Category: Generative AI

Oracle Collector Workspace: Could AI Finally Transform Collections from a Chasing Process into a Strategic Function?

~ K8 Mead ~ Leave a comment

For many finance teams, collections remains one of the most manual activities in the order-to-cash process. Collectors spend their days switching between screens, reviewing ageing reports, checking customer histories, chasing updates from colleagues and deciding who to contact next. The challenge has never really been a lack of data. Most organisations have plenty of it. The problem is knowing which information matters, which customer needs attention now and what action is most likely to improve the outcome. That is exactly the challenge Oracle is aiming to address with the new Collector Workspace Agentic Application in Oracle Fusion Cloud ERP.

One of the themes running through Oracle’s latest Agentic Applications is a shift from helping users complete tasks to helping organisations achieve outcomes. Rather than presenting information and leaving users to work out what to do next, these applications continuously monitor data, identify priorities, recommend actions and help drive work forward.

Collector Workspace applies this approach to collections management. The objective is straightforward: improve cash collection performance whilst reducing the effort required from collections teams. Oracle states that the application is designed to support more predictable cash collection, reduced DSO, stronger customer engagement and higher collector productivity. What makes this interesting is that the application doesn’t rely on a one-size-fits-all AI model. Instead, it operates within the framework of an organisation’s own collections policies.

Perhaps the most significant aspect of Collector Workspace is the role of the Collections Policy Document. Rather than allowing AI to make arbitrary decisions, organisations define the rules that drive prioritisation and recommended actions. The policy document can include business metrics, prioritisation criteria, allowed actions and guidance for next best actions in different collection scenarios.

For example, a business could define that customers with a high percentage of overdue balances should receive immediate attention, while lower-risk accounts are handled differently. The application uses these rules to prioritise work and recommend actions. This should feel reassuring to finance leaders who are interested in AI but remain concerned about governance and control. The AI is not replacing established collections processes. It is helping teams execute them more consistently.

One of the reasons collections can be inefficient is the amount of context switching involved. Collectors often need to review payment history, disputes, promises to pay, customer communications and account status before deciding how to proceed.

Collector Workspace brings this information together into a single view, providing account snapshots and historical interaction data in one place. This alone could save significant time. But Oracle has gone further by adding conversational AI capabilities that allow collectors to ask natural language questions about customer accounts and transactions directly within the workflow. Instead of navigating through multiple screens to locate information, collectors can simply ask questions and receive answers within the context of their work.

Another area where Collector Workspace stands out is customer engagement. The application can generate contextual emails and AI-assisted call scripts, helping collectors communicate more consistently and efficiently. It can also create Promise-to-Pay requests and support follow-up activities.

This isn’t about replacing human interaction. Collections often requires judgement, negotiation and relationship management. Instead, the technology aims to remove preparation effort so collectors can focus their attention on the conversation itself.

For organisations with large collections teams, consistency can sometimes be difficult to maintain. AI-generated communications may help ensure that messaging remains aligned to company policy and best practice.

One capability I find particularly compelling is incoming email intelligence. Collector Workspace can summarise customer emails, detect intent and convert responses into actionable follow-up items. It can also identify Promise-to-Pay commitments contained within customer communications.

Anyone who has spent time in finance operations knows just how much effort can be consumed interpreting emails, updating records and determining the next step. Automating these administrative activities could have a meaningful impact on productivity.

Oracle’s roadmap suggests this capability will continue to grow, with future plans to identify additional intents such as disputes, invoice copy requests, purchase order updates and contact corrections.

What interests me most about Collector Workspace is what it represents. For years, ERP innovation has focused on making transactions faster and processes more efficient. Agentic Applications feel different. They are designed around outcomes rather than transactions.

In the case of collections, success is not measured by how quickly someone can create an activity record or send an email. Success is measured by improving cash flow, reducing overdue balances and helping collectors focus on the accounts that will have the greatest business impact.

Collector Workspace is one of the clearest examples yet of how Oracle is applying agentic AI to solve a real business problem. If it delivers on that promise, collections teams may spend less time deciding what to do next and more time achieving the outcomes the business actually cares about.

And perhaps that is the real story here. The future of enterprise AI may not be about doing the work for us. It may be about helping us focus our expertise where it delivers the greatest value.

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

Oracle’s Ledger Agent Is Evolving: What You Need to Know About the New Ledger Agentic App

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If you’re already using Oracle’s Ledger Agent, there’s an important change arriving with the Oracle Fusion Cloud ERP 26C release. Oracle has introduced the new Ledger Agentic App, which represents the next stage in the evolution of AI-assisted accounting within Fusion. While many of the capabilities will feel familiar, Oracle’s long-term investment is now focused on the Agentic App, and a retirement timeline has been confirmed for the existing Ledger Agent. The good news is that there’s plenty of time to prepare, and getting started is simpler than you might expect.

The Ledger Agentic App builds on the foundations of Ledger Agent and brings together accounting insights, proactive monitoring and conversational AI in a dedicated Ledger Workspace. For existing users, the experience will feel familiar. You can still ask questions using natural language and receive financial insights without needing to navigate reports or dashboards. What changes is the overall experience. Rather than offering standalone AI interactions, Oracle is creating a single workspace where accountants can investigate issues, review insights and interact with AI in one place.

In the 26C release, Oracle has enhanced the application’s understanding of accounting terminology and business context, helping it deliver more accurate responses and maintain the flow of conversations more effectively when users ask follow-up questions. This is more than a simple name change. The Ledger Agentic App provides the platform Oracle will use to deliver future accounting-focused AI capabilities.

What happens to the existing Ledger Agent? There’s no immediate pressure to switch. Oracle has confirmed that Ledger Agent will continue to be supported during both the 26C and 26D releases. However, it is scheduled for retirement in 27A, which means organisations currently using Ledger Agent should start planning their transition. 

Oracle recommends evaluating the Ledger Agentic App during 26C and completing the move during 26D, ensuring everything is in place well before Ledger Agent reaches end of life. Importantly, any monitoring prompts and generated insights you’ve already configured within Ledger Agent will remain available through the new Ledger Workspace, making the transition considerably easier. 

One of the most important considerations is licensing. For production environments, the Ledger Agentic App requires Oracle’s Agentic Apps SKU, which is licensed separately from your standard Oracle Fusion subscription. The Agentic Apps licence provides access not only to the Ledger Agentic App, but also to a growing portfolio of agentic applications across Oracle Fusion, including areas such as collections, payables close and billing operations. It also includes access to Oracle’s AI App Builder and agent orchestration capabilities, along with an annual allocation of AI units that can be shared across applications. Licensing models and pricing can vary, so it’s worth discussing your options with your Oracle Account Director to understand what this means for your organisation. 

