Tag: Agentic Apps

Fusion Claw: Stop Looking for AI Use Cases. Start Looking for Work.

~ K8 Mead ~ Leave a comment

One of the questions I hear most when talking about AI in Oracle Fusion is: “What are the use cases?” It’s a perfectly reasonable question. But I’m increasingly convinced it’s also slightly the wrong one. We have spent the last couple of years looking for places where we can add AI to a business process. Where can we summarise something? Where can we generate something? Where can we make an interaction more conversational?

With Fusion Claw, I think we need to turn that thinking around. Don’t start by looking for an AI use case. Start by looking for work. Specifically, look for the work that people are still stitching together manually across Oracle Fusion. The things that involve checking multiple sources, applying judgement, producing an output, chasing exceptions and deciding what needs to happen next. That distinction matters because Fusion Claw isn’t designed simply to answer another question or perform another transaction.

Oracle describes a Fusion Claw use case as“a business result that someone owns and wants handed over.”  I think that is probably one of the most useful ways of thinking about agentic AI that I’ve seen.

Imagine someone in Finance asks: “What is the balance for account 3210?” That isn’t really a Fusion Claw use case. There is a question. Oracle already knows the answer. We just need the quickest and most appropriate way of retrieving it. Now compare that with: “Get us ready for month-end close and show me anything that could prevent us closing.” That is very different.

To achieve that outcome, somebody might need to check the ledger and subledgers, compare information against close policy, identify variances, investigate exceptions, work out which ones actually matter, escalate anything unresolved and produce evidence explaining what they found. There isn’t one answer. There is work to be done. And that, to me, is where Fusion Claw starts to get interesting.

Oracle’s guidance suggests a useful rule of thumb: if a knowledgeable person would normally spend 30 minutes to several hours researching, comparing information, producing something or chasing follow-ups across systems, it may be worth considering Fusion Claw. That immediately gives us a much more practical way of finding opportunities.

Forget brainstorming futuristic AI scenarios for a moment. Go and talk to the people actually using Fusion and listen for sentences like: “Every Friday I pull together…”; “I download this into Excel and then…”; “It takes me half a day to…” etc. Oracle highlights very similar phrases in its guidance because they often reveal processes involving research, judgement, multiple steps and exceptions. Those are potential Claw conversations. And they are much more interesting than asking a room full of people,“What could we do with AI?”

So what might those use cases actually look like? Once you start looking at Fusion through that lens, there are opportunities everywhere.

Finance: “Tell me if we’re actually ready to close.” Don’t just show the close tasks. Review the position across the ledger and relevant subledgers, check it against policy, identify unusual balances or incomplete activity, investigate exceptions and give the Finance team an evidence-backed view of what’s stopping them being close-ready. The outcome isn’t another dashboard. It is confidence about whether you can close.

Procurement: “Find the things we should be worried about.” Imagine regularly reviewing purchase orders, suppliers, invoices and relevant policies to identify unusual activity. Which transactions deserve attention? Which exceptions can be explained by the available information? Which need a human to investigate? And importantly, can you provide the evidence alongside the recommendation? That is considerably more useful than simply producing a list of transactions which meet a predefined query.

HR: “What needs my attention today?” A manager doesn’t necessarily want to navigate five different parts of HCM to understand what is happening across their team. They want to know: What do I actually need to deal with? That could mean looking across outstanding approvals, absence, recruitment activity, upcoming talent actions and other relevant signals, then identifying what matters and why. The deliverable isn’t the individual pieces of data. It’s the prioritised brief.

Projects: “Tell me where this project is going off track.” Imagine analysing project costs, commitments, milestones and relevant operational information, then investigating significant exceptions rather than simply flagging every variance. The value isn’t telling a project manager something is red. It’s doing some of the work required to understand why it is red.

Oracle identifies seven characteristics that indicate a job may suit Fusion Claw:

  1. Multi-step: the work needs to be planned and carried out in stages.
  2. Long-running: it can take minutes or hours rather than requiring an instant response.
  3. Research-heavy: somebody needs to examine multiple records, documents or policies.
  4. Cross-functional: the outcome spans several objects, agents or business areas.
  5. Exception-heavy: investigating what went wrong is part of the job.
  6. Produces something: the outcome might be a report, spreadsheet, plan or evidence pack.
  7. Governed action: the process may ultimately make changes, but only through controlled and approved capabilities.

Not every use case needs all seven. But if you’re ticking several of them, it’s probably worth investigating. I would add an eighth question of my own: Would somebody be genuinely pleased not to have to do this anymore? Because that’s often where the real value is hiding.

Not everything needs Claw. This may actually be the most important part. One of the risks whenever a new technology arrives is that we immediately try to apply it to everything. That would be a mistake.

If somebody simply needs to ask “How many open requisitions do I have?”, a conversational experience such as Ask Oracle may be more appropriate. If the requirement is “Route every new requisition through the same approval steps”, that’s a workflow. If someone has already made a decision and simply needs to execute a transaction, the existing Fusion action may already be the right answer.

Oracle explicitly makes this distinction: lookups, quick status questions, fixed workflows and straightforward transactions don’t suddenly become better because we put a more autonomous agent in front of them. Use the simplest technology that solves the problem. Agentic doesn’t automatically mean better.

There’s another idea in Oracle’s guidance that I particularly like. Good Fusion Claw opportunities may actually be two use cases rather than one. The first researches. The second acts. For example: Research: Review the available information, investigate the exceptions, apply the appropriate policy and recommend what should happen. Then Action: Once that recommendation has been reviewed, carry out the approved next step.

Oracle describes this as starting in Research mode, reviewing the evidence and recommendations, and gradually handing over actions as confidence develops. For enterprise AI, that feels like a much more sensible adoption model than jumping straight to full autonomy.

Trust is earned. Let the agent demonstrate that it can research the problem properly. Let people inspect the evidence. Let them challenge the recommendation. Then decide what you’re comfortable allowing it to do.

My advice: don’t start with your biggest process. I’d be tempted to start somewhere much more boring. Find something that happens every week. Something with a clear owner. Something that currently takes an experienced person an hour or two. Something involving enough research and judgement to be annoying, but not something so business-critical that everyone becomes terrified of changing it.

Run it in Research. Compare what Fusion Claw produces with what the person would have produced. Understand where it gets stuck. Understand where your data, policies or processes aren’t quite as clear as you thought they were. Then improve it.

A weekly process may teach you far more about agentic working than attempting to automate an enormous quarterly process on day one. Oracle makes a similar recommendation: start with a recurring use case that can be proven in Research mode.

For a long time, enterprise AI conversations have started with: “Where can we use AI?” With Fusion Claw, I’d change the question. Ask your Finance team: “What takes you half a day every week?” Ask HR: “What information do you have to pull together before you can make a decision?” Ask Procurement: “What exceptions do your experienced people spend their time investigating?” Ask managers: “What do you repeatedly have to check before you’re comfortable taking an action?” And perhaps most importantly: “What work are our people doing today where the value is in the outcome, not in them personally clicking through every step required to get there?”

That’s where I’d start looking for Fusion Claw. Because the interesting thing about agentic AI isn’t another clever way of interacting with Oracle Fusion. It’s the possibility that, increasingly, we can give Fusion an objective and let it help get the work done. And that feels like a much bigger change.

Please note all screenshots are the property of Oracle and are used according to their Copyright Guidelines.

Oracle AI World Day 1: From Helping People Work to Getting Work Done

~ K8 Mead ~ 2 Comments

I wasn’t in Las Vegas for Oracle AI World this year. Like many Oracle customers and partners around the world, I followed the event remotely using Oracle’s free On Air pass, catching up with the keynotes from the UK rather than from the conference floor. While watching remotely means missing some of the atmosphere and conversations that happen between sessions, it does give you the opportunity to focus entirely on the messages being delivered.

Having watched both Mike Sicilia’s opening keynote and Chris Leone’s product keynote, one theme stood out above everything else. Oracle is no longer talking about helping people work faster. Oracle is talking about getting the work done. At first glance, that might sound like a subtle distinction. I don’t think it is. In fact, I think it represents one of the biggest shifts we’ve seen in enterprise applications for years.

For the last couple of years, most AI conversations have been centred around productivity. How can AI draft content faster? How can AI summarise meetings? How can AI help employees find information more quickly? All of those things matter. They deliver value. But fundamentally, the human remains responsible for completing the task.

