Infrastructure monitoring tools can confirm degradation, and application performance monitoring can show response times. But neither explains how orchestrated workflows behaved under pressure: how dependencies interacted, where contention formed or why service-level agreement (SLA) risk accumulated.

As orchestration expands across SAP landscapes, cloud-native services, data pipelines and external APIs, that blind spot becomes harder to ignore. Automation platforms generate telemetry continuously, so the challenge isn’t collecting data, but preserving its context.

Without that context, your teams may find themselves working backwards, which often means piecing together timelines, comparing dashboards and explaining outcomes after the fact. With it, they gain something closer to a panoramic view that makes risk visible earlier and turns automation data into a feedback loop they can actually use.

Redwood Software addresses this directly with Redwood Insights for RunMyJobs, embedding observability into the orchestration layer itself — not bolting it on.

Evolving from system signals to orchestration intelligence

Observability platforms were built around applications and infrastructure. They excel at collecting distributed telemetry and tracking system performance.

Enterprise orchestration introduces a different dimension of complexity:

• Cross-platform workflows with layered dependencies
• SLA-bound business processes such as financial close or order-to-cash
• High-volume batch and event-driven workloads
• Deep SAP integration across ERP and SAP Business Technology Platform (BTP)

When an issue emerges, teams often pivot between different monitoring tools, logs and dashboards to reconstruct the sequence of events. The signals are there, but the intent is missing. Correlation must be manual. Thus, mean time to resolution (MTTR) grows because the orchestration logic — how workflows were designed to behave — lives somewhere else (e.g., in RunMyJobs by Redwood).

Redwood Insights closes that gap by keeping execution data tied to workflow relationships, orchestration intent and historical context. Instead of reviewing isolated metrics, you can see how workflows behaved as connected systems.

What changes first is the quality of investigation. Rather than chasing symptoms across tools, engineers start with the workflow itself. Root causes can surface faster and patterns are easier to spot. Less energy has to be expended for reacting and preventing the same issues from repeating.

Native operational visibility in RunMyJobs

Redwood Insights is available to every RunMyJobs SaaS customer, offering:

• Pre-built dashboards that surface execution trends, runtime variance and failure clustering across environments
• Bottleneck visibility that prevents escalation into SLA breaches 
• Immutable audit visibility and summarized execution history for administrators — without exporting data to external tools
• A high-level dashboard for engineers to move directly into specific workflow executions, eliminating platform switching or manual correlation

The views above create a shared operational baseline. Your automation health becomes easier to understand, explain and improve upon, no matter if your goal is faster triage, cleaner audits or shorter processing windows.

The impact shows up in measurable ways:

• Root causes take less time to uncover
• Mean time to repair drops
• Recurring bottlenecks surface earlier
• System behavior becomes more predictable across distributed environments

Orchestration gets its own observable voice.

Redwood Insights Premium: Extending visibility to enterprise scale

With automation becoming increasingly central to business operations, observability needs to support more than incident response.

Redwood Insights Premium, introduced in RunMyJobs 2026.1, builds on the native foundation with:

• A no-code dashboard designer for customized views
• Easy sharing of custom dashboards across the business
• 15 months of historical data retention

For many organizations, this marks a shift from short-term visibility to longer-term performance management, moving from “what just happened” to “what keeps happening, and why.” 

Custom dashboards and KPI alignment

Different stakeholders require different perspectives. For example, auditors look for records of changes made to automation environments. And Finance leaders care about SLA adherence and process completion risk.

Redwood Insights Premium allows IT to define custom dashboards for tracking KPIs tied directly to orchestrated workflows. Automation performance can then be measured against declared business objectives rather than generic system metrics.

Secure sharing gives process owners and domain leaders self-service access to their own views, while governance remains centralized. This ultimately changes how insights flow through the organization, because IT is no longer the default intermediary. Business teams can have direct visibility into the processes they depend on, too.

Long-term telemetry for planning and governance

Short monitoring windows are useful for resolving today’s incidents, but they don’t help much with planning.

With 15 months of historical data retention, it’s possible to:

• Benchmark year-over-year workload performance
• Identify seasonal execution patterns
• Evaluate the impact of architectural changes
• Support audit and compliance preparation with a continuous execution history

For CIOs and transformation leaders, this longer view supports more grounded ROI conversations. Decisions about scaling orchestration, modernizing SAP landscapes or optimizing cloud consumption can be based on how systems actually behave over time. Observability, therefore, becomes a planning instrument instead of merely a diagnostic tool.

Correlating automation across the broader observability ecosystem

Many enterprises already rely on multiple observability platforms. Infrastructure and application telemetry continue to flow into tools such as Splunk, Dynatrace, New Relic and AppDynamics. RunMyJobs integrates automation telemetry with these platforms, enabling teams to correlate workflow behavior with application and infrastructure performance.

For SAP-centric organizations, the out-of-the-box SAP Cloud ALM connector synchronizes RunMyJobs execution data, including status, start delays and runtime, directly into SAP Job and Automation Monitoring. Automation health becomes visible in the operational interface that SAP teams already use.

Instead of losing orchestration context as data moves between systems, it’s easy to retain a clear picture of how workflow behavior contributes to business risk.

Observability as an architectural decision

Observability is often framed as a DevOps concern. But in distributed enterprises, it’s an architectural one.

As orchestration spans SAP, cloud-native services, hybrid infrastructure and external APIs, leaders need confidence that critical workflows will remain predictable and transparent. Modernization initiatives, from SAP Cloud ERP transformations to multi-cloud adoption, depend on reliable execution.

By embedding observability, RunMyJobs creates a continuous feedback loop:

• Telemetry highlights friction
• Teams optimize workflows
• Reliability improves
• Business outcomes follow

Automation already underpins your most critical processes. With Redwood Insights and Redwood Insights Premium, it becomes fully observable — not only at the system level, but at the orchestration level where business risk actually resides.

Already a Redwood Software customer? Review all the features released in 2026.1.

Ready to democratize your data? Request a demo of RunMyJobs, including Redwood Insights Premium, and see how tailored observability changes how your teams work.

This shift comes at a decisive moment, preceding SAP Business Warehouse (BW) reaching the end of mainstream maintenance in 2027, with extended maintenance ending in 2030. SAP BW/4HANA remains supported until at least 2040, but the long-term direction is clear. If you’re running SAP today, you’re likely moving from primarily on-premises, centralized data warehousing toward a cloud-based, multi-service data architecture.

That change is structural, and structural changes introduce new operational realities. As you modernize your data landscape as part of a broader SAP Cloud ERP or SAP Cloud ERP Private journey in GROW with SAP or RISE with SAP, the goal isn’t just architectural alignment. It’s to accelerate transformation while keeping operating costs predictable and avoiding new layers of technical debt.

What fundamentally changes with SAP Business Data Cloud

In a traditional SAP BW landscape, most data warehousing functions lived inside one system boundary. Data extraction, transformation, modeling, scheduling and reporting were tightly coupled. Even in complex SAP ERP environments, there was a central anchor point for enterprise data.

SAP BDC operates differently. Instead of one primary platform, you’re working across a set of tightly integrated services on SAP BTP. SAP Datasphere, SAP Analytics Cloud , SAP BW and BW/4HANA, Databricks and Snowflake form a broader data fabric.

SAP Datasphere, evolving from SAP Data Warehouse Cloud and incorporating capabilities from SAP Data Intelligence Cloud, is positioned as the core enterprise data management platform. It integrates with SAP Analytics Cloud for analytics and planning, and with Databricks and Snowflake for data pipelines, advanced analytics and AI scenarios.

From a data perspective, integration is stronger than ever. Semantics, metadata and access across SAP systems are more aligned than in previous generations.

But integration isn’t orchestration. As your landscape expands across these services, you still need a way to coordinate how jobs, dependencies and business processes execute across them.

Where orchestration becomes operationally critical

In SAP BDC environments, each component has its own scheduler and automation capabilities. 

• SAP Datasphere runs replication flows and transformations
• Databricks executes machine learning pipelines
• Snowflake processes large-scale analytics workloads
• SAP Analytics Cloud refreshes dashboards and publishes stories
• SAP BW and BW/4HANA continue to run process chains

Individually, these systems work. The challenge appears when those jobs are part of a larger end-to-end business process.

Take a straightforward example. You run an extract, transform and load (ETL) or replication flow in SAP Datasphere. Once the data is updated and validated, you need to publish a new SAP Analytics Cloud story based on that refreshed dataset. Both steps can be scheduled locally. What connects them? What ensures the SAP Analytics Cloud publication only happens after the upstream process has completed successfully?

The same pattern applies if you’re using Databricks or Snowflake instead of SAP Datasphere. A machine learning or analytics job runs overnight. When it finishes, downstream reporting or operational updates need to be triggered. Each platform can manage its own workload, but the dependency between them isn’t governed unless you introduce orchestration across systems.

A second, equally common scenario is nightly batch processing across multiple services. You may schedule jobs independently inside SAP Datasphere, Databricks, Snowflake or SAP BW. Each executes reliably, but you don’t have a consolidated view of what’s happening across SAP BDC as a whole. There’s no single operational window into cross-platform execution, and understanding overall status may require reviewing several consoles.

That’s where orchestration extends the value of SAP BDC — by coordinating native schedulers and providing transparency across the ecosystem. It also reduces operational overhead. Instead of managing multiple schedulers, agents and custom scripts across environments, you establish a unified control layer that scales with your architecture. That’s particularly important in RISE with SAP environments with SAP Cloud ERP Private, where clean core principles discourage custom code inside the ERP and where unnecessary infrastructure adds cost and complexity.

The role of RunMyJobs in the SAP BDC era

RunMyJobs by Redwood provides that orchestration layer. It’s the only workload automation platform that’s both an SAP Endorsed App and included in the RISE with SAP reference architecture. RunMyJobs’ secure gateway connection to a customer’s RISE with SAP environment can be installed, hosted and managed by the SAP Enterprise Cloud Services team, eliminating the need for additional infrastructure and supporting clean core strategies from day one. Recognized as a Leader in the Gartner® Magic Quadrant™ for Service Orchestration and Automation Platforms, RunMyJobs centralizes scheduling, dependency management and monitoring across SAP and non-SAP systems.

For SAP BDC environments, RunMyJobs offers out-of-the-box connectors for:

• SAP Datasphere
• SAP Analytics Cloud
• SAP Business Warehouse
• Databricks
• Snowflake

Because RunMyJobs uses a secure gateway connection, very similar to how SAP Cloud Connector works, rather than requiring agents to be deployed across every SAP system, you avoid the operational costs and upgrade friction associated with agent-heavy architectures. That reduces maintenance effort, lowers total cost of ownership (TCO) and minimizes risk during SAP upgrades or RISE with SAP transformations.

In practice, you can:

• Trigger downstream analytics only after upstream data validation completes
• Coordinate nightly batch processes across multiple cloud services
• Establish a single pane of glass for visibility into SAP BDC execution

You don’t have to stop scheduling locally if that works for your teams, but by introducing an orchestration layer, you gain consistent control across the full landscape.

Supporting your path forward

There isn’t one correct response to the end of SAP BW mainstream maintenance. You may accelerate toward SAP Datasphere and a cloud-centric architecture. You may move selectively while continuing to run SAP BW/4HANA well into the next decade. Or, you may operate a hybrid model for years.

RunMyJobs supports all of the above, offering orchestration for classic SAP BW environments and all major components of SAP BDC. Whether you’re stabilizing existing SAP BW process chains or orchestrating new cloud-based workflows, the objective is the same: maintain control over execution across your environment.

You don’t have to complete a migration to benefit from orchestration. And you don’t have to abandon SAP BW to modernize your control layer. In fact, many organizations introduce orchestration early in their RISE with SAP and SAP Cloud ERP transformation to de-risk migration, retire legacy schedulers and create a scalable SaaS control tower before complexity compounds. That approach helps reduce disruption during go-live while positioning your automation strategy for long-term innovation.

A foundation for AI and advanced analytics

SAP BDC is also positioned as the foundation for enterprise AI and advanced analytics initiatives. Clean, harmonized data enables machine learning models and advanced analytics use cases.

But AI pipelines introduce additional operational dependencies. Training jobs, scoring runs, data refresh cycles and reporting updates must align across systems. As those chains grow, so does the need for consistent governance and monitoring. With RunMyJobs, the leading orchestration platform for the autonomous enterprise, you can apply consistent governance, monitoring and error handling across both traditional data warehousing processes and new, AI-driven workflows. That consistency is what turns experimentation into enterprise-grade transformation, without introducing new layers of manual oversight or operational costs.

See how RunMyJobs provides a coordination layer across SAP BTP, SAP BDC and your broader landscape:

Architect for control

As your SAP data landscape becomes more distributed across SAP BTP services, execution coordination becomes more important. Data integration continues to improve across SAP’s ecosystem. The next question is how you want those integrated systems to run together.

If you’re evaluating how to orchestrate SAP Datasphere, SAP Analytics Cloud, SAP BW, Databricks or Snowflake, particularly as part of a RISE with SAP and SAP Cloud ERP journey, the goal isn’t just coordination. It’s to modernize your execution layer in a way that supports clean core principles, reduces TCO and accelerates transformation across your enterprise.

The next step is practical: understand how orchestration connects to each of these platforms in your landscape.

Explore the full set of RunMyJobs SAP connectors and see how they extend SAP BTP and SAP BDC with enterprise-grade orchestration.

That framing doesn’t match how payment systems operate in practice.

Modern payment environments are built around multiple settlement models, risk controls and reporting obligations. Some payments need to move immediately, but others can’t. Many require both real-time decisioning and delayed settlement. Speed alone doesn’t determine whether a payment flow works reliably.

Most enterprises today process payments across credit cards, debit transactions, ACH payments, account-to-account transfers and alternative payment methods, which behave differently once a transaction is initiated. Some depend on immediate authorization, and others on settlement windows tied to business days. Many combine both.

As a result, organizations are rarely deciding between real-time and batch payments. They’re managing both models at the same time, often inside the same customer or partner journey. The harder problem is coordinating them across payment systems, gateways, processors and banks without creating fragile workflows or time-consuming manual intervention.

In practice, most payment journeys already operate as hybrid workflows. A transaction may begin with a real-time checkout or authorization, then move through batch-based settlement, reconciliation and reporting later. That’s why payments modernization isn’t about replacing batch processing with real-time rails. It’s about designing payment workflows that coordinate both models reliably across the payments stack, from initiation through settlement and post-payment operations.

Payments modernization, at its core, is an orchestration challenge.

Both models in modern payment environments

Real-time and batch payments exist because payment ecosystems serve different business needs. Each execution model reflects tradeoffs between speed, control, liquidity and operational effort.

Enterprise payment systems are rarely simple. A single payment operation may touch customer-facing apps, payment gateways, PSPs, acquirers and multiple financial institutions before funds actually settle. Each step introduces different timing, risk and data requirements. Real-time execution supports fast decisioning and customer experience, while batch processing supports liquidity management, reporting and auditability.

What are real-time payments?

Real-time payments are designed to move funds from payer to payee within seconds, with confirmation returned almost immediately. Settlement doesn’t wait for end-of-day cycles or multi-day clearing windows.

In the United States, real-time payment systems include the RTP network operated by The Clearing House and the FedNow Service from the Federal Reserve Banks. Participating financial institutions use these networks to support immediate payments between bank accounts, including account-to-account transfers and request-for-payment scenarios.

Similar systems operate globally. Countries such as Brazil and Australia have adopted real-time payment infrastructures that support local payment methods through banking apps, fintech platforms and digital wallets.

Common real-time payment use cases

Real-time payments are used wherever immediacy changes the outcome of a transaction. That includes P2P transfers, instant disbursements for the gig economy, insurance payouts and time-sensitive B2B payments where delays impact cash flow or customer satisfaction. Request for payment scenarios also rely on real-time execution so payers can respond and funds can move without waiting for business days to pass.

While credit cards feel instantaneous, real-time bank payments behave differently. They move funds account to account and settle immediately through real-time payment systems, which creates different liquidity and risk considerations for payment operations teams.

How real-time payments actually run

Real-time payments are event-driven and API-based. Execution begins when something happens: a checkout is completed, a request for payment is approved, a disbursement is triggered.

From there, everything must happen quickly. Payment routing decisions, authorization checks, tokenization and fraud detection occur in milliseconds. If liquidity isn’t an option, or a downstream system is unavailable, there is little time to recover. This immediacy improves customer experience and conversion rates, but it also raises the stakes for payment operations. Failures are visible right away. Retries must be automated. Fallback paths must already exist.

Because failures surface immediately, real-time payment flows depend on automation. Retries have to happen without human intervention. Not to mention, fallback paths need to be defined in advance so a single outage doesn’t stop payments entirely.

This is where payment orchestration becomes critical. Without an orchestration layer, every real-time failure becomes a visible customer issue. With orchestration, transactions can be rerouted, retried or deferred into batch workflows when conditions require it without breaking the overall payment experience.

What is batch payment processing?

Batch payment processing takes a different approach. Transactions are grouped together and processed on a schedule rather than individually as they occur.

Batch processing persists because it solves problems real-time execution can’t. Grouping transactions reduces processing costs, simplifies reconciliation and makes liquidity planning more predictable. For ACH payments and large-scale disbursements, these efficiencies matter more than speed.

