Process Intelligence Maturity Index For AI - Board Governance 

Your Board is accountable for AI. Can it see the processes AI runs on?

The TOCH Process Intelligence Maturity Index for AI shows directors where their organisation really stands, and puts in place the practices and structures to govern AI with evidence, not opinion.

Why it matters

AI is now a board matter. Many boards can't yet answer three basic questions.

Automating a process nobody has measured just makes its problems run faster. Before AI is trusted with real decisions, directors need evidence of how work actually flows today.

Where is AI acting in our processes?
Which steps are automated, which use AI, and the knock-on effects upstream and downstream.

Who is accountable when it goes wrong?
A named owner for every AI-touched process, and clear points where a human must step in.

Is it creating value, or just activity?
A clear line of sight from AI to business value. Process intelligence measures the change in cycle time, rework and cost, not a vendor's promise or a pilot's anecdote.

Managing AI at board level

The structures and practices we put in place

Governance sized for your organisation, built on process evidence rather than a heavy framework for its own sake.

Structures

• Board AI sponsor and accountability map: One director accountable for AI, every AI-touched process mapped to an owner, and an AI assistant connected to process intelligence to test strategy against how the business really runs.
• Audit and Risk Committee oversight: AI risk reported alongside financial and operational risk, with process evidence behind every rating.
• AI and Process Steering Group: Operations, finance, IT and risk deciding together where AI goes next, using shared process models.
• Process owners with real data: Owners who see their process as it runs, not as it was drawn, and answer for its performance and its risks.
• Process Intelligence Specialist: Operational and technical support that keeps process intelligence running and the evidence current.

Practices

• Evidence before investment: No AI business case without a measured process baseline behind it.
• AI risk register: Every AI use case logged with its process, owner, failure points and controls, and reviewed as you move up the Index.
• Human-in-the-loop thresholds: Clear rules for when AI decides alone and when a person must review.
• Agent monitoring: Every AI agent's actions mined like any other process, so errors, loops and policy breaches surface before they reach customers.
• Data lineage and audit trail: A traceable path from source data to every AI-assisted decision, ready when regulators, auditors or customers ask.
• Board AI dashboard and quarterly maturity review: Live process data on the board dashboard, and a quarterly check on your stage in the Index.

What the board gets: value

• The cost of doing nothing: Your invisible workforce, the capacity broken processes consume, costed in pounds and people-equivalents.
• Value committed before capital is spent: Every AI investment approved against a KPI, a baseline and a target.
• Value proven in the board pack: Before-and-after evidence on business KPIs, not reports of AI activity.
• Capital allocated on evidence: AI investments ranked by proven return, so the board scales what works and stops what doesn't.

Risk and value are two sides of the same evidence: the process data that shows where AI could go wrong also proves where it pays.