The good news is that Oracle allows customers to explore the Ledger Agentic App in non-production environments without purchasing the Agentic Apps licence. This provides an opportunity to evaluate the experience, understand the potential benefits and prepare for the transition before making any production licensing decisions. 

For organisations already using Ledger Agent, enabling the Agentic App is straightforward. There’s no requirement for additional security roles or data access configuration. Administrators simply need to enable the Ledger Agentic Application feature through the General Ledger functional area’s feature opt-in settings. 

Once enabled, users can access both the existing Ledger Agent and the new Ledger Workspace, providing a smooth transition period while teams become familiar with the new experience. If you haven’t yet adopted Ledger Agent, Oracle’s recommendation is even simpler: skip directly to the Ledger Agentic App. 

The introduction of the Ledger Agentic App offers a glimpse into Oracle’s wider vision for the future of finance operations. Oracle is investing heavily in AI capabilities that help finance teams move beyond basic enquiries and into areas such as exception management, root cause analysis and guided decision-making. Future enhancements are expected to help accountants investigate variances, resolve accounting issues and receive recommendations based on organisational policies and financial context. The aim is not to replace finance professionals. It’s to reduce the time spent searching for information and investigating routine issues, allowing teams to focus on higher-value analysis and decision-making. If your organisation is already benefiting from Ledger Agent, the new Ledger Agentic App is a natural next step. 

With support for Ledger Agent continuing through 26D, there is no urgent deadline. However, the transition period provides an ideal opportunity to explore the new experience, understand the licensing implications and begin preparing for Oracle’s future direction. The Ledger Agentic App is clearly where Oracle’s investment is focused, and organisations that start evaluating it now will be well positioned to take advantage of the new capabilities arriving over the next few releases. 

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

Oracle’s Cash Processing Agent: Could This Be the End of Manual Cash Matching?

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Anyone who has worked in Accounts Receivable or treasury knows that some tasks simply consume more time than they should. Matching receipts to invoices, investigating unidentified payments, tracking cash positions across multiple accounts, and working out how to address funding shortfalls are all activities that can eat into the working day. They are important, but they’re also repetitive, manual, and often frustrating.

That’s why Oracle’s new Cash Processing Agent caught my attention. Rather than simply helping users analyse information faster, it actively performs many of the activities that finance teams traditionally carry out themselves. It creates receipts, matches payments to invoices, identifies exceptions, monitors liquidity, and even recommends actions when it detects potential cash shortfalls. For finance teams, this has the potential to change the way day-to-day cash management is handled.

Applying customer receipts can be one of the most labour-intensive processes within Accounts Receivable. Payment information arrives from different sources. Bank statements show money arriving in the account, remittance advice may be sent separately by email, and someone then needs to connect the two before applying the payment to the correct invoices.

The Cash Processing Agent automates much of that process. It can create receipts directly from bank statement transactions, extract information from remittance emails and attachments, identify the related invoices, and apply receipts automatically where sufficient information is available. When something doesn’t match, the agent doesn’t simply leave users with an unexplained exception. Instead, it surfaces the issue along with the information needed to investigate and resolve it.

What I particularly like is the conversational approach. Rather than navigating between multiple screens and work areas, users can investigate receipts, review supporting information and take corrective action from within the same experience.

For finance teams, the practical benefit is straightforward:

  • Less manual receipt processing
  • Faster application of customer payments
  • Reduced exception backlogs
  • More time spent resolving genuine issues rather than routine matching

The second area where the Cash Processing Agent adds value is cash position and liquidity management. Many treasury teams still spend a significant amount of time monitoring balances, identifying funding gaps and determining how those shortfalls should be addressed. Traditionally, this has been a reactive process. An analyst identifies a problem and then investigates the underlying cause before deciding what action to take.

The Cash Processing Agent takes a more proactive approach. It continuously monitors cash positions and highlights potential shortfalls as they emerge. Instead of simply raising an alert, it provides context by identifying the transactions contributing to the position and highlighting available surplus funds elsewhere. The agent can then recommend transfers that could be used to address the shortfall.

Users remain in control of the decision, but much of the investigation work has already been completed for them. This means treasury teams can spend less time gathering information and more time making informed decisions.

Like most AI capabilities, outcomes will depend heavily on the quality of the information available to the system. If bank statement imports are unreliable, customer payment data is incomplete, or cash management configurations need attention, those issues should be addressed before enabling the agent. The better the underlying data, the more accurately the agent can perform.

Fortunately, Oracle has kept the setup relatively straightforward. Organisations need to configure how bank statement transactions are processed, define which transactions the agent should work with, and establish how remittance information is captured. For liquidity management, threshold balances can be configured to determine when potential shortfalls should be highlighted.

There’s no shortage of AI announcements in the ERP market at the moment, but many still focus on helping users analyse information or generate content. What makes the Cash Processing Agent different is that it performs work that finance teams have traditionally completed themselves.

By automating receipt processing, identifying exceptions, monitoring liquidity and recommending actions, it has the potential to remove a significant amount of repetitive effort from both Accounts Receivable and treasury operations. For users, that means spending less time matching transactions and investigating balances, and more time focusing on the activities that genuinely require human judgement.

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

AI Configurator Is Being Retired. Here’s What You Need to Do Before Release 26D

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If you’ve ever tweaked an Oracle AI prompt for a generative AI feature because the standard response didn’t quite fit your organisation’s needs, there’s an important change coming in Oracle Fusion Cloud.

With release 26C, Oracle has officially deprecated AI Configurator, the tool within HCM Experience Design Studio that allows administrators to override delivered AI prompts. Oracle is replacing it with AI Agent Studio, and whilst the change is optional in 26C, it becomes mandatory from release 26D.

That might sound like plenty of time, but organisations that have customised AI prompts should start planning now. This isn’t an automatic migration, and any existing prompt overrides will need to be reviewed, recreated and tested in the new platform.

AI Configurator was introduced to give administrators greater control over Oracle’s embedded AI experiences. It allowed prompt text to be tailored so that AI-generated responses aligned more closely with organisational terminology, tone of voice or business requirements.

While useful, it was also fairly limited. Administrators could edit the wording of prompts, but they couldn’t introduce new variables, remove existing ones or properly evaluate changes before publishing them. Managing prompt customisations also sat separately from the rest of Oracle’s AI tooling, creating a fragmented experience.

