From ERP capability to business execution

Insights

• Oracle Fusion Applications are embedding AI, automation, and intelligent workflows directly into the flow of work.
• Yet many organizations still face approval delays, manual coordination, and fragmented user experiences that slow execution.
• As a result, ERP value is increasingly determined by how easily employees can move from insight to action.
• Capabilities such as Redwood, embedded intelligence, and AI agents help bring decisions, information, and tasks together within a single workflow.
• The organizations seeing the strongest results are those that integrate AI into day-to-day work, decision-making, and execution.

Enterprise software is becoming more intelligent. AI can identify risks, recommend actions, automate routine activities, and support decisions directly within business processes. AI agents can assist users by gathering information, monitoring exceptions, recommending actions, and automating defined process steps within business workflows. Modern enterprise resource planning (ERP) platforms increasingly provide these capabilities within everyday workflows.Oracle Fusion Applicationsreflects this shift through capabilities such as Redwood, embedded intelligence, AI agents, and workflow automation, which together bring information, decisions, and actions closer to where work happens.

Yet many employees still spend their day searching for information, chasing approvals, and moving between systems to complete routine tasks. Organizations are increasingly measuring ERP success by how effectively it helps employees make decisions, complete tasks, and advance work to the next stage of the process — and by that measure, more capability inside the system hasn't automatically closed the gap.

From systems of record to systems of work

Traditional ERP systems were built to capture transactions, enforce controls, and provide visibility across the enterprise. They became the system of record for finance, procurement, supply chain, and operations, and helped organizations standardize processes and maintain consistency at scale.

Cloud ERP has strengthened these foundations over the past decade. Organizations have modernized core processes, connected data across functions, and adopted continuous innovation models that introduce new capabilities through regular releases. As these platforms have matured, so have expectations: Employees now expect applications to be intuitive, to surface relevant information when it's needed, and to support decisions without extensive searching or context-gathering.

Combined with embedded analytics, AI, and workflow automation, these capabilities can reduce the time and effort required to complete everyday work. As a result, ERP increasingly serves as the operational environment where employees make decisions, execute processes, and collaborate across functions.

When ERP capabilities outpace user adoption

Leading cloud ERP platforms now provide role-based experiences, embedded analytics, workflow automation, generative AI capabilities, and AI-assisted recommendations. While ERP capabilities have advanced further, many organizations still struggle to translate those capabilities into smoother execution. Routine approvals are delayed, exception handling remains manual, and employees frequently leave the workflow to search for the context needed to complete the task at hand.

Much of this comes down to legacy design patterns. Many ERP processes were built around transactions, controls, and reporting — not around how a specific employee moves through their day. Users still consult separate reports, hunt for policies, and coordinate approvals across multiple stakeholders to complete routine work. That fragmentation slows onboarding, increases the chance of manual error, and creates delay whenever a decision depends on information scattered across several sources.

This gap shows up clearly in the data on AI investment. Infosys AI Business Value Radar 2025 found that only 19% of AI use cases achieve most, or all, of their intended business objectives. The findings suggest that technical implementation alone does not guarantee business value. Workflows, decision patterns, operating models, and user adoption often play an equally important role in determining outcomes. Approval handoffs and manual interpretation often persist because the underlying workflows, roles, and operating practices remain unchanged.

One reason these problems persist is that most ERP implementations were built to standardize processes, enforce controls, and improve visibility across the enterprise. Information was typically organized around transactions, reports, and functional handoffs, leaving employees responsible for gathering the context needed to make decisions and complete their work. This often meant reviewing reports, checking policies, consulting colleagues, and validating decisions across multiple systems before a task could be completed. Modern AI capabilities can accelerate parts of that process, but they do not automatically remove the structural dependencies that create delays. Unless organizations redesign the workflow itself, employees continue to carry much of the coordination burden.

Organizations are learning that how employees encounter intelligence matters as much as the intelligence itself. Information delivered in context, clear guidance on what to do next, and workflows with less friction all determine whether a capability gets used or gets worked around.

Where experience and intelligence come together

Organizations getting more value from modern ERP connect analytics, approvals, transactions, and workflow guidance within a single process. Bringing these elements together reduces dependence on separate reports, queues, and screens. That consolidation cuts delays caused by manual handoffs and gives managers earlier visibility into where a process is slowing down.