What struck me during the Day 1 session was how little time Oracle spent talking about AI as an assistant and how much time it spent talking about AI as an executor. Across both keynotes, the discussion repeatedly returned to outcomes. Not generating insights. Not producing recommendations. Delivering results. That feels like an important evolution in enterprise AI.

One of the things I liked most about Mike Sicilia’s keynote was that the focus wasn’t really technology. It was people. The message wasn’t that AI should replace people. It was that organisations should be able to remove the repetitive, burdensome activities that consume time and energy, allowing people to focus on higher-value work.

For years we’ve accepted certain activities as simply being part of work. Reconciling data. Chasing approvals. Managing exceptions. Navigating complex processes. Collecting information from multiple systems. What if those activities become the responsibility of software instead? That was the question I kept coming back to throughout the keynote. The vision wasn’t more intelligent software. It was more capable organisations. That’s a subtle but important difference.

If Mike Sicilia explained the vision, Chris Leone demonstrated how Oracle intends to deliver it. The progression shown during the keynote was fascinating. We have already seen Oracle introduce embedded AI capabilities across Fusion. We have seen AI agents emerge. Now Oracle is talking about agent teams, workflow agents, agentic applications and autonomous execution.

The key message was that AI is evolving from supporting processes to actively participating in them. That shift matters. Historically, enterprise software has recorded work. Increasingly, Oracle appears to be building software that performs work. Not without governance. Not without controls. But certainly with a higher degree of autonomy than most organisations are accustomed to today.

There were plenty of announcements during the keynote, but Fusion Claw was the one that immediately stood out to me. Not because it was the most futuristic. Actually, quite the opposite. What interested me was the emphasis on governance.

Most organisations are no longer questioning whether AI can generate content, identify patterns or make recommendations. The real question is whether AI can be trusted to execute business processes. That is where many current AI discussions begin to stall. The answer is rarely a technology problem. It is a governance problem.

Throughout the demonstrations, Oracle repeatedly emphasised operating boundaries, permissions, policies, approvals and organisational controls. In other words, autonomy with accountability. That feels much more relevant to enterprise customers than unrestricted AI experimentation. The organisations I speak to rarely say, “We need AI to make decisions independently.” What they actually say is, “We need AI to help us achieve outcomes without introducing risk.” Fusion Claw feels like Oracle’s response to that challenge.

Another concept that caught my attention was Oracle’s discussion around systems of outcomes. For decades, enterprise applications have been systems of record. They store information. Track transactions. Enforce processes. Maintain business truth. Those capabilities remain essential.

Oracle’s argument is that organisations increasingly need something more. A layer focused on achieving business outcomes rather than simply recording business activity. The more I think about it, the more significant that feels. After all, organisations don’t exist to process invoices. They don’t exist to approve purchase orders. They don’t exist to complete workflow transactions. Those activities support the real objectives. Better customer experiences. Stronger financial performance. Improved workforce productivity. Increased organisational agility. More resilient supply chains. Those are the outcomes that matter. If AI can continuously coordinate activity towards those outcomes, then we are moving into genuinely new territory.

Fusion Claw will probably attract most of the headlines. However, I suspect Fusion Companion could prove just as important over time. As enterprise landscapes become increasingly complex, users don’t think in terms of applications anymore. They think in terms of work. They move between systems, channels, data sources and processes. The value increasingly comes from connecting those experiences together.

The vision Oracle presented for Fusion Companion suggests a future where intelligence follows the user rather than remaining confined within individual applications. If delivered successfully, that could become one of the most practical and impactful developments announced during the keynote.

Anyone who has read my recent blogs will know I’ve already posted about Fusion Setup and Configuration Registry (FSCR). Seeing it formally announced during the keynote only reinforced my belief that this could become one of the most important long-term developments for the Oracle ecosystem.

For years, implementation partners have built methodologies, accelerators, templates and frameworks to help customers configure Fusion applications. FSCR has the potential to fundamentally change that model. If Oracle can successfully connect requirements, process design, configuration and AI-driven implementation, then the way organisations deploy Fusion could look very different in the future. It is still early days. But of all the announcements made on Day 1, FSCR may ultimately have one of the biggest impacts on how Oracle projects are delivered.

When I finished watching the keynotes, I kept coming back to one simple thought. For the last two years, the industry has largely been asking: “How can AI help people do their jobs faster?” Oracle now seems to be asking: “How can AI help organisations achieve outcomes with less human effort?” That is a much bigger question. And potentially a much more transformative one.

Whether discussing agentic applications, Fusion Claw, Fusion Companion or FSCR, the underlying message felt remarkably consistent. The goal is no longer simply helping people work. The goal is getting the work done. That doesn’t mean removing people from the process. It means allowing people to spend less time managing the mechanics of work and more time focusing on the decisions, creativity, relationships and innovation that humans are uniquely positioned to provide.

Watching remotely, that was my overwhelming impression of Oracle AI World Day 1. The conversation has moved beyond AI assistance. Oracle is now focusing on AI execution. And that feels like the start of a very different chapter for enterprise applications.

Please note all screenshots are the property of Oracle and are used according to their Copyright Guidelines.

Fusion Claw: Is This Oracle’s Most Important AI Announcement Yet?

~ K8 Mead ~ Leave a comment

Over the past couple of years, we’ve seen Oracle steadily evolve its AI story. It began with AI capabilities embedded within Fusion Applications. Then came AI Agents, followed by AI Agent Studio, giving customers the ability to build, extend and govern their own AI-powered processes. Oracle has unveiled the next piece of the puzzle: Fusion Claw.

I’ll admit that when I first heard the name, I wasn’t entirely sure what to make of it. Another AI agent? A new orchestration framework? Perhaps just another acronym to add to the ever-growing AI dictionary? Having sat through the recent webinar, I came away thinking this may actually be one of the most significant Oracle AI announcements we’ve seen so far.

Not because it introduces more AI. But because Oracle appears to be addressing one of the biggest challenges organisations face today: how do you move from AI recommendations to AI-driven outcomes whilst maintaining governance, accountability and human oversight?

Most enterprise AI today is still fundamentally assistive. Whether it’s generating content, summarising information, analysing data or helping users complete transactions, AI is primarily helping people do their jobs more efficiently. Even many AI agents stop at recommendations. They identify an issue, gather information, analyse options and suggest a course of action. A human then reviews the output and decides what happens next. Fusion Claw appears to be Oracle’s answer to that gap.

Oracle positions Fusion Claw as a governed execution runtime that sits between AI planning and enterprise execution. People define objectives, policies and desired outcomes. Agentic Applications orchestrate business processes. Fusion Claw plans and executes activities. Fusion Applications remain the system of record. That distinction is important.

Oracle is not proposing a future where AI operates outside enterprise controls or invents its own ways of working. Instead, Fusion Claw operates against the same business objects, security model and governance framework that organisations already trust. In simple terms, Oracle is creating a mechanism for translating business outcomes into governed action.

Fusion Claw is designed for complex, high-value work rather than simple transactional activities. Its core capabilities are Analyse; Reconcile; Simulate; Optimise; Predict; and Execute. Those first five capabilities are becoming increasingly common across enterprise AI platforms. The sixth is what makes Fusion Claw different. Execution has traditionally been the point where AI recommendations hand off to humans. Oracle is now seeking to close that gap.

Imagine an organisation identifying a workforce issue, supply chain disruption, financial variance or operational risk. A traditional AI capability might identify the problem. An AI agent might recommend a solution. Fusion Claw is designed to take things further by evaluating multiple options, simulating outcomes, optimising the approach and, where authorised, executing approved actions. That feels like a notable evolution in Oracle’s AI strategy.

The focus is no longer: “How can AI help me complete this task?” Instead, the question becomes: “How can AI help achieve this business outcome?”

One of the most interesting concepts introduced during the session was the distinction between two the operational models Deep Research vs Full Auto. In Deep Research mode, Fusion Claw investigates, analyses and simulates. It evaluates scenarios, compares potential outcomes, surfaces recommendations and presents options for human review.

Importantly, the human remains responsible for making the decision. For many organisations, particularly those operating in highly regulated environments, this will likely be the most comfortable starting point. The value comes from faster analysis and better decision support, whilst maintaining a clear human approval process.