Batch workflows also support downstream activities like reporting, chargeback handling and audit preparation. These processes depend on complete payment data and structured settlement cycles, which is why batch execution remains embedded in payments infrastructure even as real-time capabilities expand.

Why real-time payments can’t replace batch processing in enterprise environments

The expansion of real-time payment capabilities has not removed the need for batch processing, and it’s unlikely to do so.

Many payment methods still require scheduled settlement. ACH payments, reconciliation activities and certain cross-border flows depend on batch execution to ensure traceability and compliance. Financial institutions and service providers rely on these cycles to manage risk.

Liquidity is another constraint. Real-time payments require immediate funding, which can introduce pressure at scale. Treasury teams use batch settlement schedules to manage cash positions across accounts, regions and legal entities.

There’s also the reality of downstream work. A payment doesn’t end when funds move. Chargebacks, retries, reporting and metrics collection often happen later — and in batch. Even when a payment is initiated in real time, the work around it usually isn’t.

Consider a digital checkout that authorizes and confirms payment in seconds. The customer sees an immediate result, but settlement may still occur later through batch processing. Reconciliation, reporting and metrics collection often follow scheduled workflows tied to business days and regulatory requirements.

Bringing real-time and batch together with unified payment orchestration

Modern payment orchestration solutions are designed to manage this complexity without forcing all payments into a single execution model.

A payment orchestration layer sits above payment gateways, processors and banks. Orchestration doesn’t replace payment processors, PSPs or acquirers. It coordinates them. The orchestration layer defines how payment flows move across systems, how routing decisions are made and how exceptions are handled when something goes wrong.

By centralizing this logic, organizations avoid hardcoding payment behavior into individual applications. Governance, monitoring and control move into a single platform, which makes it easier to manage both real-time and batch execution consistently as volumes and payment options grow.

This layer becomes especially important as organizations expand into new markets or support additional payment options. Different geographies rely on different payment rails. Local payment methods behave differently than global card networks. Without orchestration, each variation adds more custom logic to applications.

What orchestration handles

In practice, a payment orchestration platform manages functions such as:

• Routing transactions based on availability, geography or cost
• Supporting fallback paths during outages
• Automating retries when transient failures occur
• Applying fraud detection and secure payment controls consistently
• Centralizing payment data and operational metrics
• Managing payment data consistency across workflows
• Coordinating tokenization and fraud detection across payment methods

Centralizing these functions reduces duplication and makes payment operations easier to scale. Instead of updating logic in every app or integration, teams adjust orchestration rules once and apply them across the entire payment ecosystem. 

Real-time vs batch payments: Key differences in practice

Teams often talk about real-time and batch as if they’re competing approaches, but day-to-day payment operations usually rely on both. The differences below aren’t about which model is “better.” They’re the practical constraints that shape how you design payment workflows, choose payment rails and set up routing, retries and fallback paths across payment systems.

This comparison is also useful when you’re deciding where to standardize controls like fraud prevention, tokenization and monitoring. Real-time execution compresses the timeline for decisioning, while batch processing creates structured cycles for settlement, reporting and reconciliation.

In most modern payment stacks, these models don’t exist in isolation. Real-time execution often handles initiation, authorization and confirmation, while batch workflows handle settlement, reconciliation and reporting across business days. The goal isn’t to force one timing model onto every payment method. It’s to coordinate them so payment data stays consistent, exceptions stay manageable and success rates hold steady as volumes grow.

Benefits of payment orchestration in modern payment operations

As payment ecosystems grow more complex, payment orchestration helps organizations manage volume, variation and risk without adding fragility to their payment operations.

Higher payment success rates

One of the most immediate benefits of orchestration is improved success rates. When a payment fails due to a temporary outage or routing issue, orchestration enables automated retries or rerouting to alternative payment paths. Without this capability, many failures surface as manual exceptions that slow down operations and impact revenue.

Centralized visibility and monitoring

Payment orchestration provides a centralized view across omnichannel payment flows. Metrics such as success rates, authorization rates and failure patterns can be monitored in one place rather than across disconnected systems. This visibility helps teams diagnose issues faster and respond before failures cascade.

Lower operational overhead

By centralizing routing logic and monitoring, orchestration reduces the effort required to maintain separate integrations for each payment method, processor or gateway. Changes can be made once at the orchestration layer instead of being repeated across multiple applications, which saves time and reduces operational risk.

More consistent customer experiences

Orchestration helps deliver consistent payment behavior across checkout flows, apps and digital channels. Customers are less likely to encounter unavailable payment options or failed transactions based on geography, timing or temporary outages.

Scalable payment operations

As payment volumes grow or new payment methods are introduced, orchestration allows organizations to extend payment capabilities without reworking existing workflows. This makes it easier to scale payment operations while maintaining reliability and control.

Payment orchestration in the modern payments stack

In a modern payments stack, orchestration connects applications, payment gateways, PSPs, acquirers and banks through a single control layer. Rather than embedding routing logic in each system, orchestration centralizes decision-making. When outages occur, fallback rules can be adjusted centrally. When new payment options are added, they can be introduced without rewriting core applications.

In this model, applications initiate payments, orchestration governs execution and downstream systems handle processing and settlement. The orchestration layer becomes the control point for routing, retries and monitoring, while existing payment infrastructure continues to do what it does best.

This separation improves scalability. New payment methods, processors or geographies can be introduced without reworking core workflows, reducing downtime and integration effort over time.

Designing payment workflows for a hybrid world

Real-time and batch payments will continue to coexist as payment technologies evolve. Payment ecosystems are expanding, not converging. Modernizing payments means coordinating both models across payment flows, applying consistent governance and supporting new capabilities without disrupting what already works. Organizations that take this approach build payment systems that are resilient, scalable and ready to evolve as payment technologies and business needs change.

Designing payment workflows for a hybrid environment starts with understanding where real-time execution adds value and where batch processing remains essential. From there, orchestration rules can be defined to align routing, settlement and reporting with operational and regulatory requirements.

As payment infrastructure continues to evolve, the ability to orchestrate real-time and batch payments within a single framework will shape how effectively enterprises manage risk and deliver reliable digital payment experiences.

Learn more about the orchestration-focused approach to payments modernization.

That’s understandable. Revenue growth, AI innovation, cloud agility and customer experience dominate modernization conversations because they’re visible to boards and clients. But inside most financial institutions, the systems coordinating settlement, cutoffs, retries and reporting were designed long before real-time expectations became standard.

We’ve seen this pattern before. During cloud migrations and earlier digital transformation cycles, front-end capability advanced quickly while the operational foundation evolved more cautiously. Payments modernization is now encountering the same imbalance.

In many institutions, particularly large banks and card issuers, the orchestration model was built 25 or 35 years ago for batch windows and predictable cycles. It still works, but layering real-time controls, in-line fraud scoring and API-driven flows onto a clock-driven coordination model introduces complexity that accumulates.

For CIOs, CTOs and enterprise architects, this creates a growing tension. Legacy workload automation and batch orchestration remain deeply embedded in revenue flows, reporting cycles, regulatory controls and settlement processes. Touch them carelessly, and you risk disruption. Ignore them, and modernization efforts stall under their own weight.

The biggest risk in payments modernization today isn’t moving too slowly. It’s assuming the orchestration model you’ve relied on for decades will keep working while everything around it changes.

How modernization unfolds in the industry

Payments modernization rarely arrives as a single, declared program. It unfolds through a series of cautious, tightly scoped decisions, each designed to limit operational and regulatory risk.

• A new payment rail is introduced, requiring ISO 20022 translation, prefunding and intraday liquidity controls
• A real-time fraud check or anti-money laundering (AML) engine is deployed to score transactions in-line in milliseconds rather than overnight
• An API gateway is implemented to expose payment initiation, status and routing to fintech partners or corporate clients

Each change is reviewed carefully, implemented incrementally and monitored closely. Individually, these decisions make sense. Collectively, they change how payments move through the organization. And what often goes unexamined is the execution layer coordinating that work. 

Legacy systems remain in place because they’re stable, familiar and deeply intertwined with settlement, reconciliation, governance and reporting. Modernization rarely centers on replacement. It progresses through selective isolation of functions and the introduction of new capabilities at the edges of the system. The architecture that emerges is layered, as each addition addresses a defined requirement. 

New payment rails change the rules of execution

What’s surfacing now isn’t confusion about how new payment rails work. It’s a growing mismatch between those rails and the execution models many financial institutions still rely on to run them.

Instant payment rails like FedNow and Real-Time Payments (RTP) remove timing buffers that legacy batch coordination quietly depended on. When funds move immediately from the issuing bank to the recipient’s bank, recovery paths narrow and accountability shifts upstream into the orchestration layer itself.

At the same time, payments workflows are becoming more asynchronous and distributed. Tokenization introduces lifecycle events that don’t align neatly with batch windows. Open banking APIs and embedded payments extend payment journeys across third-party providers, payment processors, fintech platforms and institutional counterparties. Cross-border payments introduce dynamic routing, intermediaries and real-time compliance checks across payment networks like SWIFT, SEPA and card rails.

Legacy orchestration models were designed for stability in predictable environments. New payment workloads demand adaptability across hybrid ones.

The “new workload” strategy

A more pragmatic approach is emerging. Instead of forcing legacy workloads into modern patterns, leading teams are deploying modern orchestration only where it’s required:

• New payment rails and faster payments services
• New customer-facing payment options
• New API-driven and data-intensive payment flows

Existing batch workloads — ACH payments, recurring payments, settlement cycles, reporting — continue running where they are. They’re stable, governed and understood. They don’t need reinvention to support innovation elsewhere. Modernization expands outward from new payment capabilities, rather than backward into stable legacy flows.

What qualifies as a “new payment workload”?

Not every payment flow is created equal. Across banks, card networks and payment platforms, the workloads that demand modern orchestration share one trait: they can’t wait.

Examples include:

• Real-time payments and instant settlement
• Token lifecycle management
• API-driven payment initiation and partner ecosystem orchestration
• In-line fraud and risk decisioning tied to live transaction events
• Cross-border payments with dynamic routing and compliance logic

These flows run on live signals, not schedules. Recovery has to be automatic and context-aware, because there’s no safe pause button in the middle of a real-time payment.

The foundation for disciplined modernization

Modernizing forward only works if your orchestration layer evolves alongside those new workloads. Payment rails, fraud engines and APIs introduce speed and distribution, and orchestration determines whether you can safely gain speed without losing control. If your logic remains tied to clock-driven execution, your new capabilities will just inherit old constraints. Deliberate, modern orchestration helps them operate in real time without destabilizing your existing systems.

Why this reduces risk instead of increasing it

The instinctive fear is understandable: introducing new orchestration alongside legacy systems feels like adding complexity. In practice, it does the opposite.

Running modern orchestration in parallel:

• Avoids disruption to revenue-generating payment systems
• Eliminates forced migration of fragile legacy logic
• Creates a clear separation between systems of record and systems of innovation

Instead of turning every change into a platform-wide event, you contain the impact to the new flow. A FedNow exception doesn’t have to spill into ACH payments, and a routing issue doesn’t necessitate a war room just to understand what broke.

Just as importantly, this containment model prevents modernization costs from compounding, so there are fewer emergency fixes, one-off integrations and expensive upgrade projects designed solely to keep the lights on. 

Hybrid orchestration isn’t a compromise

Payments modernization will remain hybrid for the foreseeable future. Cloud-native payment platforms, SaaS services, on-premises systems and external payment networks will continue to coexist.

Chasing a perfectly unified architecture is a distraction; what matters is whether the work moves cleanly across boundaries — cloud to on-premises, internal systems to payment processors, batch to event-driven paths — without creating new failure points.

Modern orchestration becomes the connective tissue across cloud, SaaS and on-premises environments, aligning payment instruction flows, routing decisions and downstream processing without forcing everything into a single model. This is how organizations escape orchestration technical debt without risking operational stability.

Over time, this approach changes the economics of modernization by shrinking upgrade cycles, lowering operational overhead and freeing capacity for new initiatives instead of constant maintenance.

A quieter form of transformation and why it works

The most effective payments modernization programs rarely announce themselves loudly. They don’t arrive as sweeping transformation initiatives or architectural resets. Instead, they introduce new capabilities deliberately, with clear operational boundaries and a strong bias toward stability.

This approach aligns with how regulated financial institutions actually manage risk. Change is evaluated in context, scoped tightly and introduced where it delivers clear value without increasing operational exposure. 

“Boring” is often the point. It means exceptions are handled predictably, and investigations start with answers instead of guesswork. Teams can explain what happened in a payment flow without reconstructing the story after the fact. It also means audits and regulatory reviews are routine rather than disruptive, because the execution trail is clear and defensible from the start.

Change the cost curve of modernization

When new payment capabilities are introduced without reworking what already runs, modernization stops drawing from the same operational budget year after year. In that environment, digital transformation becomes more cost-effective by design. Your teams can spend less time maintaining orchestration debt and more time delivering new value.

Explore how modern orchestration supports new payment workloads without disrupting legacy operations or allowing excess costs to accumulate.

But backstage, the performance is still running.

What many CFOs are presented with is confidence theater: a polished view of progress that suggests finality without proving that the work behind the scenes is finished. In finance, that gap matters. Because when visibility replaces execution proof, financial statements can look settled while the general ledger is still changing.

Dashboards create confidence, not certainty

Dashboards are designed to present progress, not verify completion. They summarize workflow steps, timelines and metrics that imply the financial close process has reached its final scene. For accounting and finance teams under pressure, this presentation is reassuring. For executives, it signals stability.

The problem is that dashboards rarely confirm whether financial transactions have actually landed in the accounting system. Progress indicators show that tasks were reviewed or approved, not that journal entries were posted and reflected in the trial balance, balance sheet, income statement or cash flow statement.

This is where risk creeps in. Leadership believes results are stable, while accruals, reclassifications and other adjustments are still being created post-close. The finance and accounting teams may still be reconciling accounts, updating templates in spreadsheets or correcting discrepancies across subledgers.

An example was when a CFO of a SaaS organization presented “100% closed” results to lenders and the board. The dashboards showed a clean close period. Days later, late intercompany reclassifications moved revenue between business units. Fixed assets depreciation was corrected. Variances emerged between prior period assumptions and actuals. Financial reporting still needed to be revised.

The numbers changed because execution never stopped, and that meant what leadership saw wasn’t a close. It was a preview. Without execution confirmation, visibility becomes performance, and decision-making confidence disappears.

“Done” does not mean posted

Most close management systems define “done” as task completion. A reviewer signs off. A close checklist item turns green. But none of that guarantees ledger impact.

Journal creation, approval and posting remain decoupled from close status in many automation tools. A journal can be approved yet still sit outside the general ledger. Accounts payable adjustments, receivable corrections or bank statement accruals may exist only in Excel files or email threads. Until posting occurs, account balances are provisional.

This matters because material activity stays invisible until it becomes a problem. The accounting process looks complete even as manual processes continue behind the curtain. Data entry errors, unresolved discrepancies and missing financial data surface late, usually after executives believe the close period is locked.

With the CFO of the SaaS organization, additional journal entries hit the ERP five days after the apparent month-end close process. Revenue recognition was updated. Liabilities tied to credit cards and bank accounts shifted. The accounting records had diverged from what leadership had already reviewed, which forced explanations and revisions that undermined trust in reported results. Because if journals weren’t posted, the close simply wasn’t defensible.

False confidence becomes an audit and credibility risk

Clean dashboards can hide operational instability. They smooth over bottlenecks, time-consuming reconciliations and unresolved issues that sit outside the reporting process.

Auditors don’t review dashboards. They follow execution. Late adjustments appear during audit walkthroughs, not executive reviews. Auditors trace financial transactions through subledgers, trial balance movements and period-end postings. That is where post-close activity is exposed.

The downstream effects are predictable with audit delays, process bottlenecks, extended year-end close cycles and, in some cases, revenue restatements. Accounting and finance teams are pulled into firefighting mode because they’re answering why variances exist and why accounting records changed after reporting.

In the CFO example for the SaaS organization, revenue had to be reexplained once the journal entries finally aligned with the general ledger. Forecasting assumptions were questioned. Strategic decisions made earlier had to be revisited. What looked efficient became a credibility issue. What leadership saw as a fast, efficient close turned out to be a delay waiting to surface. What felt like efficiency in real time became exposure under audit.

Real close control requires execution-level proof

True close control is not about workflow progress. It’s about verified journal execution.

Execution-level proof means knowing that journals are created, validated and posted based on business logic and data readiness instead of human memory. This is where orchestration changes the model.

Orchestration ties automation, ERP data, subledgers and financial transactions into one coordinated flow. When prerequisites are met, journals post automatically. When data changes, adjustments are recalculated. Visibility reflects what is actually in the ledger, not what is assumed to be finished.

Finance Automation by Redwood applies this orchestration approach across the financial close process, from journal entries and account reconciliation to intercompany activity, accruals, provisions and reclassifications. Dashboards show only posted, final results. The accounting system becomes the source of truth, not a presentation layer.

