Process intelligence: The foundation of agentic AI success

The vast potential of agentic AI – which outstrips even that of generative AI – is quickly driving businesses to explore how they can implement the technology to enhance and streamline operations. While current AI models can perform tasks such as generating text, they must be “prompted” to do so. That is going to change with agentic AI. By 2028, 33 percent of enterprise software applications will include agentic AI, up from less than 1 percent in 2024, Gartner predicts. As a result, 15 percent of day-to-day work decisions will be made autonomously, it adds.

However, while the opportunities surrounding this emerging technology are huge, a crucial factor in driving successful agentic AI value lies in process intelligence.

What is process intelligence?

Process intelligence refers to the actionable insights generated by detailed analyses of process data. It is an increasingly vital component of modern process excellence. Process intelligence leverages process mining, task mining, process simulation and AI to autonomously highlight bottlenecks and sources of inefficiencies and recommend changes and interventions to optimize end-to-end processes.

Ultimately, process intelligence provides a data-driven foundation enabling business teams across the organization to detect value creation opportunities and to provide consistent return on investment (ROI) from process improvement initiatives.

In the PEX Report 2025 – PEX Network’s flagship industry research report – more than a quarter (27 percent) of surveyed business leaders said they use process intelligence as a key component of their business analytics strategy. Meanwhile, 12 percent of organizations implement it to support operational excellence (OPEX) and business transformation, with 16 percent planning to increase investment in process intelligence in the near future.

Why is process intelligence so important for agentic AI?

When process intelligence is combined with agentic AI, the notion of self-improving and adaptive processes edges closer to reality. The insights from process intelligence not only inform and improve the development and use of agentic AI but also support its continuous monitoring and optimization of end-to-end processes.

Without a strong foundation of process intelligence, agentic AI investments can not only fail but also risk causing more harm than good. For example, deploying agentic AI to automate a process bottleneck may simply shift the constraint downstream, resulting in no net improvement. Worse, if the AI introduces defects that go undetected, it could trigger rework loops, compounding inefficiencies and exacerbating downstream bottlenecks.

Agentic AI relies on large amounts of data. Its ability to access, process and leverage vast datasets is what makes it so effective. Successful enterprise adoption of agentic AI is dependent on comprehensive data regarding processes, workflows and systems specific to an organization.

Inaccurate or incomplete data can result in AI models making incorrect decisions, leading to flawed process automation and potential operational inefficiencies. This is where process intelligence is invaluable. By collecting and analyzing precise process data, process intelligence paints a comprehensive picture of an organization’s operations that acts as a thorough and reliable blueprint for improvement and innovation.

How process intelligence enhances agentic AI

There are multiple ways in which process intelligence can make agentic AI more powerful, with many varying from one organization to another. However, some examples include:

• Automatically discovering manual processes and creating virtual models of the steps involved. For instance, process intelligence can streamline accounts payable processes, highlighting redundant or overlapping work. This information helps agentic AI understand the context of a step or activity in a process and make smarter, more informed decisions.
• Monitoring agentic AI activities that use traditional robotic process automation (RPA). Process intelligence can record and assess the performance of agentic AI systems and pinpoint workflow frictions for action and adjustments.
• Enabling agentic AI to assess outputs and predict future impacts (such as bottlenecks created by overproduction that can’t be handled downstream). This feedback loop allows agentic AI to make adjustments in real-time and maintain smooth operations.

7 steps to process intelligence

There are several key factors for implementing and maintaining effective process intelligence. Here are seven steps to success:

1. Clear business objectives: Results depend on how well objectives are understood across the organization.
2. Sponsorship and support: Process intelligence needs sponsorship from senior leaders in an organization so that, when data highlights changes that need to be made, appropriate personnel make the decision to achieve better business outcomes.
3. Change management: Change management takes on great significance when embarking on process intelligence. To manage resistance, mitigate risks and facilitate organizational change, implement effective communication, stakeholder engagement and employee empowerment.
4. Data and systems management: Process intelligence must be able to access clean, appropriate data as well as systems.
5. Continuous improvement: Process intelligence is not a one-time project; it is an ongoing initiative that requires continuous monitoring and improvement. Adopt an iterative approach that allows for continuous refinement and optimization based on feedback and evolving business needs.
6. Business alignment: A collaborative, business-aligned approach is key to process intelligence success. Implement a strong, group-wide strategy execution framework to focus on strategic execution and process improvement.
7. Digital accelerators: Digital accelerators play a pivotal role in supporting process intelligence efforts too, ensuring rigorous control over process execution and working together to automate routine tasks, uncover inefficiencies and provide insights.

Conclusion

Agentic AI is making all the headlines – and that’s not surprising. Its potential to transform businesses is startling and adoption of the technology will only increase as end-to-end automation surges up the enterprise agenda. However, as agentic AI evolves and spreads into ever more business operations, it is vital that process intelligence remains a central element of its application, serving as both a foundational structure to build upon and a source of insights for ongoing maintenance and optimization of enterprise-wide agentic AI deployments.