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.

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.

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.

That’s because most systems and teams just aren’t built for AI yet. The hype is running ahead of the architecture. According to Redwood Software’s “Enterprise automation index 2026,” nearly 40% of organizations admit they’re not ready to adopt or implement AI-driven automation. 

This isn’t about getting access to a model. It’s not even about building one. Readiness is the boring stuff under the hood: data quality, process consistency, orchestration, exception handling. That’s what makes AI useful (or useless).

Redwood’s data shows that fewer than 6% of companies have reached autonomous automation in any major business process. Most are still crawling, even in high-stakes areas like quote-to-cash. So when an AI tool misfires — maybe it reorders the wrong part, flags a non-issue or misroutes a service ticket — the real problem isn’t the AI. It’s everything feeding into it.

You invested in automation, but did you stabilize it?

More than 73% of companies say they increased automation spending last year. Only 36.6% feel ready to apply AI. That should tell you something. A lot of teams bought the tools, but the groundwork isn’t there.

If your workflows are still rule-based and brittle, your exception handling is half-human, half-spreadsheet and your APIs are duct-taped together from three systems ago, using AI won’t solve that. It’ll just scale the mess instead. This is what stalls pilots: not bad models — broken environments.

About your workflows…

You’ve got platforms everywhere. CRM, ERP, maybe a ticketing system for customer support or field service. They work (sort of), but they’re not exactly talking to each other.

You’ve got automation, but it’s scattered across tools. And it’s hard to know what’s actually happening when something goes wrong. Now imagine dropping AI into that mix and asking it to make decisions, in real time and with real impact. 

We’ve seen this movie. The pilot works, and the chatbot answers a few questions. The predictive model makes a recommendation. But there’s no feedback loop, no clean handoff, no way to measure if the output actually helped. So the pilot ends, and nobody wants to own it anymore.

Here’s what you’re skipping — and shouldn’t

Most AI efforts start with the use case. 

• Let’s automate onboarding
• Let’s forecast demand
• Let’s personalize support

Fine goals. But what about:

• Who owns the outcome when AI makes a choice?
• Can you see — and explain — how it got to that choice?
• What happens when something breaks at 3 AM?
• Can the system ask for help? Does it even know it needs to?

Governance. Data orchestration. Exception flows. Alignment with actual business outcomes.

These are boring, but when you skip them, you end up with a demo, not a system.

What the mature teams are doing

They’re not chasing shiny tools; they’re tuning the engine. Here’s what we see from the AI-ready crowd:

• End-to-end workflow automation that connects systems, not just apps
• Clean, version-controlled datasets that support real-time decision-making
• Clear governance rules that define what AI can (and can’t) do
• Exception handling that doesn’t depend on someone checking their inbox

Redwood customers who follow this approach are:

• 2x as likely to cut manual workloads by 50%
• 1.6x as likely to improve operational efficiency
• More likely to reduce costs by over 50%

These teams aren’t more enthusiastic about AI-powered solutions. They’re just more prepared.

Are you actually ready for AI?

It’s a fair question. Before you deploy another chatbot or predictive model, take a hard look at what’s underneath:

1. Are your business processes clearly mapped and automated?
2. Can your systems handle complex tasks — or just repetitive ones?
3. Are your AI models getting data they can actually trust?
4. Do your outputs link back to measurable outcomes?

AI won’t magically fix disorganized operations. But it will accelerate what’s already there — good or bad. If you haven’t mapped your exceptions, validated your inputs or built real-time visibility across systems, you’re not ready yet. And that’s okay. That’s fixable.

But don’t plug in AI and expect it to clean things up. Clean first, then scale.
Want to know how your automation foundation stacks up? Download the full report and benchmark your AI readiness.

While 73% of companies increased their automation spend last year, less than 30% are fully utilizing their tools. The data is clear: the issue isn’t a lack of investment or technology — it’s a stubborn execution gap.

In a climate where every budget line is under the microscope, automation is still getting the green light. That’s because the business case is solid.

• 37% of organizations report that automation reduced costs by over 25%
• 43% have cut manual workloads by at least a quarter
• 49% say it increased efficiency by the same amount

Those are meaningful results, but they’re not the norm. The data also reveals a widespread failure to scale. 

From where I sit, working alongside enterprise teams on automation migration and orchestration every day, I can tell you this isn’t a technology issue. It’s a stubborn execution gap.

The 4 traps of underperforming automation

Too many organizations treat automation like an arms race, adding new tools to plug gaps and hoping for the best. My team sees the consequences of this approach daily, typically in these four traps:

1. Ad hoc tool sprawl: Marketing, Finance and IT all buy their own automation tools, creating “shadow automation.” These siloed, ungoverned processes don’t share data, follow security protocols or align with a larger strategy, undermining enterprise-wide visibility.  
2. Stopping at the task level: Teams often automate the simplest, low-hanging fruit and then declare victory, ignoring the cross-functional processes where the real value lies. This technical debt accrues until a critical process, like month-end close or supply chain fulfillment, inevitably breaks, leading to frantic, manual interventions.
3. Legacy tech dependency: Many enterprises still run their most important processes on outdated schedulers or basic scripts. These tools lack the visibility, error handling and security features required for today’s business. When they fail (and they do), the business impact is immediate and severe, but migrating off them is perceived as too difficult.
4. No automation strategy: Without a plan to consolidate, migrate and optimize, the collection of tools becomes a digital junkyard. The organization has technically invested in automation, but operationally, nothing has changed. The tools are there, but they’re underutilized, misaligned or completely isolated.

These execution pitfalls are symptoms of a deeper issue, one that consistently derails even well-funded automation projects.

Complexity: The #1 blocker to automation ROI

According to Redwood’s research, the top challenge isn’t budget, talent or tools — it’s complexity. Nearly 20% of professionals point to complex workflows as their number-one barrier to scaling automation. 

That echoes what I see in the field. Enterprises are sitting on decades of custom scripts, legacy architecture, fragile integrations and undocumented processes. And every time someone says “We’ll automate that later,” the mess grows.

When you delay migration or fail to redesign around orchestration, you lose the ability to scale. You automate the easy stuff and stall out at the first sign of friction. If you want automation to deliver, you need to:

• Standardize before you automate. Don’t just pave the path. A chaotic manual process will only become a faster chaotic automated process. Take the time to map, simplify and standardize workflows first. This initial investment pays dividends in scalability and resilience.
• Migrate strategically. A simple “lift-and-shift” of old jobs to a new platform just moves the problem. Strategic migration involves analyzing, consolidating and redesigning workflows to take full advantage of a modern orchestration platform’s capabilities.
• Orchestrate across systems. True value is unlocked when you manage processes end to end, from the mainframe to the cloud and across all applications. This breaks down the silos between IT operations, data pipelines and business applications, which the report identifies as a key challenge for industries like finance.
• Align to business outcomes. The goal isn’t just to run jobs successfully; it’s to reduce costs, accelerate innovation and improve data visibility — the top three business priorities cited in the research. Frame every automation initiative around these goals.

The path to mature automation: A call to action 

If your automation investment isn’t delivering, it’s a critical warning sign. Don’t fall into the trap of simply adding more tools. The path forward requires a shift in mindset: focus on orchestration, elevate automation to a C-suite priority and build a cohesive strategy. It’s the only way to transform it from a tactical fix to a genuine growth lever for your entire organization. 

Download the full report to get:

• Automation maturity benchmarks across industries
• Barriers and drivers of automation success
• What separates top performers from the rest
• Guidance for aligning automation with business goals

69% of organizations call automation “mission-critical,” but only 10% are actually prioritizing it at the executive level.

That gap isn’t theoretical — it’s operational. And for leaders trying to move the needle on cost, innovation or speed of execution, it’s a red flag.

I’ve spent my career scaling technical and product teams, supporting global platforms and helping businesses modernize their operations. Here’s what I’ve seen consistently: Every business outcome is the result of process mechanics. If you’re not looking at automation through that lens, you’re missing the point.

Spending more ≠ Doing it better

It’s easy to assume more investment equals progress. But the data shows otherwise:

• 73% of organizations increased automation spending in the past year
• Yet only 28% say they fully utilize their tools
• And less than 6% have achieved autonomous automation in a single critical process

That’s not a funding issue. It’s a prioritization and ownership problem.

Too often, automation lives in a silo: owned by IT, disconnected from business outcomes and fragmented across departments. When that happens:

• It lacks alignment to core strategy
• It can fail to connect to key operational insights to drive better results
• It lacks the exec-level sponsorship required to scale the impact

The result? Your investment in tools doesn’t translate into an operating capability for the business.

Automation works — when it’s an aligned operating capability

Done right, automation delivers measurable results:

• 37% reduced costs by 25% or more
• 49% improved efficiency by at least 25%
• 43% cut manual workloads by a quarter

These aren’t marginal improvements. They’re operating-model shifts. But they only show up in organizations that treat automation as an integrated operating capability — not a patchwork of IT point solutions.

What do they do differently?

• They don’t just ask “What can we automate?”
• They ask “What outcome are we optimizing?” and work backward
• They measure process volume, yield, throughput and cycle time
• They build automation architectures that span systems and teams to focus on value-stream processes and outcomes
• They begin with operational objectives, identifying where current processes underperform, why those gaps exist and how automation can significantly improve the outcome.
• They treat automation not as a siloed initiative but as an embedded capability that works across Finance, Operations and Product to drive measurable improvements.

Your automation strategy should reflect your operating model — not just your tech stack.

It needs ownership.
It needs a business case.
And it needs to be framed as an operating capability, not a toolset.

I’ve seen firsthand how teams unlock transformative value when they integrate automation as an operating capability at the strategic level.

Get the full story

If these findings resonate with you, I encourage you to dive deeper. Redwood’s “Enterprise automation index 2026” unpacks:

• How teams across industries are investing in automation
• Benchmarks for tools utilization and maturity
• The most common barriers to adoption (Spoiler: It’s not budget!)
• How leaders are preparing for AI-driven automation
• What sets top-performing organizations apart

Download the full report to learn how you can move from fragmented tasks to orchestrated outcomes.

Earlier this year, radar failures at Newark Liberty International Airport grounded flights across the United States, not because the aircraft failed but because coordination broke down. A combination of aging systems, staff shortages and manual overrides created a chain reaction that left passengers stranded and schedules in chaos.

Enterprise IT isn’t so different. Cloud systems, data platforms, ERP modernizations and AI pilots are all taking off, but the control layer that’s supposed to orchestrate them is often still stuck on the ground.

When the automation “tower” fails, everything stops.

Who’s guiding your IT traffic?

CIOs and CTOs are moving fast. They’re focused on cloud-first, generative and agentic AI and workflow automation. Under all that progress is a quiet problem: The automation architecture powering it all hasn’t kept up.

Companies are building smarter systems but still relying on old job schedulers and hard-coded scripts to orchestrate between them. That creates delays, disconnects and blind spots. The sky might look clear now, but storms are coming.

