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

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

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

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

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

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

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

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

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

2. AI governance will move from policy to operating model

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

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

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

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

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

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

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

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

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

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

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

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

4. AI will amplify experienced teams, not replace them

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

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

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

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

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

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

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

5. Resilience will matter more than efficiency

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

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

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

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

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

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

What this means for 2026 and beyond

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

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

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

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

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

AI today largely operates in silos:

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

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

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

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

Why leaders often think they’re ready

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

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

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

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

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

Before autonomy: The architectural fundamentals

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

Unification: One orchestration layer to coordinate the work

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

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

Boundaries: Guardrails for scaling intelligence — not risk

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

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

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

Transparency: Governance that keeps humans in control

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

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

Governance turns autonomy into something accountable, rather than opaque.

Coexistence: A blended environment of deterministic and dynamic automation

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

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

How experimentation becomes an operating model

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

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

Read more about building the best foundation for agentic orchestration.

A platform-first evolution in automation

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

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

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

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

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

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

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

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

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

The advantage will go to those preparing now

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

That means they’re:

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

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

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

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

AI built for the enterprise

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

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

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

What’s new in RunMyJobs 2025.4

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

Redwood RangerAI Product Assistant for RunMyJobs

Get guidance right where you work

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

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

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

Redwood RangerAI Automation Co-pilot for RunMyJobs

Build better automations, faster

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

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

A robust, intelligent ecosystem

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

Redwood RangerAI Learning Assistant

Learn continuously

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

Redwood RangerAI Support Assistant

Resolve issues instantly

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

A foundation for agentic orchestration

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

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

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

The business impact

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

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

What it means for you

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

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

Redwood Software has spent over 30 years at the forefront of this industry and lived through every evolution of automation. We’ve heard your challenges loud and clear, and we understand that the critical question is no longer just, “Is our process automated?” but, “Is our automation strategy resilient and truly ready for the future?”

With Redwood RangerAI, we’re introducing the next step in that journey: a comprehensive suite of AI capabilities woven into the RunMyJobs by Redwood platform — and previews of more advancements coming soon. These enhancements extend across every stage of the automation lifecycle: design, execution, monitoring and optimization. 

Not only will Redwood’s automation fabric solutions continue to help you orchestrate complex operations across hybrid environments, but its new built-in AI will make every interaction smarter, faster and easier. Rather than just asking whether a process can run automatically, it’s time to begin asking if it can learn, predict and optimize output as it goes.

The friction in modern operations

Modern IT and operations teams face a kind of friction that wasn’t there before. Systems multiply and workloads expand, but expertise gets spread thin. Instead of driving innovation, a lot of teams are stuck reacting, using disconnected tools that don’t speak the same language.

The challenges are clear:

• Teams face growing complexity, and orchestrating thousands of interdependent jobs across clouds and containers makes even simple changes risky
• Expertise is scarce, with critical knowledge often locked in the minds of a handful of specialists
• Tool sprawl and fragmentation add even more friction, creating inefficiency and risk
• In a flood of “AI-washing,” it’s hard to tell what’s trustworthy and enterprise-ready

If you feel your organization has reached this tipping point, you don’t need more automation. You need smarter automation that can reason, assist and adapt without adding more noise.

Introducing Redwood RangerAI: Efficiency for every team

Redwood RangerAI is not another add-on or experimental AI feature. It’s a collection of AI-powered enhancements built directly into the RunMyJobs platform you already trust. It’s designed to simplify how you get work done, so you can learn and troubleshoot faster and scale automation confidently without having to manage yet another system.

These capabilities come to life through two complementary layers.

Effortless expertise

Redwood RangerAI turns experience into a shared asset. Instead of relying on tribal knowledge or lengthy manuals, users can ask questions in natural language and get immediate, accurate guidance.

• The Redwood RangerAI Learning Assistant, a new conversational AI tool on Redwood’s public documentation site, offers 24/7 access to technical insights 
• The Redwood RangerAI Support Assistant, embedded in Redwood’s Support portal, delivers instant answers to common issues, so you can resolve them on the spot and free up your experts to focus on your most complex challenges

Intelligent operations

Every automation team dreams of having more time to focus on meaningful work instead of maintenance. Redwood RangerAI makes that possible by acting as a partner who’s by your side, making you more productive and efficient.

• The Redwood RangerAI Product Assistant for RunMyJobs is built directly into the platform and provides conversational, context-aware guidance and one-click error analysis right where your work happens
• The Redwood RangerAI Automation Co-pilot for RunMyJobs assists with generating complex scripts and creating clear, concise documentation at the click of a button. With this co-pilot on your side, you’ll dramatically speed up the development lifecycle, reduce manual errors and ensure your automated workflows are well-understood and easy to maintain

Together, these help you go from a simple concept to a production-ready automation in less time, with higher quality and fewer interruptions.

