Ecosystem and experience

How clients, partners and professional communities can work together without overstating evidence or authority.

In Ecosystem and experience

10 articles
Evaluation & assurance6 MIN READ

How to design a joint AI evaluation lab

A joint lab should be a governed way to create decision evidence across organisations, with shared methods but explicit ownership of systems, data and conclusions.

Strategy & transformation6 MIN READ

Turning an AI workshop into a decision artefact

Design an AI workshop backwards from a named decision, then capture evidence, assumptions, disagreements, owners and triggers in an artefact that survives the meeting.

Evaluation & assurance6 MIN READ

What makes an AI assurance partnership useful?

A useful assurance partner improves the quality of a specific AI decision while leaving ownership, evidence and operational accountability with the organisation.

Evaluation & assurance6 MIN READ

What good client-controlled AI evaluation looks like

Client control means owning the decision standard, representative cases, evidence access and release authority—not forcing every test to be built or run internally.

Strategy & transformation6 MIN READ

Vendor-neutral does not mean anti-vendor

Neutral advice compares dependencies and evidence against the client's decision; it does not pretend every platform is interchangeable or that vendors add no value.

Governance & privacy6 MIN READ

The responsibility map every AI partnership needs

A useful responsibility map connects each AI decision and artefact to a named accountable owner, a performing role, required contributors and evidence of completion.