The more systems you modernize, the more complex your operations become. And as this modernization goes faster and faster over time, the harder it is to coordinate workloads with high fidelity, especially across legacy systems that require custom-coded connectors, manual refactoring for continuous integration and automation designed for a different era. While it feels like you’re accelerating, legacy systems beneath the surface are quietly pulling the brakes.

Modernization without orchestration is like asking your control tower to manage new aircraft using equipment they’ve never trained on. The sky is getting more crowded, but the systems guiding the traffic are stuck in the past.

The illusion of progress

The problem with mainframes didn’t begin and end in the early 2000s. It lingered for decades. Even as businesses moved to the cloud in the 2010s, their most critical workloads and data remained locked inside monolithic, closed mainframe applications with no APIs, no agility and shrinking pools of technical talent.

During the COVID-19 crisis in 2020, the issue broke into public view when multiple U.S. states issued emergency calls for COBOL programmers to stabilize aging unemployment systems. Rather than isolated IT issues, these were architectural bottlenecks that made rapid response impossible. No DevOps, no iterative improvement, no access to real-time data. Just batch cycles, manual updates and fragile processes buried under decades of technical debt.

Today, many enterprises are facing the same limitations, just in a different disguise. Legacy job schedulers and automation tools are the modern mainframe, standing in the way of AI adoption, API-driven integration and autonomous orchestration across cloud-native ecosystems.

These schedulers were designed for predictable workflows and tightly coupled environments, not for hybrid cloud, continuous delivery and interconnected platforms like SAP Business Technology Platform (BTP), Salesforce and Snowflake. As a result, they can’t scale, they can’t adapt and they certainly can’t keep pace with AI-driven transformation.

Static scheduling vs. intelligent orchestration

Orchestration requires controlling systems with precision and context, rather than just connecting them. That’s where event-based architecture becomes critical.

Unlike traditional scheduling, which runs on fixed times or batch jobs, event-driven orchestration allows your processes to respond dynamically to business and system events. You react to what’s happening now, not just what’s scheduled. Orders get fulfilled the moment inventory updates. Reports run the second data hits the warehouse. Downtime shrinks. You meet service-level agreements (SLAs).

At Redwood Software, we call this architecture an automation fabric: a unified layer that weaves together cloud and on-premises systems and AI innovation with full visibility, scalability and control. What makes it different?

• Built for hybrid: Connect SAP, Oracle, cloud services and custom apps across environments.
• Agentless integration: Connect systems without installing or maintaining local agents, so no need for custom scripts. Reduce risk, friction and security vulnerabilities.
• AI-powered observability: Identify SLA risks and optimize performance before problems arise.
• Unified monitoring: View everything through a single pane of glass.

Why would you custom-code or patch together manual workflows when intelligent orchestration can adapt autonomously?

Avoid a Newark moment: Your flight plan

Let’s say your global energy company is modernizing for sustainability and scale. You’re juggling regulatory demands, transitioning to RISE with SAP, piloting AI in financial planning and managing dozens of custom systems. But your core automation is still dependent on a legacy scheduler designed for batch processing and nightly jobs.

You’re not alone.

This is where modernization breaks down. It’s not in the cloud migration or the AI launch, but in what keeps it all together. By upgrading to a modern orchestration platform, your company could retire fragile custom scripts, slash risk across compliance-heavy processes and move faster with fewer people.

Rather than just picking a tool, it’s essential to choose a partner with a forward-looking vision. RunMyJobs by Redwood is designed to be air traffic control for the modern enterprise. Even if you’re not feeling the turbulence yet, the future is coming faster than you think. 

Don’t wait until delays, outages or compliance gaps force your hand. Modern orchestration isn’t optional — it’s foundational.

See it in practice: Read our guide to learn how automation fabrics are helping teams orchestrate SAP and non-SAP data across industries.

This year, operations and IT leaders need to go beyond the buzzwords and align with five essential enterprise automation and technology trends that will define the next era of efficiency and innovation.

Citizen-led automation paired with artificial intelligence

One of the most exciting advancements in automation is the democratization of technology. In 2025, businesses will increasingly empower non-technical users, often referred to as citizen developers, to design, execute and oversee automated processes. This will come into play much more heavily as enterprises look to make both AI and automation more accessible, actionable and relevant for real-world use cases.

Traditionally, AI has been the domain of data scientists and IT specialists. That’s changing. With the availability of low-code and no-code platforms, the user experience is shifting. Everyday business users have a vast opportunity to integrate AI into workflows without deep technical expertise. For example, a supply chain manager can automate inventory forecasting by leveraging AI-driven predictive analytics, or a finance team member can create a bot to streamline invoice approvals.

Citizen automation is particularly valuable in two areas of AI adoption:

• Configuration: Non-technical users can build AI-driven workflows tailored to their specific needs. Processes like order-to-cash or record-to-report can more directly align with measurable business objectives as a result.
• Monitoring and management: Citizen developers can also oversee and interact with AI systems using a plain-language, conversational approach.

AI shouldn’t be a standalone initiative limited to technical experts, and in the upcoming year, it will be more possible than ever to embed it in everyday operations.

Autonomous AI agents gaining ground

Autonomous, or agentic, AI models are poised to revolutionize how businesses approach problem-solving and decision-making. Intelligent agents can operate independently, analyzing data, detecting anomalies and taking corrective action without human intervention.

In a financial institution, this might look like deploying autonomous AI tools to monitor transactions for fraud. When it detects suspicious activity, the AI agent can freeze the account, notify relevant teams and instigate an investigation.

To reap the full benefits of this emerging technology, it’s critical to build your automation systems on robust workflow engines capable of orchestrating complex business processes. They must be able to seamlessly integrate across IT and business applications so AI agents can operate with precision and agility.

The enhancement of human contributions

While discussions about AI often emphasize disruption and job displacement, the reality for enterprise IT is far more nuanced. In most cases, AI will not replace human workers but will amplify their capabilities.

In 2025, the most impactful AI implementations will focus on the optimization of existing systems rather than reinventing the wheel. In IT operations, it will continue to refine monitoring, automate repetitive tasks and bring inefficiencies to light.

Gartner reports that 92% of CIOs say their organizations will implement AI in 2025, but 49% of those involved in AI say it’s hard to estimate and demonstrate its value. Forward-thinking enterprises will resist the hype of all-encompassing AI solutions that promise an overnight, futuristic digital transformation. Instead, they’ll apply AI strategically to boost skilled individuals’ contributions, starting with mission-critical use cases.

These include:

• Enhancing system performance
• Delivering better customer experiences using real-time responses
• Improving data analytics with built-in generative AI (genAI) like ChatGPT

A dissolution of automation islands

Disjointed automation, created by a massive increase in isolated tools and platforms, remains a persistent challenge. I predict organizations will increasingly move away from this fragmented approach and embrace unified automation ecosystems.

There’s an urgency to consolidate disparate systems into single platforms capable of managing legacy applications and supporting modern, cloud-native solutions. There is a world in which all your systems — past, present and future — can work together without friction.

The single-platform model is the way to go to position your enterprise for whatever challenges or opportunities 2025 will bring.

Automation fabrics as the norm

The days of siloed processes and disconnected data are rapidly fading. In their place, we see automation fabrics coming to the forefront and solving problems via effortless connectivity: integrated applications, data and workflows.

Think of an automation fabric as the connective tissue of your organization, making smooth communication and coordination between your varied tech stack components possible. Systems become more reliable. Error rates drop. Downtime is a non-issue.

Beyond operational benefits, automation fabrics are the foundation for innovation. Why wouldn’t you move toward reducing technical debt and focusing on your larger strategy for 2025 rather than devoting excess resources to maintenance?

3 tips to prepare your IT team for 2025

1. Audit your automation landscape: Build a holistic view of your current systems. Where are there gaps or redundancies?
2. Empower your workforce: Scaling automation starts with giving more people the power to innovate. Invest in tools that enable non-technical users to participate in design and development.
3. Partner with experienced industry leaders: Working with experienced consultants can accelerate your journey. Look to established automation leaders for new solutions and actionable advice.

Looking ahead

Enterprise IT will never be about chasing tech trends, but there is much to be said for remaining open to leveraging new technologies to drive meaningful change. Learn how to do so thoughtfully and strategically in 2025 and beyond by engaging in a conversation with the Redwood Software team.

A well-rounded, cross-functional team can drive successful automation initiatives. This team must align with your business goals and be able to integrate processes across departments by focusing on high-value workflows. Structured well, an automation team is the precursor for a well-scaled automation Center of Excellence (CoE) in the future.

This guide explores the essential skills, key roles and collaborative practices necessary to form an effective automation team, whether you’re just beginning your automation journey or aiming for full-scale transformation.

Why should you have a dedicated automation team?

A dedicated automation team can propel your automation progress by focusing on execution, organization-wide alignment, scalability and accountability.

Developing and executing an automation roadmap is only possible if your automation efforts aren’t fragmented. A carefully crafted team works to correct inefficiencies and streamline workflows. They’re the central hub for all things automation, keeping all eyes focused on the same objectives.

As your automation needs grow, having a team in place allows you to scale with confidence and ready your organization to automate more complex workflows. Integrating advanced technology while maintaining accountability and sticking to governance frameworks becomes achievable with a structured group of people with a singular focus.

Essential skills for a strong automation team

The best automation teams must balance technical expertise, soft skills and domain knowledge. Look for the following skills to ensure your team can optimally design, execute and refine automations.

Technical proficiency

Automation tools and platforms often include low-code interfaces, API integration capabilities and orchestration features that reduce the need for extensive coding, but at least some of your team members should be proficient in these technologies. They need a deep understanding of how to design workflows, troubleshoot and implement automation.

Those with technical skills can minimize the team’s reliance on developers, especially in the early stage of planning and execution.

Cross-functional collaboration

Automation often touches multiple departments, from finance to HR to IT operations. Team members must possess the ability to collaborate across these silos and remain empathetic about the challenges and objectives of various business units. Building trust among stakeholders ensures smoother implementation and makes it easier to scale automation efforts to more functions as your organization matures.

Change management expertise

Introducing automation can be disruptive if not handled carefully. Effective team members need to understand how to implement new technologies in a way that minimizes disruption and resistance. This includes driving user adoption through training and communication. Your employees should feel empowered rather than threatened by automation.

Governance and compliance knowledge

Automation initiatives must comply with regulatory standards and internal governance policies. That’s especially true when you handle sensitive data. Team members with expertise in these areas will ensure that security and compliance are prioritized from the start, which can reduce the risk of costly mistakes or violations.

Key roles and stakeholders

A successful automation team is not just about skills — it’s also about having the right people in the right roles. The following are the key roles you’ll need to fill.

The visionary leader

This could be a CoE lead or an Automation Director. Their primary responsibility is to set the strategic direction for company-wide automation. Forward-thinking and influential, they advocate for resources, align with executive leadership and keep the team focused on long-term goals.

The visionary leader is an anchor, connecting the team’s efforts back to the organization’s broader objectives.

Process champions

These are representatives from specific business units, such as IT operations, Finance or Supply Chain. They’re liaisons between their departments and the automation team. With deep knowledge of their workflows and an openness to digital transformation, they identify pain points and propose automation opportunities.