Full Auto mode is where things become significantly more transformative. In this model, Fusion Claw can plan and execute authorised activities automatically in pursuit of a defined business outcome. That naturally raises questions. Who controls it? Who is accountable? What prevents unintended actions?

Anyone who has read my blogs over the past year will know that governance is often the first question that I and customers ask. The technology itself is rarely the challenge. Trust is. The encouraging aspect of Fusion Claw is that governance appears to have been designed into the platform from the outset rather than added afterwards.

Fusion Claw operates inside a private runtime container within the customer’s Fusion environment and works directly with authoritative Fusion business data. Perhaps most importantly, Fusion Claw does not grant itself authority. Organisations decide what actions can be taken, under which circumstances and with what level of oversight. Human accountability remains in place. For customers concerned about explainability, risk and compliance, that message will be every bit as important as the AI capabilities themselves.

One feature that particularly caught my attention was the concept of an Outcome Receipt. Every execution performed by Fusion Claw generates a detailed record that explains: What happened? Why it happened? What information was used? Which controls were applied? Which actions were executed?

At first glance, this sounds like an audit feature. In reality, it could become one of the most important capabilities in the platform. As AI becomes responsible for increasingly valuable business activities, organisations will need evidence. They’ll need to demonstrate how decisions were reached, what data was considered and who authorised the action. For public sector organisations, financial institutions and heavily regulated industries, this level of transparency could prove essential. Many organisations are no longer asking whether AI can make decisions. They’re asking whether they can prove how those decisions were made.

Most enterprise systems operate on a transaction basis. Users initiate activity, the system responds and the process ends. Fusion Claw appears designed to work differently. It is runtime that continuously monitors conditions, reassesses plans and adjusts activities in pursuit of an outcome. Execution can be triggered by user requests; scheduled activities or configured triggers. Rather than waiting for users to notice and react, Fusion Claw is designed to continuously work towards a defined objective. That’s a subtle but potentially significant shift in how we think about enterprise applications.

One other key point of note is Fusion Claw’s ability to work across business functions. Rather than being constrained to a single process or module, it is intended to support end-to-end activities spanning multiple Fusion pillars. This cross-functional capability is important because real business outcomes rarely exist within the boundaries of a single application module.

Most organisational challenges span departments, processes and data sources. Fusion Claw appears designed with that reality in mind. It should also be noted that whilst Fusion Claw is firmly rooted within Fusion Applications, it can interact with external services and other agents where appropriate access has been configured.

A practical concern for many customers will be whether existing investments become obsolete. Fortunately, Oracle’s answer appears to be no. They have confirmed that existing agents can be reused and enhanced rather than rebuilt from scratch. Given that many organisations are only just beginning their AI Agent Studio journey, that will be welcome news.

For those already thinking ahead to roadmap planning, there are a few key details you should know. Fusion Claw is currently targeted for the 26D timeframe. If you have already purchased the licence for the Agentic Applications, you will automatically be able to use Fusion Claw.

By the time you receive 26D, Oracle will have provided 75 Agentic Applications out of the box, of which 25 will be Fusion Claw-powered applications focused on more complex enterprise activities. You won’t be limited to just these 25 Fusion Claw applications, as with all AI Agents and Agentic Applications, you can build your own. This can be via the No Code methods in AI Agent Studio or the Low Code options using CLI.

So, Is This Oracle’s Most Important AI Announcement Yet? Maybe. It’s certainly one of the most ambitious. AI Assist features help users work faster. AI Agents help users make better decisions. Fusion Claw appears to be aiming for something larger: helping organisations achieve business outcomes through governed AI execution. Whether customers are ready to embrace that level of autonomy remains to be seen.

Some organisations will begin with Deep Research, keeping humans firmly in the decision-making loop. Others will be attracted by the possibility of allowing approved activities to execute automatically within carefully defined guardrails. Either way, the conversation is changing. We’re moving beyond asking how AI can assist users and starting to explore how AI can help organisations achieve measurable outcomes. For me, that’s the real significance of Fusion Claw.

The technology itself is impressive, but the bigger story is what it represents: Oracle’s vision for moving from AI that advises, to AI that acts, whilst still maintaining the governance, security and accountability that enterprises demand. And that may ultimately make Fusion Claw one of the most important steps yet in Oracle’s AI journey.

Please note all screenshots are the property of Oracle and are used according to their Copyright Guidelines.

Oracle Fusion Cloud Procurement 26D: Making Procurement Easier for Everyone

~ K8 Mead ~ Leave a comment

It’s quarterly release time again and Oracle has delivered a surprisingly practical set of updates for Procurement in 26D. There’s plenty of AI in this release, which won’t come as a surprise given Oracle’s recent direction. What I found interesting, though, is that many of these features aren’t about replacing procurement professionals or automating entire processes. Instead, they’re focused on removing friction. Making it easier to buy something. Easier to raise a request. Easier to manage contracts. Easier for suppliers to work with you. And honestly, that’s where I think the biggest value often comes from.

Most employees aren’t procurement experts. They don’t know the difference between a catalogue requisition, a non-catalogue request or an intake process. They simply need a laptop, a contractor, some software or a piece of equipment. The more effort they spend working out how to navigate procurement processes, the less time they’re spending on their actual job. That’s the theme that runs through Oracle Procurement 26D.

One of my favourite additions in this release is the new Procurement Request Concierge. If you’ve followed Oracle’s investment in AI across HCM, you’ll probably recognise the concept straight away. Rather than expecting users to understand which process they need, Oracle lets them start with a simple conversation.

An employee can describe what they’re trying to achieve and the Concierge works out the most appropriate route. Behind the scenes it might direct them to a catalogue purchase, an intake request, a procurement policy adviser or another specialised procurement assistant. I think this is an important shift.

For years, organisations have invested significant effort in training employees how to use procurement systems. Oracle is increasingly turning that around and teaching the system how to understand employees instead. That may seem like a subtle difference, but it has the potential to improve adoption, reduce incorrect requests and make procurement feel far less intimidating for occasional users.

Oracle has also continued enhancing the Intake Request Creation Assistant. The individual improvements are relatively small, but together they make the experience significantly more natural. The assistant can now understand units of measure, handle currencies more intelligently, capture supporting documents and retain information that doesn’t neatly fit into predefined fields. Oracle has also introduced AI-generated request summaries to help reviewers quickly understand what’s being requested.

What I like about these enhancements is that they’re focused on a real problem: people don’t speak in structured procurement forms. Users naturally describe what they need in their own words. Historically, procurement systems expected employees to translate those requirements into a format the system could understand. Increasingly, Oracle is teaching the system to do the translation instead. That’s exactly how AI should be used. The best AI isn’t always the most visible AI. Sometimes it’s simply the technology that quietly removes the annoying bits of a process.

Two enhancements to the Purchase Requisition Creation Guide stood out for me because they address challenges many organisations face every day. The first is multilingual support. For global organisations, English often becomes the default language for business systems, even when it isn’t the first language of many employees. Allowing employees to interact with procurement assistants in their preferred language reduces barriers and makes the technology accessible to a much wider audience.

The second enhancement improves visibility into internal material sourcing. If stock isn’t available from a preferred internal source, requesters can now see alternative sourcing options immediately, rather than discovering availability issues later in the process. Neither feature is particularly flashy. Both are likely to save users frustration. And that’s often a much better measure of success.

One of the updates that caught my attention has nothing to do with AI. Oracle has improved the way approval errors are communicated to users. Historically, requisitions could sometimes appear stuck with little indication of what had gone wrong. That inevitably leads to support calls, emails and frustration as users try to work out what happened. In 26D, error messages are clearer, more informative and provide better diagnostic information when something goes wrong. Whilst this isn’t ground breaking, improvements like this often have a disproportionate impact on user satisfaction because they address genuine everyday frustrations.

The biggest enhancements in 26D arguably sit within Enterprise Contracts. Both features focus on tasks that have traditionally required substantial manual effort. The first is AI-powered contract ingestion. Many organisations have large collections of contracts sitting in shared drives, document repositories and archive systems. Bringing those contracts into a structured contract management solution has often required significant manual effort and review. Oracle can now use AI to help classify those documents, extract key information and create contract records. If you’re undertaking a contract transformation programme, consolidating legacy systems or simply trying to improve contract visibility, this could significantly reduce the effort involved.