Unlike generic, bolt-on AI tools, Redwood RangerAI is engineered for the precision and reliability required in enterprise automation. Leveraging Redwood’s deep automation expertise, this enhancement for RunMyJobs is fine-tuned for your specific needs.

Rather than feeding a general-purpose large language model (LLM) that returns broad information with every query, Redwood’s generative AI models are purpose-built. The results are highly consistent and cost-effective, with enhanced security thanks to minimal data being sent with each request. Through rigorous testing, Redwood delivers a scalable solution that’s immediately effective for your most critical problems.

The future is autonomous 

The roadmap for Redwood RangerAI takes you beyond predefined workflows to a model where you define a high-level business goal, and the system figures out the best way to achieve it.

RunMyJobs will orchestrate intelligent AI agents that enhance workflows across your entire enterprise. These agents will predict the steps to take, dynamically leverage AI tools and reason through complex dependencies to meet the desired outcome.

As part of this evolution, Redwood continues co-innovating with SAP, and one example of this is in the interaction between RunMyJobs and SAP Joule. Today, you can turn complex actions in RunMyJobs into simple, intelligent Joule skills. Users can query the status of a process, execute workflows and more using natural language, bridging the gap between business intent and IT execution.

Redwood’s commitment to practical, secure innovation

The launch of Redwood RangerAI this month marks the beginning of a new era in automation — one where you are no longer limited by complexity or skills gaps, where you can confidently scale automation across your business, enhance operational resilience, accelerate the pace of innovation and future-proof your IT strategy without costly disruptions.

Redwood’s approach to AI is fundamentally different: it’s truly integrated rather than bolted-on, grounded in decades of real-world automation data and built with enterprise-grade security, governance and observability to ensure transparency and trust.

It’s time to eliminate the operational friction that holds your business back. Move from simply managing tasks to truly achieving outcomes. Do more than imagine what’s possible — start your journey with a visionary partner. 

Request your personalized demo of RunMyJobs and Redwood RangerAI today.

The victim? Accuracy. The weapon? Our outdated systems. 

They’d struck again, leaving behind a trail of missing data, mismatched numbers and enough manual rework to make a team of accountants weep. As CFO, I’m supposed to be the financial wizard, not buried alive under a mountain of spreadsheets. I swear, I could hear the “Jeopardy!” theme song playing in my head.

Now, I’ve escaped that spreadsheet purgatory, thank goodness. But I hear the same horror stories from our customers every single day. It’s like a support group for survivors of bad accounting software. And it’s clear: Accounting technology is still holding the entire profession hostage. Forget reporting deadlines, regulatory pressures or even the ever-growing data monster. Our industry’s real nemesis? Limited, clunky, soul-crushing software. It’s the digital equivalent of using an abacus while everyone else has a supercomputer.

I wrestled with outdated accounting programs for years, and it’s appalling to me that this is still something accountants are facing in the era of automation and AI. Clunky systems eat away at efficiency and keep you siloed and stuck in mountains of manual work.

There’s a massive opportunity to break free from legacy software and embrace modern solutions that are built to scale with today’s complex businesses. 

How did we get here, and why are some accounting teams still resisting change?

Tools made for a simpler time

If you walk into any typical Accounting department, you’ll find a patchwork of solutions that looks something like this:

• An on-premises ERP with accounts payable/receivable or reporting modules
• Multiple invoice scanning and data capture tools
• One or more billing systems
• A travel and expense management platform
• A set of spreadsheets to fill gaps in each function
• Add-on applications for tasks like asset management or reconciliation

That list doesn’t even scratch the surface in terms of payroll, vendor management, collaboration tools and other key activities.

Along with the sheer volume of platforms, their inability to connect is just not working for the modern enterprise. Many businesses now operate across multiple jurisdictions with different compliance standards. They rely on real-time data to make decisions on spending, hiring and new ventures. Stakeholders demand faster, more precise reporting to guide strategic moves. The core software accounting teams are using was never meant to keep up with these demands. Most legacy accounting systems are disconnected, creating bottlenecks and confusion and requiring manual data preparation, rework and manual reconciliation.

Each delayed financial statement has a ripple effect on the Finance team’s ability to deliver strategic insights and forecasts.

Every manual data entry or copy-paste action is a potential source of errors.

Disconnected systems isolate information and force you to spend far too much time chasing down key data.

The good news: This doesn’t have to be the reality. Automation solutions can address many of these issues head-on, but cultural and technological resistance often stands in the way.

Areas of resistance: Why an upgrade seems difficult

The word “risk” has a special place in an accounting context. Accountants are keenly aware that a single oversight in a ledger can result in hefty penalties, compliance breaches or reputational damage. Risk aversion, while ultimately a smart instinct, creates a culture where introducing new tools or processes is seen as a disruption that could invite more errors, not fewer.