Process champions are crucial for securing buy-in and making sure automations address real-world needs.

Automation architects or solution designers

These team members map out workflows and design automation solutions that integrate seamlessly with existing systems. With strong technical expertise and an understanding of the company’s tech stack, they translate business requirements into scalable technical solutions.

Automation architects and solution designers give your automations room to grow as your needs evolve.

Subject matter experts (SMEs)

With an eye for detail and a knack for problem-solving, SMEs bring an in-depth knowledge of specific processes. That may be HR onboarding, supply chain logistics or key finance processes. Their input will help your team optimize automations for real-world applications. 

SMEs play a critical role in refining and validating workflows and dependencies.

Data analysts

Automation initiatives should be data-driven, and data analysts are essential for this purpose. They define and track KPIs, evaluate the performance of automation efforts and report on ROI. As they tend to be incredibly detail-oriented, these team members look for ways to continuously improve.

Data analysts put your team on the path to measurable automation outcomes.

How to cultivate collaboration

An automation team’s success hinges on how well they work together. There are ways to set your team up for easy collaboration.

1. Host regular meetings and workshops. While these should not interfere with high-priority work, they should be frequent enough to keep automation top of mind. Monthly or quarterly sessions help everyone align on automation project progress, celebrate wins and discuss areas of improvement.
2. Create feedback loops with end-users. Once automations are in place, you need mechanisms for gathering feedback from the people impacted by them every day. This is invaluable for giving your team insights for building future automations.
3. Leverage external expertise. Partnering with third-party consultants or automation specialists can accelerate your efforts with fresh perspectives and industry insights that complement your in-house capabilities.
4. Track and celebrate wins. Highlighting successful automation projects boosts team morale and reinforces the value of their work.

Growing your team’s automation competency

Automation maturity can only increase proportionally to your team’s structure and capabilities.

If you’re just getting started with automation:

Begin with a lean team focused on high-impact, low-complexity automations. Prioritize process champions who understand business workflows and pair them with an IT representative familiar with automation tools. That way, you can build momentum without overextending resources.

If you’ve automated some tasks, processes and exceptions:

Once you have quite a few automations running successfully, it’s time to scale and involve more departments. At this stage, roles like automation architects, automation testing pros and change management experts become increasingly important in designing scalable solutions and ensuring smooth adoption.

If you use a centralized automation platform and are looking to innovate:

When you’ve achieved a centralized approach to automation already, consider adding specialized roles, such as data scientists or security specialists. Diversifying will help you tackle complex automations and explore advanced use cases, such as predictive analytics or machine learning integrations.

Expand your automation potential: Final considerations

As automation becomes more of a priority across your organization, it’s important to invest in being able to execute it well. Encourage an automation-forward culture, supported by continuous learning and innovation, by thinking about all the ways you can mature your automation.

Take our free, five-minute automation assessment to determine your starting point and get specific recommendations for your stage.

An automation Center of Excellence (CoE) can serve as the hub for all automation activities across your organization, bringing structure, expertise and alignment. Regardless of the size of your organization, a CoE can help you use the resources you have to their fullest potential.

Here’s how to use your automation CoE to stay focused on a big-picture strategy that will drive your organization confidently into the future.

Why a Center of Excellence matters for automation

A CoE can enable you to move beyond disparate, siloed automation. Properly set up, it can:

• Centralize knowledge strategy and governance to make onboarding and training easier while reducing redundancy.
• Establish clear objectives and metrics to align automation projects with business goals and help generate measurable results.
• Drive consistency and scalability, so you apply the same standards and best practices across your business.

Most importantly, this team makes your automation initiatives technology-agnostic, so you’re not hopping from one angle to the next based on which systems or tools you happen to have in place.

Best practice #1: Develop a clear vision 

What it does: Aligns your automation initiatives with overall business objectives

The CoE must start with a vision that will guide its activities and gain buy-in. The first step is to create a roadmap that includes:

• Short-term wins: Quick-win automation projects that deliver immediate ROI to build momentum and show tangible benefits
• Long-term transformation: A plan for strategic, large-scale automation projects that can transform core processes over time to support business resilience

You should also identify and assign key roles and responsibilities. Who will be your:

• Key decision-makers: Individuals who approve automation projects and ensure alignment with business objectives?
• Automation champions: Leaders who advocate for automation and inspire teams to adopt new tools and processes?
• Subject matter experts (SMEs): Experts in various departments who can help identify automation opportunities, provide insights on current processes and assist in implementing new solutions?

Finally, no vision is complete without the practical parts. In this case, those include documentation, governance and auditing. Decide how you’ll maintain thorough documentation for each automation that covers workflows, dependencies and regulatory requirements. It’s important to create guidelines around design, deployment and monitoring , and you’ll want to audit each automation regularly.

Tips for the first stage of setting up your CoE:

1. Create a CoE charter that outlines objectives, responsibilities and guidelines.
2. Designate a leader responsible for executing the vision and managing the team.
3. Set up an automation review board to evaluate and approve new initiatives.

Best practice #2: Build a cross-functional team

What it does: Ensures diverse perspectives and buy-in

It’s essential to include representatives from various business units in your Coe to align your automation goals with the unique needs of each department. Each team member brings firsthand insights into everyday pain points and can help the CoE understand where automation can make the most impact. Consider roles for people in IT, Operations, Finance, HR and more.

Tips for building an effective team:

1. Conduct regular meetings and workshops.
2. Kick off your CoE with pilot projects to test cross-team efforts.
3. Leverage collaborative tools to maintain visibility.

Best practice #3: Prioritize training and upskilling

What it does: Increases team competency and confidence

A CoE’s success relies on a well-trained team that’s confident and capable of executing advanced automation initiatives. Adequate training and upskilling allow individual team members to explore new technologies and further improve processes.

Remember that training doesn’t just mean technical help with using automation technology; it means helping your team develop a deeper understanding of automation’s potential impact and helping them think critically about how to use it responsibly.

Tips for training and upskilling: 

1. Enroll CoE members in certification programs offered by your software providers.
2. Create an internal drive or portal for easy access to resources.
3. Pair seasoned automation experts with less experienced team members.

Best practice #4: Standardize processes and metrics

What it does: Facilitates easy and consistent automation adoption across your organization

Your CoE should have a framework for adopting automation from planning to execution. This will reduce barriers and make it easier to transition processes from manual to automated.

It’s also helpful to determine performance metrics from the start — cost savings, time reduction, error rates, etc. 

Tips for standardization:

1. Create a library of standardized templates and workflows — or use a platform that has them built in.
2. Automate reporting and share performance metrics regularly.
3. Conduct quarterly audits of processes to maintain quality.

Best practice #5: Implement scalable solutions

What it does: Demonstrates value quickly

Begin with high-impact, low-complexity automations to increase time-to-value. Start with things that help you achieve better data accuracy, faster processing times or other day-to-day efficiency-boosting KPIs. Examples include report generation or data transfer.

If you have a small to mid-sized organization, this might mean creating a lean CoE that can scale gradually and using tools with out-of-the-box integrations.

Tips for scalability:

1. Achieve quick wins with straightforward automations and use them as proof of concept for larger projects.
2. Set up feedback loops with users after each automation deployment.
3. Replicate successful automations in other departments.

An automation CoE is a vital piece of your automation strategy, especially as your organization grows and your needs mature.

You’ll be able to tell how well you’ve implemented all of the above based on the scope of your automated processes, the depth of those processes and whether your organizational setup is helping or hindering your automation rollouts.

Take our free, five-minute assessment to determine your stage of automation and pinpoint the perfect time to launch or refine your automation CoE.

For many employees, automation still represents a threat. 22% of United States workers worry about technology making their jobs obsolete.

This shadow of uncertainty about the future of their roles magnifies a natural fear of the unknown. As much as you and fellow leaders may know that you’re not itching to replace human contributions, it’s a challenge to communicate the message that you’re looking to amplify them instead.

The right automation strategy frees up your people to leave repetitive tasks and outdated apps behind and do more of what humans excel at: strategic thinking, creative problem-solving and relationship-building. Unlocking all of these will require more than just deploying cutting-edge technology. We’ll cover the cultural shift you should invest in to help your employees view automation as a tool for enhancement rather than replacement.

Mindset matters in times of great change

When you’re in the midst of a significant digital transformation, like a cloud or ERP upgrade, collective mindset is everything. It’s a grueling process, and if the people you’re depending on to adopt new technologies don’t believe in or agree that there’s a need for them, your progress could grind to a halt.

Silos form, resistance builds and innovation fades away.

It’s essential for leaders to develop a partnership with employees — to set the tone for automation with an open, supportive culture to avoid these pitfalls. Use the guidance and real-world examples below to start making the change.

3 ways to help your employees prioritize automation

1. Align on automation-centric values

Values are the foundation of your organizational culture. Aligning automation plans with these values can help you get widespread buy-in. Moreover, making automation an original element of this foundation generates better results for your automation initiatives.

When automation is an afterthought, you might notice inconsistent outcomes. One Redwood Software retail customer had challenges with consistently meeting deadlines for their forecasting and replenishment (F&R) process for more than 10–15 days in a row, which resulted in not enough or the wrong items being stocked on the shelves. As they thought about how to improve the process, end-to-end automation and orchestration became key pillars in their strategy, along with improvements in their underlying ERP and planning systems. In the end, they achieved 30–40X the consistency, meeting SLAs 450 days in a row.

The key is to avoid embarking on your automation journey with the possibility of fragmentation. If your entire business moves as a unit with harmonized beliefs and intentions, your employees will feel more empowered and motivated to engage — a key ingredient for sustained operational efficiency and scalability.

This could look like:

1. A pharmaceutical company places a strong emphasis on safety and compliance and uses automation to minimize human error in manufacturing. By framing automation as a way to uphold its commitment to patient safety, the leadership team turns potential resistance into enthusiasm. Employees understand that the technology will enhance their ability to deliver high-quality, safe products.
2. A tech company values creativity and introduces end-to-end automation to eliminate administrative work and free up employees to innovate and develop new products. Leadership organizes “innovation days” or fun competitions to celebrate the time saved and further promote the idea that automation fuels rather than stifles creativity.

2. Establish a strong structure and dedicated automation team

When it comes to how you set up your organizational structure and team, automation cannot be something you’re merely exploring. It’s not a hobby but a primary means of getting where you want to go. Automation initiatives can only flourish in an environment where resources and responsibilities are well-defined.  

At Redwood, we see that our most successful customers have a lifecycle-oriented approach driven by a centralized automation team. The first part of that lifecycle is educating teammates and customers about the art of the possible. 

Fear and uncertainty thrive in an ambiguous environment. Clear, consistent communication is the antidote. Regular, straightforward dialogue can demystify automation and help your team understand how it will impact their specific use cases. It also reinforces the idea that automation is a collective effort and they’re not alone in experiencing frustration or having questions.