The second enhancement builds on Contract Expert. Rather than relying solely on traditional rules, Oracle can now evaluate contracts against policy guidance and recommend or automatically apply appropriate clauses. For me, the real value isn’t the AI itself. It’s consistency. As organisations grow, maintaining consistent contractual terms becomes increasingly difficult. Features that help ensure the right clauses are included at the right time can improve governance and reduce risk without creating additional work for contract authors. That’s the sort of practical AI adoption I like to see.

Several of the 26D enhancements focus on suppliers rather than internal users. Supplier contacts can now manage multiple supplier relationships using a single account, reducing the need for multiple logins and simplifying administration. Oracle has also introduced a new Redwood experience for supplier invoicing and payment management, creating a more modern and streamlined interface for suppliers submitting invoices and tracking payments. It’s easy to focus entirely on employee experience when reviewing release notes, but supplier experience matters too. The easier you make it for suppliers to do business with you, the smoother your procurement processes become overall.

Like almost every Oracle release at the moment, 26D also continues the wider Redwood journey. Additional Supplier Management setup pages are now available in Redwood, reducing the need for administrators to move between classic and Redwood experiences. It’s not the biggest story of the release, but it reflects Oracle’s continued progress towards delivering a more consistent user experience across the entire procurement suite.

What I like about Procurement 26D is that Oracle hasn’t tried to solve procurement with a single headline-grabbing feature. Instead, we’re seeing dozens of improvements that make procurement easier for everyone involved. Employees get a simpler starting point. Requesters receive more intelligent guidance. Contract managers gain tools that improve consistency. Suppliers benefit from a more streamlined experience.

Yes, AI is everywhere in this release. But the most valuable features aren’t necessarily the ones shouting about AI. They’re the ones quietly helping people complete tasks faster, make better decisions and spend less time fighting the system. That’s not always the most exciting story. For most procurement teams, though, it’s probably exactly the right one.

Please note all screenshots are the property of Oracle and are used according to their Copyright Guidelines.

Oracle ERP AI Agents in 26D: Less Processing, More Decision Making

~ K8 Mead ~ 2 Comments

If Release 26D tells us anything, it’s that Oracle’s vision for finance is no longer about giving users better screens. It’s about giving them less work to do in the first place. That might sound like marketing speak, but this quarter’s ERP updates genuinely feel different. Across Payables, Payments, Expenses, Billing, Fixed Assets and Budgetary Control, Oracle continues to move beyond AI assistance and deeper into agent-led execution. In many cases, the software isn’t just helping users do the work. It’s identifying issues, recommending actions and increasingly resolving routine tasks on their behalf. The common theme throughout 26D is simple: finance professionals spend less time chasing transactions and more time focusing on decisions that genuinely need human judgement.

If you’ve read any of my previous AI blogs, you’ll know I keep coming back to Payables. That’s because it’s one of the clearest examples of where agentic AI can deliver measurable business value. Invoice processing is still surprisingly manual in many organisations. Someone uploads an invoice, someone investigates an exception, someone releases a hold, and someone else spends time trying to understand why the same issue keeps appearing month after month. Oracle is steadily removing those touchpoints.

The Payables Agent now supports more of the invoice lifecycle than ever before. Invoice ingestion has been enhanced with broader document support and improved integration options, making it easier to bring invoices into Oracle regardless of the format or source system. For organisations dealing with different supplier standards, languages and document types, that’s a practical improvement that should reduce manual effort from day one.

The feature that stood out most to me, however, is Actionable Insights. Rather than finance teams having to trawl through reports looking for recurring issues, Oracle now highlights patterns automatically. If users are repeatedly overriding exceptions or releasing holds without addressing the root cause, the system identifies the behaviour and recommends corrective action. This is where AI starts becoming genuinely useful. Rather than simply helping users process invoices faster, it helps organisations understand why problems occur in the first place.

Oracle has also expanded automated exception resolution, allowing more invoice import errors to be addressed through predefined resolution paths rather than manual intervention. Combined with enhanced matching controls and business-specific tolerance rules, organisations have more flexibility to reduce unnecessary holds while still maintaining appropriate financial controls.

Another welcome addition is the new Payables Period Close Workspace. Month-end close is often less about processing transactions and more about coordinating people. Teams spend significant time identifying what still needs attention, chasing outstanding items and managing exceptions across multiple reports and spreadsheets. Oracle now brings those activities together into a single workspace that highlights priority actions, identifies high-value exceptions and guides users through the close process. For customers still relying on spreadsheets, email chains and manual status updates during close, this could make a significant difference.

One of the criticisms I’ve had of some early AI assistants is that they often stop at telling users what they should do next. That’s useful, but it’s only half the journey. In 26D, Oracle is starting to close that gap. The Payments Agent can now support activities such as virtual card offers, dynamic discounting and supply chain financing, allowing payment specialists to move from opportunity identification into execution within the same experience.

What I find particularly interesting is Oracle’s focus on working capital optimisation. Rather than treating financing options as separate initiatives, the platform can compare multiple strategies and help users determine which approach is most appropriate for a particular supplier or payment population.

There’s also a practical enhancement that many finance teams will probably value more than the headline AI announcements. When payment processes fail, the agent can now explain the issue in business language and recommend a course of action. Anyone who has spent time trying to decipher technical process logs during a payment run will understand the value of that immediately.

Let’s be honest. Expense management isn’t usually anyone’s favourite process. It’s one of those areas where frustration tends to come from lots of small inefficiencies rather than one major issue. Missing receipts, incomplete itemisation, attendee information, rejected claims and lengthy approval cycles all add up. Oracle is tackling several of those issues in 26D.

The Expenses Agent can now collect missing itemisation and attendee details through email interactions, allowing employees to provide information using natural language rather than navigating back into the application. That may sound like a small change, but anyone involved in expense administration knows that itemised expenses and entertainment claims often generate disproportionate amounts of back-and-forth communication. Capturing that information before the approval process begins should reduce delays and improve approval cycle times.

The new Expenses landing page is another welcome addition. Employees and delegates now have a single view showing reports requiring attention, reports ready for submission, recent payments and other key activities. For delegates managing expenses on behalf of multiple employees, this is particularly useful. Instead of jumping between multiple users and screens, they can manage activity from a single location and quickly identify where action is required. Oracle has already confirmed this will become the default experience in 27B, so now is probably a good time for organisations to start evaluating it.

If there is one release area that genuinely feels different, it’s Billing. The new Billing Operations Workspace isn’t simply another dashboard. It’s built around the idea that agents should continuously perform routine work, while people focus on exceptions and decisions. The workspace covers project billing readiness, invoice generation and dispute resolution.

On the project billing side, the agent monitors overdue approvals, sends reminders and escalations according to defined procedures and identifies billing issues before they become month-end surprises. It can even update existing draft invoices when held transactions are released, eliminating manual rework that many project accounting teams know all too well.

For invoice generation and delivery, the agent handles qualifying activities automatically and surfaces only those situations requiring human review. Dispute management follows a similar pattern. Customer billing queries received by email can be converted into disputes, assessed by the agent and, where appropriate, resolved automatically or routed for review with supporting evidence. This is probably one of the strongest examples in 26D of Oracle’s broader vision for agent-led business processes.

Fixed Assets rarely gets the same attention as Payables, Procurement or Financials, but Oracle has introduced several genuinely useful assistants in this area. New capabilities support asset additions, depreciation forecasting, acquisition cost analysis and capital expenditure planning. What’s interesting is that Oracle hasn’t focused on flashy demonstrations here. Instead, the emphasis is on reducing spreadsheet dependency.

The Asset Additions Assistant helps users create assets through a guided conversational experience. The Depreciation Projection Assistant provides on-demand forecasting from live data. Additional tools analyse acquisition costs and support capital expenditure planning activities. None of these capabilities are likely to dominate conversations, but they address real activities that finance teams perform every month. In my experience, that’s often where the biggest productivity gains come from.

The Budget Adjustment Assistant first appeared in 26C, but Oracle has continued to build on it in 26D. The most significant enhancement is policy validation. Budget transfers can now be validated against organisational policies before they enter the approval process, helping prevent invalid adjustments from progressing and creating unnecessary rework.