In the CFO of the SaaS organization example, leadership would never have seen provisional numbers with a record-to-report (R2R) orchestration platform like Finance Automation. Dashboards would have only included posted balances from the general ledger. Financial position, metrics and financial health would align with reality. Informed decision-making would be grounded in execution instead of performance optics. With Finance Automation’s orchestration, the CFO would not have relied solely on task progress. They would have relied on proof. And that’s the shift: real close control comes from knowing what’s finished, not what’s still in progress.

End the performance. Lead with proof.

CFOs should question dashboards that cannot confirm ledger reality. Task completion does not equal financial completion. A close checklist does not guarantee that period-end numbers are final.

Traditional automation software and tools focus on tracking work. Finance Automation focuses on executing it. By orchestrating journals, reconciliations and postings directly within the ERP, Finance Automation delivers verified, final execution that supports confident financial reporting.

The theater ends when the numbers stop moving.

Take the automation maturity assessment to see what’s really happening backstage in your close and whether your financial close process is built on performance or proof.

Redwood Software began addressing this challenge last year with the introduction of Redwood Insights, bringing observability directly into RunMyJobs by Redwood through standardized dashboards and operational analytics. That foundation gave teams clearer visibility into automation health and compliance without relying on disconnected tools.

RunMyJobs 2026.1 builds on that momentum with a broad set of observability-focused updates across the platform. This update expands how automation data is surfaced, shared and trusted, combining default insights, deeper analytics, tighter ecosystem integration and strengthened security. Together, these enhancements give teams a clearer context across their automation environments and greater confidence as automation becomes more central to daily operations.

Democratizing automation intelligence

At the center of RunMyJobs 2026.1 is Redwood Insights Premium, an expansion of the analytics and observability capabilities already available to RunMyJobs customers.

Redwood Insights Premium is designed for organizations that need deeper analysis and longer historical context as automation becomes more central to their operations. It extends observability beyond platform administrators to the business and domain teams that rely on automation outcomes.

Key capabilities include:

• A no-code dashboard designer that allows IT to create role-specific dashboards for different teams
• Extensive visibility into workflow health, execution patterns and emerging trends
• 15 months of historical data retention, expanding the existing analytics window for trend analysis, capacity planning and ROI conversations

IT teams can curate views for different teams and control access, while stakeholders gain self-service access to insights in their own context. This reduces reporting overhead and removes the “IT-as-translator” bottleneck without sacrificing consistency.

Unified transparency across SAP and the broader ecosystem

RunMyJobs has long supported integration across enterprise environments. In 2026.1, that integration extends more deeply into observability workflows.

For SAP-centric organizations, the out-of-the-box SAP Cloud ALM connector brings RunMyJobs execution data directly into SAP’s native Job and Automation Monitoring. Automation health becomes part of the same operational view SAP teams already use, improving coordination and reducing mean time to resolution (MTTR).

At the same time, RunMyJobs continues to integrate with leading observability platforms such as Splunk, Dynatrace, New Relic and AppDynamics. These integrations strengthen full-stack correlation, allowing teams to connect automation behavior with application and infrastructure performance using tools already in place.

Enhanced security and trusted AI, built in

In 2026.1, RunMyJobs’ security and governance foundations are further strengthened.

New capabilities include automated malicious file detection for all UI uploads with full audit logging, along with enterprise-grade moderation applied to all Redwood RangerAI interactions. These controls allow teams to benefit from AI-assisted troubleshooting and scripting while maintaining strict governance boundaries.

Support for Java 25 ensures the platform continues to align with the latest long-term support runtime for performance and security.

Modern deployment: Cloud Gateway

As automation environments become more distributed, reliable connectivity becomes essential. Observability and execution depend on consistent communication across cloud, hybrid and on-premises infrastructure.

The updated Cloud Gateway in RunMyJobs 2026.1 improves how the platform connects to these environments. It supports multiple active gateways at the same time, enabling higher throughput and load distribution across gateways. Intelligent routing allows traffic to be segmented by network or domain, while automated failover ensures continuity if a gateway becomes unavailable.

Together, these enhancements strengthen availability and performance across complex network topologies. Observability and execution data remain reliable even as infrastructure becomes more segmented and automation spans multiple environments.

Velocity through usability

Alongside these enhancements, RunMyJobs 2026.1 includes hundreds of usability and performance refinements. These changes focus on reducing friction in daily operations rather than introducing new workflows that teams need to learn.

Improvements across navigation, responsiveness and issue detection help users move faster and identify potential problems earlier. Routine interactions require fewer steps. Signals that once required manual investigation are surfaced more clearly within existing views.

Together, these updates extend RunMyJobs’ observability capabilities into a broader, more actionable intelligence layer. Automation becomes easier to understand, easier to manage and easier to optimize over time.

Already a Redwood customer? Review all the features released in 2026.1.

Ready to democratize your data? Request a demo of RunMyJobs, including Redwood Insights Premium, and see how tailored observability changes how your teams work.

The data was there. The warning signs weren’t obvious. By the time the impact surfaced, the moment to intervene had already passed.

This is a familiar tension for many enterprise leaders. Visibility exists, but understanding doesn’t always follow. Monitoring tools confirm that systems are running, but they rarely explain how automation behaves under pressure and how delays ripple across dependencies or where risk is quietly accumulating.

The single pane of glass was an important step forward. It brought fragmented information into a shared view and reduced blind spots. What it doesn’t consistently provide is depth: the ability to move from status to meaning without manual interpretation.

That gap becomes clear the moment questions turn from “Is it running?” to “Can we rely on it?”

When insight depends on translation, risk increases

Most enterprises already collect enormous amounts of operational data. Automation platforms generate execution logs and performance metrics. And applications and infrastructure emit their own signals. So on paper, nothing is missing. But in practice, insight is scattered.

Understanding what’s happening across critical workflows often requires translation. IT teams pull data from multiple monitoring tools, correlate timelines and explain what technical behavior means for business outcomes. Leaders then depend on these explanations to assess risk, prioritize action and answer questions they know are coming.

This model is fragile. It slows decision-making and quietly extends mean time to resolution (MTTR), even when teams are working as fast as they can. By the time an issue is fully understood, the opportunity to intervene early has often passed, turning what could have been a minor disruption into a larger operational event.

Observability reduces that dependency. Correlating automation data and presenting it with context, it allows different audiences to access the insight they need without waiting for interpretation.

Why consolidation alone doesn’t create clarity

The promise of a single pane of glass is powerful when the goal is shared visibility into a specific domain — one platform, one set of processes, one operational context. It creates a common reference point and a shared understanding of what’s healthy and what’s not.

The challenge emerges when that same approach is stretched to cover the entire enterprise. A single view can only show so much. When automation spans applications, infrastructure, data pipelines and business services, compressing everything into one window often flattens the story instead of explaining it. 

Over time, this leads to dashboard fatigue, especially when green statuses can mask issues that matter deeply to specific teams. Different roles need different windows into the landscape:

• Process owners need to understand whether end-to-end workflows will complete on time 
• SAP teams need to see how automation execution affects business services and applications
• Platform teams need to connect workflow behavior to application performance and infrastructure health

Effective observability builds on the single-pane-of-glass approach with more of a panoramic view, where multiple, connected panes together reveal the full landscape. Each pane provides the right context for the person looking through it, while still drawing from the same underlying source of truth.

Context changes how problems are handled

Monitoring answers a narrow question: did something happen?

Observability answers a more useful one: why did it happen?

With cross-domain, correlated, up-to-date data, teams can see how workflows behave as part of the enterprise ecosystem, how dependencies influence response times and where delays originate — insight that directly shortens MTTR by narrowing focus to the point of failure instead of the symptom. 

The real impact shows up in consistency. Fewer surprises reach leadership. More importantly, service-level agreements (SLAs) stop feeling like commitments you hope to meet and start becoming outcomes you can actively manage. Ultimately, the organization spends less energy reacting and more time improving how critical processes perform.

So, the control room still exists, but it stops being a wall of indicators. It becomes a place where cause and effect are visible.

Resilience requires a longer memory

Operational resilience isn’t built in a single incident. It’s built over cycles.

Short-term monitoring captures what happened today, while observability preserves history and makes it actionable. With extended data retention, leadership teams can look across quarters instead of weeks. They can compare peak-period performance year over year, identify recurring bottlenecks and understand how changes in architecture or volume affect outcomes.

This longer view supports better planning and more credible conversations with the board. It also simplifies governance and audit preparation. Instead of assembling evidence manually, you can rely on a consistent execution history that reflects how systems actually operate.

A 15-month narrative, rather than the two- or three-month one many teams work with today, creates continuity. It allows leaders to explain not only what changed, but why it changed — and how those decisions improved reliability, protected SLAs during peak periods and strengthened the return on automation investments in the long run.

A more sustainable role for IT

When observability is done well, something subtle but important changes inside the organization.

IT teams stop being the place everyone goes for explanations. They’re no longer stuck translating technical signals into business impact after the fact. Instead, they set the conditions for shared understanding. The right information is available earlier, in context and in language that different teams can actually use.

That shift frees technical managers to focus on improving how systems perform rather than defending why something failed. It also changes how leaders engage. Conversations become less about status and more about trade-offs, priorities and what to improve next. Visibility no longer depends on deep technical detail or last-minute briefings.

This is why observability can’t be reduced to “better dashboards.” The real value is confidence: 

✅ Confidence that the systems carrying real business risk are understood

✅ Confidence that issues will surface early 

✅ Confidence that decisions are grounded in reality, not assumptions

Continue exploring observability

As automation continues to scale via Service Orchestration and Automation Platforms (SOAPs), the ability to understand, anticipate and explain performance becomes a strategic advantage. To learn more about how modern observability supports resilient, data-driven operations, explore Redwood’s approach to enterprise observability.

What doesn’t get talked about enough is that this shift isn’t primarily about tools. It’s about us as humans.

That’s why I find the 2025 Gartner® Magic Quadrant™ for Service Orchestration and Automation Platforms (SOAPs) so relevant as both a technology lens and a talent and operating-model conversation.

SOAPs aren’t simply better schedulers. They orchestrate end-to-end business services with precision, context and intelligence. And when an organization adopts one, it doesn’t just modernize its automation stack, but reshapes how teams work together, learn and create value.

From my perspective, this isn’t a skills gap problem. It’s about how roles are naturally evolving. With the right guidance, encouragement and space to grow, people can thrive in change rather than just adapting to it.

The shift from task execution to service ownership

SOAPs dramatically expand the surface area that automation touches. We’ve moved from “run this job” to “run this entire business service — with events, conditions, dependencies and real consequences.” 

That evolution changes the nature of the work. You’re no longer optimizing isolated workflows inside a single system. You’re orchestrating processes that span ERP, SaaS applications, cloud platforms and external services. That requires broader thinking and deeper collaboration. The work itself becomes more strategic.

People are still central, but they’re now enabling resilience, business agility and real-time orchestration, not just maintaining automation.

What high-performing automation teams think differently about

The first real shift has to happen in how teams think about automation. Over time, I’ve seen successful teams move from:

• “Automate the task” → “Orchestrate the service”
• Siloed responsibility → Shared, cross-functional enablement
• Versioned scripts → Reusable templates and modular components
• Maintenance thinking → Platform thinking
• Support role → Strategic enabler
  

This mindset shift is subtle, but it changes everything from design decisions to how people collaborate across SAP and non-SAP landscapes.

How automation responsibilities are being redistributed

As SOAPs reshape operational models, new roles naturally emerge. Many organizations already have the talent; they just haven’t named or empowered these roles yet.

Some patterns I’m seeing more often:

• Process architect/Orchestration designer: Connects workflows across business services, APIs and cloud-based environments
• Automation data translator: Bridges operational logic with analytics, logs and business context
• Workflow monitoring and exception manager: Manages signals, dependencies and upstream/downstream impact
• Adoption lead or Change champion: Drives orchestration consistency across business ops, IT operations and development teams
• Automation culture steward: Shapes shared norms around reusable assets, platform thinking and feedback loops

Revisiting how automation work is distributed can unlock capacity you didn’t realize you had.

The conditions that make orchestration stick

Technology expands what’s possible. Culture determines what actually sticks. As automation spans more of the business, shared ownership becomes essential.

That starts with a shared vocabulary. If one team calls it “dependency mapping” and another says “event chaining,” you’ll end up with silos — not orchestration.

It continues with a learning loop that goes beyond training. People and teams need space to experiment, compare patterns and refine their instincts. As a leader, your job isn’t to prescribe every step but to create the conditions for repeatable learning that scales naturally.

And all of the above depend on clear ownership. Without visible leaders accountable for process optimization, templates and tooling standards, automation efforts remain reactive.

How to help your team thrive

If SOAPs unlock new levels of human potential, your team’s job is to take that and run. The environment you create — and the initiatives you choose to prioritize — will bring the shift to life.

At a minimum, I’d focus on the following.

1. Build capability with intention

Capability building can’t be accidental. It should be purposeful. Help teams build fluency in event-driven automation, API integration, cloud orchestration and monitoring patterns. Give legacy automation specialists room to evolve into orchestration designers or platform operators.

The goal isn’t to turn everyone into a developer. But everyone should understand how services fit together and how dependencies behave. You’ll be designing for resilience when your teams understand the “why” behind SOAP-driven workflows.

2. Create a structure that supports orchestration

Orchestration doesn’t thrive without structure. Establish an automation Center of Excellence as a guide for standard workflow patterns, exception handling and reuse. Make ownership explicit for templates, connectors and observability.

Most importantly, bring your practitioners into governance conversations. That’s how you remove friction between process design and automation design.

3. Equip teams with tools that let them excel

People do their best work when technology reduces complexity instead of adding to it. Choose platforms that make dependencies visible and support both low-code and advanced design approaches. Each persona should be able to contribute at their level.

I often ask one simple question: Will this tool make it easier for my team to design, understand and maintain end-to-end processes? If the answer is yes, the value shows up quickly.

4. Avoid the usual traps

Automation stalls when it’s treated as an IT-only scripting exercise, when adoption is an afterthought or when success is measured by output instead of outcomes. You can avoid these traps by formalizing enablement, designing for orchestration — not tasks — and tying KPIs to reliability and business impact.

Your people = your differentiator

SOAPs raise expectations for how work flows across the enterprise. But it’s your people who turn those expectations into outcomes.

When you make space for teams to think bigger about how data, work and ideas move across the business, you unlock something far more powerful than automation alone.

If you’re building that kind of culture, it helps to understand where the market is headed. The 2025 Gartner® Magic Quadrant™ for SOAPs report offers a grounded view of the Leaders and orchestration capabilities shaping the next chapter of enterprise automation — and the teams that will thrive in it.

It’s the infrastructure everyone depends on, but almost no one revisits, because it hasn’t failed loudly enough.

Banks have spent years modernizing what customers can see: digital experiences, mobile apps, real-time payment rails, cloud-native cores. Those investments were necessary. In many cases, they were overdue. And on paper, they delivered exactly what executives asked for.

So, why does it still feel harder than it should be to move money safely, quickly and predictably?

When “good enough” stops being defensible

Most enterprise architects and IT operations leaders know this feeling well. The environment works. Payments clear, and fraud is caught. Reconciliation eventually balances. When something breaks, teams step in, fix it and move on. The system absorbs stress, and people compensate. And because the compensation works, the underlying issue stays invisible.

But “good enough” becomes much harder to defend when three pressures converge at once:

1. Payments volumes accelerate
2. Time-to-decision collapses
3. Accountability increases

That convergence is happening now, and it’s visible to regulators and customers.

Real-time rails like FedNow and real-time payments (RTP) aren’t just faster versions of existing processes. They eliminate the buffer zones — overnight windows, batch retries, manual intervention points — that legacy schedulers took advantage of for decades. At the same time, regulatory scrutiny and customer expectations have converged around one assumption: you know exactly where a payment is, why it failed and what you’re doing about it.

That assumption exposes a structural weakness many banks and financial institutions have learned to work around — but not fix.

The invisible complexity behind every transaction

A modern payment doesn’t move through a straight line. It fans out across fraud detection, compliance checks, routing decisions, settlement systems, reconciliation workflows, notification services and reporting pipelines. Many of those components have been modernized individually. Few have been modernized together.

Orchestration fills the gap.

Many teams still rely on a combination of legacy schedulers, custom scripts and tribal knowledge. It’s not elegant, but it’s familiar. And familiarity is powerful, especially when budgets are tight and priorities are visible elsewhere.

The problem is that technical debt compounds fast, and it’s sticky.

Outages that weren’t supposed to matter

In May 2025, a major outage at Fiserv disrupted payment services across multiple United States banks and credit unions. Zelle transfers stalled, and online banking features and ACH processing were affected. For customers, the experience was confusing. And for banks, it was clarifying. It was a failure of coordination, not innovation.

Similar stories have played out across industries. 

• Airlines grounded by systems that couldn’t reconcile real-time data flows: Hundreds of flights were canceled in 2022 when key IT systems went offline, revealing how critical poorly coordinated back-end layers can be.
• Cloud providers experiencing cascading outages because dependency logic behaved differently under load: A major AWS outage in 2025 rippled across global services when internal automation triggers weren’t sufficiently orchestrated, showing how even modern platforms can fail without resilient control layers. 