Leadership teams that prioritize budget may also be skeptical. An automation initiative seems like a costly project with an unclear ROI. And there’s often no one fighting them to move forward — most accountants believe spreadsheets are “good enough.”

The comfort of legacy methods and the perceived reliability of spreadsheets can overshadow the cumulative (often hidden) costs of manual work: slow turnaround times, error corrections, overtime pay and burnout, to name a few.

Even if everyone agrees in principle that modernization is necessary, there are still technical challenges. Legacy systems often lack the APIs necessary to integrate with new platforms. Limited extensibility can keep you from ever achieving real-time data sharing.

Then, there’s the misconception that modernization requires customization. While custom solutions can be powerful, they’re not your only option. Leading automation platforms and cloud-based accounting solutions can be implemented without the same level of cost and complexity.

Myths vs. reality: Upgrading your accounting software

I get it: Even if they’re just beliefs that need to change, these are big hurdles to jump when you’re already overloaded with day-to-day tasks. But overcoming them must be a priority, because a technological upgrade is now imperative for companies seeking sustainable growth and resilience.

An industry wake-up call: Automation as the future

The fact is, if you’re attempting a finance transformation without automation, you’re doing it wrong. 

Unfortunately, many CFOs are skipping steps in their modernization strategy. A 2024 Gartner report found that CFOs are prioritizing AI adoption over financial technology selection, strategy and deployment in 2025. The problem? AI isn’t a shortcut — it’s an enhancement. Without automation as a foundation, AI’s impact will be minimal and siloed at best.

This disconnect is creating a fragmented approach to modernization. While some leaders chase AI capabilities, 49% of accounting firms still have no plans to use generative AI at all. Meanwhile, automation adoption remains inconsistent, even though 55% of finance executives aimed for a touchless financial close by 2025.

What I find in engaging with finance leaders who come to Redwood Software for guidance and solutions is that many of them have the best intentions but are struggling to execute on these goals. Setting your sights on automation with a capable and fully adopted platform is the right answer.

What modern finance automation enables

Modern finance automation integrates critical accounting functions like closing the books, processing invoices, reconciling bank statements and generating real-time financial reports into one seamless system.

The core benefits are clear:

• Consistency and accuracy as a result of standardizing repetitive tasks and removing the risk of human errors
• Speed achieved by faster data flows, approvals, reconciliations and more
• Scalability derived from processing large volumes of transactions without additional staff or hours
• Strategic insights that come about when accountants are free from manual drudgery

Energy Transfer’s experience using Finance Automation by Redwood is a great example: By applying the power of automation, the Fortune 500 energy company reduced the amount of time spent on bank reconciliations by 88% and streamlined capital project settlements and SAP user access provisioning.

Don’t miss out on the automation and AI movement

Even if you see the need for a tech upgrade, it’s possible you’re feeling overwhelmed by all of this. The first step doesn’t need to be a complete overhaul of your accounting and finance infrastructure. In fact, that wouldn’t be a wise endeavor.

Start small, perhaps with the tasks that contribute to a high-impact process such as record-to-report. Proving ROI on a focused project can help you gain buy-in and dispel the myth that new software is too disruptive or expensive. 

Ineffective, outdated accounting software is a liability. It’s time to automate, adapt and thrive, or get left in the dust. Redwood is here to help build an organization-wide automation fabric: Read more about how to achieve fully automated accounting.

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.

While we’re focusing on these concepts in business and reading about them in the news, many of us are still looking to the future — for a revolutionary moment to come along in AI or for it to get just that little bit better.

The latest advances in AI and machine learning mean we have many opportunities now to improve work and play. Specifically, reducing the resource burden of low-value or time-consuming tasks and enriching processes with natural language analysis and content. There are readily accessible benefits that organizations in various industries have yet to realize.

Machine learning vs. AI

Machine learning (ML) is sometimes seen as a precursor to AI, but it’s still part of the whole AI picture and is highly relevant today. Though largely unseen by end users, ML is built into many software products we already use. 

Using ML to train models to recognize patterns and anomalies is the most common use case today. In automation, this surfaces in the infrastructure needed for large-scale training. Services such as AWS Batch provide easy ways for AI developers to train models.

The search for more generalized forms of ML models brought us to the current phase of AI development.

Where AI is now

Generative AI and large language models (LLMs), such as OpenAI’s ChatGPT, offer a human-friendly way of interacting with the most recent models. While this makes them feel much closer to the “real” AI we imagine, in most cases, the capabilities we can reliably and confidently use are relatively narrow.

