The recent Oracle AI World event did not unveil much that was stunning “new” news for its business applications, because much of what has been evolving was presented already and has been in front of analysts for some months. What was on display, though, was steady progress to its advancing capabilities coming out every quarter. The company has focused on the development of agents and an agentic environment that have the potential to improve enterprise and organizational performance by boosting productivity, increasing visibility and shortening process cycles.
Oracle has two major advantages, as artificial intelligence (AI) in all of its forms substantially extends the functionality and value of business applications. The first is a broad and deep portfolio of business applications where embedding AI maximizes the decades of subject matter expertise and vertical scope. The second is the potential to achieve above-average margins, lower cost of ownership to buyers or both. This is due to Oracle’s intentional vertical integration of a full stack, from infrastructure to specific applications.
To summarize the announcements made at the event, in the current quarter and in the coming year, Oracle will roll out embedded AI agents in Fusion ERP for its general ledger, procure-to-pay, business planning, payments, automated invoice ingestion and three-way invoice matching processes. Agents will support continuous accounting, provide ongoing tracking of an enterprise’s financial condition and performance, support event-driven forecasts and reduce costs by facilitating the use of early payment discounts. Supply chain management is gaining agents for planning, product lifecycle management (PLM), purchasing, manufacturing operations, inventory management, logistics and order management. Agents provided by Oracle are included in the subscription price. There is also an included AI Agent Studio that allows users and third parties to develop agents embedded in Fusion applications, but these will involve additional costs based on resource consumption. Oracle also launched an AI Agent Marketplace and expanded support for multi-large language models.
Oracle’s stated design objectives for its office of finance software are aligned with industry and market trends. It sees AI, generative AI and agents as potentially eliminating low-value,
Beyond the productivity gains supported by this extended functionality, agents can potentially harness predictive and generative AI more effectively and efficiently than end users. This is accomplished by designing in the best choice of algorithms or canned sets of prompts that reliably deliver useful results using the optimal amount of compute resources. Customers train predictive and generative AI systems on their data to increase the speed and accuracy of business planning, streamline role-based searches and handle cash flow forecasting and management and guide process execution faster with less training. Oracle allows customers to use whatever language models they wish for their specific use cases. Customer-trained machine learning (ML) enables deeper, contextually aware anomaly detection, highlighting issues in accounting operations and possible resolutions. For instance, rather than identifying an amount that appears high or low, the system will note an amount that is out of range for the specific customer, transaction, location, time or purchasing unit. This reduces false positives and negatives—a source of frustration—and provides a better context for clarifying interactions.
Oracle has ERP AI agents available today, with many more on its near-term roadmap. In its presentations, the provider highlighted examples of how agents increase staff productivity. While their individual powers may seem trivial, agents represent meaningful incremental improvement when multiplied by millions of process motions. For example, Oracle’s software has a Document IO Agent designed to streamline and automate document-intensive business tasks such as processing invoices and payments. It can ingest, parse and characterize unstructured information in multiple languages, automatically converting incoming sales quotes and invoices into ERP system entries faster, more completely and more reliably than is typical for a human operator. Its Payments Agent helps identify the best use of working capital, accelerating vendor payments that have the biggest discounts and are strategic from a supplier management standpoint, for instance. Ledger and Reconciliation Agents provide accountants with real-time observation and automated reconciliation to support a continuous accounting approach.
Agents also make it easier for financial planning and analysis (FP&A) groups to use predictive forecasting and planning while making it more accessible to operating managers and executives. Individuals can relatively quickly perform ongoing revenue and expense forecasting, contingency and scenario planning and project cash flows, and identify cash flow or receivable issues among many use cases. There are also procurement and sustainability policy advisors that facilitate adherence to enterprise requirements.
Agentic AI holds significant promise in supply chain management (SCM) because it can enable autonomous action, reducing time spent by humans on repetitive tasks (of which there are many in SCM) and compressing task and process cycle times to enhance responsiveness and agility. It’s usually the case that a majority of decision nodes in supply chain tasks and processes follow set rules with limited or no nuance. These lend themselves to using sophisticated robotic process automation. However, the remaining are anything but cut-and-dried, requiring actors to understand multiple layers of context sensed from a set of data and actions that are sourced from scattered islands of information and execution. Those tasks, in turn, are the major source of delays in decision and execution cycles and therefore avoidable costs. Agentic AI adoption is likely to trace the familiar pattern of going from simple to sophisticated in the familiar crawl-to-run change management progression.
Data is a critical ingredient for accurate and useful predictive, generative and agentic AI used in business applications. Oracle’s strengths in this foundational element of the technology should help it be competitive as enterprises assess providers in multiple business application categories.
In the race to innovate, larger, incumbent providers have a potential advantage. This goes against the software industry disruption narrative that asserts that smaller, newer companies are better able to exploit new technology faster than established providers. In the past, the new players were able to reconceive how new technology could accomplish business tasks, unburdened by legacy code and incompatible sales or go-to-market models. In this meme, the old guard are dinosaurs, ready for extinction. That’s not the case with AI, GenAI and agentic AI because the technology amplifies the capabilities of existing software, and new arrivals would have to replicate intellectual property developed over decades to be competitive.
I strongly recommend that finance and accounting organizations have a plan and process in place to accelerate the adoption of AI, including anticipating how established processes and roles will be changed by the technology. Finance executives must concentrate on establishing the change management aspects of AI adoption, especially in communicating how AI will alter work, probably for the better, as much of the tedious, soul-deadening work will be streamlined or replaced by agentic systems. Although some workers may find it difficult to adapt, departments that fully embrace AI are more likely to attract and retain the best talent. I recommend that enterprises looking to replace an ERP, business planning or supply chain management software fully investigate application providers’ roadmap and capabilities to deliver an ongoing stream of AI-driven advances. I recommend that buyers consider Oracle for these three.
Regards,
Robert Kugel