In each case, the visible platform was modern, but the control layer beneath it was not. These incidents are foreshocks, signaling the risk of a greater problem in the near future. They indicate architectural lag — that the desire for execution speed outpaced application and data orchestration maturity.

The operational resilience question no one wants to ask

Over the past several years, operational resilience has stopped being something IT teams manage behind the scenes and started becoming something boards are directly accountable for. Regulators now expect banks to demonstrate not just recovery plans but clear tolerance for disruption, while customers and markets punish even short-lived outages with lost trust. As a result, resilience is now a governance issue.

Here’s the uncomfortable question many organizations avoid: If a critical payment flow failed right now, could you trace its path end to end quickly enough to meet your obligations without assembling a war room?

Not in theory. Not eventually. But immediately, in real time.

Could you see which system made the last decision, which dependency stalled and which downstream processes were affected? Or would your teams jump between dashboards, logs and scripts to reconstruct the story after the fact?

If the answer feels uncertain, don’t blame capability. The failure is architectural. Operational resilience is proven in the moment of impact: when systems strain, dependencies collide and decisions must be made immediately. It depends on understanding how work actually flows and how systems behave together under stress, so breaks can be proactively identified and addressed in real time, not explained after the fact.

Why this matters now

For years, banks could afford to defer this problem. Latency masked fragility, and lots of manual effort absorbed uncertainty. Institutional knowledge filled the gaps, but that tolerance is disappearing.

Real-time payments have reduced recovery windows to seconds. AI-driven fraud models are introducing asynchronous decision points. And each new payment method and provider increases the number of routing paths. Customers, retail and corporate alike expect transparency when something goes wrong. In that environment, orchestration is a strategic capability rather than background plumbing.

Orchestration as the control plane

Being successful at modern payments orchestration means establishing a control plane that understands how payment flows behave across systems.

That includes:

• Event-driven execution instead of clock-based scheduling
• Dependency awareness that prevents cascade failures
• End-to-end visibility across payment journeys
• Governance and auditability built into execution, not layered on afterward

When orchestration evolves, your ecosystem behaves differently. Failures isolate instead of spread, and recovery is not some heroic moment. You regain your margins quicker than you would’ve thought possible in the worst-of-the-worst scenarios.

Modernizing your orchestration approach is also going to prepare your organization for executing on the AI use cases you’ll need to keep up in tomorrow’s financial services world. Learn how.

The risk (and opportunity) of waiting

The greatest risk in payments modernization today isn’t choosing the wrong platform. It’s assuming the operational foundation will keep holding. Most organizations don’t modernize orchestration because something breaks. They do it because the cost of not knowing what’s happening in their payment flows —and not being able to change them quickly — eventually exceeds the cost of change itself. When competitors can launch new payment experiences in weeks and you’re stuck doing it in quarters, the limitation isn’t strategy but orchestration.

Payments modernization is already a recognized growth lever. What’s often missed is where that growth actually comes from. It doesn’t come from new payment types alone, but from the ability to operationalize, deploy and scale them into production quickly and reliably. That capability lives in the underlying application and data pipeline orchestration. When plumbing is rigid, modernization becomes cosmetic rather than transformational.

This is why payments modernization succeeds or fails long before a new rail or service goes live. Real-time processing and richer payment data enable request-to-pay, embedded finance, merchant insights and cross-border optimization. None of these are possible without orchestration that can adapt payment flows quickly, route intelligently across providers and expose consistent data across the ecosystem. Modernization creates growth only when the plumbing underneath is built to move.

The banks that act now won’t be the ones chasing outages but the ones making payments boring again. And in financial services, boring is often the highest compliment. Find out more about how to modernize your payments processes.

Most transactional reconciliation tools rely on dashboards and checklists to show progress across the financial close. Once data matching flags items as “matched,” the system often marks the task complete. From the surface, the close process appears controlled. Dashboards turn green. Workflows advance. The reconciliation looks finished.

But checklists are driven by task completion, not data movement or financial accuracy, and a “complete” status in the reconciliation tool doesn’t mean the data has been updated or validated. It only means someone flagged a match. In the financial close process, completion should mean corrected account balances in the general ledger instead of a visual signal in a reconciliation solution. This distinction matters during the month-end close, when manual processes and unresolved discrepancies can quietly accumulate.

That gap misleads CFOs into thinking issues are resolved when they are not. One healthcare controller learned this the hard way. Their team believed reconciliations were complete across bank reconciliation, sub-ledger activity and accruals. The dashboards showed no open items. Yet during an audit, $2.6 million in accrual-related journal entry corrections were still sitting in email threads, never posted to ERP systems. The financial statements looked clean on paper, but the underlying financial records told a different story.

Finance Automation by Redwood prevents this false confidence by tying reconciliation status to execution. The platform does not allow the close process to advance until required journals are created, approved and posted inside SAP to align transactional reconciliation with real financial outcomes.

“Matched” doesn’t mean corrected

In transactional reconciliation, data matching is detection, not correction. Auto-match logic highlights discrepancies between bank statements, bank feeds, bank transactions, credit cards and bank accounts, but it doesn’t fix them. Many reconciliation tools stop once discrepancies are identified, which forces finance teams to resolve issues elsewhere.

That “elsewhere” is typically spreadsheets or Excel templates used to calculate correction journals. These manual processes introduce human error, increase manual effort and slow the account reconciliation process, especially in high-volume environments handling large volumes of transactions across multi-currency entities. This time-consuming workaround introduces risks that include:

• Added burden on finance and accounting teams already stretched thin
• Late-cycle changes that disrupt the month-end close
• Lower reliability in financial reporting and audit trails
• More exposure to error-prone, manual processes

Validation functionality inside transaction-level reconciliation tools rarely touches the actual SAP posting layer. As a result, the system cannot reconcile accounts end to end. In the healthcare example, unmatched accruals required correction journals before depreciation could run. Because those journals were not posted, downstream close management tasks stalled, consolidation was delayed and financial reporting timelines slipped. The reconciliation tool checked the box, but the close process broke.

Finance Automation closes this gap by linking transaction matching directly to journal execution. When reconciliation logic is satisfied, the platform can automatically create, route and post journals based on configured rules and approvals to eliminate spreadsheet dependency.

Resolution depends on actual journal execution

A reconciliation is only complete when correcting entries are posted to the general ledger. Visual confirmation without execution is meaningless. Yet many reconciliation tools cannot natively see whether journals tied to reconciliation items are even in flight, let alone posted.

Auditors know this weakness well. During the healthcare audit, the team was asked to prove when corrections posted, with timestamps, audit trails and supporting documentation. Without proof of posting, the team couldn’t explain how those corrections affected the broader financial data or when adjustments were reflected in reporting. The reconciliation system showed completion. The ERP showed nothing. Internal controls existed on paper but not in execution.

Finance Automation enforces reconciliation completeness by embedding the entire discrepancy resolution process into ERP-native execution. It tracks discrepancy detection, journal creation, approval workflows, posting and reversal where needed. As a result, teams get audit-ready financial records with full traceability that reduce risk management exposure and support accurate decision-making.

Why most tools create journal gaps instead of closing them

Most tools separate anomaly detection from journal processing. That architectural split forces accounting processes to span multiple systems and modules, which creates manual work outside the platform. Corrections are calculated in Excel, routed through email and posted manually through ERP interfaces or APIs that break audit trails and slow down downstream SAP jobs. Even when teams try to fill the gaps manually, the process remains error-prone because they’re relying on disconnected handoffs between people and systems.

This fragmentation impacts cash flow visibility, forecasting accuracy and consolidation timing. When account balances are corrected late, pricing assumptions shift and financial management becomes reactive. The reconciliation solution reports completion, but the financial close continues behind the scenes.

Finance Automation addresses this structurally. Built as a cloud-based orchestration layer, it unifies reconciliation, journal entry and close management in a single platform. It integrates directly with data sources, bank feeds and ERP systems and removes the journal entry automation gaps that reconciliation tools leave behind by streamlining the entire close process.

Use reconciliation to trigger real action

Finance Automation transforms transactional reconciliation from passive review into active resolution. Where traditional account reconciliation software promotes visibility and certification as its key features, Finance Automation embeds execution directly into the ERP layer so reconciliation actually results in posted journal entries. Finance Automation is the leading record-to-report (R2R) orchestration platform and is designed to execute the financial close rather than monitor it.

When reconciliation logic confirms discrepancies, Finance Automation automatically generates correcting journal entries, applies approval workflows, validates posting rules and posts directly to SAP. The reconciliation process becomes a trigger for real action instead of a reporting exercise. Account reconciliation tools no longer stop at visibility. They drive execution.

In the healthcare controller’s case, this would have changed the outcome entirely. The $2.6 million in accruals would have been posted in real time, depreciation would have run on schedule and audit questions would have been answered with system-backed evidence. Finance and accounting teams would have spent less time chasing emails and more time closing with confidence.

By orchestrating close management, automated reconciliation and journal execution across ERP systems, Finance Automation reduces manual processes, improves scalability for enterprise organizations and delivers real-time insights through a user-friendly platform.

If your dashboards look clean but your journals live in email, your reconciliation is not done, and your journal entry close is not really automated. Test your journal automation maturity and see how your reconciliation breaks down into manual journals.

Yet, in my conversations with plant managers, operations leaders and CIOs, a familiar theme keeps surfacing: progress feels harder than it should. Automation initiatives keep getting approved, but then momentum slows. Improvements arrive in pockets rather than end to end.

The data in Redwood Software’s new manufacturing automation research backs that up. Seven in ten manufacturers report automating 50% or less of their core operations. Only about a quarter say they’ve automated more than half.

This isn’t a failure of manufacturing automation or a lack of commitment. What the data points to instead is a structural limitation. You reach a plateau in automation maturity because automation often stops at system boundaries, not because you lack the right tools. Over time, your organization may have built an impressive collection of automation technology, but the connective tissue between those systems never quite materialized. Returns often flatten in this scenario because automation stops compounding, not because it never worked in the first place.

The middle-stage trap

When manufacturers described their automation maturity, the pattern was striking. Nearly half — 47% — placed themselves in the “Managed” stage, where automated processes exist but orchestration is partial. Another 26% identified as “Controlled,” with most tasks automated and orchestration present. Only about 2% described their operations as fully autonomous.

In other words, nearly three-quarters of manufacturers sit squarely in the middle automation maturity stages.

That clustering isn’t random. It reflects a ceiling most organizations hit after automating the obvious, self-contained processes. Early automation wins are straightforward: scheduling jobs, triggering reports, running batch processes, stabilizing equipment routines. These improvements deliver immediate value and reduce human error on the factory floor. But once those gains are captured, what remains is harder. 

The next level of improvement depends on workflows that span multiple systems — ERP, MES, supply chain platforms, quality systems and control systems built around programmable logic controllers. That requires orchestration, not just automation.

The challenge is that middle-stage maturity feels like success because dashboards are green and production rates look healthy. But the manual work hasn’t disappeared; it’s shifted into the gaps between automated processes, where people compensate with spreadsheets, emails and workarounds.

Where automation delivers and why connection matters

Automation delivers its strongest results when applied to processes contained within a single system. The report shows that about 60% of manufacturers have reduced unplanned downtime by at least 26%, with a meaningful share reporting reductions beyond 50%. Uptime, throughput and quality control consistently emerge as areas where automation excels.

These results are real, and they matter. They represent reduced risk, stabilized high-volume operations and improved consistency across production processes.

Challenges tend to emerge when outcomes depend on coordination across systems. 

• Inventory turns remain difficult to improve even as automation improves uptime, highlighting the limits of siloed execution 
• Data accuracy also lags, especially when information must move quickly between planning, execution and supply chain functions using real-time data

Lack of coordination isn’t limited to automation initiatives. Recent McKinsey research shows that broader disruptions — from supply chain volatility to shifting manufacturing footprints — are exposing the same structural weaknesses, where disconnected systems and fragmented decision-making limit performance even in otherwise well-run operations.

You can optimize maintenance schedules inside an MES or improve machining efficiency with CNC and control systems. Those are bounded workflows with clear inputs and outputs. But improving inventory performance requires synchronized data and decision-making across forecasting, production planning, material handling, warehouse operations and supplier networks.

When automation stops at system boundaries, single-system metrics improve, while cross-system outcomes lag. Orchestration addresses this gap by connecting existing automation into workflows that span the entire manufacturing environment.

The top bottlenecks between systems

When we asked manufacturers about their automation challenges, three issues arose most often: 

1. Forecasting accuracy gaps
2. Manual exception handling
3. Lack of integration between ERP, MES and PLM systems 

Together, these account for roughly 66% of reported bottlenecks. What’s notable is what isn’t on that list. Manufacturers aren’t pointing to weak automation technology, but to breakdowns between systems.

Exception handling is a clear example. Only 40% of manufacturers have automated it, even though 22% cite manual exception handling as a top disruption. Exceptions don’t respect system boundaries. A supply delay affects production schedules, inventory positions, customer commitments and financial forecasts simultaneously. Resolving that requires coordinated action across systems, not isolated scripts.

The same pattern appears in forecasting. Forecasts depend on timely, accurate data from many sources. When those systems aren’t connected through event-driven workflows, forecasts rely on stale information. By the time data is reconciled, the window for action has already closed.

These aren’t edge cases. And they persist not because automation has failed, but because automation alone was never designed to solve them.

Fragmented data automation 

Most manufacturers automate inside systems, not between them. The data shows that 78% have automated less than half of their critical data transfers. More than a quarter still move sensitive information through email or manual methods. Nearly 30% rely on scheduled scripts rather than event-driven automation that responds to conditions as they change.

Over time, this fragmentation compounds. Each new automation initiative delivers value in isolation, but also introduces another boundary that someone must manage. Complexity increases and manual handoffs multiply. Each additional project adds less incremental benefit than the one before it.

Manufacturing environments span decades of technology: legacy MES platforms, modern cloud applications, IoT and data collection layers and enterprise systems from multiple vendors. Connecting that landscape requires orchestration that can coordinate workflows across it all, based on events and business rules rather than schedules.

Reframing the challenge

Automation hasn’t failed the manufacturing industry. It has delivered real, measurable value where workflows remain contained. Fixed automation works. Flexible automation works. Individual automation solutions continue to advance.

What needs to change is the focus.

The next phase of automation maturity will be about connecting what’s already automated rather than adding more tools. Exceptions and handoffs — the points where risk and cost accumulate — need to become primary targets for improvement. Workflows must adapt in real time. How well you handle this shift will determine whether your manufacturing automation investment plateaus or continues to scale.

🠆 See a demo of what orchestration could look like using RunMyJobs by Redwood for SAP production planning.

What gets automation moving again

Manufacturers that climb beyond mid-stage maturity share common characteristics. 

• They automate exception handling across systems
• They connect data flows between ERP, MES and supply chain platforms
• They rely on event-driven workflows instead of scheduled scripts

These organizations are also more likely to explore artificial intelligence and machine learning use cases — not as a leap into the unknown, but as a natural extension of orchestrated operations. AI models are only as effective as the data feeding them, and orchestration ensures that data is timely, complete and actionable.

Orchestration changes the question from “What should we automate next?” to “Which workflows still depend on manual coordination?” It shifts success metrics from the number of automated tasks to the reduction of human intervention across the manufacturing industry.

The plateau is real, but it isn’t permanent. Changing your outcomes starts with changing how systems work together.

Get prepared for an orchestrated future now. Download the full “Manufacturing AI and automation outlook 2026” to see how your organization compares — and what it takes to move beyond the middle.

By 2026, most enterprises will no longer be asking whether AI belongs in their automation strategy. That debate is effectively over. The harder questions are about trust, resilience and value: 

• Can automation adapt when reality does not follow the plan? 
• Can leaders rely on it when pressure is highest? 
• Does it genuinely make the business stronger, not just faster?

These questions signal a turning point. Automation is growing up. Below are Redwood Software’s top predictions for how AI, agentic systems and automation will show up in real-world IT and operations over the next year and beyond.

1. ERP will evolve from “system of record” to “system of action”

For decades, enterprise resource planning (ERP) platforms have been treated primarily as systems of record: authoritative databases and sources of truth for the business.

That’s changing. In 2026, as AI adoption expands and agentic systems move beyond chat and analysis into execution, the ERP will still be at the center of the business. But its value will increasingly come from how effectively it drives action.

This shift has been discussed for years, but only now is the surrounding ecosystem mature enough to make it practical. Many agentic initiatives struggle today because they operate in isolation, confined to a single team, department or experimental environment. They rarely deliver sustained value without deep integration into core business systems.

Service Orchestration and Automation Platforms (SOAPs) play a pivotal role in closing this gap. By connecting ERP data models via the SOAP — the orchestration layer — that span applications, integrations and infrastructure, enterprises can move from insight to execution with greater reliability. Because it allows teams to evolve processes using AI technologies with minimal disruption, a true orchestration platform enables a business’s ERP, agentic systems and traditional services to work together, making a return on AI investment far more achievable.

Watch out: Treating agentic AI as a standalone layer outside ERP and orchestration will limit its impact. The value comes when insight, decision and execution operate as one system.

2. AI governance will move from policy to operating model

Most enterprises now have some form of AI governance framework, but few have fully operationalized it. That will change quickly. 

