Why Process Intelligence is vital for success with Agentic AI

The pace of change in AI shows no sign of slowing, with new technologies emerging and seemingly being usurped on a weekly basis. For decision-makers, this can be a daunting challenge. However, the encouraging news is that AI development is largely iterative, each new tool builds on the foundations laid by its predecessors.

This has brought us to the next phase of the AI revolution, Agentic AI. This latest development describes the development and implementation of autonomous software agents, grounded in Generative AI, that can make decisions and take action independently of human input. According to Gartner, by 2028, 33% of enterprise software applications will include AI agents, and 15% of work decisions will be made autonomously. Forward-thinking organisations are already using AI agents to uncover business value and achieve goals such as accelerating software development.

Australian companies Telstra and Commonwealth Bank are ensuring they are keeping up with the pace of change by putting boots on the ground in Silicon Valley, providing employees with direct access to the tech pioneers leading the artificial intelligence revolution.

Telstra will take up residence in Accenture's offices near Google's headquarters in Mountain View, California, as part of a $700 million investment in an AI alliance.

As Telstra's Group Executive for Product and Technology Kim Krogh Andersen said at the time of the launch: "Advances in AI technology are happening at incredible speed. We've seen the conversation shift from generative AI to include agentic AI in the space of months, and this technology will change the game. To extend our network leadership and reinvent customer experiences, we will take our innovation to the next level by combining our domain expertise with the best AI minds, in the heart of technology advancement, Silicon Valley."

While being at the cutting edge of technology in Silicon Valley is one thing, AI agents need a clear understanding of business context. How can leaders ensure that AI agents comprehend how their businesses operate? The answer lies in Process Intelligence (PI). PI takes data from systems such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) software to track how events progress within an organisation. It creates a dynamic, living digital twin of business operations, offering a holistic view of how work gets done. This makes it a foundational tool for implementing AI in ways that actually deliver value.

AI agents are not a one-size-fits-all technology panacea that can solve every business problem right out of the box. For AI agents to succeed, they must be built to solve specific problems and they need insight into how the business really functions. This is where PI plays a critical role. It gathers fragmented data from dozens or hundreds of business processes, offering AI agents a 'common language' to understand events such

as invoicing and shipping, and offering high-quality, timely data which can enable AI agents to make better decisions. With a digital twin of business operations in hand, AI agents can analyse how processes truly impact each other across the whole business and uncover opportunities to drive efficiency.

Businesses are already creating AI agents built to harness the power of PI and seeing tangible results. One customer has worked with Celonis to develop an AI-driven inventory to track parts and materials. Within two months the AI tools had identified that many purchase orders were raised for spare parts that were already in stock as well as highlighting that a significant portion of spare parts were over eight years old. An additional AI Agent uses the inventory to optimise spare part availability for plant engineers, with users able to describe the parts they need using technical descriptions or common industry terms, eliminating the need for exact part numbers.

In another case, PI and Agentic AI helped a company double the speed of software delivery by improving predictability and cutting stage waiting times by 30 to 40%. AI-driven tools pinpointed bottlenecks, offered predictive alerts, and suggested mitigations ranked by potential impact. Leaders could ask simple, natural-language questions to uncover delays and risks, using an AI copilot that translated complex data into clear, actionable insights.

Agentic AI holds the potential to revolutionise enterprise operations, but its effectiveness depends on the quality of data agents have access to. PI 'bridges the gap' to provide AI with the input it needs, offering oversight of the totality of the business's processes. PI is thus a vital tool for optimising enterprise processes. Enterprise customers that try to improve their processes using AI without the vital insights from PI all too often fail. In fact 89% of business leaders globally we surveyed recently said that giving AI the context of how their business runs is crucial if it is going to deliver meaningful results.

That is why we believe there can be no effective enterprise AI without PI. Process intelligence is integrated into live systems, so even when systems change, it offers AI agents real time access to the current state of processes. Think of it like the mapping data for a GPS. Without a map, you're just following a line on a blank screen. You won't know why you were turning left and it would be all too easy to take a wrong turn. Similarly, Process Intelligence gives AI agents the essential context to navigate business complexity reliably.

Agentic AI is set to become increasingly central to enterprise success. But its impact depends on access to timely, accurate, and contextual data. Process Intelligence provides this foundation enabling AI agents to drive meaningful change across business functions, from software development to finance.

The message is clear: Agentic AI needs the right data, and the right context. That's exactly what Process Intelligence delivers.