As part of a workflow, these pre-baked models can quickly summarize information and bridge the gap between workflow and employee. The information you ask a model to summarize could be about the workflow itself or the process the workflow is automating.

Remember, to get a desired and consistent output, we need to be specific in our prompts.

Foundation models and “AI PaaS” services are pre-trained and often tuned for a specific purpose. Businesses looking to use AI models need to train them with data from business processes. Examples are Amazon Q and Amazon Bedrock.

Solution enhancements to technology using AI models are common, but with the new wave of AI technologies, we can expect many solutions to provide a more human interface for accessing knowledge and information. 

AI workflow automation potential

End-to-end process automation depends on an integrated framework that seamlessly connects automation tools, processes and data sources—an automation fabric. AI complements automation fabrics in the form of built-in features or connectors that facilitate greater process efficiency and accelerate business outcomes.

Putting the more novel or complicated advancements aside for now, let’s dig a bit more into how implementing AI-powered workflow automation can bring the benefits of AI and LLMs to your routine tasks.

What’s possible with the ChatGPT connector for RunMyJobs

We’ve built a ChatGPT integration for our workload automation solution, RunMyJobs by Redwood, so AI can further the platform’s value of unleashing human potential. For many uses, ChatGPT exists alongside workflow steps as a supplement or a way to interface with users. In some cases, it can replace existing steps or manual tasks users may do later.

Using the ChatGPT connector and job template, adding a prompt with information from a workflow is simple and works like any other step in a chain.

As with the user interface for ChatGPT, you can send data as a chat via API. The connector enables you to configure the prompt you’re sending and use that in your workflow. Effectively, you’re sending ChatGPT a question and getting a response and can optionally maintain the history for a contextual conversation. You can extend this functionality and pull information in from any source using other connectors and scripts.

Let’s talk about how organizations are expanding the power of RunMyJobs with ChatGPT.

Crafting emails

Emails and other writing needs are some of the most common reasons people currently use tools like ChatGPT. Given the immense library of previously entered content the app can draw from, it does an excellent job.

In an automation context, you’d be likely to send an email when a job is starting or has been completed. The email might simply notify, or we might embed some data, times or other information. The problem is that original formatting may break down, the data can be missing or changed in a way that affects the legibility of your email.

In RunMyJobs, you can collate a set of information using workflow parameters and other data, send that to ChatGPT and ask for an email summary, and then use the output to craft your email. You could store the data in a RunMyJobs — in a data table or parameter — or in a separate file the workflow can access.

Alternatively, you could maintain a conversation with ChatGPT: Each time a workflow progresses, you’d send a new piece of information, the time the given step was completed and the outputs to ChatGPT. At the end of the process or upon an error, it can provide a summary of events so far.

You could also send ChatGPT other information: Structured data like CSV lists or unstructured data like emails that the workflow handles as part of its processes, asking for summaries or specific queries about the data to send to users in emails. See more examples in the upcoming sections.

⚠️ Although it’s interesting to have a conversation with the AI chatbot throughout the workflow, and it can be used for ongoing enhancements, I have to point out that the latter method could be quite expensive in terms of API calls.

Quick translations and extracting text

In many use cases, you might be handling documents, emails or other data that’s in a language other than your main business language or is unstructured in nature.

Here, it’s a good idea to think about the criticality of the translation or extraction. The benefit of using ChatGPT or another general model to do this is you can instruct it generally on what to do. The downside is that leaves some room for a variable, or changing, response.

When dealing with emails or other forms of non-critical communication, we could use ChatGPT to make a quick translation pass to help any users who need to assess the data later.

Or you might need to handle a dataset with comments in a different language; you could pass comments selectively to ChatGPT for translation.

We could also ask ChatGPT to extract specific portions of text, perhaps looking for countries, place names or other recognizable information to include in a summary report or an email.

⚠️ It’s worth remembering that while ChatGPT is capable of producing translations, the model hasn’t been fine-tuned specifically for translation tasks. For critical or professional translation needs, it’s generally recommended to use dedicated machine translation models or services designed explicitly for translation.

Interpreting and summarizing data

Data analysis is a huge undertaking, so it’s particularly valuable to acquire a quick summary or identify something specific from a given dataset. Sending a question to ChatGPT could be the answer. But to do so efficiently, it’s key to learn proper prompt engineering and balance specific instructions with simple language.

A note on data security

To secure your business data while using the ChatGPT connector for RunMyJobs, you should use your own instance of ChatGPT through ChatGPT Team or ChatGPT Enterprise. This will mean your data is kept separate, though still sent to the OpenAI cloud platform.

Your organization may have policies and processes in place to remove or mask personally identifiable information (PII), but even with some data removed or anonymized, you can still ask useful questions to make decisions or share information with other people.

