Process intelligence is the use of data and AI to show how business processes actually run, why they deviate and what to change. It combines process mining of system logs, task mining of user activity, process modelling and monitoring into one continuous view of operations.

For years the market called this process mining. In 2026 the name changed, and the change matters: enterprises now want more than a map of system events. They want a complete, trusted picture of how work gets done, one that can guide improvement and feed AI agents. This guide explains what process intelligence is, how it differs from process mining and task mining, what each approach misses, and where the human steps between systems fit in.

What is process intelligence?

Process intelligence brings together several ways of understanding a process:

• Process mining reconstructs processes from event logs in systems such as SAP, Oracle and Salesforce.

• Task mining records what users do on their desktops: clicks, keystrokes and application switches.

• Process modelling documents the intended process, often in BPMN, in a governed repository.

• Monitoring and analysis compare how processes really run against targets, flag deviations and suggest actions.

The goal is not a better diagram. It is a reliable answer to three questions: how does this process really run, where does it break, and what should we change first?

Process mining vs task mining vs process intelligence

For a closer look at the first two, see task mining vs process mining vs process documentation.

Why Gartner renamed process mining to process intelligence

Until 2025, Gartner assessed this market in its Magic Quadrant for Process Mining Platforms. On 5 May 2026 it published the first Magic Quadrant for Process Intelligence Platforms instead. According to SAP's announcement, the expanded category assesses platforms that unify process mining, task mining, modelling, analysis, optimisation, monitoring, automation discovery and governed process repositories.

Three shifts explain the change:

1. Vendors had already broadened. Leading process mining platforms added modelling, task mining, simulation and automation, so "mining" described only one part of what they sold.

2. Insight had to lead to action. Buyers wanted platforms that recommend and trigger changes, not just visualise problems.

3. AI agents need process context. As enterprises deploy agents, they need a trusted source of how processes should run. Process intelligence is increasingly positioned as that layer.

For buyers, the practical takeaway is simple: "process intelligence" and "process mining" now often describe the same product category, but process intelligence implies a wider scope.

What process mining and task mining miss

Both techniques are powerful, and both have blind spots:

That gap matters most in the places enterprises care about: handoffs between systems, exception handling, and processes that run partly in legacy tools or spreadsheets. An invoice may show as "waiting for approval" for five days in the ERP log, while the real work happens in an email chain and a spreadsheet the log never sees.

Where Trupeer fits

Process mining sees system logs. Trupeer captures the human steps between systems. An expert records the task as they do it, across every application they touch, and explains their decisions as they go. Trupeer turns that recording into a process map, a step-by-step SOP and a training video.

The two work well together. Process mining shows where a process is slow or inconsistent at scale; Trupeer captures exactly how people do that part of the process, including the exceptions, so it can be standardised, trained and, where it makes sense, handed to an AI agent.

Process discovery tools

Process discovery is the work of finding out how processes really run. These are the main types of process discovery tools:

Most enterprises combine at least two: mining for scale, and capture or interviews for the human detail. Compare mapping options in our guide to process mapping tools.

How process intelligence feeds AI agents

AI agents need to know how a process should run: the steps, rules, exceptions and the point where a person takes over. Process intelligence supplies the evidence for that:

1. Find the candidates. Process mining shows which high-volume, rule-heavy processes are worth handing to an agent.

2. Capture the real procedure. Process capture records how experts actually handle the work, including the judgement calls the logs don't show.

3. Define the agent's limits. The procedure sets the rules, guardrails and handoffs the agent must follow.

4. Monitor the result. Process intelligence tracks whether the agent-run process performs better, and where it hands work back to people.

Without the second step, agents learn only from system data and miss the exceptions experienced people handle every day. See how to make your processes agent-ready and what goes into an agentic operating procedure.

How to get started with process intelligence

1. Pick one end-to-end process with clear business value, such as procure to pay or order to cash.

2. Use system data to find the problem. Where are the delays, rework and variants?

3. Capture the human steps in the problem area by recording experts doing the work.

4. Agree the standard process and document it as an SOP and process map.

5. Decide what to change: standardise, retrain, automate or deploy an agent, then measure the result.

Related reading

• Task Mining vs Process Mining vs Process Documentation

• Enterprise AI Agents: Make Your Processes Agent-Ready

• 12 Best Process Mapping Tools & Software in 2026 (Free + Paid)

• Enterprise Workflow Management Software