Process Mining is Advanced Analytics for AI Control Tower

[Martin Barclay]

AI Control Tower gives you centralized visibility and control over your AI ecosystem—what's deployed, what's in queue, where risk classifications stand. You can align AI initiatives to enterprise strategy, streamline operations, and manage compliance from a single hub.

But visibility into what is happening doesn't always explain why.

Why is mean time to onboarding 46 days when the business expects 14? Why are low-risk AI assets sitting in the same review queue as high-risk ones? When an approval that should take 2 days averages 18, where exactly is work stalling?

These are the questions that keep AI CoE teams, AI stewards, and asset owners stuck between governance rigor and deployment velocity. AI Control Tower surfaces the KPIs. Process Mining diagnoses the root cause behind them.

The diagnostic layer for AICT workflows

Because Process Mining runs on the same platform where AI Control Tower workflows execute, it connects metrics to actual process behavior—no data export, no translation, no lag between insight and action. You're not looking at dashboards in isolation. You're seeing exactly where governance effort exceeds (or falls short of) risk levels, where bottlenecks slow onboarding, and where approval handoffs break down.

More importantly, Process Mining moves you from diagnosis to improvement. Improvement Opportunity detectors surface where your governance and lifecycle processes deviate from design—evidence-based opportunities tied directly to your AICT workflows.

We've put together a brief that walks through how this works in practice across two critical AI Control Tower workflows: AI Asset Onboarding and AI Asset Approval. Each shows how Process Mining turns AICT metrics into root cause evidence—and root cause evidence into faster, governed AI deployment.

Read the brief: Extending AI Control Tower with Process Mining