Oracle’s direction of travel has been clear for some time. AI Agent Studio is becoming the central platform for building, configuring and governing AI capabilities across Fusion Applications, so it makes sense that prompt management is moving there too.

For many organisations, this is less about replacing one tool with another and more about adopting Oracle’s strategic platform for AI customisation going forward.

The good news is that AI Agent Studio doesn’t just replicate the functionality of AI Configurator; it significantly expands on it.

Prompt configurations are managed through Large Language Model (LLM) nodes within agent definitions, giving administrators much greater flexibility. Variables can be added or removed, prompts can be evaluated before being published, and configuration sits alongside the wider agent framework rather than in a separate administration tool. This provides a more consistent way of managing Oracle’s AI capabilities and brings prompt configuration into the same governance and security model used for agents.

Beyond prompt management, AI Agent Studio continues to evolve rapidly. Recent releases have introduced visual workflow design, debugging capabilities, policy models, connectors, Builder Assistant functionality and command line support. It is clear that Oracle’s future AI innovation is centred on this platform.

Does Your Organisation Need to Take Action? The answer depends on how you’ve been using Oracle’s AI capabilities. If you’ve been relying solely on Oracle’s delivered AI functionality and haven’t customised any prompts, the transition should be relatively straightforward. You’ll need to enable AI Agent Studio and validate that your existing AI features continue to behave as expected, but there is unlikely to be significant remediation work.

If you have created prompt overrides in AI Configurator, the situation is different. Oracle has confirmed that existing customisations are not automatically transferred into AI Agent Studio. Organisations will need to review their current configurations, recreate them within Agent Studio and carry out appropriate testing before moving into production.

During the transition period, AI Configurator remains available, allowing administrators to reference existing prompt overrides and use them as the basis for their new configurations. However, the responsibility for recreating and validating those customisations rests with each organisation.

If your organisation has customised prompts, I would recommend taking the following approach:

1. Document Existing Prompt Overrides. Before making any changes, review all existing prompt customisations and document them thoroughly. Capture both the prompt text and the business rationale behind each change. Understanding why an override was created is often just as important as understanding what was changed.

2. Identify the Equivalent Configuration in Agent Studio. Each customised prompt will need to be mapped to the relevant LLM node within AI Agent Studio. This is not simply a copy-and-paste exercise, so take time to understand how the functionality is represented in the new framework.

3. Enable AI Agent Studio. AI Agent Studio must be enabled before any migration work can begin. Organisations that haven’t yet activated the capability should factor this into their planning.

4. Rebuild and Review. As you recreate configurations, take the opportunity to challenge whether older customisations are still needed. In many organisations, prompt overrides were introduced to address specific requirements that may have evolved over time. Migration projects often provide a useful opportunity for housekeeping.

5. Test Before You Deploy. One of the advantages of AI Agent Studio is the ability to evaluate prompts before publishing them. Use this capability extensively. Compare outputs against existing behaviour and verify that the results continue to meet business expectations.

In most cases, very little. Oracle has stated that the end-user experience remains consistent, regardless of whether the underlying functionality is delivered through traditional prompt execution or agent-based execution. The transition primarily affects administrators, implementers and those responsible for configuring AI capabilities.

However, organisations that have invested time in prompt customisation should treat testing as a critical part of the transition. Small changes behind the scenes can sometimes produce noticeably different outputs, particularly where prompts have been heavily tailored.

The retirement of AI Configurator is about more than a product being withdrawn. It signals Oracle’s commitment to AI Agent Studio as the single platform for AI customisation across Fusion Applications.

For organisations that have not yet explored Agent Studio, this change provides a compelling reason to start. What is optional in 26C becomes mandatory in 26D, and those who begin preparing early will have far more flexibility than those who wait until the deadline is approaching.

My advice is simple: if you have prompt overrides in AI Configurator, review them now. Understand what needs to be recreated, familiarise yourself with AI Agent Studio and schedule time for testing. A proactive approach today will make the eventual transition considerably smoother.

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

Oracle Fusion Agentic Apps: From System of Record to System of Outcomes

~ K8 Mead ~ Leave a comment

I’ve written about Oracle’s Agentic Applications a few times now, covering the original announcement, the HCM workspaces and, more recently, my hands-on experience with the Agentic App Builder. Every time I think I’ve reached the end of the story, Oracle introduces something new.

This time, rather than looking at individual features, I want to focus on the bigger picture. What is Oracle actually trying to achieve with Agentic Apps, and why should Fusion customers be paying attention? Because what we’re seeing isn’t just another set of AI features. It’s a shift in how enterprise applications are designed to support work.

For years, ERP and HCM systems have been systems of record. They capture transactions, store data and provide the processes organisations need to run their business. Employees perform tasks and the system records the outcome.

Agentic Applications introduce a different approach. Rather than simply waiting for users to initiate work, the application continuously analyses data, identifies priorities, recommends actions and, where appropriate, helps execute tasks within existing security and governance frameworks.

The result is a move from recording activity to driving outcomes. That’s a significant change.

Employees aren’t removed from the process. In fact, judgement, approval and decision-making remain firmly in human hands. What changes is the amount of manual effort required to gather information, identify next steps and coordinate routine activities.

Oracle describes this as moving from systems of record to systems of outcomes, and the more I see of the strategy, the more that description feels accurate.

One thing that often gets lost in conversations about AI is that not all AI capabilities are the same. Oracle’s approach now spans four distinct layers.

At the most familiar level are generative AI features embedded directly within Fusion user experiences, helping users generate content, summaries and recommendations. The next layer introduces Answer Agents, which provide contextual information and guidance within the flow of work. Beyond that are Workflow Agents that can execute multi-step business processes on behalf of users.

Finally, Oracle has introduced Agentic Applications. These are goal-driven workspaces that coordinate multiple agents and business processes to achieve a specific outcome. The 22 workspaces announced across HCM, ERP, SCM and CX sit firmly within this final category.

For customers starting their AI journey, understanding these layers is important. Not every use case requires a fully agentic application, but they do provide a glimpse of where enterprise software is heading.

Alongside the growing catalogue of pre-built applications, Oracle has continued to invest in the tooling behind them. One of the most important additions is contextual memory, allowing agents to retain relevant information across interactions rather than treating every conversation as a completely new request.

Content Intelligence is another significant development. It enables agents to combine transactional data with enterprise content such as policies, procedures and knowledge articles. This creates opportunities for more sophisticated use cases in areas such as compliance, onboarding and employee support.