Even if your team is only comprised of two or three people, it’s crucial for inspiring all stakeholders and maintaining a shared sense of purpose.

The wrong way: A large healthcare provider attempts to introduce automation but allows each department to work independently. Lacking a unified strategy, they end up duplicating efforts and leaving critical projects unaddressed. Thus, they create inefficient workflows, realize little to no cost savings and see low adoption rates.

When it works: A global manufacturing company recognizes the complexity of implementing automation across its operations and creates a Center of Excellence (CoE) to gather input, prioritize projects and align with business goals. They apply resources in the most efficient way, thanks to an advanced workload automation platform, so they successfully automate 40% more processes and experience 64% fewer errors.

3. Support deeper automation long-term

Cultural buy-in and education are key, but they must work in practice. You need to be able to scale and integrate automation into diverse processes across your organization. That means getting the relevant teams involved in automation design and ensuring they do their part to get the systems running that can support those automations.

We recommend the following:

• Ask critical questions that push automation to new areas, such as: “How can we make our supply chain planning more predictable?” or “Could we close the books two days sooner?” These questions aren’t just about adding automation; they’re about reimagining processes to bring measurable business value, like reducing forecasting errors or accelerating financial closes.
• Scale thoughtfully by applying the lessons you learn in your first automations to more complex workflows and areas of the business. Create a cycle of continuous improvement, with your automation team evaluating and optimizing each automated process and considering how to inject automation into others.

As your automation capabilities mature, your automation team should never have to worry about people and their concerns or misunderstandings being a roadblock. Automation velocity will increase because your values, structure and scaling approach align.

Facilitate a fundamental shift

If you’re already undergoing a cloud or ERP transformation, you understand the importance of organization-wide buy-in. Maturing your automation demands the same level of cultural commitment. Clinging to archaic practices or allowing distrust to fester will only stand in the way of efficient processes and could even stall your company’s growth.

To ease the cultural change around automation and generate greater business value, first discover where you are on the path to automation maturity. Take our free, five-minute assessment.

These large-scale implementations are long-term projects that require both planning and patience. They often bring hope for more streamlined operations, reduced costs and increased operational efficiency. However, these outcomes are far from guaranteed, at least without the all-too-commonly missing piece: automation. 

Overlooking automation can bog your systems down in the same inefficiencies and silos you sought to escape with new solutions.

Many IT operations teams treat automation as a separate project — something to think about after the dust settles from a larger tech stack upgrade. But waiting to implement automation is a mistake that can cost time and resources. Embedding automation into your transformation efforts helps with short-term workloads and promotes lasting agility.

Automation: A digital transformation foundation

Whether you’re overhauling your ERP system or migrating to the cloud, you will likely struggle under the weight of inefficient manual processes. With automation as a central component of your transformation strategy, your new technologies have a better chance of delivering on their potential ROI.

Properly integrated, automation enhances the effectiveness of your modernization efforts by:

• Streamlining workflows
• Reducing errors
• Enabling real-time data processing

These improvements to business operations evolve into strategic advantages that enable faster decision-making and more agile responses to business changes. Your entire organization becomes better equipped to anticipate and adapt to demands, regulations and technological advancements.

How automation aligns with your ERP or cloud transformation journey

Let’s break down how automation fits into different phases of your cloud modernization or ERP transition and why you can’t afford to overlook it as you expand your business strategy.

Before: Preparing for success

The planning phase of a cloud or ERP implementation is crucial, and automation can help you create a stable operational environment so you don’t have to worry about cleaning up manual tasks later. Establishing automated processes early streamlines your workflows so your new tech can hit the ground running.

One of the most enticing reasons to think about automation from day one is that it helps you identify even more ways to automate going forward. Not only will you tackle inefficiencies in your current processes, but you’ll also be able to frame new processes in terms of opportunities to automate.

This early groundwork also gives your IT team a chance to define clear automation goals. A proactive approach can save your organization time, effort and headaches later.

Tip: Integrate core systems, such as managed file transfer (MFT) solutions, into your workload automation platform to prioritize data security and important regulatory factors throughout your implementation.

During: While you transform

Cloud migrations and ERP upgrades are resource-intensive processes that require a significant amount of time and effort from your IT team. Automating tasks such as data entry, report generation and system monitoring can free them up to focus on the strategic issues that inevitably arise during a major implementation. Automation during a transformation also serves as a way to validate and test your new technologies to ensure they’re successful.

Business continuity is a big concern in these long projects. If you’ve automated workflows ahead of time, your key processes can remain consistent. And if you’re working on automating them simultaneously, you’re more likely to notice and resolve problems that could create a risk of service interruption or performance lags that impact partner relationships or customer experiences. 

Automation is a safety net when you’re in the depths of a software adoption or migration, allowing your business to keep running while you unravel the more challenging elements of scalability and evolving business needs.

Tip: Identify and automate key dependencies early to avoid bottlenecks and improve data consistency across applications. This will drive operational continuity and minimize downtime.

After: Solidifying the gains

A successful cloud transformation or ERP implementation doesn’t end when you go live. To optimize cost savings and meet business objectives with your new technology in place, it’s essential to make automation part of your ongoing operations.

Automating workflows and processes in your new IT infrastructure will allow you to extract value faster. Embedding automation into the day-to-day workings of your ERP system or cloud infrastructure reduces manual workload, improves data accuracy and helps you realize ROI right away. When you’ve put your automations in place before and during a transformation, the transition will feel like a “plug-and-play” scenario.

These short-term benefits set the stage for continuous improvement. Instead of assuming you’ll need another large-scale technology overhaul in the future, you can lay an agile foundation for responding to market demands and driving new initiatives.

Tip: Implement a continuous improvement loop by monitoring system performance, pinpointing opportunities to further streamline and automating updates as needed to keep processes in optimal shape.

Don’t wait to automate

Incorporating automation from the start of your digital transformation journey helps you maximize the potential of your new ERP or cloud environment. By embedding automation across all phases, you generate an agile operational machine.
The best time to automate is now, and your path to long-term ROI starts right where you stand. Determine your stage of automation maturity by taking our quick assessment.

If you know you’re in this group or your organization faces other efficiency roadblocks, you can’t wait any longer to streamline with automation. But what if you’re not sure? Recognizing when it’s time to invest in modernization projects can prove challenging. 

This list will help you identify the indicators that your organization is poised to boost its automation maturity level to keep pace with the demands of your industry and customer base.

1. Comparatively slow processes

If you’re relying too heavily on manual effort, you’ll notice that routine tasks are time-consuming and require constant human intervention. You may notice a difference in speed and efficiency across teams and departments or compared to peers in your industry. 

For instance, in the order-to-cash process, your employees might be manually entering sales orders, which delays order fulfillment and increases error rates. Or, your procurement team may still be using spreadsheets to track purchase orders and invoices and be slow to flag issues — a sign that automation should be on your radar. 

The impact: Reliance on manual processes can act as a significant barrier to progress and prevent your organization from achieving best-in-class performance. Manual tasks slow down response time and tie up valuable human resources in work that could easily be automated. The cumulative effect is reduced efficiency and a growing gap between what your organization can achieve and what it needs to achieve to stay competitive.

Next steps: Assess which manual processes and workflows are ripe for automation. The low-hanging fruit often includes repetitive tasks that require little human judgment but are prone to error. By automating these, you gift your team the time to focus on higher-value work and protect your bottom line by reducing costly mistakes.

2. Lack of integration between systems

When your software platforms operate in silos, unable to share data seamlessly, your employees spend too much time manually transferring data from one system to another or using workarounds like exporting and importing spreadsheets. Data is difficult to find and can be inaccurate. In key business processes like meter-to-cash, disjointed systems can create a big risk of inaccurate billing.

The impact: Core business metrics should drive your decisions. Inefficiencies get in the way and become a true strategic liability. Without the functionality to integrate your most critical platforms, your team may duplicate efforts. Your entire organization becomes vulnerable to inaccuracies that mislead decision-makers and slow down your progress toward key business objectives.

Next steps: Investigate the business value of a workload automation platform that integrates with all your SAP and non-SAP systems across any IT environment to encourage seamless data flow and set the stage for future process automations.

3. Difficulty scaling operations

Struggling to handle increased workload or demand fluctuations can be a sign that you need to implement automation (or better automation solutions) as soon as possible. A surge in orders or customer inquiries could overwhelm your system if your automation tools are hard-coded and inflexible. In a period of growth, you need to be prepared to adapt to new business requirements, integrate new sales channels or onboard new team members.

The impact: Inability to scale begins as an operational challenge but can balloon into missed lucrative opportunities and a declining customer experience. Insufficient or absent automations both contribute to stagnation across all areas of your business.

Next steps: 

1. Evaluate low-code/no-code automation tools that democratize automation design and provide easy opportunities to extend the power of your growing IT team and free them up for innovation.
2. Leverage AI in areas where it matters most, such as for SLA performance.

4. High operational costs with limited ROI

Rising costs and zero or negative return on investment (ROI) point to problems with your automation strategy. You might experience this unfortunate combination if you’re using legacy systems or outdated automation tools that don’t speak to each other. Licensing fees, maintenance costs and the IT resources required to develop and monitor automations across a complex environment can result in persistent budget overruns.

The impact: High operational costs create everyday challenges, such as difficulty pinpointing the right pricing strategy. But they’re also a silent killer of innovation because they create an urgency to constantly address inefficiencies. There’s no room for investing in new technology when you’re frustrated with your current stack.

Next steps: Perform a cost-benefit analysis of your existing automation tech stack and compare it to the potential cost savings of transitioning to an automation fabric solution. 

5. Frequent compliance or security issues

An inability to maintain compliance or prevent and respond to security breaches is a red flag that your current processes are outdated or insufficiently automated. Difficulties maintaining accurate records for tax compliance, for example, can mean your monitoring and reporting aren’t where they need to be. If project managers, finance teams and other stakeholders don’t know what’s happening with critical jobs across all business units, proving you’re sticking to regulations in the event of an audit won’t be possible.

Similarly, a security breach requires an immediate response, which humans can’t always provide. Nor can they engage in the level of testing efforts that automated tools can. Continuing to rely on manual testing and quality assurance can leave your organization vulnerable.

The impact: Compliance and security are vital to your business’s existence, not just its success. Failure in these areas can result in fines or operational disruptions, but more significantly, it can damage your reputation irreparably. With both your customers and regulators, trust is everything. The risks of non-compliance are far higher than the costs of migrating to an end-to-end automation solution you can count on.

Next steps: Implement automated compliance and security monitoring with a centralized workload automation solution to ensure consistent adherence to regulations.

Mature your automation strategy now 

Recognizing these signs is the first step in transforming your organization’s approach to the automation process. But failing to act on that awareness can increase your risk of being left behind — in your industry and in business in general. 

Automation is not just where technology is going. It’s already here. The question is: Are you willing to do what it takes to make real change, so you don’t get trapped in a stalled automation project or tied to automation tools that aren’t capable of taking you where you want to go?