As AI-driven and agentic decision-making becomes embedded in day-to-day operations and core automation workflows, governance can no longer live in policy decks or steering committees alone. In 2026, effective AI governance will look much more like an operating model.

This means clearly defined boundaries for autonomous action, explicit escalation paths for human oversight and transparent validation of AI models and decisions. Just as importantly, it requires auditability that scales across complex, cross-system workflows.

Strong governance is an enabler rather than a constraint, and teams move faster when they trust the systems they rely on. Organizations that build governance directly into their automation foundations will be far better positioned to scale AI responsibly and confidently.

Watch out: Governance that lives only in policy documents will slow adoption. Governance built into workflows accelerates trust and scale.

3. Shadow AI will force agentic orchestration to the forefront of enterprise operations

As AI capabilities expand, enterprises will face a familiar challenge in a new form: shadow AI.

Just as shadow IT emerged during the early days of cloud adoption, shadow AI appears when teams deploy AI tools and agents outside enterprise guardrails. These initiatives often move quickly but operate in isolation, creating fragmentation, unpredictable downtime and security exposure from tools never designed for mission-critical use.

This fragmentation is one of the main reasons many agentic initiatives stall or fail to deliver ongoing value. Intelligence without coordination means decisions are made in isolation and can’t reliably translate across complex business environments.

2026 is the year orchestration will be widely recognized as the connective tissue that resolves this problem and makes AI useful at scale. This includes the growing role of agentic orchestration, where intelligent agents coordinate decisions and actions across workflows rather than acting as standalone tools. This year, agentic AI will move from experimentation into planning. Buyers will increasingly score vendors on “agent readiness,” asking how AI agents are governed, orchestrated and integrated into existing workflows without introducing new risk.

Rather than hardcoding every possible scenario, orchestration allows workflows to adapt in real time while maintaining visibility, accountability and control. This is what turns AI from a collection of point capabilities into something enterprises can depend on.

Watch out: Shadow AI can deliver short-term wins, but without orchestration and governance, it introduces long-term operational and security risks that enterprises cannot afford.

4. AI will amplify experienced teams, not replace them

Despite the headlines, most enterprise leaders are not trying to remove people from operations. They’re trying to remove friction. This year, AI-enabled automation will increasingly support overstretched teams by handling exception triage, diagnostics and routine decision-making more consistently and at greater scale. Skilled professionals will be able to focus on higher-value work, where judgment and context matter most.

This is already changing how teams interact with SOAPs. Natural-language co-pilots are becoming standard, helping teams build workflows and configure automations without deep scripting expertise. What once required specialist knowledge is becoming accessible to a broader range of operational and technical users.

At the same time, AI-driven anomaly detection is becoming the default for runtime operations. Instead of reacting to failures, teams increasingly rely on systems that continuously ask, “What’s unusual here?” across schedules, queues, dependencies and downstream impacts — using data that orchestration platforms already collect.

This shift is critical because the IT operations skills gap is not a future problem — it’s already here. Enterprises can’t hire their way out of complexity. AI-assisted automation offers a more sustainable path by capturing expertise and making it available when and where it’s needed.

The result is better human involvement, not less. People remain accountable for strategy and outcomes, while automation absorbs the noise that slows teams down.

Watch out: AI that only accelerates development but ignores run-time operations shifts effort, not outcomes. The biggest gains come when AI supports teams across the full automation lifecycle.

➔ 40% of automation teams don’t feel ready to adopt AI. Read the latest research.

5. Resilience will matter more than efficiency

For years, automation initiatives were justified primarily through efficiency metrics: jobs automated, tickets reduced, hours saved. Those numbers were useful, until they stopped telling the full story.

By the end of 2026, enterprise leaders will care far less about how much automation is running and far more about what it protects and enables. They’ll ask:

• Did automation prevent a disruption? 
• Did it help the business absorb change without slowing down? 
• Did it keep critical commitments on track when systems, data or partners behaved unpredictably?

As enterprises become more interconnected and event-driven, resilience becomes the real measure of process maturity. Automating individual tasks is no longer enough. What matters is orchestration: the ability to manage end-to-end processes across business domains and take corrective action when conditions change.

AI will accelerate this transition by helping automation prioritize intent over rigid execution. As agentic approaches mature, automation will increasingly be able to evaluate context, choose appropriate paths and coordinate actions across systems when conditions change midstream.

Watch out: Efficiency gains from isolated automation fade quickly. Resilience comes from orchestrating processes across domains, not optimizing tasks in isolation.

What this means for 2026 and beyond

The next phase of AI and automation will not be defined by novelty, but by trust, discipline and outcomes.

It will be essential to ground intelligence in strong operational foundations, invest in orchestration and governance and use AI to empower people and focus on orchestrating work rather than automating individual tasks. As orchestration platforms take on more responsibility, enterprises can drive transformation while lowering their total cost of ownership (TCO) by reducing tool sprawl, operational friction and rework.

Automation is no longer just about doing more with less. It’s about doing what matters most, even when conditions are far from ideal.

Want help laying the foundation for agentic orchestration in 2026? Explore Redwood’s AI hub.

This points to a deeper issue: Many organizations have automated tasks and implemented point solutions, but they haven’t fundamentally changed how work flows across their ecosystems.

Understanding why so many teams stop short of autonomous automation requires looking behind the technology curtain to examine how automation is governed and embedded into the operating model. It’s the accumulation of structural constraints that can quietly but consistently slow progress. These constraints show up less in tooling decisions and more in people and process issues.

Automation advances faster than operating models

If you introduce automation into environments that weren’t designed to support it at scale, your processes will be automated without being restructured. The risk is that ownership stays distributed and decision-making feels unclear.

There’s a practical ceiling you’ll reach in this scenario. Dependencies and exceptions will multiply, because what worked for a handful of workflows is difficult to extend across end-to-end processes. At this stage, automation won’t be slowed by technical limits, but by uncertainty around who can change what, when and under what conditions.

Autonomous automation is driven by shared accountability across IT, operations and the business. That doesn’t mean everyone owns everything, but it does mean no critical process lives entirely within one function’s control. Decisions about logic, exceptions, risk and change management have to be made in the open with a clear operating model behind them. Without that, automation can move quickly in pockets but will always stall when it reaches the seams between teams.

Complexity becomes institutionalized

The report shows that workflow complexity is the most commonly cited barrier to automation adoption. Such complexity is generally unplanned or accidental — the result of years of layered systems and incremental fixes.

Rather than being addressed directly, complexity is often worked around. Teams automate what they can without disturbing upstream or downstream dependencies. Over time, automations inherit the same structural complexity as the environment they operate in. This increases costs and makes change progressively harder to justify.

It also creates a troublesome paradox. You’re introducing automation to simplify execution, but it becomes embedded in architectures that are stuck in the proverbial mud. Autonomous automation depends on the opposite condition: predictable, observable systems designed to adapt without constant intervention.

Governance keeps automation in a holding pattern

As automation’s surface area expands, governance typically becomes more restrictive. Controls are added to reduce risk, but many times without a corresponding increase in transparency or coordination.

In practice, you end up performing cautious automation. Your teams avoid automating processes that cross organizational boundaries because changes require lengthy approvals. The automations you do have may be reliable, but they’re static and siloed.

The research shows that only 10% of organizations prioritize automation adoption at the enterprise level. This can manifest as a focus on preventing failure instead of enabling evolution. Your governance framework should support change in addition to stability.

Utilization plateaus before autonomy emerges

Most organizations own capable automation platforms, but only 27.5% fully utilize them, according to the same study. Underutilization isn’t simply a matter of missing features. It reflects how automation is positioned. Is it treated as a strategic capability or simply supporting infrastructure?

It’s common to only automate what’s immediately visible or urgent, then leave broader opportunities unexplored. You hit a plateau when you continue to do only this, normalizing automation but not expanding its reach. And it’s difficult to overcome without explicit goals tied to utilization and scale.

Autonomy requires confidence and capability

A less visible barrier to autonomy is confidence in automation itself. Many leaders hesitate to allow systems to operate without human oversight, especially when outcomes have financial, regulatory and operational consequences. That’s understandable, but only a true risk if you don’t have strong observability, auditability and recovery mechanisms in place. In which case, you have to default to manual checkpoints.

Redwood’s data suggests that organizations achieving higher levels of automation maturity tend to pair execution with visibility and control. Autonomy becomes possible only when trust in the system is established.

Be among the few that move forward

Those who progress toward autonomous automation behave differently long before they reach it. They treat automation as a coordinated capability, not a collection of tools. And they invest in simplification and accountability across IT, operations and the business — early, not after complexity has set in.

The “Enterprise automation index 2026” provides deeper insight into where most organizations stall and what differentiates those that continue to advance up the ladder of automation maturity. Use this data as a practical lens for evaluating and reworking your organization’s automation trajectory.

Few organizations achieve that kind of orchestration consistently, because their automation is fragmented. One team might manage batch jobs, and another might script data pipelines. A third could rely on manual interventions and shared inboxes to keep business processes moving.

The value of a Service Orchestration and Automation Platform (SOAP) lies in its ability to unify these silos and support the workflows that actually run the business. In its 2025 Critical Capabilities for SOAPs report, Gartner® outlines five Use Cases that demonstrate this value in action. Here’s how, in my interpretation, those capabilities show up in real operations across industries.

IT workload automation: Still essential

No matter how much technology evolves, the reliance on routine workloads never really goes away. Nightly ERP updates, hourly job chains and critical data movements between systems are fundamental processes that keep your business running.

But those workloads aren’t confined to a single mainframe or on-premises scheduler anymore. They span hybrid environments, connect to cloud-based APIs and carry tighter service-level agreement (SLA) expectations than ever before. The hard part isn’t the workload itself but the web of dependencies and recovery paths that stretch across different systems.

A robust SOAP solution lets you orchestrate all these elements in one place: SAP jobs, custom scripts, data movements and file transfers, for instance. You gain centralized control with distributed execution — the perfect balance for hybrid IT environments. I feel Gartner points to this as a foundational Use Case because it tests how well a platform performs under enterprise pressure — securely, reliably and with minimal manual intervention.

What this unlocks: With dependable workload automation, your IT teams can start each day with confidence that core batch processes ran cleanly and dependencies resolved in the right order. Not to mention, any failures were isolated and didn’t cause unwanted ripple effects. Your operational tone can shift from checking for surprises to reviewing a clean audit trail and planning ahead.

Workflow orchestration: Running the business, not just jobs

Behind every business outcome is a complex chain of tasks, approvals and exceptions that span multiple systems and departments. Take the month-end financial close: it happens thanks to finance systems, spreadsheets, validations and cross-departmental collaboration. Or consider onboarding a new hire. Beyond provisioning accounts, it requires scheduling training, initiating background checks and activating access across multiple systems.

With a SOAP platform, these workflows can be orchestrated end to end. Instead of managing each step separately, you create a unified process that flows across boundaries. You get steadier execution and cleaner handoffs, which cuts down on the small errors that tend to compound over time.

It seems Gartner emphasizes this Use Case as a marker of maturity: it’s not about more automation, but using the right automation to move the business forward. By linking actions into cohesive workflows with decision points and exception handling, you transform fragmented activities into streamlined business processes.

What this unlocks: If your workflows run end to end, you’ll feel the difference immediately. Approvals and handoffs will happen without manual nudges, and any exceptions will surface early. The work is to oversee processes instead of managing dozens of micro tasks.

Data orchestration: Automating movement and storage

Analytics live or die on the reliability of the pipeline behind the dashboard. At 3 AM, your retail data might need to move from SAP to Snowflake, be validated, then trigger an update to executive dashboards before the morning meeting. That kind of flow can’t rely on spreadsheets, email notifications or ad hoc scripts — it requires systematic orchestration.

SOAPs plug into managed file transfer (MFT) solutions, ETL tools and data lakes to manage the full lifecycle of data movement: ingestion, transformation, validation and delivery. You can build flows that validate data quality, handle exceptions and ensure downstream systems receive accurate, timely information.

I believe Gartner calls out data orchestration because the stakes are high. Poor data hygiene slows decisions, introduces risk and devalues analytics investments. With proper orchestration, your data pipeline becomes a strategic asset rather than a constant challenge.

What this unlocks: Reliable data flows remove the daily uncertainty that slows decision-making. Your analysts don’t have to wonder whether today’s numbers are safe to use. And by the time business users open a dashboard, the underlying pipeline has already done the hard work.

DevOps: Coordinating pipelines across teams

It’s relatively easy to automate a deployment, but it’s much harder to orchestrate everything that comes before and after. When your infrastructure team needs to provision environments, QA needs to run tests and compliance needs to log every step, a simple webhook or CI/CD pipeline isn’t sufficient.

SOAPs can coordinate across your entire development lifecycle, trigger event-based actions and integrate with ITSM and monitoring tools. This coordination is especially valuable when different teams use different tools but need to work together seamlessly.

In my view, Gartner includes this as a distinct Use Case because orchestration here is a force multiplier: it aligns developers, operations and compliance without slowing velocity. By automating handoffs between teams and tools, you reduce waiting time, eliminate manual coordination and maintain an audit trail of all activities.

What this unlocks: Orchestration that supports the DevOps lifecycle ensures your release cadence reflects your engineering velocity. Your dev team doesn’t have to worry whether upstream tasks are complete, and your operations team gets predictable workflows they can trust.

Citizen automation: Putting control in the right hands

Not every routine workflow warrants an IT ticket. An HR manager initiating onboarding or a supply chain planner adjusting inventory levels need their workflows to be accessible without sacrificing governance. As your organization scales, the ability to distribute automation capabilities becomes crucial.

SOAPs support low-code interaction, reusable templates and full audit trails. Users get what they need when they need it, and IT maintains oversight of the entire automation ecosystem. Gartner likely highlights this Use Case because it balances empowerment and control: you reduce shadow IT while still enabling business agility.

What this unlocks: Governed self-service changes how work gets done. You can move faster without losing control because every action runs through the same orchestrated backbone with full visibility.

Your SOAP unifies it all

Every Use Case in the Gartner report points back to a simple truth: orchestration is how you scale automation without multiplying complexity. The best SOAP platforms make that orchestration real across jobs, data, workflows and teams, providing the connective tissue that binds your digital ecosystem together.

As you evaluate your options, look for platforms that support all five Use Cases with equal strength. Your business doesn’t operate in silos, and your orchestration platform shouldn’t either. The right solution will grow with your needs, adapt to new technologies and continuously deliver value as your organization evolves.

RunMyJobs by Redwood offers comprehensive, enterprise-wide orchestration, with deep integration into SAP environments and support for hybrid cloud architectures. Download the full Critical Capabilities report to see an extended analysis of the Gartner Magic Quadrant™ and learn why Redwood was recognized as a SOAP Leader two years in a row.

Your finance and accounting SaaS tools were supposed to make finance more efficient. Instead, they’ve created complexity, disconnected workflows and competing systems that are time-consuming and don’t talk to each other. You may have adopted reconciliation, journal entry and intercompany software, but none of them address the full scope of end-to-end automation. Instead, they create the need for additional automation tools and more manual effort.

It’s time to rethink the patchwork. Learn how a platform-first strategy solution like Finance Automation by Redwood offers something different: true automation that executes your accounting and finance functions, not just tracks them.

The trap of fixing problems one tool at a time

You likely didn’t set out to build a fragmented tech stack. But when you look at your finance automation tools today, do you see a streamlined process or a collection of isolated fixes?

This happens when teams search for a solution for a problem, not a solution to the problem. You need to automate account reconciliations, so you buy a tool. Then you add another tool or module for journal entries and additional automation tools for the unaddressed manual effort. It’s logical in the moment — but over time, it creates silos.

Instead of simplifying your financial close, this approach leads to disconnected systems, inconsistent validation and more complicated audits that require constant oversight.

According to the 2025 SSON R2R automation playbook, 76% of finance leaders say automation is critical to transformation, yet only 33% have strong executive support to scale it. The results? Projects stall. ROI suffers. And finance ends up stuck in a loop of disconnected tools that never quite deliver.

It’s a familiar pattern where good intentions lead to a pile of shelfware, disconnected workflows and a finance tech stack that resembles more of a SaaS graveyard than a unified strategy.

The SaaS graveyard: When financial point tools create more problems than they solve

Most SaaS tools promise to eliminate manual work. In reality, many just shift the burden elsewhere and require you to manage handoffs between them. You might use one system for reconciliations, another to validate journals and a third to execute SAP closing tasks. But without orchestration, you’re the one bridging the gaps.

SSON’s research highlights the disconnect: 81% of finance leaders believe journal entries are highly automatable, yet just 54% have made progress. Even more telling is that only 13% are satisfied with the ROI of their financial automation solutions.

So, what’s missing? Many of these tools were built for compliance, not execution. They track approvals or store documentation but don’t handle the actual work. They weren’t designed for seamless integration or built to automate end-to-end processes across the close. That’s what sets Finance Automation apart. The platform executes tasks inside your SAP systems and minimizes your reliance on separate systems to enable faster, more accurate decision-making and capacity release to support your business needs.

When tools don’t talk to each other, finance loses visibility

Each tool introduces a new data model, interface and set of permissions. You might reconcile account balances in one system, prepare reports in another and track their status in a standalone checklist. Meanwhile, your SAP contains the truth, but your dashboards aren’t in sync.