Oracle has also expanded support for multimodal interactions, enabling agents to work with information beyond text, including images and voice-based inputs.

For organisations looking at AI as part of a broader technology landscape, Oracle’s support for industry standards such as the Model Context Protocol (MCP) is particularly interesting. It opens the door for Fusion agents to work alongside agents and services running on other platforms, helping organisations avoid creating isolated AI ecosystems.

One challenge every AI programme faces is proving value. Organisations understandably want to move beyond demonstrations and understand whether AI is delivering measurable business benefits. Oracle’s new Agent ROI Dashboard aims to address this. The dashboard tracks metrics such as usage, successful completions, time savings and cost savings, giving organisations a way to monitor the impact of individual agents over time.

While the methodology behind any ROI calculation should always be considered carefully, the availability of these metrics provides a practical starting point for conversations with project sponsors and steering committees. Instead of discussing potential benefits in theory, organisations can begin measuring outcomes based on actual usage patterns.

The HCM workspaces have received a lot of attention since their launch, but some of the ERP and SCM use cases are equally compelling.

One example is the Design-to-Source Workspace. Traditionally, moving from product design to sourcing can involve multiple teams, disconnected systems and significant manual effort. The workspace connects these activities, helping organisations move more efficiently from bill of materials creation through to supplier engagement and sourcing decisions. What’s particularly interesting is that the workspace doesn’t simply display information. It continuously monitors progress, identifies risks and highlights areas requiring attention.

Another example is the Collectors Workspace within ERP. Collections teams often spend considerable time gathering information from different sources before they can determine the appropriate next action. The Collectors Workspace brings together customer history, disputes, payment behaviour and other relevant information into a single view, helping teams focus their attention where it will have the greatest impact.

In both examples, the goal isn’t to replace experienced employees. It’s to remove the effort associated with collecting and organising information, allowing people to spend more time making informed decisions.

Oracle has continued to expand its HCM agentic application portfolio, with workspaces covering areas such as hiring, career development, workforce operations, employee support and team management. Despite the variety of use cases, a common theme runs throughout them all.

The applications take responsibility for gathering information, coordinating activities and surfacing recommendations, while people remain responsible for decisions that require judgement, experience and empathy. That balance is important because successful AI adoption isn’t about removing human involvement. It’s about enabling people to focus on higher-value work.

The most interesting aspect of Oracle’s Agentic Applications isn’t any individual workspace or feature. It’s the direction of travel. For years, organisations have invested heavily in putting consistent processes and accurate data into their enterprise systems. Agentic Applications represent Oracle’s next step, using that foundation to actively help organisations achieve business outcomes rather than simply record activity. Not every organisation will adopt these capabilities at the same pace, and governance, security and change management will remain critical considerations.

However, the conversation is already changing. Instead of asking how technology can automate individual tasks, organisations can start asking how it can help deliver broader business objectives. That is a much bigger shift than simply adding another AI feature to an application.

If you’re exploring Oracle’s AI strategy, my recommendation is to start small. Identify a business process where information gathering and routine coordination consume significant time, evaluate one of the pre-built agentic applications, and measure the results. The new Agent ROI Dashboard gives organisations a practical way to begin that journey using real data rather than assumptions.

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

Building Workflow Agents in Oracle AI Agent Studio: From Configuration to Production

~ K8 Mead ~ Leave a comment

It’s one thing to understand Workflow Agents in theory. It’s another to sit down in Oracle AI Agent Studio and start building one. In my previous blog, I explored the architecture behind Workflow Agents and how Oracle has designed them to combine AI reasoning with structured business processes.

This time, I wanted to focus on the practical side. What do you actually see when you start building a Workflow Agent? Which settings matter? How do triggers work? What’s the best way to handle errors? And which data nodes should you use for different scenarios? These are the questions that tend to come up once organisations move beyond the concept stage and start planning real-world implementations.

Creating a new Workflow Agent is straightforward, but there are a few important decisions to make from the outset. The Details tab is where you define the basics, including the agent name, code, application family and product area. While these may seem like simple administrative fields, they’re worth getting right because they determine how the agent is categorised within AI Agent Studio and how it appears within monitoring and reporting.

The LLM tab is where you select the large language model that will power the agent. Workflow Agents use GPT-5 mini by default, which is more than capable of handling many common automation scenarios such as document extraction, classification and policy-based decision making.

One point worth noting is that not all models are licensed in the same way. The OSS LLM is free to use and is provided as part of your Oracle subscription, while all other LLMs are deemed premium and therefore are charged as AI Units are consumed. For more information, please look at my previous blog on AI Agent pricing structures.

The Chat Experience tab allows you to enable file uploads. This becomes particularly useful when your workflow needs to process documents such as supplier quotes, invoices or legal correspondence. Files uploaded at runtime can then be passed directly to a Document Processor node for extraction and analysis.

One of the first questions people ask is how a Workflow Agent actually starts running. Oracle currently supports three trigger types, each designed for different use cases.

Firstly, webhook triggers. These offer the greatest flexibility. When configuring a webhook, you define one or more input variables which become available through a REST endpoint. External systems can then pass information into the workflow when triggering the agent. This approach works particularly well when integrating with Oracle Fusion processes, third-party applications or custom solutions that need to launch an automated workflow in response to a specific business event.

Email triggers are where Workflow Agents start to open up some interesting automation opportunities. After configuring an inbound Microsoft or Google email account within AI Agent Studio, every email received by that mailbox can automatically initiate a workflow. The agent has access to both the email content and any attachments, allowing it to classify, extract and process information without manual intervention.

For example, a payroll team could automatically process court garnishment orders received via email, while an accounts payable team could analyse supplier invoices arriving in a dedicated mailbox. The key benefit is that users don’t need to actively launch the process. The workflow begins as soon as the email arrives.

Schedule triggers are designed for recurring tasks. Whether you need a workflow to run every 30 minutes, every Monday morning, or on a specific schedule throughout the month, Oracle provides flexible scheduling options to support it. This makes them ideal for monitoring activities, batch processing and routine validation tasks that need to run automatically in the background.

If there’s one area that’s often overlooked during development, it’s error handling. Most people focus on getting the workflow working and only think about failures later. Unfortunately, it’s usually the first thing they need when moving into production.