When validation issues are identified, the assistant explains the issue in plain language and provides guidance on how to resolve it. The approval process and audit trail are managed as part of the overall workflow. It’s worth noting that this enhanced capability is currently available through a controlled release, so customers interested in evaluating it will need to engage Oracle Support.

When Oracle first started talking about AI agents, I think many customers wondered whether they would be genuinely useful or simply another layer on top of existing processes. Release 26D provides some of the clearest evidence yet that Oracle is serious about changing how finance work gets done.

What stands out isn’t any single feature. It’s the number of areas where Oracle is systematically removing repetitive effort. Whether that’s invoice exceptions, payment investigations, expense administration, billing disputes, fixed asset analysis or budget adjustments, the goal is increasingly the same: let the system handle routine work and focus human attention where it adds the most value.

We’re still some distance away from a fully autonomous finance function, and frankly I don’t think most organisations would want that anyway. But we are moving closer to a world where finance professionals spend less time moving transactions between stages and more time applying judgement, managing risk and supporting the business. For me, that’s where the real value of AI sits. Not replacing finance teams, but helping them spend more time doing the work only they can do.

Oracle regularly introduces additional functionality throughout the quarter, so it’s worth keeping an eye on the What’s New documentation as Release 26D progresses. If any significant new ERP capabilities appear, I’ll be sure to cover them in a future update.

Please note all screenshots are the property of Oracle and are used in accordance with Oracle’s Copyright Guidelines.

Oracle Recruiting 26D: From Reactive Hiring to Proactive Recruiting

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It’s that time again, quarterly release time, and Oracle Recruiting 26D is shaping up to be one of the more interesting releases we’ve seen for a while. At first glance, it’s easy to assume this is simply another AI-heavy update. After all, almost every major enhancement includes some form of advisor, agent or generative capability. But looking a little deeper, I think there’s a more interesting story here.

For years, recruiting technology has largely focused on helping recruiters process candidates more efficiently once a vacancy exists. A requisition opens, recruiters search for candidates, screening begins, interviews are arranged and eventually an offer is made. Release 26D starts to challenge that model.

Many of the new capabilities are focused on helping organisations anticipate hiring demand, build pipelines earlier, make more informed decisions and reduce administrative effort throughout the recruitment lifecycle. In other words, moving from reactive hiring towards proactive recruiting.

The feature that caught my attention most is Candidate Sourcing Workspace. Most recruitment teams know the pressure that comes with a new vacancy. As soon as a requisition is approved, the clock starts ticking. Recruiters begin searching, hiring managers want candidate shortlists immediately, and the race is on to identify suitable people. Oracle is trying to change that.

The new Candidate Sourcing Workspace allows recruiters to build and maintain talent pipelines before a requisition even exists. Using previous hiring patterns and anticipated demand, Oracle can help recruiters identify and nurture potential candidates in advance, creating a pool of talent ready to engage when opportunities arise.

Rather than starting every search from scratch, recruiters can draw from existing talent pools, review AI-generated candidate summaries and quickly identify the strongest matches for anticipated hiring needs. For organisations that regularly recruit into similar roles, this could have a significant impact on time-to-hire. More importantly, it allows recruiters to spend less time hunting for candidates and more time building relationships with them. That’s a much more strategic use of everyone’s time.

Supporting the new sourcing workspace is an enhanced outreach experience that brings together all candidate engagement activity into a single view. That might not sound particularly exciting on paper, but anyone who has ever managed a large recruitment campaign knows how easy it is for conversations to lose momentum. Candidates don’t respond immediately, follow-ups are forgotten, and promising prospects gradually disappear from the process. Oracle is addressing that problem with automated reminders, consolidated outreach tracking and AI-generated summaries of candidate responses. The result is a much clearer picture of who has been contacted, where conversations stand and what action should happen next.

Another interesting addition is the new Hiring Workspace for Store Manager. Although initially aimed at retail organisations, the broader theme here is relevant to any organisation managing high-volume recruitment. The workspace allows managers to ask natural-language questions such as: “What requires my attention today?” or “Where is hiring off track?” and receive a structured response that highlights issues, priorities and recommended actions.

What I like about this feature is that Oracle isn’t simply presenting data differently. The platform is actively helping managers understand what requires attention and what they should do next. That may sound like a subtle distinction, but it’s an important one. Most recruitment systems already provide dashboards and reports. The challenge isn’t usually accessing information. It’s understanding what action should be taken based on that information. This workspace starts to bridge that gap.

Probably the most practical AI enhancement in the release is the new Compensation Recommendation and Job Offer Creation Advisor. Creating a competitive offer has traditionally involved a mixture of salary benchmarking, internal comparisons and professional judgement. It can also be surprisingly time-consuming. Oracle’s new advisor pulls together relevant information from the requisition, candidate assessments and internal salary data to provide a recommended salary range before helping create the draft offer itself.

What particularly stood out to me is Oracle’s emphasis on explainability and guardrails. The recommendations are based on defined business criteria and Oracle explicitly states that protected characteristics such as age, gender, race and disability are not used when generating recommendations. In a world where organisations are rightly paying more attention to AI governance and pay equity, that’s an important message. Will recruiters still want human oversight? Absolutely. But reducing the administrative effort involved in preparing offers while supporting consistency of decision-making feels like a genuinely useful application of AI.

Not every valuable enhancement in 26D relies on AI. Oracle has also simplified how candidates access and respond to job offers by making offers available directly through candidate-facing experiences. External candidates can access offers through career sites, while internal candidates can access them through Opportunity Marketplace. They can review offer documentation, accept or decline offers and access completed documentation after acceptance.

It’s a relatively simple change, but one that removes a common point of friction. Offer emails can be missed, filtered or buried in busy inboxes. Giving candidates a consistent place to access offer information feels like a sensible improvement that should help remove avoidable delays from the process.

While most of the release focuses on new innovation, there is one update that deserves immediate attention. Oracle has confirmed that Digital Assistant (ODA) Fusion Apps templates for Recruiting will no longer be supported from November 2026. If your career site currently relies on Oracle Digital Assistant, now is the time to begin planning your transition. Oracle’s recommended path forward is Career Coach, which becomes the strategic candidate assistance experience going forward. This isn’t something I’d leave until the last minute. November 2026 will arrive much faster than many organisations expect, and early planning will help avoid unnecessary disruption.

What stands out to me about Recruiting in 26D is that Oracle appears to be focusing on the right problems. The most valuable features aren’t necessarily the ones generating content faster. They’re the ones helping organisations build stronger talent pipelines, make more consistent hiring decisions and remove friction from the recruitment process.

That’s why Candidate Sourcing Workspace was the feature that caught my eye. If organisations can move from scrambling to find candidates every time a requisition opens to maintaining an always-on pipeline of talent, that has the potential to transform recruitment performance far more than any chatbot ever could. There are still plenty of AI announcements in this release, but for once they feel connected to genuine business outcomes rather than being technology in search of a problem. And if you’re still relying on Oracle Digital Assistant on your career site, don’t overlook the deprecation announcement. November 2026 will arrive faster than you think.

Please note all screenshots are the property of Oracle and are used in accordance with Oracle’s Copyright Guidelines.

Oracle Fusion Talent Management 26D: Less Administration, Better Talent Conversations

~ K8 Mead ~ Leave a comment

It’s quarterly release time again and, while Talent Management doesn’t have quite the same headline-grabbing AI announcements we’ve seen elsewhere in Oracle Fusion Cloud, Release 26D delivers something arguably more valuable. It removes friction.

Across performance management, succession planning and skills, Oracle continues to focus on helping managers and HR teams spend less time navigating processes and more time focusing on their people. There is a clear theme running through many of the enhancements in this release: making the information you already have easier to access and turning time-consuming tasks into something much simpler.

Oracle introduced the Team Talent Calibration and Review Workspace in 26C, but Release 26D feels like the point where it starts delivering real value. Managers can now see AI recommendations suggesting whether an employee’s overall performance rating should be adjusted or remain unchanged. What makes this more interesting than a simple AI-generated recommendation is the amount of information Oracle is now evaluating behind the scenes.

Rather than looking at a narrow set of data, Oracle is drawing on feedback, check-ins, goals, questionnaire responses and other performance information to build a richer picture of an employee’s contribution throughout the year. Let’s be clear, AI should never replace managerial judgement, particularly when it comes to performance discussions. However, it can help surface information that might otherwise be forgotten or overlooked during calibration sessions, especially in larger teams where managers are balancing multiple priorities.