Disconnected tools create data silos and force your accounting and finance teams to align information manually across systems. This delays reporting, increases risk and undermines confidence in your numbers.

Finance Automation eliminates this fragmentation by embedding execution and validation inside your accounting and finance systems to provide a consistent, audit-ready view of every close task and its current status.

The longer you try to squeeze more value out of disconnected tools, the deeper your organization sinks into its own SaaS graveyard.

The costs you didn’t budget for

Task-level point solutions may seem cost-effective, but their hidden costs add up fast:

• Building and maintaining custom integrations
• Continuous onboarding and training across platforms
• Delays in processing time and misaligned dependencies
• Duplicate effort from manual data entry and rework
• Inconsistent data and risk exposure across disconnected systems

SSON’s 2025 data confirms it. 88% of organizations report moderate or lower satisfaction with their automation ROI. Fragmented tools are a major reason why. However, Finance Automation avoids this spiral by offering a scalable automation model — no per-user fees and no per-task charges — just unified, coordinated execution across your accounting and financial processes.

What a platform-first strategy really looks like

A true automation platform doesn’t just plug gaps. It optimizes how you run finance. Finance Automation unifies fragmented business processes across your people, processes and technology, encompassing the entire record-to-report (R2R) process, into one connected solution.

Here’s what that looks like in real time:

• Configurable controls to support multi-entity, multi-region finance teams
• Coordinated, rules-based workflows that link one step to the next
• Live views that show current status, bottlenecks and ownership
• Native SAP execution
• One shared data model for financial operations, tasks and compliance records

Instead of managing work, Finance Automation completes it. Instead of tracking outcomes, it delivers them.

Move from tactical fixes to strategic execution

Some accounting and finance teams confuse adoption with impact. If your automation is still dependent on people to push it forward by having them launch jobs, confirm steps and update dashboards, you’re still running the process manually. You’ve just added more interfaces.

Finance Automation takes a different approach. It removes manual intervention by design. The platform handles execution in SAP, tracks validation and results automatically and empowers your team to focus on what matters: analysis, strategy and making faster, smarter strategic decisions.

Instead of plugging gaps with more tools, Finance Automation helps you orchestrate your tech stack across people and processes to streamline your R2R operations with consistency and clarity.

Ready to push beyond the SaaS graveyard?

If your tech stack is full of disconnected financial and accounting software and your results still depend on manual processes, it’s time for a new approach. Finance Automation’s platform-first strategy gives you the execution power and scalability that task-level point tools can’t.

Instead of reacting to inefficiencies, you can start removing them. Instead of working around delays, you can eliminate them. And instead of managing a graveyard of SaaS, you can finally build the foundation for modern, connected finance.

Curious what your tech stack is really costing you? Explore the ROI of an end-to-end finance automation platform built to scale.

This year, we have proudly welcomed over 100 new customers to Redwood. Our partnerships span the globe, as we collectively now serve over 7,600 customers in more than 150 countries. This growth highlights how organizations are embracing true end-to-end automation, and we believe the success our customers have achieved has played a significant part in this growth. 

We’re inspired by the commitment our customers show in helping others realize the power of full stack automation. Filled with numerous speaking engagements, webinars and insightful conversations that made our shared vision a worldwide reality, this year has been exceptional.

Let’s take a look back at some of the most memorable moments of 2025.

Center stage: Event speakers

Sharing your success stories at major industry events provides invaluable, authentic insight. The customer sessions this year detailing the real-world business impact achieved with Redwood were truly inspiring.

Eugene Water & Electric Board

At the SAP for Utilities event in Denver, Leif Utterstrom and Prita Mani from Eugene Water & Electric Board (EWEB) detailed how RunMyJobs is enabling autonomous execution of complex processes like meter-to-cash while strengthening their core operations. They explained how they transformed resource-intensive work into faster execution and better business outcomes. 

RS Group

Dharmesh Patel spoke at SAP Sapphire Madrid about how RS Group now manages over one million global customers using RunMyJobs by Redwood for supply chain optimization on SAP via Amazon Web Services (AWS). The company runs approximately 150,000 executions per day to cater to its key SAP business processes.

Schneider Electric

Schneider Electric showed us how to reshape the financial close and what an 80% reduction in manual effort looks like. Stefano Oliveri hosted a workshop at Shared Services and Outsourcing Week (SSOW) Europe, where he shared how the company moved from fragmented record-to-report (R2R) processes to integrated automation strategies. With Finance Automation by Redwood at the center, they saw 86% faster close tasks and increased compliance without increasing workload.

On the air: Winning webinars

Redwood customers brought their expertise straight to the community this year through enlightening webinars and user group sessions. The major takeaway for 2025? It’s all about cost reduction and shifting focus from manual tasks to high-value strategy.

Sabari Swaminathan of Energy Transfer detailed how Finance Automation saved their accountants 45,000 hours annually, freeing them up for strategic analysis instead of time-consuming data entry. Watch the on-demand webinar here. 

In a similar vein, Mary Shiena Johnson from Siemens Global Business Services showed exactly how Finance Automation cuts labor costs and accelerates the R2R close, proving the tangible financial impact for Siemens.

Our user groups were filled with practical insights from the true experts — the people using Redwood products every day. We saw great contributions from Srikanth Nellutla (CONA Services), Srinivas Udata (Corebridge Financial) and Sumit Sinha (HHS Technology Group) at the RunMyJobs and JSCAPE by Redwood sessions, helping the community learn best practices and accelerate their own automation journeys. 

Don’t miss out on this collective wisdom — learn more about joining a user group. 

A special thanks to our most engaged advocates

While every advocate’s effort makes a difference, we want to give a special nod to those who participated in an exceptional number of activities this year.

🏆Top advocates of 2025

• Charles Sheefel from International Paper: Charles was deeply engaged this year, participating in multiple Customer Advisory Board meetings, speaking at our global kick-off and offering his insight in numerous conversations with customers and industry experts alike. Thank you!
• Daniel Sivar from American Water: Daniel engaged in Customer Advisory Board meetings, spoke on the panel at our global kick-off, recorded a video testimonial and even took last-minute reference calls. We can’t thank you enough for the time and effort you’ve put in, thank you!
• Darrin Ward from Energizer: Darrin has graciously lent his time and expertise for multiple reference calls and industry analyst conversations, plus internal feedback meetings that will help shape the future of Redwood. Thank you!

We are so grateful to all of our advocates for sharing their expertise and automation journeys this year. A heartfelt thank-you to all!

Join the movement in 2026

Your incredible efforts directly help other organizations see how Redwood’s automation fabric solutions can empower them to orchestrate, manage and monitor their mission-critical workflows.

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As soon as teams began trying to extend AI beyond isolated tasks — past the browser tab, outside the development environment or into workflows that cross departments — progress stalled. The models were perfectly capable, but in most cases, the enterprise wasn’t ready to support them.

AI today largely operates in silos:

• Summarizing a document in one tool
• Generating a draft in another
• Answering a question inside a chat window

Those applications are useful, yes. But transformational? No. And certainly not autonomous.

The next phase of AI will operate very differently. Agentic AI promises to reason, plan and participate in the work, not just advise on it. For any AI system to influence real business processes, the organization must first create the environment to support it.

It’s critical to build a foundation for the next decade of AI to operate with clarity, coordination and control.

Why leaders often think they’re ready

When AI experiments stall, the reflex is to look at the model.

• Should the prompt be rewritten?
• Should the model be retrained? 
• Should the team switch providers?

In fact, most AI slowdowns have nothing to do with model quality. They’re caused by the operational surface the model enters. Across enterprises, the same foundational gaps appear again and again, regardless of industry or scale.

1. Work happens in silos. AI has no shared control layer. Automations, scripts, SaaS workflows and departmental tools all run independently. This fragmentation increases the likelihood of “shadow AI” — and the blind spots in security and cost that come with it.
2. Every department uses different guardrails. Access, approvals and policies vary wildly across teams. AI simply can’t follow rules that don’t exist consistently.
3. Workflows assume predictability, but reality doesn’t. Static, rule-based logic breaks the moment conditions change. AI becomes another exception handler instead of a force multiplier.
4. Leaders lack cross-system visibility. Throughput, failures, bottlenecks and downstream impacts are scattered across tools. You can’t operationalize intelligence you can’t see.

These gaps don’t make agentic AI unrealistic, but they reveal what’s missing. To safely give AI the ability to plan and act, enterprises need coordination, governance, adaptability and visibility working together under a unified orchestration approach.

Before autonomy: The architectural fundamentals

Across enterprises making real progress toward AI readiness, one theme is clear: they’ve perfected the architecture underneath the model. These organizations are doing more than just experimenting with clever tools. They’re building the conditions for intelligent systems to operate safely and consistently.

Unification: One orchestration layer to coordinate the work

Imagine an AI system evaluating a delivery delay. It checks order data in one application, inventory in another, customer records in a third and workflow timing in a fourth. Without orchestration, those steps become disconnected guesses. With it, they become a single, synchronized, visible and aligned action path governed by business rules.

A unified layer provides the control plane that keeps all forms of work — human, automated or AI-assisted — moving in the same direction.

Boundaries: Guardrails for scaling intelligence — not risk

Guardrails vary in format, but they all answer the same question: What is safe for this system to do? Instead of a long list, the most effective enterprises keep it simple with:

• Actions that are always permitted
• Actions that require verification or approval
• Actions that are never allowed

When these rules are applied consistently across departments, intelligent behavior becomes predictable. AI stops guessing how decisions should work and starts following the same standards everyone else does.

Transparency: Governance that keeps humans in control

As soon as automation can influence workflows, visibility becomes non-negotiable. Leaders need to see how a decision unfolded, what it touched and why it behaved the way it did. That requires:

• Observability into processes
• Clear documentation of decision paths
• Audit trails that withstand scrutiny
• The ability to unwind or adjust actions when needed

Governance turns autonomy into something accountable, rather than opaque.

Coexistence: A blended environment of deterministic and dynamic automation

Enterprise leaders sometimes assume they must choose between traditional automation and AI-driven adaptability, but the highest performers do the opposite. They preserve their deterministic backbone: the scheduled workflows, validations and rule-based logic that keep operations steady. Then, they layer adaptability where variability actually occurs.

In other words, it’s reinforcement, not replacement. Rule-based processes handle what is predictable, adaptive decision loops handle what isn’t and orchestration brings the two together.

How experimentation becomes an operating model

AI experimentation is happening everywhere at once. Marketing might test a summarization tool, Finance could be exploring anomaly detection and Operations may pilot an automation assistant. The activity is high, but the impact is uneven. Some pilots work, others stall and many echo work already happening elsewhere in the organization.

What’s missing is structure. Modern AI only becomes meaningful when it’s connected, governed and repeatable. That requires shifting from scattered experimentation to an operating model that gives every team the same foundation to build upon.

Read more about building the best foundation for agentic orchestration.

A platform-first evolution in automation

The transformation underway resembles the moment when analytics matured from isolated dashboards into full data platforms. AI is undergoing a similar transition. What begins as a collection of tools eventually becomes an operational discipline shaped by shared infrastructure, shared controls and shared context.

In practice, this means we have to start thinking differently about how AI gets introduced and supported. Investment decisions move away from individual tools and toward foundational capabilities that every team can rely on, like interoperability and visibility. Talent evolves as well, with roles focused on designing supervised automation, not just building models in isolation.

Metrics also expand. Instead of measuring AI success through cost savings alone, executives are beginning to track the health of end-to-end processes: throughput, order delivery rate, consistency, service quality and customer satisfaction, for example. These are the signals that show whether the enterprise is truly becoming more adaptive.

Risk posture changes, too. Rather than waiting for AI to cause a problem, leaders establish guardrails and safety patterns before AI touches a core workflow. True autonomy starts with boundaries.

This evolution marks a larger shift: the move from experimenting with AI to preparing the enterprise for it. When you treat orchestration and governance as shared capabilities instead of departmental add-ons, innovation becomes faster, safer and easier to scale. AI moves from being something scattered teams try out to something the entire organization can trust.

What agentic orchestration will unlock (when the foundation is ready)

Agentic AI at scale remains a future capability, but the directional value is already clear. Once you have orchestration, governance and interoperability in place, you can unlock an entirely new class of capabilities:

• Systems that adapt faster than conditions can destabilize them
• Cross-system decision-making that reflects real business context
• Self-service interactions where users request outcomes, not workflows
• Operations that continue running even when inputs, timing and exceptions change
• Insight that spans applications, dependencies and data in motion

Your teams can gain a level of clarity, context and control that may be elusive today.

The advantage will go to those preparing now

Organizations making progress toward autonomous operations share a common pattern. They’re not racing toward agentic AI, but building the scaffolding that will support it.

That means they’re:

• Consolidating automation under a unified orchestration layer
• Strengthening governance to define how decisions and actions occur
• Insisting on interoperability across systems and tools
• Using AI assistance to improve deterministic workflows
• Piloting new AI patterns in controlled, low-risk environments
• Defining KPIs that reflect throughput, delivery, consistency and service quality

Preparation accelerates innovation, creating an environment where AI can be introduced safely, evaluated clearly and scaled confidently. Enterprises that begin now won’t just be ready for agentic AI. They’ll be structurally positioned to benefit from whatever comes next.

To explore the now, next and beyond of AI, read “The autonomous enterprise” and get a deeper look at how orchestration, governance and preparation shape the path to more intelligent operations.

It’s a fair question. And the answer isn’t just focused on why you should add another automation tool. Instead, considering a Service Orchestration and Automation Platform (SOAP) means you’re ready to think about the operational model behind how work moves across your organization.

A scheduler triggers tasks, an iPaaS connects applications, but a modern SOAP coordinates end-to-end business processes across systems, teams and environments in a way that maintains reliability at enterprise scale. That difference shows up directly in operational resilience, business agility and cost control.

The 2025 Gartner® Critical Capabilities for SOAP report is the clearest framework I’ve seen for tying platform strengths to financial outcomes. Here’s how I help leaders like you use that framework to build a credible business case.

The framework: Mapping Use Cases to your P&L

The Critical Capabilities report doesn’t start with architecture diagrams or methodology. It starts with how platforms perform against five operational Use Cases, each representing a measurable part of your business. I find these especially useful because they line up almost perfectly with the major categories of cost, risk and productivity that executives care about:

• Operational resilience
• Business agility
• Cost optimization
• Risk management
• Speed to insight

Instead of thinking of them as technical buckets, think of them as the five pillars that determine whether your automation investments actually return value. A SOAP that scores well across all five transforms automation from a technical initiative into an engine for enterprise performance.

Pillar 1: IT workload automation and the ROI of unbreakable operations

The business challenge:
A pervasive pattern I see is IT teams stuck in a reactive mode. Excessive time is spent on firefighting and manual monitoring, which draws focus away from strategic process improvement. This reactive posture results in costly consequences, including missed service-level agreements (SLAs), silent failures in critical overnight processes and a constant backlog of expensive incidents.

What this Use Case measures:

Gartner considers this the foundation of SOAPs: Can the platform run critical workloads reliably across hybrid and multi-cloud environments? Think financial close, inventory syncs, regulatory reporting — with real-time awareness and automated recovery. This means it must offer dependency management that understands system context, recovery paths that prevent cascading failures and observability that lets operators diagnose and resolve issues quickly.

Where the ROI shows up:

• Reduced downtime costs: Preventing failures before they hit the business
• Lower operational overhead: Fewer hours spent monitoring or intervening
• Strategic consolidation: Eliminating multiple schedulers, licenses and skillsets

This is the first place most organizations find real cost savings, because reliability is expensive when you’re compensating for it manually.

Pillar 2: IT workflow orchestration and the value of cross-team agility

The business challenge:
Most delays don’t come from individual tasks. They come from the handoffs: the approvals that get stuck in someone’s inbox, the data that wasn’t validated, the system that didn’t trigger the next step. Teams often automate inside their own domains but leave the gaps between them unmanaged.

What this Use Case measures:
Gartner looks at how well a SOAP can coordinate entire processes, not just tasks:

• Cross-application workflows (ERP + ITSM + SaaS + custom apps)
• Conditional logic and exception handling
• Orchestration spanning on-premises and cloud environments

Where organizations see ROI:

• Shorter cycle times: End-to-end processes move without waiting on human intervention
• Higher throughput: Fewer restarts, errors or duplicate work
• Greater adaptability: Workflows that adjust as business requirements change

The payoff is simple: people get hours back. Not to mention, change doesn’t feel risky anymore.

Pillar 3: Data orchestration and the payoff of faster, smarter decisions

The business challenge:
Analytics teams can only move as fast as the data feeding them. Many organizations are still juggling multiple disjointed ETL solutions, insecure file transfers or inconsistent handoffs between systems. The result is predictable: delays, inconsistent data and missed windows for decision-making.

What this Use Case measures:

Gartner evaluates a SOAP’s ability to orchestrate reliable, governed data pipelines:

• Event-driven movement from systems like SAP to data warehouses like Snowflake
• Managed file transfers with dependency tracking
• Data validation, reconciliation and exception handling
• Automated triggers to BI, AI or other downstream applications

Where organizations see ROI:

• Faster time-to-insight: Data arrives validated and on time
• Improved compliance: Centralized audit trails remove the risks of custom scripts with no single source of truth
• Eliminated bottlenecks: Analytics teams spend less time waiting and more time analyzing

This is where organizations often unlock value they didn’t realize they were losing.