Oracle provides a dedicated Error Handling tab where you can configure automated notifications whenever a workflow encounters an unrecoverable issue. Rather than sending a generic failure message, you can include contextual information such as:

  • The workflow name
  • Trace ID
  • The node that failed
  • The error message
  • Business data being processed at the time

This makes troubleshooting far quicker because support teams receive meaningful information rather than simply being told that something went wrong.

It’s also worth remembering that individual nodes can have their own error-handling paths. Rather than terminating the workflow entirely, you can redirect failures to alternative branches that perform logging, update status values or notify users before ending gracefully. In practice, combining workflow-level notifications with node-level recovery paths creates a much more resilient solution.

One of the more confusing aspects of Workflow Agent development is knowing which data node to use. Although several of the available nodes appear similar at first glance, they serve very different purposes.

The Document Processor is the starting point for document-based workflows. It extracts content from files such as PDFs, Word documents, HTML pages and scanned images, making the information available for downstream processing.

Vector Write creates embeddings and stores them within a vector index. Think of this as the step where knowledge is prepared and stored so that it can be searched later.

Vector Read retrieves relevant content from an existing vector index. This is typically used when a workflow needs to locate policies, procedures or reference material to support a decision.

The RAG Document Tool combines retrieval and grounding into a single step. Instead of manually connecting retrieval and generation components, the node retrieves relevant content and uses it to produce a grounded response from the LLM.

For knowledge-based workflows, the most common pattern is: Document Processor → Vector Write → Vector Read → LLM. Understanding this flow early can save a considerable amount of redesign later.

Perhaps the most important feature within Workflow Agents is the Human Approval node. This allows the workflow to pause and request approval before carrying out a specific action. The workflow only continues once an authorised individual has reviewed and approved the decision.

This approach strikes a balance between automation and governance. Routine tasks can be handled automatically, while higher-risk scenarios remain subject to human oversight. This is particularly important in HR, payroll and finance processes, where an incorrect decision can have a direct impact on employees or suppliers.

In many ways, this ability to blend AI-driven automation with structured controls is what makes Workflow Agents different from many other agent frameworks.

One of the most significant developments to AI Agent Studio is METRO (Measurement, Evaluation and Testing for Real-time Observability). METRO provides visibility into how agents are performing through dashboards covering:

  • Accuracy
  • Latency
  • Token usage
  • Execution tracing
  • Evaluation scoring

For Workflow Agents, detailed tracing is particularly valuable because it allows teams to see exactly how an instance progressed through the workflow, identify bottlenecks and pinpoint failures.

Oracle have also introduced AI governance capabilities, including guardrails on requests and responses, instruction protection and integration with Oracle’s AI Governance framework. For organisations looking to deploy Workflow Agents into production environments, these additions provide greater confidence around monitoring, governance and ongoing optimisation.

Ready to Start Building? The best advice I can give is to start small. Choose a process you understand well. Something with clear inputs, defined outcomes and a manageable number of exceptions.

Build a simple proof of concept first. Get the trigger working. Add a data node. Introduce an LLM step. Test the error handling. Then gradually expand the workflow as your confidence grows.

Oracle has made Workflow Agents surprisingly accessible, and the observability features make it much easier to understand what’s happening behind the scenes as you develop and refine your solution. If you’re considering Workflow Agents and want to understand where they could deliver value in your organisation, now is a good time to start exploring the possibilities.

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

Oracle’s AI-Powered Future for Recruiting: Connecting the Entire Hiring Journey

~ K8 Mead ~ 2 Comments

Recruitment teams are under pressure from every angle. Candidates expect faster responses, personalised interactions and greater transparency throughout the hiring process. At the same time, organisations are increasingly shifting towards skills-based hiring and talent management, often while juggling disconnected systems, inconsistent processes and growing workloads. This is where Oracle’s latest AI-powered recruiting capabilities are aiming to make a real difference. Rather than introducing another standalone AI tool, Oracle is embedding intelligent agents throughout the recruitment lifecycle to help organisations attract, engage, hire and onboard talent more effectively.

Oracle’s AI Agent Framework for Recruiting brings together a collection of specialised AI agents that support different stages of the hiring process. Instead of acting as isolated tools, these agents work together across the recruitment journey, sharing context and information to create a more seamless experience for both candidates and hiring teams. The objective is straightforward: improve candidate experiences, reduce manual effort and help organisations make better hiring decisions more quickly.

One of the most visible innovations is Career Coach, Oracle’s candidate-facing AI assistant. Integrated directly into career sites, Career Coach helps candidates discover relevant opportunities based on their skills, experience and career interests. Rather than searching through dozens of vacancies, candidates receive personalised recommendations and guidance throughout the application process. They can upload CVs, ask questions and complete applications through a conversational experience that feels far more natural than traditional online forms.

Perhaps most interesting is Career Coach’s ability to provide personalised application feedback. Candidates can receive recommendations on how well their application aligns with the role requirements, highlighting skills, qualifications or experiences that may strengthen their submission. For organisations, this has the potential to improve application quality while creating a more supportive and engaging candidate experience.

Maintaining regular communication throughout recruitment remains a challenge for many organisations, particularly when managing high application volumes. Oracle addresses this through AI-powered messaging capabilities that can answer routine candidate questions and provide updates through channels such as email, SMS and WhatsApp. Candidates can receive information about interview stages, scheduling, timelines and next steps without waiting for a recruiter to respond. The result is a more responsive recruitment process while allowing recruiters to focus their attention on higher-value interactions.

Recruiters are not the only beneficiaries of Oracle’s AI investment. Hiring managers are often required to review large volumes of candidate information, interview feedback and assessment results before making decisions. Oracle’s Hiring Assistant helps simplify this process by generating concise candidate summaries and surfacing key information from across the recruitment lifecycle.

Instead of reviewing multiple documents and feedback forms, hiring managers can quickly understand a candidate’s suitability, relevant experience and alignment with role requirements, helping them make more informed decisions while reducing administrative effort.

Interviews remain one of the most important stages of the recruitment process, yet they can be time-consuming to organise and document. Oracle is introducing new capabilities that help streamline interview management. Interview Companion, arriving across upcoming releases, will assist interviewers before, during and after interviews.

Before the interview, it can generate interview guides tailored to the specific role and candidate. During the interview, it can help interviewers track discussion topics and ensure key areas are covered. Afterwards, it can automatically generate transcripts, summaries, interview notes and suggested follow-up actions.

These capabilities have the potential to improve consistency across interviews while reducing the administrative burden placed on hiring managers and interviewers. Oracle is also expanding its partner ecosystem through integrations with organisations such as Eightfold.ai and Phenom, bringing additional capabilities including AI-driven interviewing and skills assessment.

