Build governed AI capability across all nine domains.

Domain 1 of the AI for Business Institute practitioner standard, built on the five canonical criteria. Formulate an AI strategy that traces to business objectives; establish a taskforce with real decision rights; prioritise and resource a roadmap that removes as well as adds; publish a policy and approval route staff will actually use; and communicate, scan and review on a defined cadence.

Domain 2 of the AI for Business Institute practitioner standard. Assess a technology stack against named use cases rather than in the abstract; close the readiness gaps that stall first deployments; plan for scale where cost rises with success; evaluate tools on exit cost and adoption rather than demonstrations; and manage vendor dependency so a deprecation notice is a plan rather than a crisis.

Domain 3 of the AI for Business Institute practitioner standard. Find candidates by observing where processes lose time rather than by collecting ideas; prioritise on impact, feasibility, alignment and risk; measure gains against a baseline captured before deployment; build an ROI case that survives a finance review; and run a pipeline that moves pilots into production rather than accumulating them.

Domain 4 of the AI for Business Institute practitioner standard. Choose and adapt a delivery method for work whose outcome cannot be specified in advance; build assurance gates that hold when a date is under pressure; establish shared ownership in objectives rather than in meetings; apply the delivery practices specific to AI, from prompt versioning to pilot exit criteria; and govern AI investment alongside everything else the organisation funds.

Domain 5 of the AI for Business Institute practitioner standard. Build AI competence in leadership through the challenge decisions receive; design literacy programmes that change behaviour rather than record attendance; develop role-based capability against a framework and evidence it in real work; diagnose and retain scarce AI talent; and manage the workforce dimension of AI-driven role change before deployment rather than after.

Domain 6 of the AI for Business Institute practitioner standard. Diagnose and evidence AI's effect on customer experience before acting; distinguish genuine employee experience applications from surveillance; design the controls that protect a customer in AI contact; capture feedback through mechanisms that reach the people the tool failed; and close the loop by telling users what changed.

Domain 7 of the AI for Business Institute practitioner standard. Build a data governance framework that covers the whole AI lifecycle; judge data quality by representativeness rather than accuracy; assess residency and data protection where inference and logging actually occur; defend against prompt injection and treat model outputs as untrusted; and control AI agents that can act with scoped permissions, thresholds and an accessible kill switch.

Domain 8 of the AI for Business Institute practitioner standard. Embed ethics across the AI lifecycle through a stage gate rather than a final review; determine regulatory obligations by classifying each system on intended use; detect bias and understand why removing a protected field does not remove it; give affected people plain-language explanations with a route to human review; and check the licence, IP and provenance of training data before use.

Domain 9 of the AI for Business Institute practitioner standard. Monitor live AI systems for output quality and act on leading indicators; enforce an evaluation and testing discipline built on golden datasets and regression; run an improvement and release process proven by exercised rollback and shipped changes; establish post-deployment ownership and a post-implementation review that tests the business case; and run corrective action that knows when to decommission.