Pillar 4: Citizen automation and the advantage of empowered teams

The business challenge:
IT teams become bottlenecks when every routine request — from report generation to onboarding steps — has to be manually actioned. The backlog grows and the business slows.

Yet handing automation directly to business users without governance isn’t an option.

What this Use Case measures:
Gartner evaluates this capability by looking at how well a platform can distribute automation safely without losing control. It’s essentially a test of whether your Operations team can create guardrails that let business users trigger approved workflows on demand without introducing risk. A strong score here reflects a platform that supports low-code execution, reusable templates and full auditability, so non-technical users can initiate routine actions while IT retains oversight. This Use Case ultimately measures how effectively a SOAP can push automation closer to the edge of the business without allowing fragmentation or shadow IT to creep back in.

Where organizations see ROI:

• Faster turnaround: Teams get what they need without waiting days or weeks
• Reduced IT ticket volume: Freeing technical staff to focus on higher-value work
• Fewer errors: Standardized workflows eliminate risky and error-prone manual steps

When done right, citizen automation is not “shadow IT.” It’s a controlled extension of enterprise automation.

Pillar 5: DevOps automation and the competitive edge of continuous delivery

The business challenge:
Software teams often automate their CI/CD pipelines but leave the surrounding processes — environment provisioning, test data setup, dependency coordination — untouched. Those manual steps are what slow releases and introduce inconsistencies.

What this Use Case measures:
For DevOps automation, Gartner focuses on how deeply the platform can integrate into modern delivery pipelines and how reliably it can coordinate the steps surrounding deployment. It’s about assessing whether automation can move at the same pace as engineering, from provisioning and testing to promotion and release. High-performing platforms demonstrate support for automation-as-code practices, event-based triggers and consistent orchestration across environments. Use these parameters to gauge a provider’s ability to remove bottlenecks from the software lifecycle so teams can deliver changes quickly without compromising reliability or governance.

Where organizations see ROI:

• Shorter release cycles: Faster, safer path from commit to production
• Higher developer productivity: Fewer manual tasks around the deployment lifecycle
• More reliable deployments: Consistency enforced across environments

This is increasingly a strategic differentiator for teams moving toward cloud-native delivery models.

An undeniable case for a strategic investment

A scheduler reduces manual effort, whereas a SOAP reduces friction across your entire operating model. When a single platform delivers across all five Gartner Use Cases, you’re not just buying automation capabilities — you’re buying:

• Fewer outages
• Faster decisions
• Higher team velocity
• Lower integration costs
• Stronger risk posture
• A future-proof automation foundation

This is the business case every CFO wants: clear outcomes tied to operational, financial and strategic value. The next time you evaluate platforms, don’t ask, “What does it automate?” Ask, “Where does it impact my P&L?”

That’s the difference between a tool and a transformation partner.

Evaluate SOAP vendors with our scorecard, and download the full Gartner Critical Capabilities report to compare how leading platforms perform against these five essential Use Cases.

Starting with RunMyJobs by Redwood version 2025.4, that vision is on its way to full realization. Redwood RangerAI is live and integrated across the platform, bringing embedded AI assistance and AI-driven development to the automation solution enterprises already trust to run their mission-critical operations.

AI built for the enterprise

Redwood RangerAI is a product of Redwood Software’s 30-year legacy of enterprise-grade automation. From contextual guidance to AI-assisted workflow creation, its capabilities are built directly into RunMyJobs, not bolted on. It’s AI designed to work within your governed, secure automation environment.

All of Redwood’s solutions are shaped by direct, high-touch engagement with customers, including regular feature-focused user advisory boards and strategic customer advisory boards that directly impact near-term and longer-term roadmaps. 

Redwood RangerAI is no exception. Redwood’s teams have focused first on delivering practical upgrades to accelerate users’ tasks and cut down on their backlogs. These focused enhancements lay the groundwork for upcoming AI capabilities that will further expand your ability to build and scale automation within your enterprise.

What’s new in RunMyJobs 2025.4

Redwood RangerAI introduces a range of AI-powered features that help users learn, build and operate faster within a secure framework.

Redwood RangerAI Product Assistant for RunMyJobs

Get guidance right where you work

Embedded directly into the RunMyJobs interface, the Product Assistant provides on-hand guidance tailored to your tasks. It can:

• Suggest one-click error resolutions and best practices
• Reduce reliance on specialized knowledge and support tickets
• Accelerate onboarding for new users

With dynamic, situational guidance, the Product Assistant makes RunMyJobs more intuitive for every user, not just automation experts.

Redwood RangerAI Automation Co-pilot for RunMyJobs

Build better automations, faster

The Automation Co-pilot helps teams translate intent into execution. Using natural-language input, it can automatically generate scripts, complete with built-in guardrails to maintain compliance and prevent common errors.

Engineers can now focus on design and innovation, not repetitive maintenance. The Automation Co-pilot dramatically shortens the cycle from concept to deployment while producing consistent, high-quality outputs that align with enterprise governance standards.

A robust, intelligent ecosystem

Redwood RangerAI doesn’t stop at RunMyJobs. Its intelligence extends across Redwood’s entire product portfolio to give you a consistent experience from discovery to deployment.

Redwood RangerAI Learning Assistant

Learn continuously

Available through Redwood’s public documentation site, the Learning Assistant gives users 24/7 conversational access to product knowledge. It provides instant answers to technical questions, with precise contextual references. This reduces the learning curve for new features and capabilities, in addition to helping users find the exact information they need without manually searching.

Redwood RangerAI Support Assistant

Resolve issues instantly

Integrated within the Redwood Support portal, the Support Assistant instantly analyzes issues and suggests resolutions for common scenarios. It reduces first response time to seconds (every time) and allows technical experts to focus on higher-value challenges.

A foundation for agentic orchestration

Redwood RangerAI is the next step in your enterprise’s journey toward true autonomy. With its capabilities now embedded across RunMyJobs, your enterprise gains the secure footing you’ll need to evolve from deterministic workflows to goal-driven, adaptive automation.

Designed to support industry standards such as the Model Context Protocol (MCP) and Agent2Agent (A2A) protocol for interoperability, Redwood’s open, future-proofed approach allows you to securely connect to your broader agentic ecosystem, including SAP Joule. Your AI systems can make informed decisions while IT retains complete visibility and control.

Redwood provides the trusted bridge to carry you from today’s automation to tomorrow’s autonomous enterprise, ensuring you can leverage existing investments and scale AI securely.

The business impact

Every organization adopting Redwood RangerAI has one thing in common: a drive to accomplish more with the same resources. These intelligent capabilities amplify human efforts, automating what can be automated and guiding what requires expertise. Most importantly, learning continuously from both. Here’s what Redwood RangerAI can enable for your teams.

Redwood RangerAI doesn’t replace people. It extends their reach, generating measurable improvements in efficiency, accuracy and time-to-value.

What it means for you

Already a RunMyJobs customer? Redwood RangerAI capabilities are available now as part of the 2025.4 release. SaaS customers will automatically benefit from the Redwood RangerAI Support Assistant, and upgrade options are available to activate additional features. Take a tour of Redwood RangerAI in RunMyJobs.

To experience what’s next in AI-powered automation, visit the AI hub or request a personalized demo of RunMyJobs and Redwood RangerAI.

Similarly, in the IT world, your organization cannot leap overnight from task-level automation to full enterprise orchestration. You must build the capabilities carefully.

The 2025 Gartner® Magic Quadrant™ for Service Orchestration and Automation Platforms report flags that we are at a pivotal inflection point: what was once the domain of job schedulers and workload automation is now evolving into enterprise orchestration fabrics that span IT, business services, data pipelines and multi-cloud ecosystems. The vendors leading this shift are not just automating but orchestrating. And the business consequences could not be clearer.

We’ll walk through five stages of evolution, from manual operations to autonomous orchestration, framed in the language of orchestration, and pull insights from the Magic Quadrant™ on what to watch for as you chart your next move.

Stage 1: Manual operations

The pre-automation era

Before any driver-assist features, the human driver handled everything: steering, braking, monitoring blind spots, judging distances. In this phase, your automation may not yet be formalized. Work is isolated. Dependencies are hidden. Risk is unmanaged. 

As Gartner notes, the shift away from purely workload automation is driven by exactly this: organizations finding that scheduling jobs and hoping they succeed is no longer enough. 

Key action: Inventory the manual work: what processes are being run by hand, what dependencies are invisible and which handoffs are subject to errors or delay? Capturing the map of manual operations is the first step toward orchestration.

Stage 2: Assisted automation

The rise of workload automation

Just as cars added features like cruise control, lane-keeping or adaptive cruise assist, organizations added scheduling tools, workload automation platforms and event-driven triggers. At this stage, you’re automating within domains: IT ops can schedule jobs, data teams can automate transfers, Finance may have some back-office automation — but it remains siloed.

In Gartner’s framing, however, this stage is no longer sufficient, considering the future of automation will be AI-driven. The market shifted to SOAPs precisely because these assisted workload platforms cannot orchestrate across hybrid, multi-domain, service-oriented environments. 

Key action: Push beyond individual automations. Ask: Which tasks are being automated but still reside in silos? Which workflows span applications and teams and, yet, are still manually handed off? Start identifying cross-domain opportunities for integration, handoffs and orchestration.

Stage 3: Coordinated orchestration

SOAPs transform the enterprise

Now, we reach the point in automotive evolution where vehicles begin to talk to one another, sharing speed, position and intention. It’s no longer isolated, driver-assist features but real coordination. 

In enterprise terms, this is where orchestration comes into play. A platform can integrate across multiple domains (IT operations, business services, data pipelines), across environments (on-premises, cloud, edge) and across organizational boundaries (business users, DevOps, operations).

This is exactly the space Gartner defines for SOAPs:

“Service orchestration and automation platforms are essential for delivering business services through complex workloads. SOAPs unify workflow orchestration, workload automation and resource provisioning, extending across data pipelines and cloud-native architectures.” 

In the 2025 Magic Quadrant™, vendors who sit in the Leader quadrant are those who already deliver across those boundaries. One of the key shifts is moving from automation as tasks to orchestration as services. Workflows are no longer just sequences of jobs but deliverables to the business. 

Key action: If you’re in this stage (or aiming for it), your focus should shift from “How many tasks are automated?” to “Which end-to-end services do we deliver, how well are they orchestrated and how visible and responsive are they?” Define service-level outcomes, identify orchestration gaps and consider a platform that supports orchestration rather than just job scheduling.

Stage 4: Intelligent orchestration

Where SOAPs are heading

The next generational shift in driving is full autonomy, when cars not only sense lane, distance and speed but adapt to traffic, make decisions and even anticipate hazards. The comparable shift in orchestration is when platforms begin to embed intelligence, analytics, machine learning and predictive capabilities, turning from reactive to proactive.

Gartner’s commentary on SOAPs points to this evolution from scheduling. Orchestration enables business outcome optimization, real-time responsiveness, hybrid execution and data-driven insights. 

What makes this stage distinct:

• The platform monitors SLA slippage, process deviations and event patterns and intervenes automatically
• It adapts workflows based on business outcome metrics, not just runtime metrics
• It integrates across domains (IT, business, data) with a unified observability and orchestration layer

Key action: Ask whether your orchestration approach is still reactive (executing defined workflows) or becoming intelligent (monitoring, adapting, optimizing). Consider adding observability dashboards, SLA tracking, anomaly detection and business-metric alignment. Ensure that your orchestration platform supports and surfaces these capabilities.

Stage 5: Autonomous orchestration

The inevitable destination

In the automotive metaphor, this is fleet-wide coordination, vehicles and infrastructure orchestrating together — with no human driver in the loop. In enterprise automation, this is where orchestration spans entire business ecosystems: external partners, supply chains, digital services and beyond. Platforms anticipate demand, compose new service flows, self-heal and self-optimize.

The Gartner report points toward this future: by 2029, Gartner predicts “90% of organizations currently delivering workload automation will be using service orchestration and automation platforms (SOAPs) to orchestrate workloads and data pipelines in hybrid environments across IT and business domains.” 

Thus, the destination is a space where orchestration is the only way to stay competitive. Businesses that stay in scheduling or siloed automation risk being outpaced.

Key action: Visualize not just automation within your walls, but orchestration across your value chain. Consider how automation fabrics can link partner systems, customer-facing services, external data flows and more across business functions. Begin constructing the operational model for autonomous orchestration: monitoring, governance, AI-assisted workflows and outcome-based orchestration.

Accelerate your journey

Gartner is signaling that this shift has already arrived. The question isn’t whether enterprises need orchestration platforms but who will have them and how effectively they’ll deploy them. The vendors in the Leader quadrant have already embraced the orchestration fabric, not just automation modules. 

For CIOs, heads of infrastructure and operations (I&O) and automation leaders, here are the implications:

• Siloed automation tools (task → script → schedule) are no longer sufficient for scale, complexity or agility
• A SOAP platform becomes the anchor for hybrid, cloud and business service-oriented orchestration
• The technology investment moves from standalone tools to orchestration fabrics: connectors, observability, low-code orchestration and event-driven services

Your roadmap must reflect the above stages of automation and orchestration maturity to ensure you can deliver business outcomes at speed. Use analyst frameworks (like the Magic Quadrant™ and Critical Capabilities) as strategic lenses — not just vendor checklists — to benchmark your progress and maturity.

Just as automakers gradually moved through mechanical driving, driver assist, autonomy and vehicle-to-vehicle coordination, enterprises must traverse these orchestration stages deliberately. And the 2025 Gartner Magic Quadrant™ for SOAP provides the framework for what good looks like today and which vendors are leading the charge.

Download the full report now and use it to choose a partner with a proven track record of enterprise orchestration.

Applying clean core strategies reduces complexity by emphasizing configuration instead of customization. With less customized code buried in the ERP, updates install cleanly, integrations behave predictably and technical debt can be avoided as much as possible. It also shortens the time it takes to roll out new functionality and enhancements, so the system evolves in step with your business.

When clean core principles are paired with orchestration and side-by-side extensibility, your enterprise can handle complex operations without sacrificing flexibility or creating long-term maintenance issues.

What is an SAP clean core?

A clean core describes an SAP Cloud ERP or SAP S/4HANA on-premises environment that remains as close as possible to standard SAP. Enhancements are built externally using approved extensibility frameworks such as SAP Business Technology Platform (BTP). This keeps the foundation stable and cloud-ready while giving your teams room to innovate.

A clean core strategy enables consistent upgrades and simpler integration of new SAP and non-SAP capabilities. Guiding principles include:

• Relying on standard SAP functionality whenever possible
• Extending through platforms like SAP BTP
• Governing all custom development to maintain transparency
• Preserving compatibility across future SAP releases
• Allowing key user extensibility without altering core logic

ASUG calls clean core a foundation of agile ERP and business transformation. Standardization accelerates innovation, trims long-term maintenance and streamlines compliance while leaving room for business-specific differentiation.

At SAP Sapphire Orlando, SAP leaders reiterated that clean core is not optional anymore. It’s essentially a baseline for every modern ERP strategy.

The problem with customizations: Technical debt and inflexibility

Many long-standing SAP landscapes still rely on layers of custom ABAP code built to address one-off requirements. Over time, these changes pile up, forming a cluttered landscape of custom code. The further a system drifts from SAP standard, the more expensive, fragile and time-consuming every future change becomes.

Common consequences include:

• Upgrade delays: Each new release requires re-testing and re-work
• Higher total cost of ownership (TCO): Maintenance grows harder as custom logic ages
• Reduced adaptability: Connecting new technologies, such as AI or analytics, becomes more complicated
• Performance concerns: Additional code can slow processing and increase resource use
• Security exposure: Non-standard modifications introduce potential vulnerabilities

NTT DATA notes that once an ERP environment diverges from SAP standard, even minor upgrades demand heavy manual validation.

Reducing this dependency sits at the heart of the clean core approach. Externalizing logic and integrating through APIs rather than internal code changes restores agility and simplifies ongoing maintenance.

How future-ready orchestration enables clean core extensibility

Maintaining a clean core while still running large, interconnected business processes efficiently requires coordination. Orchestration is the answer.

Modern Service Orchestration and Automation Platforms (SOAPs) can link SAP and non-SAP systems without embedding logic in the ERP. They handle workflow sequencing, manage dependencies and automate handoffs across applications. Instead of writing isolated scripts, your IT teams gain a central control layer that unifies automation.

The advantages are practical:

• Streamlined integration through standardized APIs and connectors
• Faster response to business or process changes
• Less testing and regression effort with each upgrade
• Centralized visibility and stronger governance
• Consistent behavior across hybrid and multi-cloud landscapes

With orchestration managing process flow, the SAP core stays clean, upgrade-safe and easier to evolve. As a result, you reduce the technical debt that too often slows and, what’s worse, limits innovation in transformation projects.

Leveraging SAP BTP for extensibility

SAP BTP provides the official foundation for extensibility in SAP Cloud ERP. It lets your teams build, integrate and operate new applications that enhance functionality without altering core code.