Luckily, a push for prioritizing automation can pair wonderfully with other technology transformations. If you’re transitioning to SAP S/4HANA Cloud, for example, you don’t have to wait until that move is over to begin building automation technology like Redwood Software’s suite of automation fabric solutions into your plans and actionable steps. 

It is important, however, to know exactly where you stand before getting started. 

Identify your organization’s level of automation maturity and get personalized recommendations with our new, convenient assessment.

For big software purchases, your company likely faces a choice between a subscription-based (SaaS) model, which requires ongoing payments and often comes with regular updates and improvements, and perpetual licensing, which enables you to use the software indefinitely. Both models offer distinct advantages, but it’s important to understand the broad potential impact on your business if you commit to a perpetual license.

While they may initially seem more cost-effective, the long-term implications tell a different story. They can’t live up to their promise of unlimited future usage. SaaS offers the true perpetual value. 

Cost considerations

It’s easy to assume that perpetual licenses save money in the long run. After all, a one-time payment seems more economical than recurring subscription fees. However, this perspective overlooks the long-term costs.

When you buy a perpetual license, you’re purchasing a version of the software that will not evolve with your organization’s needs. As your company grows and changes, you’re locked into technology that hasn’t grown alongside it — and a provider you may not be happy with. This creates “tech debt” that’s difficult to overcome and will likely necessitate another significant investment within a short period of time. Moreover, your IT team may face the added burden of maintaining or integrating outdated software with newer technologies, which can generate more hidden costs.

SaaS solutions, on the other hand, offer ongoing value. With regular updates and enhancements, they evolve with your business and ensure you always have access to the latest features and capabilities. Adaptability extends the lifespan of your software, helping you stay competitive without needing to overhaul your entire tech stack.

Security implications

Cyber threats are constantly evolving, so it’s best to choose software providers that can help you maintain strong security measures. A SaaS model gives you access to regular security updates and new features that can protect your business. A proactive approach reduces the risk of data breaches and system intrusions that can lead to significant financial and reputational damage.

Perpetual licenses can create risk because there’s less of an incentive for providers to invest in ongoing security improvements. And without consistent updates, your software could become vulnerable and leave your data and operations exposed. Even if you invest in additional security measures, the lack of integrated updates from the provider can leave gaps in your defenses.

The flexibility factor

As your business needs change, a SaaS model will allow you to scale your software usage. You can add new features, users or integrations without a significant upfront investment. Especially if you’re in an industry that experiences rapid fluctuations, this is a major upside: You can quickly adapt to market changes, customer demands or new regulatory requirements without a software overhaul.

SaaS models also typically include seamless updates that occur in the background, so you experience fewer disruptions.

Upgrading to a newer version of perpetual software may require a separate purchase, and integrating new capabilities can be both time-consuming and costly. Ultimately, this rigidity can hinder your ability to innovate and stay competitive.

Long-term viability

The longevity of software code is a crucial factor when you’re choosing between perpetual and subscription licenses. While the idea of a perpetual license suggests indefinite use, the reality is that code typically becomes outdated within three to five years if it’s not continually updated. With innovation happening faster than ever and software becoming more complex, even the most robust solutions will eventually become obsolete.

A subscription gives you the best chance of extending the value of your investment because you have access to updates as your needs or industry standards change.

Resist the myths and go with SaaS

Outdated software, security vulnerabilities and limited flexibility are significant drawbacks that outweigh the initial savings of a perpetual license. Succeeding in business today requires finding solutions that evolve in tandem with your business goals.

If you want to make sure your software not only cuts short-term costs but also drives long-term growth, security and agility, choose a SaaS model.

Powerful automation fabric solutions offered as SaaS, like RunMyJobs by Redwood and JSCAPE by Redwood, give you peace of mind that your investments will drive value for years to come. Learn about this power combo of workload automation and seamlessly integrated managed file transfer in a demo. 

The shift from manual to automated processes

In the thick of this digital revolution, the finance function is witnessing a significant reduction in the reliance on Excel spreadsheets and manual tasks. This curtails the time and manual effort consumed in day-to-day finance activities, such as journal entry and internal controls, along with month-end close tasks, such as reconciliations and accruals. Digitally transformed finance processes also minimize human errors, prevent fraud, enhance controls and improve audit trails, which drives greater precision and accuracy in financial statements. The use of robotic process automation (RPA), despite its initial allure, has seen limitations in its practical application within R2R processes. Financial institutions and organizations in other sectors have realized the need for more robust and advanced solutions that offer better flexibility to manage changing requirements, scalability and more resilient integration with existing ERP systems beyond screen scrapping to transform their operations effectively digitally.

Real-time data and operational efficiency in finance

Real-time data processing has emerged as a critical component of this transformation, enabling CFOs and finance teams to access instant financial insights for better decision-making. Integrating digital technologies into R2R processes is an operational upgrade and a strategic initiative that aligns with broader finance transformation goals. Automating manual tasks opens up new opportunities such as continuous automated internal controls, daily reconciliations with automated adjustments, real-time intercompany transaction management and more. It propels financial operations towards greater transparency, enabling stakeholders to better understand the organization’s financial health on a day-to-day basis throughout the accounting period, not just at month end.

As providers of technology that enables digital transformation in finance continue to innovate, deploying comprehensive automation solutions within the finance function simplifies complex workflows by eliminating manual tasks and activities while providing greater end-to-end transparency of finance operations. This marks a decisive step towards achieving the holy grail of finance — operational efficiency and speed without sacrificing accuracy or compliance. With these advancements, digital transformation in finance is not just about keeping up; it’s about setting a new pace for finance with continuous improvement and rapid adoption of changes in new ways of working and more sophisticated financial analysis and reporting.

Fintech developments and RPA have provided the initial steps toward automation, yet the intricacies of the R2R process demand a more sophisticated approach. Organizations now recognize that to achieve a seamless financial close cycle, they must look beyond RPA to robust solutions when things change and deeply integrate with ERP systems with real-time data analytics capabilities for financial process transparency and multi-dimensional reporting.

Through digital transformation, finance professionals can harness the power of real-time analytics based on more precise and reliable data to offer timely and accurate forecasts, improve pricing and profitability strategies, and enhance overall financial planning and corporate performance. Automating journal entries, account reconciliations, accruals, continuous internal controls and other R2R tasks ensures that financial statements and analytics are both timely and reflective of a fully transparent and compliant finance operation.

Empowering finance professionals

As we crest the wave of digital transformation, the finance leader’s role is more crucial than ever. They must steer the ship through the evolving seas of R2R automation, ensuring their teams are empowered with the most advanced and reliable automation tools and solutions. Digital transformation enables finance departments and the finance function to move beyond being considered number crunchers and as pivotal contributors to strategic planning and business decisions, the foundation for enhanced corporate performance.

By adopting sophisticated automation and digital technologies, finance teams can eliminate bottlenecks, delays and errors, delivering real-time insights that propel the business forward. This transformation ensures that financial analytics and reports are the dependable compass by which the business navigates, bolstered by the speed, accuracy and efficiency that automation brings. As finance professionals harness the power of these transformative tools, they reinforce their role in shaping the future trajectory of their organizations, ensuring they remain competitive in a digital-first world.

The digital transformation journey in finance through R2R automation has challenges. For instance, replacing spreadsheets with automation technologies can be a significant cultural shift for organizations steeped in traditional manual processes and Excel spreadsheet-heavy workflows. The introduction of automated finance operations requires a coherent plan and a new transformational mindset, focusing on process improvement and organizational change management, which includes redefining roles within the finance team.

Embracing this digital shift isn’t just about staying relevant; it’s about leading the charge in a financial revolution that promises to redefine the role of finance within the business and unlock new avenues of growth and success.

The future of finance in a digital-first world

In this era of rapid digitalization, new technologies are the linchpin of the finance industry’s transformation. With each passing day, repetitive tasks are automated, allowing finance professionals to upskill and pivot towards more strategic roles within their organizations. This digital finance transformation isn’t just about efficiency; it extends to further transformation projects that reimagine the entire finance ecosystem, integrating apps, web services, new digital assets and ways of working that enhance the customer experience and developing roadmaps for future continuous innovation.

As companies embark on these transformational journeys, they’re crafting resilient ecosystems responsive to the evolving demands of the marketplace and regulatory environment within which global businesses operate. They are building frameworks that incorporate the latest digital tools and foster a workplace where continuous learning and adaptation are part of the organizational culture. With a keen eye on the roadmap, finance is poised to embrace a digitally transformed organization where automation and human expertise converge to create unprecedented value. Sign up for a demo of Finance Automation by Redwood to learn more about digital transformation through R2R automation.

When we travel around the holidays, most of us expect a certain amount of stress, and there’s always a risk of weather-related delays. With the impending “bomb cyclone,” Winter Storm Elliot, this period of 2022 was fraught with uncertainty for people on the move.

Weathering two storms

Starting on December 23, the powerful cyclone grounded more than 17,000 flights nationwide. Many airlines, including Delta, American and United, faced bottlenecks, an all-too-familiar story. Despite heavy investment in systems and careful orchestration by leading IT experts, technology failed to perform under extreme pressure.

Southwest Airlines, for one, found itself in a dire situation. Even after the storm cleared, its systems were still struggling. Between December 26 and 31, the popular airline had to cancel up to 70% of its flights every day — largely due to its inability to assign crews to the flights that were still scheduled. Millions of passengers were stuck.

Southwest expressed sincere apologies: “Our employees and crews scheduled to work this holiday season are showing up in every single way. We are beyond grateful for that. Our shared goal is to take care of every single customer with the hospitality and heart for which we’re known.”

Despite these sentiments and round-the-clock efforts to correct the problem, operational disruptions continued beyond that week, making many travelers hesitant to book future trips. The debacle cost Southwest nearly a billion dollars in net outlay and lost revenue. Compounded by viral negative stories circulating on social media, the financial repercussions dealt a considerable blow.

Runway to ruin: Origins of an industry predicament

The cause of the storm after this storm wasn’t the snow and ice blanketing the runways. It was invisible — embedded in the legacy infrastructure underpinning many airlines’ operations.

This was not merely an instance of management neglecting to anticipate weather impacts but a fundamental flaw in backend IT setup. Many large corporations continue to use the same technology that drove successful outcomes for years without realizing the magnitude of risk involved, sometimes thinking any system upgrade or migration would be too much. Relying on outmoded systems for critical processes is costly yet common.

Four root causes of the aviation industry’s struggles stand out.

1. Archaic technological architecture

At the heart of operational dysfunction is often a struggling gate check-in and check-out system, which may rely on an outdated job scheduler. Ill-equipped to handle the complexities and rapid changes required in modern air travel logistics, old systems don’t perform well under stress. Any disruption in the schedule could lead to widespread systemic failure.

Just a couple weeks after Elliot passed, the Federal Aviation Administration experienced an outage of its 30-year-old NOTAM system, which is critical for the safety of all US flights. Thus, the effects of obsolete technology aren’t limited to private companies but also regulatory bodies.

