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.
Deciding where agents belong, what readiness looks like, and how to sequence adoption without over-committing.
A useful responsibility map connects each AI decision and artefact to a named accountable owner, a performing role, required contributors and evidence of completion.
Rank agent opportunities by decision value, workflow evidence, feasibility, consequence and reuse—not by enthusiasm, executive volume or demo polish.
Live agents cut across product, operations, risk and technology. One accountable service owner needs explicit decision rights supported by named control owners.
An agent is not reliable if people avoid it, over-trust it or route around its controls. Adoption evidence belongs beside technical performance evidence.
KEEP EXPLORING
Read the method, assumptions and questions to ask.
A little more clarity
Start with the questions that shape a better decision.
The blog examines where agents create business value, how teams define release evidence, how authority and tool access should be bounded, and how engineering, security and governance owners can make a shared decision.
They are written for product, engineering, AI platform, security, governance and transformation leaders responsible for moving an agent workflow from idea or pilot towards accountable operation.
Articles explain the problems and decision methods behind the consultancy’s service direction. They are educational material and do not replace a scoped review of an organisation’s actual system.