Oracle AI for Fusion Apps

Want to make AI work for your business? In today’s episode, Lois Houston and Nikita Abraham continue their discussion of AI in Oracle Fusion Applications by focusing on three key AI capabilities: predictive, generative, and agentic.

 

Joining them is Principal Instructor Yunus Mohammed, who explains how predictive, generative, and agentic AI can optimize efficiency, support decision-making, and automate tasks—all without requiring technical expertise.

 

AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/

 

Oracle University Learning Community: https://education.oracle.com/ou-community

 

LinkedIn: https://www.linkedin.com/showcase/oracle-university/

 

X: https://x.com/Oracle_Edu

 

Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.

 

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Episode Transcript:

00:00

Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started!

00:25

Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs.

Lois: Hi there! In our last episode, we explored the essential components of the Oracle AI stack and spoke about Oracle’s suite of AI services. 

Nikita: Yeah, and in today’s episode, we’re going to go down a similar path and take a closer look at the AI functionalities within Oracle Fusion Applications.

00:53

Lois: With us today is Principal Instructor Yunus Mohammed. Hi Yunus! It’s lovely to have you back with us. For anyone who doesn’t already know, what are Oracle Fusion Cloud Applications? 

Yunus: Oracle Fusion Applications are a suite of cloud-based enterprise applications designed to run for your business across finance, HR, supply chain, sales, services and more, all on a unified platform. They are designed to help enterprises operate smarter, faster by embedding AI directly into business process. That means better forecasts in finance, faster hiring decisions in HR, and optimized supply chains, and more personalized customer experience. 

01:42

Nikita: And we know they’ve been built for today's fast-paced, AI-driven business environment. So, what are the different functional pillars within Oracle Fusion Apps?

Yunus: The first one is the ERP, Enterprise Resource Planning, which supports financials, procurements, and project management. It's the backbone of many organizations, or day-to-day operations.

HCM or Human Capital Management, handles workforce-related processes such as hiring, payroll, performance, and talent development, helping HR teams operate more efficiently. SCM, the Supply Chain Management, enables businesses to manage their logistics, inventory, and suppliers and manufacturers in the business. It's particularly critical in industries with complex operations like retail and manufacturing.

The CX, which is the Customer Experience, covers the full customer life cycle, which includes sales, marketing, and service. These models help the businesses connect with their customers more personally and proactively, whether through the targeted campaigns or responsive support. 

03:02

Lois: Yunus, what sets Fusion apart?

Yunus: What sets Fusion apart is how these applications work seamlessly together. They share data natively and continuously improve with AI and automation, giving you not just tools, but intelligence at scale. 

Oracle applications are built to be AI first, with a complete suite of finance, supply chain, manufacturing, HR, sales, service, and marketing, that is tightly coupled with our industry and data intelligence applications. The easiest and the most effective way to start building your organization’s AI muscle is with AI embedded in Fusion applications.

For example, if the customer needs to return a defective product, the service representative simply clicks on Ask Oracle for the answers. Since the AI agent is embedded in the application, it has contextual information about the customer, the order, and any special service, contract, or any other feature that is required for this process. The AI agent automatically figures out the return policy, including the options to send a replacement product immediately or offer a discount for the inconvenience, and also expedite shipping.

Another AI agent sends a personalized email confirming details of the return, and different AI agent creates the replacement order for fulfillment and shipping. Our AI-embedded Fusion Applications can automate an end-to-end business process from service request to return order to fulfillment and shipping and then accounting. 

These are pre-built and tested so that all the worry and hard work is removed from the implementation point of view. They cover the core workflows. Basically, they address tasks that form part of the organization's core workflow. User requires no technical knowledge in the scenarios. 

05:16

Lois: That’s great! So, you don’t need to be an AI expert or a data scientist to get going.

Yunus: The outcomes are super fast in business softwares and context is everything. Just having the right information isn't enough. This is about having the information in the right place at the right time for it to be instantly actionable. They are ready from day one and can be optimized over time. They are powerful out of the box and only get better with day-to-day processes and performance.

05:55

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06:20

Nikita: Welcome back! So, when we talk about the AI capabilities in Fusion apps, I know we have different types. Can you tell us more about them? 

Yunus: Predictive AI is where it all started. These models analyze historical patterns and data to anticipate what might happen next. For example, predicting employee attrition, forecasting demand in supply chain, or flagging potential late payments in finance workflows. These are embedded into business processes to surface insights before action is needed.

Then we have got the generative AI, which takes this a step more further. Instead of just providing insights, it creates content, such as auto-generating job descriptions, summarizing performance reviews, or even crafting draft responses to supplier queries. This saves time and boosts productivity across functions like HR, CX, and procurement.

Last but not the least, we have got the agentic AI, which is the most advanced layer. These agents don't just provide suggestions, they take actions on behalf of the users. Think of an agent that not only recommends actions in a workflow, but also executes them, creating tasks, filling tickets, updating systems, and communicating with stakeholders, all autonomously but under user control. And importantly, many business scenarios today benefit from a blend of these types. For example, an AI assistant in Fusion HCM might predict employees turnover, which is predictive AI, generates tailored retention plans, which is generative, and it is generative AI, and initiate outreach or next steps, which is done by the process of agents, which is called agentic AI. So, Oracle integrates these capabilities in a harmonious way, enabling users to act faster, personalize at scale, and drive better business outcomes. 