One feature I particularly like is the new Talent Summary presentation. Oracle can now generate a ready-made presentation covering each employee’s performance and talent indicators, along with AI-generated strengths and development opportunities. Anyone who has ever spent hours building calibration packs before a talent review meeting will immediately understand the value of that enhancement.

Check-ins have quietly become one of the strongest parts of Oracle Performance Management and 26D continues that steady evolution. Several enhancements have been introduced that make the experience more structured and, perhaps more importantly, easier for managers to prepare for.

Organisations can now define default templates for check-ins, helping to drive consistency while reducing the amount of setup required when creating new conversations. Oracle has also introduced the ability to include questionnaires directly within check-ins and surface recent employee feedback alongside the discussion. What I like about these changes is that they help managers come into conversations with context already available.

One of the biggest challenges with performance management isn’t collecting information. It’s getting the right information in front of managers at the point they need it. By bringing feedback, discussion points and supporting information together in one place, Oracle is helping make those conversations more meaningful.

The AI-generated summaries introduced in earlier releases have also been enhanced, allowing Oracle to use a broader range of information when generating check-in notes and conversation summaries. Individually, these enhancements may seem relatively small. Together, they make the entire check-in process feel much more mature.

One of my favourite enhancements in this release is also one of the least complicated. Managers can now assign performance and development goals to multiple employees at the same time from the Team Activity Centre This isn’t an exciting AI announcement. It isn’t revolutionary. It’s simply practical.

Many organisations establish common goals across teams or departments, yet managers have traditionally had to assign those goals employee by employee. It’s not difficult, but it is repetitive and time-consuming. Release 26D removes that unnecessary administrative effort and allows managers to assign goals to multiple team members in a single action. Features like this don’t usually make marketing headlines, but they’re often the ones users appreciate the most because they save time every single day.

Most organisations recognise the importance of succession planning. The challenge is often finding the right information when you need it. Even organisations with well-maintained talent profiles and strong skills data can struggle to identify suitable successors quickly and consistently. Release 26D aims to simplify that process through enhancements to the Succession Planning Agent. HR teams can now describe the type of candidate they’re looking for using natural language and receive recommendations based on available workforce, talent and skills information.

What stands out to me is not necessarily the AI itself, but the accessibility it creates. For years, organisations have invested significant effort building richer talent profiles, managing skills data and maintaining employee information. Features like this finally make that data easier to use during real-life talent decisions. That’s where the true value lies.

Skills continue to be a major focus area across Oracle HCM, with many organisations looking beyond traditional job-based workforce planning and moving towards a more skills-driven approach. For customers using Techwolf, Oracle has introduced a new integration that synchronises Techwolf skills directly into Oracle Fusion HCM.

Now, I appreciate this won’t apply to every organisation. However, for those already using Techwolf as part of their skills strategy, this could remove a significant amount of ongoing administration. Maintaining multiple skills repositories is rarely a sustainable long-term approach. Automating the movement of skills data between systems helps improve consistency and gives organisations greater confidence in the information they’re using to support talent decisions. As skills initiatives continue to mature, integrations like this become increasingly important.

A smaller enhancement that shouldn’t be overlooked is the additional flexibility Oracle has introduced within All-in-One Evaluations. HR teams can now make greater use of star-based ratings, define read-only sections within evaluation documents and customise some of the terminology displayed to managers and employees. None of these changes are transformational on their own, but they provide welcome flexibility for organisations that have invested time tailoring performance processes around their own ways of working.

The ability to lock down completed sections is particularly useful for organisations that want tighter control over different stages of the evaluation process while still maintaining visibility for managers and employees. Sometimes success isn’t about introducing entirely new functionality. It’s about giving organisations more control over the tools they already use.

Talent Management 26D isn’t a release packed with revolutionary new capabilities, and honestly, I think that’s perfectly fine. Instead, Oracle has focused on making existing processes easier, smarter and less time-consuming. Calibration becomes more informed, check-ins become more effective, goal management becomes simpler, succession planning becomes more accessible and skills information becomes better connected.

What links all these enhancements together is a common objective: helping managers and HR teams spend less time administering talent processes and more time focusing on the people behind them. Sometimes those are the releases that deliver the biggest impact. As always, Oracle may add additional functionality to the release over the coming weeks. If any significant talent features appear, I’ll be sure to cover them in a future update.

Please note all screenshots are the property of Oracle and are used in accordance with Oracle’s Copyright Guidelines.

Oracle Help Desk 26D: AI Gets More Practical and the Redwood Clock Keeps Ticking

~ K8 Mead ~ Leave a comment

It’s quarterly release season again and Oracle has already revealed a healthy batch of new features for Help Desk in 26D. What struck me most this quarter wasn’t necessarily the number of new features, but how much Oracle continues to invest in making support part of the wider employee experience. The latest updates are less about helping agents work faster and more about helping employees find answers, complete actions and stay on top of requests without having to navigate multiple areas of HCM.

There’s also an important reminder for anyone still running Classic HR Help Desk. Oracle has reconfirmed that the Redwood migration deadline is fast approaching and, if you haven’t started planning yet, now is the time. As always, Oracle may add further features as the release progresses, but let’s take a look at the highlights from the first wave of 26D announcements.

The standout feature in 26D for me is the new My Help Workspace for Employees. If you’ve ever watched employees navigate Oracle HCM, you’ll know one of the biggest frustrations isn’t necessarily finding information. It’s remembering where everything lives. Help requests sit in one place, learning assignments in another, expenses somewhere else, and absence requests somewhere else again. Oracle is clearly trying to tackle that problem.

The new workspace gives employees a single place to see the activities and requests that need their attention. Whether that’s a Help Desk request waiting for a response, an outstanding task, or useful knowledge content, employees can quickly see what requires action without having to navigate around multiple parts of Oracle HCM.

What I particularly like is that it isn’t just a reporting page. Employees can take action directly from the workspace. If a Help Desk analyst is waiting for additional information, the employee can respond immediately. If a request seems to have gone quiet, they can follow up without having to navigate elsewhere.

Oracle has also made it easier to get support when employees need it. The Ask Oracle search capability is available directly within the workspace, with access to a full AI-powered chat experience without taking users away from what they’re doing. This feels very much like the direction Oracle is heading with many of its AI investments. Rather than forcing users to jump between modules, Oracle is bringing information and actions together in a single experience and letting AI help guide employees to the next thing they should do.

For organisations exploring Oracle’s Agentic AI strategy, this is probably one of the clearest real-world examples we’ve seen so far. It’s practical, employee-facing and solves a genuine problem rather than introducing technology for technology’s sake.

The Employee Help Desk Self Service Assistant continues to mature with every release, and 26D includes a collection of smaller enhancements that together make a significant difference. The most interesting addition is the use of the Classification Agent when requests are created through the assistant. Instead of employees having to work out categories, priorities and classifications themselves, the AI can now determine much of that information automatically. That might not sound particularly exciting on the surface, but anyone who has spent time reviewing Help Desk data will immediately recognise the value. Better categorisation means better reporting, more consistent routing and less manual effort for support teams.

Oracle has also improved how knowledge content is surfaced by filtering results based on Business Unit. In practice, that means employees are more likely to see information that is relevant to their part of the organisation rather than scrolling through articles that may not apply to them.

One enhancement that I think many customers will appreciate is the ability to identify requests created through the AI assistant using a dedicated source code. Until now, measuring AI adoption has often been surprisingly difficult. Organisations can now report on how many requests originated through the assistant, making it far easier to understand whether employees are actually using the capability and whether it is delivering value. For me, that’s an important step. AI shouldn’t just be implemented. It should be measurable.

Oracle has also continued its push to make AI Agent Studio the centre of AI configuration by allowing the assistant to be run and customised directly from the platform. That gives organisations more flexibility to tailor the experience as their AI strategy evolves.

One of the more practical additions in 26D is the ability to associate external links directly with Help Desk requests. Whether your teams work with Jira, SharePoint, Confluence, Google Workspace or other business systems, agents can now link relevant records directly from within a service request.

This isn’t a headline-grabbing feature, but it’s exactly the sort of improvement that saves people time every day. Instead of searching through emails, chat messages or separate applications to find supporting information, agents can access everything they need from the Help Desk request itself. For organisations where support processes span multiple systems and teams, small improvements like this can have a surprisingly positive impact on efficiency and collaboration.