In a workflow handling invoice or sales data, you might anonymize the data and send a list to ChatGPT and be able to ask some specific questions — like our earlier example to look for outstanding invoices. Or, you could ask it to produce summaries of the data to push quick insights to other teams via email rather than them needing to access reports when they have time.

At the most simple level, we could ask for “a short summary of this invoice data” and receive an output similar to what’s shown below to use in an email.

Generative AI for more efficient orchestration

Even if AI is not quite yet an omnipotent being, you can start weaving ChatGPT and other AI-powered tools into your workflows to enrich and streamline processes, save time, give your team members additional insights and speed up decision-making.

If your organization is already using generative AI to a significant degree, there may be more integrated ways to enhance your workflows. A model that understands more about your business could answer specific and unique questions to help you achieve intelligent process automation.
And remember, the ChatGPT integration is just one way to incorporate AI into your workload automation with RunMyJobs and a familiar user experience.

Connecting to other AI systems via REST API is easy with the Connector Wizard.

Despite the high-stakes nature of SLAs, failing to meet them is a common problem for IT automation departments. The ripple effects on customer trust and loyalty are significant. 

To improve your SLA performance, it’s essential to investigate why your team is coming up short and develop a strategy for meeting every SLA, no matter how much your business grows or how complex your processes become.

Why do SLAs fail?

There are many reasons why you may not be able to meet your customers’ expectations. We’ll cover a few of the most common ones.

Inadequate tools

Often, the issue begins with not having the appropriate tools — limited communication channels,  insufficient job scheduling software or insufficient systems for predicting and remediating automation issues, for example. There are so many steps that contribute to successful SLA outcomes, and each one must execute perfectly. 

Without proper data analysis tools, it’s almost impossible to identify where a workflow or process went off track and get a clear sense of the scale of your SLA non-compliance. Team members may also be discouraged and less productive: According to a survey by Airtable, employees mainly disengage from a task because it’s too hard to find the data they need to complete the job. 

Lack of visibility

Even when the right tools are in place, you could lack proper visibility into your automated processes if they don’t work well with one another. Disjointed notifications cause sheer overwhelm and make it hard to determine the root cause of SLA failure. Making informed decisions also becomes a challenge.

Information silos

Compartmentalized tools and processes lead to siloed information, which complicates SLA management. Communication between your teams could be fragmented, which delays your response to SLA-related issues. Redundancy can also crop up and waste resources. Plus, each silo might collect and store data differently, skewing your SLA insights. 

The strain of managing SLAs at scale

When you can focus on providing on-time service to just a few customers, it’s possible to address any issues as soon as they appear.  But scale up, and you’re likely to run into roadblocks:

• Data overload: Managing hundreds or thousands of SLAs requires continually collecting and analyzing real-time data. It’s critical to derive meaningful insights to ensure compliance and optimize SLA performance. However, the effort required for extensive data handling and analysis diverts your IT team’s time and brain power away from strategic, value-added work.
• Resource allocation pressure: Only when you apply resources optimally can you feel confident that you won’t miss deadlines or fail to deliver on what your customers expect. Human capital and non-human resources must be distributed wisely to support SLAs, but without the right tools, that ideal distribution won’t be obvious.
• Shifting priorities: As your organizational strategies evolve, so must your SLAs. However, with numerous agreements in place, it’s hard to keep track of which are outdated. Without dynamic monitoring and management systems, your SLAs can quickly become irrelevant and create misunderstandings with customers.
• Stressful compliance tracking: Staying on top of compliance requirements for multiple SLAs can be overwhelming, especially if you’re in a heavily regulated industry. Without efficient tracking mechanisms, it’s easy to overlook milestones that could result in penalties and damaged relationships.

Predictive analytics for SLA optimization

Automation offers a way to overcome these challenges — specifically, workload automation (WLA) technology that’s poised for the artificial intelligence (AI) wave.

WLA software can be your foundational tool for modernizing your tech stack, in turn improving all the factors that contribute to SLA management. Today, WLA solutions are evolving to provide built-in AI features or integrate with AI, such as predictive analytics tools.

With predictive analytics, you can leverage historical data to forecast future events, including SLA issues. The ability to anticipate potential failures and make decisions to prevent them is a competitive differentiator in today’s business climate. Moreover, by analyzing trends and patterns over time, AI tools can signal when an SLA is misaligned with current business objectives or customer needs. 

The combination of predictive modeling and machine learning algorithms can significantly enhance the utility of WLA platforms. From predictive maintenance of systems for avoiding downtime to improved outcome prediction, these emerging technologies offer a future-proofing opportunity for organizations ready to drive efficiency and increase visibility.   

The best automation platforms are those that innovate to enable you to:

• Get an early warning if critical deadlines are predicted to slip, which allows your team to address potential issues before they impact the business. 
• Configure process SLAs and thresholds with customizable escalations and alerts to ensure the right people have the right information at the right time.
• Leverage dynamic scheduling capabilities to ensure your at-risk SLA processes meet their deadlines.