For organisations operating in sectors such as retail, hospitality and frontline services, recruitment often centres around hiring large numbers of employees quickly. Oracle’s Hiring Workspace for Store Managers has been designed specifically for these environments. The workspace provides managers with a simple, action-focused view of recruitment activity, highlighting vacancies requiring attention, identifying potential pipeline issues and recommending next steps.

Managers can quickly identify strong candidates, progress applications and manage recruitment activity without needing extensive experience with a traditional applicant tracking system. For organisations managing high-volume recruitment, this could significantly improve hiring speed and reduce delays in filling critical roles. It should be noted that this is an Agentic App and as such requires the Agentic Application Platform licence.

Finding talent remains one of the most challenging aspects of recruitment. Oracle is investing heavily in AI-powered sourcing capabilities to make this process more efficient. Recruiters can use natural language search to identify candidates based on skills, experience and other criteria, without relying on complex search strings. AI-powered recommendations help identify suitable candidates from existing talent pools, reducing the risk of overlooking qualified individuals who may already exist within the organisation’s database. Combined with automated outreach and personalised engagement, these capabilities aim to help recruiters build stronger pipelines while reducing manual sourcing effort.

The recruitment experience does not end when an offer is accepted. Oracle is continuing to strengthen the transition between recruitment and onboarding by helping organisations maintain communication with new hires from offer acceptance through to their first day. Automated communications, onboarding updates and personalised engagement help reduce the risk of candidate drop-off while creating a more consistent experience. This continuity can be particularly valuable in competitive talent markets, where maintaining engagement after offer acceptance is just as important as attracting candidates in the first place.

Oracle’s vision for recruiting is becoming increasingly clear. Rather than viewing AI as a collection of disconnected features, Oracle is building an ecosystem of specialised agents that work together across the entire hiring lifecycle. Many of these capabilities are already available today, while others will be introduced through upcoming releases such as 26C and 26D. Together, they represent a significant shift towards more intelligent, proactive and personalised recruitment experiences.

For organisations looking to improve candidate engagement, increase recruiter productivity and support skills-based hiring strategies, these innovations offer a glimpse into how recruitment technology is evolving from process automation to genuinely intelligent talent acquisition.

Please note that some capabilities discussed may require additional Oracle licensing, including Oracle Recruiting Booster. Organisations should confirm availability and licensing requirements with Oracle before implementation.

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

Oracle Fusion HR Help Desk: What AI Actually Looks Like in Practice

~ K8 Mead ~ Leave a comment

When Oracle talks about AI in Fusion Applications, it’s easy to assume we’re talking about a chatbot sitting on top of an existing process. In reality, Oracle has been quietly embedding AI throughout the entire HR Help Desk lifecycle, from the moment an employee has a question through to the point a case is resolved and closed.

What’s particularly interesting is that these capabilities aren’t isolated features. They work together to create a service experience that is increasingly proactive, intelligent and efficient for both employees and service teams.

Let’s walk through what that journey looks like in practice. The best help desk case is often the one that never needs to be raised. Oracle’s AI-powered employee assistance capabilities, available through the My Help experience, allow employees to ask questions in natural language and receive immediate guidance based on knowledge articles and HR process information. Instead of navigating FAQs or searching through documentation, employees can simply ask questions in their own words and receive conversational responses. Whether an employee wants to understand a policy, find information about benefits, or work out how to complete a process such as requesting leave, the AI assistant acts as the first point of contact and attempts to resolve the issue through self-service.

This is also the direction Oracle is taking strategically. Organisations still using Oracle Digital Assistant (ODA) skills for HR Help Desk should be aware that Oracle is moving towards the newer Redwood-based AI Agent framework, with legacy ODA capabilities being phased out in favour of the new experience.

Of course, self-service won’t solve every issue. When additional support is required, Oracle’s Help Desk Request Creator AI Agent can automatically create a help desk request using the details already captured during the conversation. Information such as the request description, category and other key details can be populated automatically, avoiding the frustrating experience of employees having to repeat everything they’ve already explained. For employees, that means a smoother support experience. For service teams, it means better-quality requests arriving in the queue with the right information from the outset.

One of the most time-consuming parts of any service desk operation is getting requests to the right team. In many organisations, someone still needs to review incoming requests, determine the nature of the issue and assign it appropriately. Oracle uses machine learning to automate much of this process. When a help desk request is created, whether through self-service, email or another channel, Oracle can analyse the content and predict the most appropriate category before routing it to the correct queue.

Imagine an employee emails HR because their tax code is incorrect on their payslip. Rather than waiting for manual triage, the system can identify the request as payroll-related and route it directly to the relevant team. What makes this particularly valuable is that the model improves over time. Oracle trains the categorisation engine using historical case data, allowing it to learn from previously resolved requests and continually improve its accuracy. Administrators can monitor performance through dedicated insights dashboards, helping them understand how effectively the model is classifying requests and what impact it is having on resolution times.

Once a case reaches a service agent, Oracle introduces another layer of AI support. A common challenge within HR Help Desk is the amount of time agents spend reviewing case history before taking action. Long-running cases often contain multiple updates, conversations and handovers, making it difficult to quickly understand what’s happened so far.

Oracle addresses this through a series of AI-powered summarisation capabilities. Agents can generate instant summaries of requests, review concise overviews of recent activity and use AI-generated handover notes when transferring cases between teams. When the case is resolved, AI can also generate draft resolution notes to help document the outcome.

These may seem like small improvements individually, but collectively they remove a significant amount of administrative effort from the case management process. In 26B, Oracle enhanced these capabilities by rebuilding them as configurable AI Agents managed through AI Agent Studio. While the experience remains familiar to end users, organisations gain much greater flexibility over how these capabilities operate.

Beyond summarisation, Oracle also provides a Resolution AI Agent that can analyse an open case and suggest a potential response. Rather than manually searching for relevant knowledge articles or previous resolutions, agents receive a recommended draft response based on the details of the current case. Importantly, the AI does not make decisions on behalf of the service agent. It provides recommendations, while the agent remains responsible for reviewing and approving the final response.

Of all the AI capabilities currently available within HR Help Desk, Case Analyzer is arguably the most transformative. Complex cases often involve multiple stakeholders, tasks, updates and handovers. Understanding the history of a case can sometimes take longer than resolving the issue itself. Case Analyzer is designed to address that challenge.