08:39

Lois: Ok, let’s get into the specifics. How does Oracle use predictive AI across its Fusion apps, helping businesses anticipate what’s coming and act proactively. 

Yunus: So in HCM, things like recommended jobs, in this, candidates visiting a potential employer’s website encountered an improved online experience, whereby if they have uploaded their resumes, they will be shown job opportunities that match their skills and experience mix. This helps candidates who are unsure what to search by showing them roles and titles they may not have considered.

Time to hire provides an estimated as to how long it will take for an HR team to fill an open role, but this is really useful not only in terms of planning, recruitment, but also in terms of understanding whether you might need some temporary cover and for how long will it actually take the process to complete. In the process of supply chain management, the predictive AI is leveraged to revolutionize transit time and estimated time of arrival, which is called as the predictive analysis, enhancing efficiency, and optimizing operations. It can flag abnormal patterns in supply or inventory. For example, if a batch of parts is behaving differently in the production line and predict future demands, helping avoid overstocking or stockouts is a process that can be done by using the SCM predictive analysis or predictive AI.

In ERPs, where you can audit your expenses, plan for future expenses, and do dynamic discounting for vendors who are likely to accept earlier payments or earlier payment discounts, it can also speed up reimbursements by automated expense entries. In CX, you have the options to go with adaptive intelligence for sales, which helps representatives prioritize the leads and the likelihood that a specific lead will close, helping representatives focus their time and effort. So predictive scheduling and routing in service delivery ensures that the right resource is assigned to the right customer at the right time, boosting operational efficiency and customer satisfaction, also known as fatigue analysis.

11:23

Lois: Now let’s shift our focus to generative AI. How does Oracle implement generative AI across HCM, ERP, Supply Chain, and CX?

Yunus: So, in HCM, the generative AI can automatically generate performance review summaries from raw data, saving time for HR teams, and can help you in providing candidates with summaries of their interview process, feedback, and next steps, all auto generated.

With AI assistance, goal creation for employees can be automated, and the system analyzes performance data and trends to propose meaningful and attainable goals, aligning them with organizational objectives and employee capabilities. In SCM, similarly, the generative AI process helps you in defining drafting summaries of purchase orders. So generative AI can automatically create clear, readable synopses, and can be summarized with complex negotiations and discussions, making it easier for supply chain managers to analyze supplier proposals, track negotiations, processes, and understand key takeaways. With predictive AI embedded, it is helping you to leverage to help generate the repairs of master definitions of summaries, and can generate descriptions for item based on their specification, helping product teams automatically generate catalog contents.

With ERPs, you can automate the creation of business reports, offering more insights and actionable narratives, rather than just showing the raw data. The AI can provide context, interpretations, and recommendations. AI can also take raw project data and generate a comprehensive, easy-to-read project status, reports that stakeholders can quickly review.

In CX, we have got service request summarization, which can provide these long summaries for the customer services and the tickets that have been requested by the customers, allowing support teams to understand the key points in the fraction of time, and can also create knowledge base articles directly from common service requests or inquiries, which not only improves internal knowledge management but also empowers customers by enabling self-service. So generative AI can automatically generate success stories or case studies from successful opportunities or sales, which can be used as marketing content or for internal knowledge sharing.

14:20

Nikita: And what about Oracle's Agentic AI? What are its capabilities across the different pillars?

Yunus: In HCM, Agentic AI handles the end-to-end onboarding experience, from explaining policies to guiding document submissions, even booking orientation sessions, allowing the HR staff to focus on human engagement. It can further support HR teams during performance review cycles by surfacing high potential employees, pulling in performance data, and recommending next actions like promotions or learning paths. It helps manage time with requests by checking eligibility, policy constraints, and suggesting appropriate substitutes, reducing administrative frictions and errors.

With SCM, the Agentic AI Fusion Applications act as a real time Assistant to ensure buyers follow procurement policies, and reducing compliance risk and manual errors. It can also support sales representatives with real-time insights and next best actions during the quoting or ordering process, improving customer satisfaction and sales performance.

With ERP, you can handle document intake, extraction, and routing, saving significant time on manual document management across financial functions using Fusion Applications. AI automates reconciliation tasks by matching transactions, flagging anomalies, and suggesting resolutions. It helps you in reducing close cycle timelines and continuously analyzes profit margins. And it recommends the pricing adjustments that can be done in your ERPs.

In CX, the Agentic AI Fusion Application supports staff by instantly compiling full customer histories, orders, service requests, interactions, and can act like a real-time assistant, summarizing open tickets and resolutions, helping agents take over or escalate without needing to dig through the notes, and dynamically adjust technicals and technician routes based on traffic, priority, or cancelation, increasing the field efficiency and customer satisfaction.

17:04

Lois: Thank you so much, Yunus. To learn more about the topics covered today, visit mylearn.oracle.com and search for the AI for You course.

Nikita: Join us next week as we cover how AI is being applied across sectors like healthcare, finance, and retail, and tackle the big question: how do we keep these technologies aligned with human values? Until then, this is Nikita Abraham…

Lois: And Lois Houston, signing off!

17:30

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