2. Increasing enterprise complexity

The complexity of today’s enterprise environment further complicated the 2022 event and presents an ongoing dilemma. Digital transformation has, for many organizations, meant increasing the number of tools used for everyday work. On average, a large enterprise uses 473 SaaS applications. 

If integrations aren’t seamless, this expansiveness breeds information silos and a disjointed system that isn’t equipped for large and intricate workloads. Delayed responses in critical times are, therefore, not surprising.

3. No true workflow orchestration system

Many airlines also lack effective workflow orchestration, depending on custom-scripted automations that weren’t designed for rapid adaptability or scalability. The absence of a robust, integrated workflow system means they’re not prepared to recalibrate in the worst-case scenario.

4. Missed signs of impending breakdown 

Technology crises don’t generally occur without warning. For instance, a smaller system outage occurred two years prior in Southwest’s Atlanta hub, highlighting vulnerabilities that were bound to worsen if not addressed. It’s understandable that this would have been overlooked, as it’s not necessarily obvious that a particular outage across such an intricate system is the one signaling a major problem in the near future. However, more sophisticated systems would be able to predict system issues before they grow to the scale of the 2022 debacle.

The value of seamless data flows in preventing operational chaos

In this tale are priceless lessons for complex enterprises in any industry. It’s a stark reminder of the role of modern, resilient technology in a crisis response strategy.

The most prominent message? Seamless data flows aren’t just part of a secure IT foundation. Implemented correctly, they can be a business advantage and ensure your architecture stands up to pressure when your competitors’ may not. Reliable, powerful technology is instrumental in preventing delayed responses, misinformation and downtime.

Good data is anticipatory — it allows you to predict challenges. Organizations with agile, data-driven processes can see what’s coming and pivot effectively, a significant competitive advantage when external factors like weather impact an entire industry.

Should an unforeseen event occur, continuous data flows are the best form of insurance, as they protect your business in real time. When data moves unhindered across departments and systems, it minimizes downtime and the costs associated with it. In turn, this reduces the indirect costs of lost customer trust and potential market share.

An alternate route

How could things have gone differently for airlines with a more modern IT environment in the midst of this major storm? 

Fewer, newer and more tightly integrated systems would have removed the manual burden of disaster recovery and reduced the scale of the consequences. Specifically, end-to-end automation could have dramatically improved crew scheduling and reassignment to reroute resources where they were most needed. Real-time data inputs from weather services and airport traffic updates could have preemptively adjusted flight schedules. 

The customer experience, while not entirely protected from disruption, could have been far less catastrophic, with automations triggering customer alerts and eliminating the need to stand in long lines for answers.

How to navigate an application-heavy landscape

Still not convinced it’s time for IT infrastructure modernization initiatives at your organization? Let’s look to the future.

Sophisticated technologies are arriving on the business scene at an exponential rate. On top of the immense number of apps you already use, you’ll likely be looking to fully utilize the power of machine learning and AI to transform your business operations and keep up. 

However well-intentioned, these moves increase the chance of system failure if they’re not done well. Stacking more complexity on top of an already overwhelmed mainframe or legacy infrastructure is a sure way to kill any amount of operational efficiency you’ve maintained until now.

Interconnection and interdependence are mandates in today’s (and tomorrow’s) IT operations, and every enterprise must evolve to support them. You need your business processes to run at scale — millions of times per year, across your architecture, uninterrupted. To achieve this and stay competitive, you simply must automate.

A storm-ready modernization strategy

Automating single tasks in silos is not the answer. Instead, aim to implement an automation fabric — a single, integrated framework that unites all of your activities, applications and environments and orchestrates mission-critical business processes. This approach is essential for modernizing your infrastructure and solving for the complexity of the application explosion.

Here’s how to stay ready for the equivalent of a severe winter storm during the holiday travel period in your industry.

• Don’t overlook warning signs. Your systems speak! The longer you ignore a “check engine” light, the bigger and more costly the issue could become. While your backend may not be revenue-generating, it is revenue-protective.

• Choose forward-thinking software providers. Invest in relationships with software providers who prioritize innovation and adaptability while considering your business needs. They’ll offer solutions that are adaptable to changes in technology to ensure your ecosystem doesn’t become obsolete.
• Safeguard your organization with workload automation. Instead of fighting to make monolithic, inflexible systems do what you want, streamline and build efficient end-to-end processes you can rely on.

Clear your organization for takeoff 

Imagine a plane from the 1960s taking off next to one built in 2024. While it could theoretically get off the ground, problems verifying the safety of parts, finding pilots who can fly it and speaking to air traffic control and more would make it impossible to try.

Today’s plane is your competition. They will modernize whether you do or not. Therefore, trouble is inevitable if you fall behind, whether it manifests as a few days, hours or minutes of chaos.

Modern enterprise scheduling can pull you forward from an archaic space to help you level with the best in your market today, tomorrow and beyond. The key is to find a solution that can do the job. Many job schedulers haven’t shifted their architecture or offerings in 15 to 25 years. 

Redwood Software, on the other hand, has been live with SaaS for over 10 years. The full stack automation platform that’s purpose-built to orchestrate your mission-critical business processes, RunMyJobs gives you a single pane of glass to turn your business operations into a business advantage. 

Don’t wait to get off the ground. Demo RunMyJobs today.

As the former CEO of one of the enterprise software leaders in analytics, I had a front-row seat to this “data fabric” revolution.  While it was easy to get caught up in the marketing hype around new terms like “big data” and “predictive analytics,” the reality was that the most competitive companies in the world were increasingly differentiating their ability to serve their customers based on how well they collected, managed and utilized their data.  By eliminating data silos, these leaders were able to consolidate and organize data from multiple sources and capture a unified view of the customer across all touchpoints.  

The inevitable domino effect

Today, the use cases and benefits of a modern data fabric architecture are apparent. And now, this revolutionary interwoven approach is happening in the automation industry. The result of this will be a requirement for every modern enterprise to build “automation fabrics” in order to effectively compete and profitably grow.  

An automation fabric is a cohesive and integrated framework that seamlessly connects various automation tools, processes and data sources. It acts as a central nervous system, enabling seamless communication and collaboration among disparate business activities, applications and environments, driving mission-critical business processes across any tech stack. Think things like procure-to-pay, just-in-time delivery, record-to-report.  

The core market change driving this revolution and the need for automation fabrics isn’t rocket science. It’s simply a number of market shifts that we have all been investing in for some time. For starters, IT is no longer relegated to being a simple enabler of the back office. Lines of business leaders expect their technology investments to drive core business outcomes, with delivering a superior customer and employee experience being the new competitive battleground. For example, how do I close the books in record time? How do I translate an online order into cash collections without error? Or, how do I massively improve the resilience of my supply chain? Each of these business outcomes starts with some kind of end-to-end business process transformation.

However, achieving that end-to-end business process transformation is now quite complicated. As best-of-breed products replaced business suites for more superior, targeted functionality, the number of applications that house these business processes, and their underlying transaction data, has absolutely exploded over the last two decades. 

The good news is these highly specialized, process-oriented applications have made many individual tasks easier and more forgettable. But the bad news is they’ve created an endless sea of silos that do everything incredibly efficiently alone but do virtually nothing together. Today, almost no business outcome — including mission-critical ones — is accomplished with just one application. Furthermore, most mission-critical business outcomes still require working with established transaction systems of record, like your ERP system. As a result, the transaction data and business processes needed to come together to drive these business outcomes require coordination across multiple applications — cloud, on-premises or hybrid — working in an orchestrated fashion.

To make things more complex, all these bespoke applications and systems often run on tech infrastructure that is constantly changing. Enterprise modernization efforts are no longer just considering a simple lift and shift from on-premises to the cloud. Instead, leaders are conducting a careful reassessment and refactoring of their entire tech stack, as they are on a mission to tear down monolithic systems and refactor their vast tech stacks to microservices architectures while putting everything into containers, including modernizing their CI/CD and DevOps pipelines for faster delivery.  

When companies start refactoring their entire tech stack into microservices and containers spinning up and down on this massive a scale, you need an immense amount of automation because human beings cannot handle this manually — it’s an n-dimensional problem. This great replatforming has created a real problem for enterprises, as their legacy automation platforms simply do not have the ability to automate business processes end to end across this full stack of mission-critical applications and underlying, ever-changing tech infrastructure. This n-dimensional complexity requires a new approach to automation. One that’s purpose-built for a best-of-breed application world but also provides the flexibility to work across any IT infrastructure you may encounter. It’s why automation will become the pervasive operation system fabric powering today’s modern enterprises. 

Choose your partner wisely

In the same way data fabrics revolutionized our ability to make more informed decisions for our companies, customers and employees, automation fabrics will now revolutionize our ability to deliver superior customer and employee experiences. Like building data fabrics, building your automation fabric requires making critical decisions around your automation platform and software partner. After all, your automation fabric will be the pervasive operation system driving your entire company. So, it’s an important decision! Some points you may want to consider in choosing your automation partner include:

• Connecting applications and systems: Can I connect deeply to all the applications and systems I need to connect to ensure seamless, end-to-end business process automation? Does this include connections to my ERP system and my SaaS and legacy applications?

• Composability: Can I create new automations quickly and at scale without extensive programming resources? Can I easily create a new automation with a drag-and-drop approach and pre-built components rather than creating code? 
  
• Monitoring and control: Can I monitor and control the myriad of processes in real time and have confidence that the processes will run to completion? Can I predict, manage and take action on SLA performance? 
  
• Confidence: How confident am I in the platform’s ability to scale its performance in a highly secure manner? Does it come with global 24/7 support?  

Harness the power of automation

You will hear a lot of buzz around enterprise businesses turning their attention to the automation fabric. But in its essence, it’s simply about tying every mission-critical business process together into a seamlessly orchestrated effort. And at its core, it’s about freeing up the time and mind space for you and your team to focus on the bigger picture and more strategic initiatives that will drive your business forward. You just need the time and space to see the forest! Your automation fabric will help you do just that.  

You want to ensure (as best you can) that you’re making the right moves and investments now to withstand tech developments and inevitable innovations along with your scaling business needs.

Poor planning when it comes to automating your critical business processes will have your team running at a deficit, unable to keep up with workloads and working ineffectively. This will negatively impact operations, any competitive advantage and, ultimately, business outcomes.

As the leader in workload automation, we’re sharing our predictions of what’s on the horizon for automation in the coming year. We hope this information equips you to adapt and scale through the changes and drive greater financial success. 

We’ve talked about the Great Replatforming, the reckoning of automation and the application boom, and we’ve all experienced AI infiltration, seeping into almost every technology and business application conversation, including workload automation.

So, now, what’s next?

Automation will become easier to use

As enterprises focus on creating greater efficiency and unlocking savings, many will wisely look to automation. Most IT teams already have a backlog of automation opportunities and “low-hanging fruit.” 

Most smart IT leaders recognize it’s more urgent than ever that automation becomes faster and easier to set up. Many are exploring a self-service approach, with employees becoming more hands-on in managing their automated business processes. 

Stakeholders will need to participate actively, forcing automation providers to make their products easier to use. The barrier for workload automation will be broken down even further with less technical and more business process-oriented skillsets needed. 