Getting proactive with SLAs using workload automation

There are clear benefits of using WLA enhanced with AI capabilities to improve SLA performance. Here, we’ll look at practical use cases in various industries.

1. Financial institutions: A multinational bank faces an unexpected surge in transaction processing due to a market event, which requires a high volume of record processing jobs to be completed for end-of-day reporting SLAs.
   
   A critical job failure prediction model powered by AI within WLA software identifies potential failures in the job queue that could put SLAs at risk. By alerting operators in advance, the system enables preemptive action to reroute or reprioritize tasks and ensure compliance despite the sudden increase in demand.
2. Healthcare: A large hospital network experiences an overload of patients one day. Although patient intake finishes within SLA parameters, the excess pressure on the system delays the compliance jobs that run overnight. Thus, they do not meet compliance SLAs.
   
   WLA software could prevent this scenario by scheduling and running data updates during off-peak hours to maintain system performance during high-traffic times. The network can supplement its automations with predictive analytics to better allocate resources and keep up with SLAs despite spikes in demand.
3. IT services: A leading IT service provider notices some automations are running slow on a machine with a new version of anti-virus software. They have trouble identifying whether this will create a problem downstream for SLAs.
   
   The team could use WLA to manage software updates and security patches across thousands of endpoints and incorporate AI to anticipate security risks and predict when it’s most efficient to deploy updates. They can rest easy knowing all client systems are up to date and that they’ll be alerted in the event of equipment failure.

Protect your SLA commitments 

Attempting to improve your SLA performance with piecemeal automation tools isn’t an effective long-term answer to the universal SLA problem. Today, smart enterprise teams implement a WLA platform with a centralized portal to monitor automations across their tech stacks.

RunMyJobs by Redwood offers advanced SLA management with automatic alerting for missed milestones, detailed process execution tracking and end-to-end visibility of all business and IT processes.

WLA can act as a catalyst, accelerating your decision-making and scalability and boosting your ability to deliver an exceptional customer experience. Demo RunMyJobs today.

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 pinnacle of intelligent automation

Intelligent business process automation (IBPA) integrates three key technological components: robotic process automation (RPA), artificial intelligence (AI) and machine learning (ML) to elevate and streamline business workflows. Unlike traditional automation, which simply follows prescribed rules, IBPA employs a sophisticated approach to automate complex processes.

With the application of RPA, tasks that are repetitive and rule-based can be automatically completed without human intervention. This not only enhances efficiency but also reduces the possibility of human error.

AI, on the other hand, provides the capability to analyze data, recognize patterns and make decisions. It enables systems to comprehend complex scenarios and respond appropriately, often engaging in processes that require a level of decision-making and adaptability.

Incorporating machine learning means that the automation system has the ability to learn and improve from experience. The system analyzes data from previous operations, learns from it and utilizes this knowledge to improve future process executions. It essentially enables the system to adapt and optimize its performance over time without being explicitly programmed to do so.

IBPA involves deploying bots that can execute repetitive tasks autonomously, guided by smart algorithms, freeing up human resources to focus on more strategic activities. Chatbots can instantaneously respond to customer inquiries, providing real-time support and improving customer interaction. Additionally, intelligent tools process and analyze vast and varied data, converting it into actionable insights that can inform strategic decisions and enhance operations.

IBPA is not merely about automating tasks. It’s about embedding a level of intelligence and continuous improvement within the automated workflows, ensuring they are not only efficient but also progressively evolving and adapting to changing business environments and demands.

The difference between RPA and IPA

Robotic process automation (RPA) and intelligent process automation (IPA) both play vital roles in the automation landscape, yet they serve distinctly different purposes based on their inherent capabilities.

Robotic process automation

Let’s talk about RPA first. It’s like your trusty worker bee, systematically handling repetitive, rule-based tasks with a stoic precision that ensures accuracy and consistency. RPA follows a script, diligently performing tasks like data entry, processing transactions, or managing data without the need for a human to be involved. It does its job and it does it well, but it doesn’t stray from the prescribed path or make decisions — it sticks strictly to the rules and patterns it was programmed to follow.

Intelligent process automation

Enter IPA, which essentially takes the robust, rule-following nature of RPA and gives it a bit of a cerebral boost. By integrating machine learning and natural language processing, IPA not only automates tasks but also brings a level of adaptability and decision-making to the table. It doesn’t just follow set patterns — it learns from the data it processes, adapts to evolving scenarios and makes nuanced decisions based on the insights it gathers. With IPA, you’re not just automating — you’re enabling a system that can think, learn and decide in a way that’s inherently more dynamic and responsive.

