Institute learning · Nine-domain standard
Choose a focused domain or follow the complete programme. Every course translates the Institute's standard into decisions, practice and evidence you can use at work.
Programme pathways
One purchase unlocks every published domain now and adds each remaining course when it is released.
Build practical capability across all 45 criteria, with every domain course included.
Complete the learning pathway, then demonstrate your capability in the Institute assessment.
Purchasing does not award certification. Accreditation is awarded only after you successfully complete the assessment.
Build your own pathway
9 of 9 domains available · Select individually or add several to one cart.
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.