What to do now

Use a workload automation platform that can ramp up reliable automation quickly and easily, taking into account the different applications and environments within your tech stack. Look for things like automation wizards, drag-and-drop interfaces, pre-built templates and intuitive tools for configuration and deployment. How is the platform leveraging AI? 

Question the provider’s migration process early if you’re exploring a new solution. We all know the commitment and risk of moving to a new system, so check all your bases.

Enterprises will move from silos to connections to hyperautomation 

Silos across different platforms will continue to cause problems and breakdowns within automation. The multitude of solutions like Service Orchestration and Automation Platforms (SOAPs), robotic process automation (RPA) solutions, integration platform as a service (iPaaS) tools, business process management (BPM) solutions and enterprise applications integration (EAI) tools will need to work together in a unified ecosystem.

SOAP will drive the interoperability of automation platforms by progressing the orchestration capabilities of the hybrid IT tech landscape. This is necessary, with so many companies managing a diverse, hybrid environment. The automation rate will rise sharply as more and more customers use this interoperability to realize their own vision of automating all the processes they can across their entire organization.

Hyperautomation will be within reach for IT leaders as it becomes feasible to connect a variety of automation, and even AI tools, to drive business outcomes. Enterprises will continue to elevate how technology is used for business growth and profitability. Going into 2024, there will be an increased focus on how your IT landscape, including automation platforms, works in tandem to drive end-to-end automation and realize desired business outcomes.

What to do now

Evaluate your business processes and automation systems. Use a full stack workload automation solution that automates end-to-end with the flexibility to work across and orchestrate any application, middleware and IT infrastructure. This is the only way to ensure that business process automation will continue to work seamlessly, no matter what the tech stack is now and in the future.

Generative AI will bring exciting innovations

Generative AI can provide valuable insights into a company’s overall usage of an automation platform and even recommendations on improving automation — everything from how workflows are built to how jobs are scheduled for better optimization and fewer failures. 

The integration of generative AI into various automation platforms, such as SOAP, RPA and low-code application platforms (LCAP), will further improve the capabilities of these platforms. 

Generative AI can summarize data, create reports, generate personalized responses for customer service and rapidly handle level 1 support to improve CSAT scores — and that’s just scratching the surface. Generative AI will expand the scope of automation, moving from only automating repetitive, rule-based tasks to handling more complex, creative and data-driven processes, opening up new automation possibilities. 

Expectations are that generative AI (and we’ve already seen some of this) will begin automating content creation, design generation and product customization, along with personalized marketing campaigns, sales communications and customer support. 

Many believe that generative AI tools will revolutionize the automation experience so that non-technical users can be empowered to automate their own tasks and processes easily. More advanced AI models will help automate components of humans’ decision-making process by analyzing vast amounts of data, identifying patterns and making informed recommendations. 

Think of a loan approver where AI can analyze dozens or hundreds of variables and recommend approving or rejecting an applicant. AI will handle this complex manual process with the ability to comb through and make sense of massive amounts of data in a way unattainable by humans.

What to do now

Begin embracing generative AI and keep yourself informed on the latest developments. The key here is to leverage technology in a way that helps your people do more and focus on what they do best. Evaluate where human talents and capabilities would be most effective within your IT team and organization. Make sure your SOAP platform has the capability to integrate AI to automate decision-making tasks to improve your automation coverage. At Redwood, our mission is to unleash human potential through enterprise automation, and we believe the focus needs to be on empowering and optimizing your human workforce.

Automation ROI will be measured on targeted business outcomes

Some C-suite leaders are tracking automation ROI based on basic metrics or alignment with corporate KPIs. Going forward, many CEOs and CFOs will demand that automation platforms be connected to business intelligence dashboards and advanced data visualizations to track critical metrics and KPIs that show automation’s impact on business outcomes.

Many digital companies with automation baked into their DNA have experienced a tremendous competitive advantage. The vast majority of other companies have been playing catch-up. 

IT and business teams will increasingly work together to develop dynamic and flexible measurement frameworks for automation, taking into account evolving circumstances and goals, all in an effort to improve business agility.

What to do now

Ensure your workload automation solution connects with critical business intelligence tools and tracks what executives want to know. This will inevitably show the value that automation brings to the business while also unveiling potential areas of improvement. Greater efficiency and productivity can be directly attributed to automation, and its contribution needs to be visible across the company to prove value. This will make sure your automation journey continuously bears rewards along the way.

Are you ready for 2024?

This year will bring even more significant advances in automation and more opportunities for enterprises to lean into these powerful solutions. While automating more of your business processes may initially feel like an intimidating lift, it’s the only way to stay competitive and drive better business outcomes.

With the right platform, your business will unlock so much more potential and achieve more significant milestones than you could have imagined before. May the automation force be with you! 

The essence of workload optimization

Workload optimization ensures the right resources are in the right place at the right time. It allows businesses to align IT resources with their evolving needs, whether they have on-premises infrastructure or public cloud platforms. With real-time metrics, businesses can monitor application performance, ensuring they meet the expected SLAs. Whether you’re using Azure, AWS or any multi-cloud environment, workload optimization ensures the effective use of resources.

The data center of yesteryear was static and hardly dynamic. However, with the advent of cloud computing, the paradigm shifted. Growing businesses have recognized the value of cloud migration and the necessity of tools like workload automation and orchestration to automate the allocation and provisioning of resources.

Workload optimization in action

For instance, when looking at a virtual machine (VM), the operating system and CPU metrics can help pinpoint where the resources are most stretched. Tools like Cisco Workload Optimization Manager (CWOM) have been designed to help businesses optimize these VMs. For businesses navigating a multi-cloud world using VMware or any cloud service, the capabilities of CWOM can offer invaluable insights.

However, it’s not just about tools and metrics; it’s about understanding your business needs. Whether you’re a Microsoft enthusiast, a DevOps team pushing code in real-time or a business considering cloud cost and cost optimization, there’s a need for a systematic approach.

However, workload optimization isn’t just about IT. It’s about how IT aligns with the broader business. Whether you’re considering cloud optimization, looking to automate using Kubernetes or leveraging the power of APIs to consolidate resources, the end game remains the same: efficient operations.

When it comes to workload performance, Redwood offers a unique perspective. With a focus on IT operations and automation, Redwood’s suite of tools helps streamline and orchestrate complex processes, bringing harmony to IT resources (read more about workload automation trends here). Integrating Redwood’s solutions into businesses, particularly for IT processes, can dramatically shift how you handle IT operations.

Workload optimization remains a focal point for businesses looking to scale and optimize as the IT landscape evolves. It’s not just about automation; it’s about aligning IT operations with business objectives. As you venture further into the world of workload optimization, remember to explore what Redwood has to offer and how these solutions can redefine your IT operations.

Why business process management matters

Business process management is optimizing and managing business processes to achieve more efficient outcomes. The objective? Streamline operations, reduce inefficiencies and enhance the overall customer experience.

Picture this: A smoothly running assembly line with each part working in perfect harmony, propelling the entire machine forward. That’s the power of effective BPM. From workflow management to the digital transformation of traditional processes, BPM is pivotal in ensuring businesses remain agile in a fast-paced world.

The role of BPM tools and software

Remember when businesses relied heavily on manual processes like Excel sheets to track operations? Today, we’ve evolved. BPM tools and software allow businesses to automate workflows, optimize processes in real-time and even incorporate artificial intelligence to make data-driven decisions.

Whether onboarding new employees or managing intricate project management tasks, BPM tools work behind the scenes to deliver for your business. For solutions that can drive BPM forward, look into Redwood’s Business Process Automation and innovative cloud-based workflow solutions. These platforms offer a holistic approach, from process mapping to end-to-end process management.

How BPM and digital transformation go hand-in-hand

In this age of digital transformation, BPM is not just a methodology; it’s a necessity. Businesses increasingly look to BPM systems to drive their digital strategies, streamline operations and enhance customer satisfaction. Integrating low-code platforms, robotic process automation (RPA) and BPM creates a potent mix that drives process improvement, eliminating bottlenecks and redundancies.

For a deeper dive into the power of low-code automation, check out this insightful article by Redwood.

In conclusion

BPM is the backbone of operational success, from large corporations to budding startups. By embracing BPM tools, businesses can achieve their goals faster and more efficiently. But remember, while tools play a significant role, it’s the strategic implementation and continuous improvement that truly unlock BPM’s potential.

Ready to experience the magic of advanced BPM tools firsthand? Consider scheduling a quick demo with Redwood today!

At Redwood Software, our commitment to innovation positions us uniquely in this dynamic landscape. We not only understand the immense potential of RPA but also recognize its limitations. As we guide businesses in their automation journey, our focus remains on offering tailored solutions that truly resonate with their unique needs and challenges.

In this article, we’ll explore the shortcomings of RPA to ensure you make informed decisions for your organization’s digital future.

Robotic process automation (RPA) defined

In the digital age, RPA solutions emerges as powerful tools, allowing businesses to automate tasks that were once repetitive and manual. Through software robots or “bots,” RPA mimics human actions, efficiently handling tasks ranging from data entry to user interface navigation. This efficiency, combined with its scalability potential, has made RPA a sought-after solution in various industries.

However, while RPA offers many benefits, it’s essential to understand its limitations.

The disadvantages of RPA

1. Handling complex tasks: RPA excels in handling straightforward, rule-based tasks. However, when it comes to more complex operations that lack a structured rule set, RPA often falls short. Human expertise, with its deep analytical and decision-making capabilities, remains unmatched in such scenarios.
2. Initial investment: Beyond the evident cost of acquiring RPA software, businesses must also consider expenses related to training, system integration and deployment. These costs can add up, making the initial phases of RPA adoption financially demanding.
3. Scalability hurdles: While RPA promises adaptability, some tools may struggle to scale, especially when interfacing with outdated legacy systems. This can lead to operational challenges as businesses grow.
4. Impact on employment: The rise of bots can sometimes stir concerns about job loss. However, it’s crucial to note that while bots can automate tasks, they cannot replace the nuanced human touch, problem-solving capabilities or creative insights.
5. Software dependency: Over-dependence on RPA can pose challenges. Bots, while efficient, can face issues with software updates or unforeseen changes in user interfaces, leading to operational disruptions.
6. Security challenges: RPA bots operate across multiple platforms, necessitating comprehensive access permissions. This expansive access can sometimes pose security challenges that businesses need to address proactively.

Navigating these challenges can seem daunting, but with the right approach and guidance, it’s manageable. Redwood offers solutions like digital business process automation and advanced IT automation workflows, helping businesses seamlessly integrate RPA while addressing its inherent challenges.

How RPA compares to other automation strategies

Automation is a buzzword in the modern business landscape and while RPA stands as a significant player, it’s just one among various automation strategies available to enterprises. Understanding how RPA stacks up against other methods can offer clarity when deciding which approach best suits your business needs.