While RPA and IPA might seem quite different, they complement each other in a balanced automation strategy. RPA provides a solid foundation, ensuring repetitive tasks are handled efficiently, while IPA brings the intelligence and adaptability needed to manage more complex, decision-centric processes. The key is to understand the strengths and applications of both, ensuring that your business leverages RPA for unwavering accuracy in repetitive tasks and employs IPA when processes demand a level of decision-making and adaptation.

How intelligent automation impacts various industries

Intelligent automation is not industry-specific — its applications are diverse and transformative across various sectors. In healthcare, bots automate administrative tasks, reducing manual workloads and enabling professionals to dedicate more time to patient care.

Within financial services, sophisticated algorithms are utilized to inform decision-making, enhancing accuracy and efficiency. In the realm of supply chains, automation ensures that processes are optimized from inception to delivery, minimizing disruptions and elevating customer satisfaction by ensuring timely and accurate product deliveries.

In the last few years, numerous industries have accelerated their digital transformation, recognizing the indispensable role of automation in maintaining and enhancing business continuity and efficiency.

If you’re curious to see how businesses are utilizing these advancements, our Redwood case studies offer in-depth insights. Especially post-pandemic, industries are going full-tilt into digital transformation, realizing automation is key to keeping business smooth and continuous.

Incorporating cutting-edge automation tools into business process management

In the realm of business process management (BPM), the spotlight is increasingly shining on digital process automation (DPA), offering a roadmap to bridge traditional practices with forward-thinking initiatives. Through the utilization of innovative automation tools, businesses are not only mitigating the need for time-consuming manual tasks but are also carving pathways toward more intelligent, adaptive workflows.

Automation platforms play a pivotal role in this evolution, serving as the linchpin that connects robust apps and digital interfaces to enhance the overall user experience. Here, every interaction, whether internal or customer-facing, is designed to be as seamless and intuitive as possible, ensuring that businesses operate with a blend of efficiency and precision.

Moreover, the integration of cognitive technologies into BPM shifts the paradigm from mere task execution to more thoughtful, strategic automated actions. It’s an environment where the technology doesn’t merely perform but learns, adapts and assists in informed decision-making, driving processes that are not just replicated but intelligently optimized.

Imagine a future where each component of your business process management — from manual tasks to strategic initiatives — is seamlessly intertwined with an intelligent automation platform, empowering your operations to be more efficient, responsive and user-centric. Redwood Software offers this reality, guiding businesses towards a horizon where operational processes are not just performed but perfected. It’s not merely about stepping into the future but crafting it, with Redwood anchoring your journey towards operational excellence.

Why Redwood?

At Redwood, our business process automation solutions are tailored to address modern challenges. Our cloud-based platforms are crafted with a priority on user and customer experiences, ensuring every interaction is smooth, intuitive and user-friendly. We’ve seen firsthand what the future holds for business processes and how intelligent automation acts as an engine driving next-generation operating models.

For businesses that are ready to embrace intelligent business process automation, the path forward is illuminated with possibilities of enhancing operational efficiency and elevating customer satisfaction. If you’re prepared to propel your business processes into a future characterized by smart, efficient operations, we invite you to sign up for a quick demo of Redwood’s cutting-edge solutions today.

It’s 2023, and if there’s one aspect of the IT industry that refuses to slow down — the consistent evolution of automation platforms. The landscape of IT automation has been significantly shaped by integrating new technologies, market demands and ongoing innovation. It’s become clear that automating business processes is incredibly beneficial to enterprise companies across the spectrum, ranging from utilities and finance to cybersecurity and healthcare.

Even before the pandemic, when many businesses needed to rethink how they got things done, including their IT operations, the automation market was on fire. But some things are hotter than others. And as we wind down 2023, it’s a good time to look at the transformative shifts that happened, and how that bodes for the future.