Another area receiving attention in 26D is administration. A number of Help Desk configuration pages have now been brought into the Redwood experience, including Categories, Severities, Channels and Visibilities, Message Types and SmartText. On the surface, this might not feel like a particularly exciting enhancement. However, administrators who regularly manage Help Desk configurations will probably appreciate it more than most.

One of the frustrations during Oracle’s Redwood journey has been moving backwards and forwards between Redwood pages and older administration screens. Every additional configuration page that moves to Redwood helps create a more consistent experience. It’s not transformational, but it is another sign that Oracle’s commitment to Redwood remains firmly on track.

Normally I finish these blogs by talking about what’s coming next. This quarter, I want to finish with a warning. Oracle has reconfirmed that Classic HR Help Desk will no longer be available from 27B. If you’re already running Help Desk in Redwood, that’s not a problem. If you’re still using Classic HR Help Desk, however, this deadline should be firmly on your radar. The reason isn’t simply that Oracle is retiring the old experience. Increasingly, the innovation is happening exclusively in Redwood.

Many of the capabilities organisations are looking at today, including AI-powered self-service, modern employee experiences, enhanced case management capabilities and the new My Help Workspace for Employees, are only available in the Redwood experience. As each quarterly update arrives, that gap is becoming more noticeable. That’s becoming a recurring theme across Oracle HCM as a whole. The question is no longer whether organisations should move to Redwood. The question is whether they’re leaving themselves enough time to get there.

Migration isn’t something I’d recommend leaving until the final few months before an update. There are prerequisites to review, validation activities to complete, testing cycles to run and potentially business processes that need to be reviewed along the way. Starting early gives you time to resolve issues before they become critical path activities. Oracle has provided migration guidance, workbooks and checklists to support the transition, but the key message remains the same: if you’re still on Classic HR Help Desk, this should be a priority for the remainder of 2026.

Overall, this isn’t a release packed with dozens of transformational features, but it does show a very clear direction of travel. Oracle continues to invest heavily in AI-assisted support, employee self-service and Redwood-first experiences, while gradually bringing together information and actions that have traditionally been scattered across multiple areas of HCM.

For most organisations, the new My Help Workspace for Employees and the continued evolution of the Self Service Assistant will be the features worth paying closest attention to. They demonstrate how Oracle is moving beyond simply answering questions and towards helping employees complete tasks, resolve issues and manage their work through AI-assisted experiences. And if you’re still running Classic HR Help Desk, the Redwood migration deadline should probably be the feature getting the most attention of all.

Please note that all screenshots are the property of Oracle and are used in accordance with Oracle’s Copyright Guidelines.

Oracle Learning 26D: Smarter Learning Through Practical AI

~ K8 Mead ~ Leave a comment

It’s quarterly release season again and Oracle Learning has delivered a surprisingly strong set of updates in 26D. What stood out to me wasn’t any individual feature. It was the direction of travel. Over the last couple of years, we’ve seen Oracle talk extensively about AI, skills intelligence, digital assistants and, more recently, Agentic Apps. For many customers, the question has been whether these capabilities would genuinely change the way people work or simply provide another layer of technology to navigate. With 26D, Oracle Learning starts to answer that question.

Several of the headline updates focus on something far more practical than flashy AI demonstrations. They help managers understand whether learning is driving development, reduce administrative effort for learning teams, and make it easier for employees to manage their own learning journeys.

What’s particularly noticeable is that Oracle is investing across the entire learning ecosystem. Managers gain better visibility into skill development, learning specialists receive more powerful AI-assisted administration tools, and learners benefit from a significantly upgraded self-service experience. As always, Oracle may add additional functionality as the quarter progresses, but let’s take a look at the highlights announced so far.

If you’ve been following Oracle’s Agentic Apps journey, the Team Learning and Development Workspace for Managers is perhaps the most interesting Learning enhancement in 26D. One of the challenges managers often face is connecting learning activity with actual employee development. Completion rates and learning hours tell part of the story, but they rarely answer the question that really matters: is this learning helping people develop the skills they need? Oracle is clearly trying to bridge that gap.

The previous Skills Development by Skill and Skills Development by Direct panels have been replaced with two new views: Directs’ Progress on Development Objectives and Directs’ Learning Alignment with Development Objectives. Rather than simply showing learning activity, managers can now see whether development goals are progressing and whether completed learning is contributing towards those goals.

The Directs’ Progress on Development Objectives panel highlights which team members are making progress against assigned development skills during a 90-day evaluation period. Managers can take action directly from the workspace through delivered email actions, either nudging employees who haven’t started progressing or recognising those who have. The Directs’ Learning Alignment with Development Objectives panel provides a different perspective. It helps managers understand whether completed non-compliance learning aligns with an employee’s development objectives, using completion outcomes and learning context to make those connections visible.

One of the concerns I often hear when discussing AI in HR is whether we’re removing too much human judgement from people processes. Oracle has struck a sensible balance here. The agent analyses learning activity, identifies who may need support and drafts appropriate communications, but managers remain firmly in control. Nothing is sent automatically. The manager decides whether intervention is needed and whether the suggested communication is appropriate. To me, that’s where enterprise AI delivers the most value. Not by replacing managers, but by helping them spend less time finding issues and more time acting on them.

You may remember the Learning Creation Assistant, first introduced in 25D and enhanced further in 26B. In 26D, it has been renamed the Learning Catalog Management Assistant, and Oracle continues to expand what it can do. The main enhancement is support for questionnaire-based self-paced learning.

Learning specialists can now provide a questionnaire title or code and instruct the assistant to create the associated learning item. The assistant validates the questionnaire, determines whether it should be implemented as an assessment or observation checklist, and creates the learning in draft status ready for review and activation.

At first glance this might feel like a relatively small enhancement. In reality, it’s another step towards natural language administration. Learning specialists can focus on what they’re trying to achieve rather than remembering every configuration step required to get there. By allowing administrators to describe the learning experience they want to create and letting the assistant build the underlying structure, Oracle continues to remove friction from content administration. It’s exactly the type of repetitive setup activity that AI should be helping with. The assistant can also capture additional details such as mastery scores, attempt limits and visibility settings as part of the prompt, reducing the amount of manual configuration required.

Customers who already implemented the earlier Learning Creation Assistant should note that the property on the agent must be updated to reference the new delivered runnable agent. Any customisations made against copied templates will also need to be reapplied.

One of my favourite enhancements in 26D is also one of the simplest. Learning specialists can now create task-based self-paced learning directly from rich text instructions, removing the need for a supporting content asset. That may not sound revolutionary, but anyone who has implemented Learning Cloud will recognise the problem it solves.

Many organisations need to track activities that happen outside the platform. Reading a policy, completing an offline activity, attending an external event or carrying out a workplace task are all valid learning experiences, yet administrators have often needed to create workaround content simply to make those activities available in Learning Cloud. 26D removes that complexity entirely.

Learning specialists can simply enter instructions directly within the Redwood learning experience. Learners see those instructions as part of the enrolment details page and complete the activity according to the configured completion rules. Where learner confirmation is enabled, employees can mark the activity as completed themselves. Where administrator verification is required, that option is hidden and completion remains under administrative control.

It’s not necessarily the most exciting feature in the release, but I suspect it will become one of the most useful. The feature is enabled automatically and requires no additional configuration.

This enhancement originated in Oracle Ideas Lab, which is always encouraging to see. Learning specialists can now add self-paced learning directly into a course structure, allowing learners to access online content through courses in the same way they access instructor-led or virtual offerings. From the course details page, administrators can either attach an existing self-paced learning item or create a new one directly within the workflow.

The standout capability, however, is the new Replace with Self-Paced Learning action. I particularly like the inclusion of the replacement capability. Replacing learning content in large catalogues can be surprisingly challenging. Organisations need to modernise content without losing visibility of historical completions, compliance records and in-progress learning.

Oracle has clearly thought beyond simply enabling a new feature and considered the operational reality of managing learning catalogues over time. When a legacy offering is replaced, completed and in-progress learning records are preserved for reporting and audit purposes. Learners already progressing through an offering can still complete it, while future assignments are redirected towards the new self-paced learning. Deprecated offerings become read-only and are no longer available for enrolment, assignment or discovery within the catalogue.