When launched, the AI Agent reviews the entire case record, including notes, messages, tasks, action plans and other relevant information. It then presents that information through a conversational interface that allows agents to explore the case in far greater detail. Agents can view a timeline of events, identify important developments, ask specific questions about the case and receive suggested next-best actions. The tool can also highlight potential escalation risks and help agents understand whether additional attention may be required.

What I particularly like about this capability is that it moves beyond simple summarisation. Rather than giving you a static overview, it allows you to have an interactive conversation with the case itself. For newer service agents, this can significantly reduce the time needed to understand a complex issue. For experienced agents, it provides confidence that important details haven’t been overlooked. Oracle has continued to invest in this capability, with 26B introducing configurable starter prompts that help guide users towards the types of questions they can ask.

A controlled availability feature that is also worth watching is the evolution of My Help into a broader employee workspace. Rather than focusing solely on conversational support, the workspace brings together employee tasks, open requests and relevant knowledge content into a single AI-driven experience. While still in the early stages of adoption, it provides a useful glimpse into how Oracle sees the future of employee support. It should be noted that this utilises the Agentic Application Platform which requires a separate licence.

The real value is not any single AI feature. It’s the way they work together. Employees receive assistance before a case is raised. Requests are categorised and routed automatically. Agents receive summaries, recommendations and drafted responses. Complex cases can be analysed through a dedicated AI assistant. Resolution notes can be generated automatically at closure. Taken together, these capabilities represent a shift away from traditional ticket management and towards a more intelligent service model that actively supports both employees and HR service teams throughout the lifecycle of a request.

There is another important point worth mentioning. All of these capabilities are embedded within Oracle Fusion Applications and operate within the existing security and access framework. For organisations handling sensitive HR data, that matters just as much as the AI functionality itself.

If you’re already using Oracle HR Help Desk, it’s worth taking a fresh look at what’s available in your environment. Many of these capabilities have arrived gradually over recent releases, making them easy to overlook. Taken together, however, they represent one of the most comprehensive examples of how Oracle is embedding AI directly into everyday business processes.

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

Choosing the Right LLM for Oracle AI Agent Studio: Balancing Cost, Performance and AI Unit Consumption

~ K8 Mead ~ Leave a comment

When Oracle introduced AI Agent Studio, it gave organisations the ability to build and deploy custom AI agents directly within Oracle Fusion. As customers move from experimentation into production, one question comes up repeatedly: So, which LLM should I use? At first glance, this looks like a technical decision. In reality, it has significant implications for cost, performance and the way your AI agents operate at scale. With Oracle’s new AI Unit pricing model, the LLM you choose can directly affect how quickly you consume your AI allocation, making model selection an important part of both solution design and budget planning.

The Good News is that Oracle Gives You Choice, Oracle AI Agent Studio allows organisations to select the LLM that best fits their requirements. For some use cases, a basic model may provide everything you need. For others, a premium model delivers the additional reasoning and conversational capabilities required for more complex business processes.

The important thing is that Oracle is not forcing customers into a single approach. Instead, organisations can align model selection to the specific needs of each AI agent. This flexibility is particularly valuable because not every business process requires the same level of intelligence.

I get asked a lot, how can I check which LLM my agent is using. If you go to AI Agent Studio, find your agent and click on the edit button and then the cog button, the below options will appear. Click on the LLM tab and review what is selected. In this example, the default was GPT-4.1 Mini which is a premium LLM, so I’ve switched it to the free GPT OSS. Just click update and it is all saved.

One of the biggest misconceptions surrounding AI agents is that every use case requires the most powerful model available. In practice, many agents perform relatively focused tasks:

  • Classifying documents
  • Extracting information from forms
  • Routing requests
  • Generating standard responses
  • Performing workflow actions

For these kinds of activities, a basic LLM may deliver perfectly acceptable results while keeping AI Unit consumption lower. The challenge is that many organisations instinctively select a premium model for every use case without fully understanding the cost implications. As with any cloud service, right-sizing matters.

Another key consideration is the impact on AI Unit consumption. Oracle’s new pricing model is based on AI Units rather than traditional licensing metrics such as users, agents or tokens. Every Fusion customer receives a monthly allocation of AI Units, with additional capacity available if required.

Where this becomes relevant is the relationship between the selected LLM and AI Unit consumption. During Oracle’s recent pricing briefing, examples showed that general actions using a premium model consume AI Units, while actions using a basic LLM can often be performed without additional AI Unit charges. This means the model choice behind an agent can have a direct effect on ongoing operational costs. For organisations planning multiple custom agents, this quickly becomes an architectural consideration rather than simply a design preference.

When deciding whether to use a basic or premium model, I encourage customers to focus on business risk and business value. Ask yourself:

  • What is the impact if the answer is wrong?
  • Does the process involve customer interactions?
  • Is there financial or compliance risk?
  • Does the agent need to reason across multiple pieces of information?
  • Is a human reviewing the output before action is taken?

For low-risk activities, a basic model may be entirely appropriate. For more complex scenarios, such as HR advisory services, financial decision support or customer-facing interactions, the additional capability of a premium model may justify the additional AI Unit consumption. The goal should not be to minimise AI Unit usage at all costs. The goal should be to use the right model for the right task.

One particularly important point that emerged from Oracle’s pricing session is that AI Units consumed in non-production environments also count towards usage when premium models are being used. This means development, testing and experimentation activities can contribute to overall AI consumption.

Many organisations are accustomed to thinking about production workloads when budgeting cloud services, but AI introduces a different dynamic. Agent testing, prompt refinement and user acceptance testing all have the potential to generate usage. As AI adoption grows, development and testing practices will need to evolve to take this into account.

The reality is that most organisations do not yet have mature governance processes for AI consumption. That will need to change. Oracle has already introduced observability and analytics capabilities to help customers understand performance, accuracy and AI Unit consumption. Future releases will also provide additional controls such as budgets and consumption caps. These capabilities will become increasingly important as organisations deploy larger numbers of AI agents across HR, Finance, Procurement, Supply Chain and Customer Experience. Without visibility, it becomes difficult to understand whether the business value being generated justifies the cost being incurred.

When building agents in Oracle AI Agent Studio, my recommendation would be to always start with the business outcome rather than the technology. Not every use case requires a premium model, and not every process should be optimised purely for cost. The most successful organisations will take a balanced approach, matching model capability to business need. Use premium models where reasoning, accuracy and user experience are critical. Use simpler models for straightforward, repetitive tasks where the additional intelligence offers limited value.