• RPA vs. business process automation (BPA):
  • RPA: Primarily focuses on automating repetitive, rule-based tasks, often at the user-interface level. It’s like teaching a bot to mimic specific human actions on software applications.
  • BPA: Encompasses the broader strategy of automating entire business processes. It’s more holistic, often integrating multiple systems and automating workflows end to end.
• RPA vs. IT process automation (ITPA):
  • RPA: Often operates on the surface level, interacting with applications but removing the human error, making it application-agnostic.
  • ITPA: Dives deeper, automating backend IT processes. It can include server reboots, backups and restores or routine maintenance tasks and often requires integration with the system’s APIs.
• RPA vs. intelligent automation (IA):
  • RPA: Operates based on predefined rules without the capability to learn from data patterns.
  • IA: Combines RPA with artificial intelligence (AI) and machine learning (ML). It doesn’t just follow rules; it learns and evolves based on data input, making it more adaptive.
• RPA vs. workflow automation (WLA):
  • RPA: Focuses on discrete tasks, often independent from a broader process or workflow.
  • WLA: Concerned with automating a sequence of tasks that make up a specific workflow, ensuring tasks are passed along to the right individuals or systems in a coordinated manner and with real-time monitoring.

How to navigate RPA’s challenges

To truly harness the advantages of RPA, a strategic perspective is essential. Redwood believes that for RPA tools to be transformational, they must be integrated thoughtfully and purposefully. Here’s a roadmap to make the most of this powerful automation technology.

• Intelligent RPA deployment: While the allure of scaling up with numerous RPA robots is tempting, it’s pivotal to focus on quality over quantity. An efficient bot management system ensures that each RPA tool complements your business needs without creating an unmanageable maze of bots.
• Clear task allocation: One of the profound advantages of RPA is its adeptness at executing simple tasks. By allowing RPA technology to tackle these time-consuming, repetitive tasks, you free up human resources to handle roles requiring intuition and creativity. But remember, even the most advanced RPA robot has its limitations. Strike a balance. Engage humans in tasks that demand a personal touch, ensuring harmony between automated and manual business operations. This not only optimizes efficiency but also paves the way for upskilling opportunities across various departments.
• Rigorous process evaluation: Prior to embarking on automation, it’s imperative to put potential tasks under the microscope. Testing each process before automation uncovers potential pitfalls, ensures the end-user experiences seamless operations and amplifies productivity dividends.
• Synergizing with allied technologies: While RPA is formidable, its real power shines when combined with complementary technologies. For instance, integrating DevOps principles can further streamline operations, while other automation solutions can address areas beyond RPA’s scope. This multi-technology approach fortifies your automation strategy, making it more resilient and adaptable to evolving business landscapes.

Maximizing RPA with Redwood

RPA systems are invaluable in the era of digital transformation, but they’re not without quirks. By understanding both its strengths and limitations, businesses can wield it more effectively. As you consider enhancing your automation initiatives and expanding to new use cases and automated processes, Redwood offers the expertise to navigate these challenges effectively.

Experience the next step in automation: Sign up for a demo of RunMyJobs by Redwood and witness the transformation firsthand.

While RPA simplifies many processes by autonomously performing specific, structured tasks, the journey isn’t without its hurdles, especially when navigating the intricate landscape of modern business operations of various industries, including healthcare and finance.

A closer look at RPA’s limitations

Robotic process automation is not just about bots. RPA is about replicating human actions in digital tasks. These software robots or RPA bots can carry out specific, rules-based tasks, often without the need for human intervention. For example, data entry tasks in Excel or CRM systems, which were traditionally manual, can now be performed by these bots.

However, as the demand for RPA solutions grows, we’ve observed certain limitations of RPA:

Grasping beyond rules

While RPA shines in structured, rules-oriented tasks, it encounters significant limitations with tasks that eschew straightforward rules. For example, content creation or customer complaint resolution, where human empathy and creativity are crucial, can only partially be managed by bots. The pure logical processing of RPA fails to capture the nuanced, emotional and creative dimensions that human operators bring to the table.

The inescapable need for human intuition

Although highly efficient for structured tasks, bots are at a roadblock when faced with roles demanding cognitive or emotional intelligence, such as strategic decision-making or nuanced customer interactions. The cognitive capacities, creativity and emotional responsiveness of humans, or even more sophisticated technologies like AI, become paramount in these contexts.

Scaling woes

Scalability emerges as a prominent issue for RPA. A solution that might be fitting for a smaller organization or limited tasks may falter when stretched to accommodate the burgeoning needs of a growing enterprise, emphasizing a profound need for adaptable and scalable RPA solutions.

Consider a quickly growing e-commerce platform where an RPA solution. Initially, managing orders effectively could be challenging as order volumes and customer inquiries soar, pointing towards the exigency of scalability in solutions.

The dilemma with legacy systems

Integrating RPA bots with older, legacy systems, especially those devoid of modern interfacing capabilities, could impede smooth data transfer and task execution. The absence of user-friendly APIs could pose a major hindrance, making integration a cumbersome and potentially flawed process.

When these bots, highly dependent on specific user interfaces, encounter updates or changes in the UI, it disrupts their operational flow. Imagine a bot programmed for data entry into a CRM, floundering when confronted with a UI revamp where data fields and buttons are repositioned or altered. This disrupts functionality and necessitates a painstaking reconfiguration of the bot’s operational parameters.

An expanded view on digital transformation and RPA

Embracing digital transformation via RPA tools offers a pathway to automating simple tasks. Still, it comes with unique challenges, notably in managing unstructured data and ensuring scalable and secure RPA implementations. Embedding RPA software into a broader canvas of business process management (BPM) and IT process automation (ITPA) platforms is pivotal to morphing short-term fixes into sustainable, long-term solutions.

Addressing RPA implementation intricacies, from deciphering handwritten documents to ensuring robust governance and security amidst stringent regulations, is indispensable. The journey towards an efficient digital transformation using automation software involves leveraging RPA tools for simplicity and embedding them within a secure, scalable and integrated technological framework, ensuring a future-ready and resilient automation trajectory.

RPA vs. WLA — What’s the difference?

While RPA and workload automation (WLA) seek to enhance efficiency through automation, they embody different approaches and are apt for varying contexts.

• Scope and focus: RPA primarily focuses on automating rule-based, repetitive tasks and is often utilized for front-end processes. It mimics human actions, such as mouse clicks and keyboard strokes, to perform tasks. In contrast, WLA is more rooted in back-end processes, automating data workflows between applications and systems without human intervention.
• Integration and scalability: WLA is typically more scalable and deeply integrates with IT systems and applications, addressing comprehensive workload needs across an enterprise. On the other hand, RPA might confront scalability issues, particularly when dealing with enhanced task volumes or complexities, as it’s mainly used to automate tasks at a surface level.
• Use cases: RPA is ideally suited for processes that involve interacting with user interfaces or dealing with structured data. In contrast, WLA finds its stronghold in managing complex data workflows, especially in an IT environment where tasks are triggered by specific events or schedules, managing dependencies between different jobs.
• Technological depth: WLA usually necessitates a deeper technological understanding of system architectures, as it often interacts with applications at a database or API level. RPA, however, is often praised for its user-friendly nature and is generally considered easier to implement for business users without deep technical expertise.

Interestingly, the differences between RPA and WLA can shed more light on these aspects, and understanding the power of IT automation beyond efficiency can offer more depth to this discussion.

Merging RPA with advanced technologies

When RPA intertwines with intelligent automation, a dynamic synergy is formed, enabling bots to learn, adapt and manage tasks that require a dash more ingenuity and flexibility than conventional RPA assignments. Integrating blockchain with RPA introduces an unassailable layer of security and traceability, ensuring transparent and secure data handling and tracking in various processes such as finance and supply chains.

RunMyJobs by Redwood doesn’t just automate; it digitizes. Your automated processes aren’t merely efficient but also resilient and adaptable to evolving business landscapes with our solutions. We transform not only tasks but also the overarching workflow, introducing your operations to a digital era where technology and human insight integrate seamlessly.

Overcoming RPA challenges with Redwood’s solutions

Redwood’s automation solutions are adept at navigating the complexities and potential obstacles of RPA, ensuring fluid and scalable automated processes that evolve with your business. Appreciating the unique strengths of both automated and human-driven processes, our offerings ensure that while bots manage repetitive, rule-based tasks with meticulous precision, human teams guide where intuition and creativity are paramount.

Your journey towards intelligent automation begins now. It’s one thing to read about solutions and another to experience them firsthand. Together, let’s ensure every step is secure, navigating through challenges toward a future where your business processes are not just efficient but agile, robust and intrinsically aligned with your unfolding needs.

If you’re looking to understand how Redwood can help your overcome some of these limitations, consider signing up for a demo here.

Attendees of Gartner IT Infrastructure, Operations & Cloud Strategies Conference on December 5-7 in Las Vegas, NV can attend the same session presented by Redwood’s Chief Product Officer, Abhijit Kakhandiki.

Why attend “Taming the Complexity, Full Stack Automation for IT Leaders”

Offering future-proof adaptability and scalability by seamlessly integrating across systems and applications, like SAP ERP and other business-critical software, full stack workload automation ensures the reliable end-to-end execution of any business processes. Attend the session and hear real-life use cases demonstrating how customers achieved superior business outcomes, including greater efficiency, reduced operational costs and improved customer experiences. 

“With ongoing digital transformation, IT teams are tasked with connecting processes across an ever-evolving hybrid technology stack with new applications constantly being added and creating more disconnect between systems. This is why the majority of digital transformation initiatives fail — IT teams are not set up for success,” said Redwood Chief Product Officer Abhijit Kakhandiki. “Full stack automation is the only way to successfully automate IT and business processes end-to-end because it has the flexibility to work across any application, middleware and IT infrastructure you may encounter now and in the future.”

Abhijit will share his expertise on utilizing the advanced automation of today and what businesses need to know.

The results of full stack automation

The proof is in the outcomes achieved by Redwood customers. “Whether large utility companies, global financial institutions or major manufacturers, businesses are seeing significant improvements using full stack automation — 30% reduction in operational costs, 40% improvement in processing time, 45% faster reporting. Companies can confidently migrate to Redwood’s full stack automation solution using our proven migration process with hands-on support and specialized data migration tools,” said Devin Gharibian-Saki, Redwood’s SVP of Business Development and Strategy. 

Session details

Wednesday, December 06, 2023 / 01:35 PM – 01:55 PM PST in Las Vegas, NV, presented by Abhijit Kakhandiki, Chief Product Officer at Redwood.

Redwood Software: Taming the complexity, full stack automation for IT leaders

In today’s era of the great replatforming, IT leaders are confronted with unprecedented complexities. With more systems and data than ever, spread across on-premise and private, hybrid and public cloud, leaders must simultaneously innovate and modernize while maintaining operational stability. A key component to addressing this challenge is full stack automation. Offering future-proof adaptability and scalability, full stack automation seamlessly integrates data and systems, like SAP and other business-critical software, ensuring the reliable execution of any business processes.

Visit Redwood at booth #437 for Gartner IT Infrastructure, Operations & Cloud Strategies Conference in Las Vegas, NV.