For example, Oracle's approach increasingly focuses on reducing the coordination employees must do themselves. Redwood provides a modern role-based user experience that surfaces relevant information, tasks, and actions more intuitively within Fusion applications. Role-based experiences focus employees on the actions relevant to their responsibilities, while embedded analytics, predictive insights, and generative AI provide decision support within the workflow. Oracle has begun introducing AI agents across Fusion applications to automate activities such as monitoring exceptions, gathering relevant information, following up on pending actions, and helping users complete process steps more efficiently. Workflow automation helps maintain consistency and control. Together, these capabilities reduce the effort required to move from information to action.

A large US utility company illustrates the pattern. Persistent delays in procurement and finance stemmed from fragmented workflows across approvals, supplier onboarding, and invoice handling — despite the company already having forecasting tools, operational reporting, and exception alerts in place. Infosys redesigned the experience and workflow model around business roles: Procurement and finance teams could see approvals, supplier information, risks, and exceptions in one unified view. Policy guidance appeared during transaction creation; invoice anomalies were flagged during validation, eliminating the need to wait for a later review cycle to identify them. Approval thresholds, audit trails, and escalation paths stayed embedded in the workflow, so the process got faster without losing oversight.

Careem Groceries shows a similar result with a narrower process. It automates invoice processing using Oracle AI-powered process automation, embedding intelligence into extraction, matching, and validation, reducing invoice processing time while improving accuracy and efficiency.

Both examples point to the same underlying shift. As AI agents take on more coordination and decision-support work, governance has to be designed into the workflow from the outset — audit trails, escalation paths, and human review points aren't an afterthought to be layered on later.

The priorities shaping modern ERP

As ERP evolves, organizations have an opportunity to rethink how employees access information, make decisions, and move work forward. The greatest value comes from combining modern user experiences, embedded intelligence, and continuous innovation in ways that improve how work is done.

1. Prioritize high-friction workflows

Look for processes where employees spend a lot of time gathering information, coordinating approvals, or resolving exceptions. Procurement requests, invoice processing, supplier onboarding, journal approvals, and period close are common starting points because the delay and manual effort involved are visible and measurable. A single invoice, for instance, can require sign-off from multiple approvers based on rules like amount thresholds, cost center, or business unit. Each additional rule is a place where a process can stall, which is exactly why these workflows are easy to spot and worth fixing first.

2. Use experience design to drive adoption

A capability only creates value once people use it. Reduce navigation complexity, surface relevant information in context, and align each page to the user’s role. A procurement manager needs approvals, spend insights, and supplier alerts, while a buyer needs requisition creation and order status — not the same screen with everything on it. Oracle Redwood's role-based pages are built around this distinction, surfacing only the fields, actions, and key performance indicators relevant to the person using them.

3. Embed intelligence where the action happens

AI and analytics are most useful at the point of decision, not before or after it. An invoice processing page that shows exception risk, aging, and variance inline can improve decision-making and response times. Policy guidance during transaction creation, anomaly detection during invoice validation, and AI-generated summaries during approval keep the user inside the process instead of pulling them out to check something elsewhere. The same principle applies to enterprise policy: Instead of pausing a transaction to search a policy document, users can receive context-specific guidance directly within the workflow.

4. Treat continuous innovation as a discipline, not a series of one-off projects

Build a structured process for evaluating, testing, and adopting the regular updates a modern ERP platform delivers, and measure their effect on adoption and outcomes. New capabilities should be assessed based on the value they create, not simply their availability.

5. Build governance into AI-enabled workflows from day one

As AI agents take on larger roles, approval controls, audit trails, escalation paths, and human oversight need to be designed alongside the automation from the outset. Waiting until a problem occurs creates operational and governance risks that are harder to address later. This is already visible in the narrow scope of some deployed agents. For example, Oracle is introducing AI agents for finance processes that help automate routine activities while flagging exceptions and requiring user review where appropriate. None of this replaces investment in change management and training. Without both, employees may continue relying on existing habits and fail to realize the full value of the system.

From ERP systems to intelligent work platforms

As ERP platforms continue to add AI capabilities, the competitive advantage will increasingly come from how effectively organizations connect intelligence to everyday work. The opportunity now lies in redesigning the workflows, decisions, and experiences through which that technology creates value.

Purchase requisitions with multiple approval steps, invoice exceptions that need manual follow-up, supplier onboarding that spans several teams, and period-close activities dependent on repeated coordination are good places to look first: the current cost is visible, and the improvement is easy to measure. A visible business problem combined with measurable improvements is often what turns a pilot into a broader rollout.

Organizations that can connect experience, intelligence, and workflow execution in these areas will be better positioned to realize the value of continuous innovation and emerging AI capabilities as they evolve.