As outlined in SAP’s ”Clean core extensibility” whitepaper, BTP supports upgrade-safe enhancements through ABAP Cloud, seamless integration via standard APIs, low-code application development with SAP Build and workflow automation, without modifying core ERP logic.

When orchestration platforms tie into BTP services, they can initiate workflows, move data securely and synchronize systems across cloud and on-premises environments. So, they keep the ERP core stable while allowing innovation where it’s needed most.

SAP BTP also supports a wide range of extensions and SAP Business Data Cloud (BDC), from analytics and planning to integration and data orchestration. Solution services like SAP Analytics Cloud (SAC), SAP Datasphere and Integration Suite help teams enhance functionality without modifying ERP code.

By automating the flow of data between S/4HANA, BTP services and analytics platforms, you avoid embedding custom logic or reporting scripts in the ERP. This approach keeps core processes standard and ensures upgrades remain predictable — even as your business intelligence needs grow.

Clean core in practice: RISE with SAP

RISE with SAP offers a managed path to SAP Cloud ERP that bundles software, infrastructure and services. Clean core methodology is fundamental to that journey.

Legacy customizations often complicate RISE migrations, introducing extra testing cycles and unplanned delays. Systems designed around clean core principles migrate more easily, adopt future releases sooner and need less remediation and oversight afterward.

Orchestration strengthens this model by automating the cross-system activities that typically require manual oversight, such as:

• Financial close and reporting
• Order management and fulfillment
• Supply chain coordination and demand planning
• Master data synchronization
• Billing and receivables management

➡️ As described in detail by SAP in this community post, RunMyJobs by Redwood is part of the RISE with SAP reference architecture and is, thus, SAP’s recommended solution. RunMyJobs managed services, delivered by the SAP ECS team, offer a compliant and lower-risk approach to modernizing job scheduling within a RISE environment. Rather than relying on OS-level scripts or unmanaged third-party agents installed without oversight and control on application servers, SAP ECS provides managed connectivity and oversight for RunMyJobs. This enables customers to execute OS-level workflows, including file transfers, in a standardized, cloud-compliant way.

Automating key business functions while keeping the core clean

Clean core methodology supports automation without compromise. By managing automation outside the ERP core ABAP code and integrating through APIs and other SAP-approved methods, you maintain system integrity while improving efficiency and accuracy.

Finance automation

Financial closing often involves repetitive manual steps that slow cycle times. Orchestration can handle reconciliations, journal entries and accruals automatically using SAP standard interfaces. Enterprises using Finance Automation by Redwood have reported significant time savings — in some cases up to 90% — across record-to-report processes. It allows you to execute these tasks externally, maintaining upgrade safety and compliance with clean core principles while supporting continuous, touchless close capabilities.

Data excellence and migration preparation

Migration success depends on both clean code and clean data. Archiving, validation and cleansing are essential to maintaining data quality and readiness for future releases.

Automation coordinates these steps end-to-end, optimizing accuracy and consistency throughout migration. It minimizes manual effort and reinforces data governance across the enterprise, supporting the same discipline that defines clean core strategy.

A foundation for future SAP innovation

Clean core is a commitment to long-term stability and adaptability. It simplifies upgrades, accelerates adoption of new SAP capabilities and creates a predictable base for innovation. As enterprises extend their automation and data strategies, BDC adds another layer of value by connecting data across applications and business processes. Together with SAP BTP, BDC helps you achieve end-to-end visibility without compromising clean core integrity.

Clean core extensibility lets organizations evolve through approved frameworks rather than custom modification. This design keeps systems agile and dependable over time.

Enterprises that follow clean core principles gain measurable advantages:

• Lower maintenance costs and effort
• Faster access to new SAP features and releases
• Greater agility to adjust as business priorities shift
• Improved reliability and system uptime
• Readiness for new SAP and third-party technologies

Strategic orchestration for clean core success

Sustaining a clean core across enterprise operations requires discipline and coordination. Modern orchestration platforms supply that structure, aligning automation and governance so processes run consistently while the ERP stays untouched.

Redwood Software has worked with SAP for more than two decades. RunMyJobs by Redwood is the only workload automation solution that is a Premium certified SAP Endorsed App. It uniquely supports SAP’s clean core strategy in cloud, on-premises and hybrid environments by eliminating custom code and workarounds.

By following SAP’s clean core framework and extending it through orchestration, you can keep your S/4HANA environment adaptable, reduce technical debt and ensure every new capability builds on a solid foundation.

Explore how a leading SOAP can future-proof your clean core strategy. Book a demo of RunMyJobs.

Digital point or close management tools have replaced spreadsheets for many accounting professionals and finance teams, but the work beneath those tools is still largely manual. Critical close tasks, from SAP job execution to journal entry posting, still depend on you to run the process, confirm it’s done and update its status afterward, which is time-consuming.

Tracking is helpful, but it doesn’t equal progress. A faster, more scalable close requires intelligent automation that executes the work — not just organizes it.

That’s exactly what Finance Automation by Redwood is built to deliver. As the only financial close management software designed to orchestrate a full, touchless record-to-report (R2R) process, Finance Automation connects directly with ERP systems like SAP to automate execution, streamline dependencies and surface exceptions in real time. Explore the different ways your close management process is still manual and how Redwood Software built a tool to help you reach full automation in your financial processes, whether it’s by helping you catch discrepancies ahead of time, update account balances and balance sheets or manage other financial data.

Manual work still runs the process

Most checklist tools only centralize task management. They don’t launch SAP close jobs, start reconciliation processes or automate end-to-end journal entries. Those tasks still happen offline or inside disconnected accounting software and rely on you to return to the checklist you created in an Excel template to mark them as complete.

If your tasks go unconfirmed, successor tasks are delayed. This slows the close cycle, forces follow-ups and leaves your team with an incomplete view of progress. During high-stakes accounting periods like your entity’s close, this lack of insight leads to bottlenecks, delays in financial reporting processes and costly rework.

Finance Automation automates this handoff. Tasks are closed by the system once work is executed, not by someone trying to remember to check a box. That means your close checklist reflects truth, not approximation.

Where most tools fall short

Digital checklists and shared dashboards offer visibility, but not execution. The underlying manual effort remains and requires finance and accounting teams to manually launch close activities, manage data entry and update task statuses, usually in tools that aren’t connected to your ERP.

Without system-driven updates, your metrics, KPIs and dashboards are lagging indicators instead of real-time performance signals. That leaves CFOs, controllers and global stakeholders guessing about the true close status and unable to confidently have fast, informed decision-making.

Finance Automation integrates with SAP to automate the close at its core by removing these dependencies entirely and allowing your teams to operate in real time with full control and traceability.

What intelligent close management looks like

Modern close execution doesn’t require more checklists; it requires fewer manual steps. Finance Automation executes close activities directly within your SAP environment, then updates the task status automatically.

This includes:

• Automatically escalating exceptions and delays across workflows
• Creating, validating, approving and posting journal entries, including accruals, provisions and reclassifications
• Initiating and tracking account reconciliation workflows based on a top-down, risk-based approach
• Managing intercompany eliminations and automatically posting transactions in the core ERP with the correct trading partners
• Running SAP close programs (e.g., depreciation, currency revaluation and allocations)

This task automation is captured with a complete audit trail that gives you the controls you need without slowing down execution. Finance Automation doesn’t just track work — it performs it.

Built to scale with complexity

Global finance operations rely on consistent, reliable execution across regions, systems and teams. Finance Automation was built specifically to support the orchestration of close activity across multiple entities and ERPs, while maintaining compliance and control.

The platform automatically adjusts task calendars, handles multi-entity dependencies and routes exceptions based on business logic. Whether your team is centralizing its financial close processes or managing decentralized business units, Finance Automation ensures that your close remains consistent, scalable and auditable. You’ll also gain:

• A reduction in manual tasks and repetitive manual processes
• Enterprise-ready support, including AI-powered exception detection and 99.95% uptime
• ERP-native execution across SAP ECC and S/4HANA
• Streamlined workflows that eliminate unnecessary coordination
• Support for dynamic tasks across business units, sub-ledgers and general ledgers

Why finance teams choose Redwood

Unlike checklist tools that depend on you to move the process forward, Finance Automation automates the actions behind the scenes. It enables finance teams to:

• Complete close tasks with fewer handoffs and greater speed
• Ensure system-driven validation of critical milestones
• Improve financial performance by accelerating close and reporting cycles
• Optimize timelines across regions, departments and systems
• Provide confidence to executives and auditors with a full, traceable audit trail

And with transparent, scalable pricing — with no per-user or per-task fees — this close management solution grows with your business needs, not against them.

Stop tracking. Start automating.

If your close checklist still depends on manual inputs, disconnected tools or human coordination, it’s not built to scale.

Finance Automation transforms your checklist into an execution layer. SAP tasks run when ready. Journal entries are prepared and posted automatically. Reconciliations are initiated and completed based on actual source data. And your team members focus on analysis versus manual work.With this solution, your month-end close process becomes faster, more consistent and confidently audit-ready every time. Your modern finance organization needs more than visibility. It needs results. Schedule a demo to see how Finance Automation can help your team close smarter, reduce risk and lead with real-time performance instead of after-the-fact reporting.

In my view, the Magic Quadrant™ isn’t a scoreboard. It’s a strategic map that reflects thousands of product decisions, customer outcomes and architectural bets. Reading it strategically can help you make smarter investments in automation, extensibility and long-term innovation.

This year’s report reinforces something we’ve known for some time: not all Leaders are interchangeable. The quadrant tells you where vendors are positioned. Interpreting why they are there — and how that aligns with your own transformation priorities — is where the real insight lies.

Two axes, one strategy lens

The two dimensions Gartner evaluates — Ability to Execute and Completeness of Vision — each reveal a different layer of vendor maturity. Together, they create a framework for interpreting platform relevance not just in the present, but across the lifecycle of enterprise automation strategy.

Ability to Execute: A test of operational resilience

I believe high placement here reflects sustained operational performance under enterprise conditions.

Leaders on this axis tend to demonstrate:

• Scalable performance across hybrid and multi-cloud systems
• Deep integrations with complex applications like SAP, mainframe and proprietary tools
• Operational simplicity that reduces total cost of ownership, not just task count
• Clear expansion momentum across customer accounts

As Gartner notes, Leaders “execute strongly at scale and offer deep capabilities across a breadth of use cases.”

Execution strength is, essentially, a measure of enterprise trust. It answers the questions: Can this vendor reliably orchestrate critical business processes at scale? 

Completeness of Vision: A proxy for architectural longevity

A forward-leaning position on the Vision axis, in my opinion, speaks to how well a provider anticipates market direction and whether its platform investments are aligned with that trajectory.

Strong positioning here suggests:

• Future-ready architecture — cloud-native, API-first, event-driven by design
• Flexible, extensible capabilities that allow teams to adapt without vendor lock-in
• Alignment with ecosystem shifts, including AI, data fabric and digital ops strategies
• Strategic investment discipline, not reactive product expansion

Vision matters because today’s innovation is tomorrow’s technical debt. A platform that lacks architectural foresight may soon be outpaced by your organization’s needs.

Interpreting quadrant dynamics

In my experience, reading quadrant by quadrant makes it easier to identify tradeoffs and risks. Here’s the breakdown of each quadrant position according to Gartner methodology — and my interpretation of these positions.

Extract maximum value by connecting quadrant insights to tangible outcomes: reduced cycle time, improved SLA performance or lower integration overhead, for example. Ask vendors to demonstrate how their execution and vision translate into business impact, not just platform metrics. By tying evaluation to outcomes, you transform an analyst framework into an instrument for performance accountability.

SOAP Leaders today

Leaders who share space in the top right quadrant may take fundamentally different approaches to orchestration, extensibility and AI integration. I feel a strong Leader in 2025 is defined not only by breadth of capability but by the ability to remain competitive through:

• AI-driven operations
• Composable, event-driven architectures
• Autonomous remediation and continuous optimization 

The most important question isn’t “Who leads today?” but “Who is building for what’s next?”

Enduring leadership depends on both continuous architectural evolution and current market momentum.

How to use the Magic Quadrant™ in your vendor evaluation

Position within the Leaders quadrant should not be viewed as a stamp of parity. Vendors may share a quadrant, but not a philosophy, architecture or roadmap. 

The most strategic organizations treat the Magic Quadrant™ not just as validation, but as an input in a broader due diligence process. Map vendor placement to your operating model maturity to turn the quadrant from a static chart into a living framework for modernization. Over time, this mindset shifts the focus from comparing vendors to clarifying enterprise priorities.

Ask questions like:

• How does the platform align with our current tech stack, business model and operating environment?
• Will this vendor support our transformation roadmap — or limit it?
• What aspects of execution or vision earned the placement? Are those priorities aligned with our needs?
• Do case studies and references indicate expansion, innovation and long-term value?

Use the quadrant to inform your next questions, not to answer them outright. Read our guide to choosing the right SOAP solution for a more detailed analysis.

Strategy, not symmetry

We’re all searching for simplicity in a fast-changing world of automation tech, so it’s tempting to view proximity on the Magic Quadrant™ as a sign of equivalence. It’s not.

Go beyond the dot — use this year’s Magic Quadrant™ for SOAP as your starting point. Consider the rationale behind each placement. Investigate the executional proof points and architectural investments. And above all, choose partners who will not only deliver results in the current environment, but evolve with you as strategy, scale and complexity accelerate.

The quadrant gives you a map. The next move is yours. Download the full Gartner report to examine the landscape and learn why Redwood Software was named a Leader for the second consecutive year.

The problem isn’t a lack of automation. You have scripts, schedules and tools. The problem is a lack of orchestration. And that gap is putting your business outcomes at risk.

Modern Service Orchestration and Automation Platforms (SOAPs) exist to solve this. When you look at the leaders in the latest Gartner® Magic Quadrant™ for SOAP, you’ll find they aren’t just job schedulers with a prettier UI. They connect disparate systems. Understand business events. Anticipate outcomes and manage the mind-numbing complexity of a hybrid cloud world without increasing your team’s cognitive load.

Here are the new rules defining the modern orchestration platform.

Rule #1: Automation that listens

Time-based schedulers are still everywhere, but they’re tuned to a world that doesn’t exist anymore. Business runs on events now, not clocks.

A supply chain workflow, for example, doesn’t need to run at 2 AM. It needs to run after the fulfillment file hits your cloud bucket and the payment clears and the inventory check validates.

Modern orchestration listens instead of waiting for time to pass. In other words, it sequences jobs based on the triggers that matter. Those might be external events, upstream outcomes or system states.

Rule #2: Integration is deeper than connectivity

Every platform says it “integrates.” But in my experience, that often means little more than a basic API handshake. Real orchestration is understanding what connection means in the context of a process.

For example, did the SAP job finish successfully, or did it hit a soft failure? Is the returned dataset complete? Does the next system require a transformation before ingesting it?

Modern orchestration is built to manage this kind of nuance. It adapts to API changes, handles schema validation, triggers follow-ups, reroutes based on conditions and preserves dependencies across platforms.

Rule #3: Failure is a scenario instead of an anomaly

Legacy tools treat failure like an edge case. If a job fails, they send a generic alert and might retry once or twice. But in distributed cloud architecture, failure is expected. It’s just a matter of how you recover.

Modern orchestration platforms treat failure paths with full auditability — and no panic. They track SLAs, anticipate delays, escalate intelligently and reroute automatically. It’s not mere incident avoidance.

Rule #4: Orchestration is no longer a solo role

You don’t build processes in a vacuum. DevOps is managing CI/CD, IT Ops is overseeing runtime, Finance is owning the close — everyone needs orchestration. But that doesn’t mean everyone should write scripts.

Modern SOAP platforms make orchestration collaborative. Devs work in YAML or code. IT manages by exception. Business users trigger workflows safely via self-service portals. Meanwhile, centralized controls keep everything governed.

Rule #5: Observability must trace outcomes, not just steps

Most platforms can tell you a job ran, and some can tell you it failed. Very few can tell you why, though. Not to mention which business outcome was affected and who needs to fix it.

Modern orchestration gives you end-to-end visibility, so you can trace a late report all the way back to the missing data file and see the ripple effect through every dependent system.

Legacy automation vs. modern orchestration

Rule #6: AI only works when automation does

Enterprises are rushing to embed AI into operations, but smart models are worthless without smart orchestration. A demand forecasting model can’t adjust inventory unless the right workflows gets triggered, and an LLM can’t summarize reports unless the right data lands in the right place. If your data pipelines are fragile or manual, your AI outputs will be dead on arrival.

Orchestration is that invisible engine behind AI-powered operations. It feeds the model, triggers the action, verifies the outcome. Without that layer, your AI is like a disconnected lab experiment.

Evaluation criteria have shifted

If you’ve read the 2025 Gartner Magic Quadrant™ for SOAP report, you’ll notice the bar has been raised. At Redwood Software, we believe the evolving contents of this report are a clear signal that the market has shifted. Hybrid control, event-driven design, persona flexibility, business outcome alignment … these are now table stakes.

If you’re evaluating your next orchestration solution, use the Magic Quadrant™ as a starting point. Download your complimentary copy of the report and ask whether your current platform — or the one you’re considering — is built for the world as it is today or the world as it was a decade ago.