The final enhancement that caught my attention is the continued evolution of My Learning Assistant. We’ve moved well beyond the stage where conversational assistants simply answer questions. Oracle is increasingly positioning learning assistants as task-oriented experiences, helping users complete actions rather than just retrieve information. In 26D, learners can review assignments, explore recommendations, enrol onto learning and access key learning processes from a single conversation. That moves the assistant much closer to becoming a genuine learning companion rather than simply a search tool.

The assistant can now answer questions about active, overdue, mandatory, upcoming, self-enrolled and completed learning. It can recommend content from the catalogue, support enrolment directly where eligibility allows, and launch processes such as Record External Learning and Request Noncatalog Learning. Recommendation cards are also richer, displaying details including learning type, effort, pricing information, AI-generated reasoning and links to catalogue information.

Just as importantly, Oracle continues to ground responses in actual Learning data and workflows. If information doesn’t exist, the assistant won’t invent it. That may sound obvious, but trust is one of the most important factors in successful enterprise AI adoption. Users need confidence that recommendations and answers are based on real information, particularly when they’re making decisions about compliance or professional development.

Customers who deployed My Learning Assistant using the original 25D template should take note. Oracle recommends replacing the existing template with the updated delivered version to avoid inconsistent behaviour. Depending on your deployment approach, guided journeys used to surface the assistant through the Ask Oracle banner may also require updates.

If I had to summarise Oracle Learning 26D in a single phrase, it would be practical AI. There are no headline-grabbing announcements or futuristic concepts here. Instead, Oracle continues to focus on helping managers, learning specialists and learners spend less time navigating systems and more time focusing on development.

What I find particularly interesting is that Learning is becoming one of the strongest showcases for Oracle’s broader AI strategy. Across multiple features, Oracle isn’t attempting to remove people from the process. The technology is being used to surface insights, reduce administration and support decision-making while keeping humans firmly in control. That’s where I believe enterprise AI creates the greatest value. Not by replacing expertise, but by helping people apply it more effectively. And if Oracle continues along this path, Learning could become one of the strongest examples of AI delivering measurable business value within Oracle Fusion Applications.

Please note all screenshots are the property of Oracle and are used in accordance with Oracle’s Copyright Guidelines.

Beyond the Technology: Where Agentic Apps Could Take Oracle Fusion Next

~ K8 Mead ~ Leave a comment

Over the course of this series, I’ve explored how Oracle Fusion AI Agent Studio is changing, why the Builder Assistant matters, the building blocks that sit behind Agentic Apps, and some of the design principles that can help organisations create meaningful solutions. Throughout all of those discussions, one theme has consistently emerged. The technology itself is only part of the story. What’s really interesting is where this might lead.

Whenever a new technology appears, there’s a tendency to focus on what it can do today. That’s understandable. Organisations want to understand the practical use cases, the risks, the costs and the potential benefits before investing time and effort. But sometimes it’s worth stepping back and thinking about the broader direction of travel. When I look at Agentic Apps, I don’t see another reporting tool, dashboard framework or application development platform. I see the beginnings of a different way of interacting with enterprise software.

For most of the history of business applications, software has been built around transactions. An employee updates their details. A manager approves a request. A finance team processes an invoice. A procurement specialist creates a purchase order. Applications have traditionally been designed to help users complete specific tasks as efficiently as possible. There’s nothing wrong with that approach. In many cases, it’s highly effective.

However, organisations today are dealing with increasing levels of complexity. Decisions are rarely based on a single transaction. They often require information from multiple systems, multiple teams and multiple business processes. This is where I think Agentic Apps become particularly interesting. Rather than simply helping users complete a transaction, they have the potential to help users navigate an entire business outcome. That’s a very different objective.

One of the ideas I find most compelling is the shift from process-centred thinking to journey-centred thinking. Take something as common as onboarding a new employee. Traditionally, onboarding spans numerous activities across HR, IT, Facilities, Security, Payroll, Learning and line management. Each team plays an important role, but the overall experience is often fragmented because it spans multiple systems and processes. From the employee’s perspective, however, there is only one journey. The same principle applies to customer experiences, supplier management, project delivery and countless other business activities.

We tend to organise systems around organisational structures. People experience them as journeys. Agentic Apps have the potential to bridge that gap by bringing together information, actions and communications from across multiple processes into a single, outcome-focused experience. Instead of asking users to navigate organisational complexity, the technology could increasingly help manage that complexity on their behalf.

Many of today’s business challenges don’t fit neatly within departmental boundaries. Employee retention isn’t just an HR issue. Supplier performance isn’t just a procurement issue. Project success isn’t just a project management issue. The factors influencing those outcomes often span multiple teams, multiple data sources and multiple business processes. This is where traditional applications can sometimes struggle. Each application does its job well, but no individual application has visibility of the complete picture.

Agentic Apps offer the possibility of bringing those perspectives together. Imagine a manager being able to view workforce data, project commitments, learning progress and performance indicators in a single context when making a resourcing decision. Imagine procurement, finance and supply chain teams working from the same set of insights when managing a critical supplier issue. Imagine employees receiving support that reflects their entire journey rather than the individual processes sitting behind it. The technology is still evolving, but the direction feels increasingly clear.

One challenge I hear repeatedly from customers is that the pace of business continues to accelerate whilst organisational complexity continues to increase. There are more systems. More data. More regulations. More stakeholders. More expectations. The result is that people spend an increasing amount of time gathering information and coordinating activities rather than focusing on high-value decision making.

I don’t believe Agentic Apps will eliminate complexity. Business will always be complex. What they can potentially do is make that complexity easier to navigate. Rather than expecting individuals to manually bring together dozens of signals from across the organisation, an Agentic App can help surface what matters, explain why it matters and support the next action. In many ways, that feels like the natural evolution of enterprise software. For years we’ve focused on capturing information. The next phase may be helping organisations make better use of it.

Of course, technology alone isn’t enough. One thing I’ve learned from years of transformation programmes is that successful adoption rarely depends entirely on the software. Culture matters. Leadership matters. Governance matters. Trust matters.

Organisations exploring Agentic Apps should be thinking about these areas now. How much autonomy should agents have? Which decisions require human approval? How will recommendations be reviewed and governed? How will users understand and trust the outputs being generated? These questions are every bit as important as the technical architecture. In fact, I would argue they’re more important.

The organisations that achieve the greatest success with Agentic Apps are unlikely to be those with the most sophisticated technology. They’ll be the organisations with the clearest understanding of where AI can support people whilst maintaining appropriate governance and accountability.

Whenever discussions turn to AI, there is often concern that technology will replace human expertise. Personally, I don’t think that’s where the greatest opportunity lies. The most valuable decisions organisations make often require context, judgement, empathy and experience. These are qualities that remain fundamentally human.

What Agentic Apps can potentially do is reduce the amount of time spent gathering information, navigating systems and managing administrative tasks. That creates more time for the activities people are uniquely good at. Leading teams. Building relationships. Solving problems. Managing change. Making informed decisions. If organisations approach Agentic Apps with that mindset, the conversation becomes much more constructive. It stops being about replacing people and starts being about helping people be more effective.

Perhaps the thing that excites me most about the future of Agentic Apps isn’t a specific feature or capability. It’s the possibility of making enterprise software feel more human. For years, users have adapted to systems. They’ve learned processes, navigated menus, searched for information and worked around organisational boundaries. Agentic Apps have the potential to reverse that relationship. Instead of people adapting to software, software can increasingly adapt to people. It can understand context. It can bring together information. It can help guide users towards better outcomes. That’s a powerful idea. And whilst we’re still in the early stages of that journey, I think we’re beginning to see what that future could look like.

When I started exploring Oracle Fusion AI Agent Studio, I expected to spend most of my time learning about agents, prompts, workflows and integrations. Those things are important, and they will continue to evolve rapidly over the coming years.

But the more time I spend with these capabilities, the more convinced I become that the most important conversation isn’t about the technology itself. It’s about outcomes. It’s about helping organisations deal with increasing complexity. It’s about helping people make better decisions. And ultimately, it’s about creating experiences that feel less like software and more like support.

Whether you’re just starting to explore Agentic Apps or already experimenting with your first use cases, I think that’s the question worth keeping at the centre of the conversation. Not what the technology can do. But what outcomes it can help your organisation achieve.

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