Harnessing automation: The 2023 perspective

1. Robotic process automation (RPA): It’s hard to discuss automation trends without bringing up RPA. RPA uses software robots or “bots” to automate time-consuming, rules-based repetitive tasks that are generally well-defined to promote operational efficiency. Leveraging RPA can significantly streamline business processes and allow them to occur 24/7 without human intervention. The bots are faster than humans, which increases productivity. But they also free those same humans, including the IT team, to focus on more strategic initiatives.
2. Hyperautomation: Beyond RPA, the world is shifting towards hyperautomation, a term first coined by Gartner. Hyperautomation involves implementing multiple automation technologies, like machine learning, artificial intelligence (AI) and decision-making algorithms, to carry out more complex tasks than RPA can handle. Hyperautomation uses the full spectrum of automation tools, from basic bots to sophisticated AI-driven functionalities.
3. Artificial intelligence and machine learning (ML): AI and ML are two digital technologies that we’ve witnessed become intertwined within automation. Whether it’s chatbots powered by natural language processing (NLP) being used for an enhanced customer experience or advanced algorithms for predictive analytics, AI and ML remain at the forefront of the automation surge.
4. Low-code and no-code automation: Perhaps nothing has been more transformative than the democratization of automation thanks to low-code and no-code platforms. It has essentially eliminated the need for high-cost, highly resourced IT teams and cumbersome processes by enabling even non-tech business users to create apps, interfaces, process management workflows and more with drag-and-drop solutions. By simplifying the creation of end-to-end automation workflows, businesses have accelerated their digital transformation initiatives significantly, without heavily relying on IT teams.
5. Orchestration and workloads: One of the bigger trends In 2023 that is sure to continue is an offshoot of many of the other trends, particularly low-code automation. That trend is based on the idea of not just automating individual tasks but orchestrating entire workflows across departments and across applications. It’s becoming known as the automation fabric, with everything being woven together by platforms like Redwood’s orchestration automation software, which enables businesses to design, manage and optimize intricate processes in real time.

Looking towards 2024, we need to keep an eye on plenty of emerging technologies. Existing technologies like cloud-based automation solutions, Internet of Things (IoT) and business process automation (BPA) will continue to grow, and businesses will need to ramp up technology if they want to achieve aggressive business outcomes. The ongoing expectation of more efficient workflows, streamlined processes and higher operational efficiency pushes businesses to prioritize IT automation. RPA, AI and ML are going to continue to evolve quickly and offer more innovative functionalities that promise real-time decision-making capabilities and improved customer experience.

As for the future of Redwood, we’ve been laser-focused on automation for 30 years and will be at the forefront of this next wave. Platforms like RunMyJobs by Redwood empower companies to embrace these automation trends and developments to achieve unprecedented scalability and innovation.

So let’s clear up, once and for all, exactly what these terms mean. and why they’re useful.

Machine learning

In short, machine learning is akin to a human learning how to perform a task more efficiently (or to result in a better outcome) through a process of trial-and-error. Software, obviously, can perform this same process far faster than people and, thereby, increase its efficiency more quickly.

The result is a piece of software, device or machine that can improve its own performance through compound statistical modelling — each time there’s a new piece of data about a decision, it adds to the overall result.

As machine learning is based on statistical analysis of data sets, combined with predictive capabilities, its potential uses are widespread.

Machine learning is already the technology that powers image and voice recognition, fraud detection and high-profile projects such as IBM Watson.

Artificial intelligence

Regular readers of this blog will likely be ahead of us here in spotting the confusion between these terms: AI encompasses machine learning and other technologies.

It’s an umbrella term that brings together a collection of technologies that are deemed “intelligent.”

The problem with that, as it correlates with automation, is that it leads to confusion and mismatched expectations. If customers don’t really understand what they’re paying for, it’s going to be difficult to get buy-in from across the wider business, let alone see where AI adds true value.

The common core

Regardless of the specific technology being defined, those you often find listed alongside AI and machine learning share a common goal — to automate or imitate human activity. That’s why, at Redwood Software, we refer to all these intelligence-based technologies as augmenting human experience and capabilities.

“Augmented intelligence” might not be as catchy a name as “artificial intelligence,” but we like to think of a future in which employees are still at the core of businesses, albeit with the benefit of added technological superpowers as standard.

It’s a bit like that family member who refers to all tablets as an “iPad,” regardless of the actual device they’re holding or referring to. It’s the set of capabilities that’s important to the user, not the device itself.

As a result of this bunching together of terms, the exact capabilities of AI and how it fits with existing automation solutions hasn’t been clear for many IT and business professionals. And that in itself has led to a period of exploration and, in many cases, disappointment at current capabilities and use cases, versus the myths perpetuated by vendors jumping on the bandwagon.

But hype only lasts so long and what takes its place is a calm assessment of efficacy and return on investment. And it’s there that current thoughts around “AI” tend to go astray.

At Redwood, everything we do is focused around delivering measurable value, so we prefer to think of the overarching collection of AI technologies as augmented intelligence, rather than artificial intelligence.

What this accumulated augmented intelligence has meant for automation so far is the rise of “self-service” applications and solutions, for both customer-facing and back-office operations, and that trend will continue through technologies that are themselves an amalgam of techniques.

Take predictive analytics as an example. The process of combining and assessing data in various ways to determine potential future patterns might well be called predictive analytics for ease, but it’s really a combination of data mining, statistical analysis, machine learning and, in some cases, artificial intelligence, to make predictions about the future.

It’s there, in augmenting existing technological capabilities in a variety of ways to improve operations, that the future of AI and automation are intertwined, it’s just that the “A” stands for augmented, not artificial.