Most importantly, monitor consumption from the beginning. AI Units are becoming the new currency of Oracle AI, and understanding how your model choices affect usage will be essential to scaling AI successfully across the enterprise.

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

Turning on AI in Fusion Procurement

~ K8 Mead ~ Leave a comment

While many organisations are still working out where AI can genuinely improve business processes, Oracle has been steadily embedding generative AI into Oracle Fusion Cloud Procurement. Some of these capabilities are already well established. Both the Procurement Policy Advisor and Supplier Portal Advisor have been available for around a year and are being used by many organisations to improve self-service, increase process efficiency and reduce support overhead. More recently, Oracle has introduced agentic applications such as the new Sourcing Command Centre, released in Oracle Fusion Cloud Procurement 26B, which moves beyond answering questions to actively identifying issues and recommending actions.

The result is not simply faster access to information. These capabilities are designed to reduce administrative effort, improve compliance, support better decision-making and help procurement teams focus on higher-value activities. Three innovations illustrate how Oracle is approaching AI in procurement today: Procurement Policy Advisor, Supplier Portal Advisor and Sourcing Command Centre.

One of the biggest frustrations for employees raising requisitions is finding the information they need. Questions about purchasing policies, approved suppliers, spending limits or equipment refresh cycles often result in searches through intranet pages, policy documents or calls to the procurement team. Oracle’s Procurement Policy Advisor addresses this challenge by providing answers directly within the Self Service Procurement experience. Users can ask questions in plain English and receive immediate responses based on their organisation’s procurement policies and supporting documentation. The capability is designed to provide seamless access to procurement policies while employees are ordering the products and services they need.

Whether an employee wants to know when they are eligible for a replacement laptop or which products can be ordered through approved catalogues, the information is available without leaving the requisition process. Importantly, responses are grounded in approved source documents, allowing users to see exactly where the information has come from. This helps build confidence in the accuracy of the advice while supporting consistent policy compliance across the organisation.

For procurement teams, the benefits are equally significant. By reducing repetitive policy queries and making guidance available at the point of need, organisations can improve the user experience while reducing demand on internal support teams. Oracle specifically highlights streamlined access to policy documents and improved compliance as key outcomes of the solution. As one of Oracle’s earlier procurement AI capabilities, Procurement Policy Advisor is already a proven and mature use case, giving organisations a practical way to deliver the benefits of AI without changing core procurement processes.

Suppliers often need support with routine activities such as invoice submission, payment enquiries or updating organisation details. While these requests may seem straightforward, they can consume significant amounts of time for procurement and accounts payable teams.

Supplier Portal Advisor extends Oracle’s AI capabilities to the supplier community by providing a self-service assistant within the Supplier Portal. Oracle describes it as a chat-based experience that answers policy, process and how-to questions directly within the application using an organisation’s own supplier support content. Rather than searching through documentation or contacting support teams, suppliers can receive immediate guidance within the portal. Organisations can tailor the advisor using their own supplier-facing documentation, policies, invoicing instructions and onboarding guidance, ensuring answers reflect their specific ways of working. This creates a more consistent experience for suppliers while reducing the number of routine enquiries that procurement and finance teams need to manage.

The value becomes particularly apparent for organisations with large supplier populations, where even a small reduction in support requests can deliver meaningful efficiency gains. Suppliers are able to resolve questions themselves, while procurement teams can focus their time on supplier relationships, sourcing activities and strategic initiatives rather than handling repetitive enquiries.

Like Procurement Policy Advisor, Supplier Portal Advisor is no longer a new concept. It has been available since 2025 and is already helping organisations extend AI-driven self-service beyond employees and into their wider supplier ecosystem.

While answering questions is valuable, Oracle’s Sourcing Command Centre takes AI a significant step further by helping sourcing teams identify risks, prioritise activities and take action more quickly. Unlike the Policy Advisor and Supplier Portal Advisor, which are now established capabilities, Sourcing Command Centre is a new addition introduced as part of Oracle Fusion Cloud Procurement 26B. Oracle positions it as an AI-powered agentic command centre where specialist sourcing agents continuously analyse negotiations, supplier participation and award readiness to identify risks, opportunities and recommended actions.

Organisations interested in adopting Sourcing Command Centre should also be aware that it sits within Oracle’s broader Agentic Applications strategy. Customers will need to license the Agentic Applications Platform before they can take advantage of the capability, making it important to understand both the business case and licensing implications as part of any evaluation.

Managing multiple sourcing events often requires buyers to monitor negotiations, track supplier participation, review timelines and assess award options across numerous screens and reports. Important issues can be overlooked simply because of the volume of information involved. Sourcing Command Centre brings these activities together into a single workspace. AI-generated summaries highlight negotiations that require attention and prioritise actions based on sourcing data and business rules. Oracle states that the solution surfaces negotiations needing immediate intervention, including events with low supplier participation, negotiations approaching close dates and sourcing activities awaiting award decisions.

Rather than simply identifying issues, the solution enables users to act directly from the workspace. Recommended actions can include extending negotiations, updating schedules, resuming paused events and communicating with suppliers without navigating elsewhere in the application. The Command Centre can also assist with award decisions by analysing supplier responses and recommending award scenarios. This enables sourcing teams to evaluate options more efficiently while maintaining visibility of how recommendations have been generated. Oracle highlights AI-recommended award actions, negotiation-specific analysis and the ability to apply recommended awards directly from the workspace.

For organisations looking to increase procurement productivity and accelerate sourcing cycles, this represents a significant shift. Rather than AI acting solely as an information assistant, it becomes an active participant in managing procurement processes and helping users drive outcomes.

Although these capabilities address different aspects of the procurement lifecycle, they share a common theme. The Procurement Policy Advisor and Supplier Portal Advisor focus on providing accurate, contextual answers based on an organisation’s own policies and documentation. Both have now matured into established capabilities that are delivering value in real-world implementations. Sourcing Command Centre builds on that foundation by helping procurement professionals identify priorities and take action. It represents Oracle’s next step towards agentic applications that can not only answer questions but also recommend and execute tasks within defined business controls.

Together, these innovations demonstrate Oracle’s practical approach to AI in Fusion Applications. Rather than replacing procurement professionals, they are designed to remove friction, surface relevant information at the right time and support more informed decision-making. For organisations using Oracle Fusion Procurement, AI is no longer an experimental technology. It is becoming an increasingly practical part of day-to-day procurement operations, helping employees, suppliers and sourcing teams work more efficiently while maintaining visibility, governance and control.

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