Assess a technology stack against named use cases rather than in the abstract
AI for Business Institute · D2
Infrastructure & Technology
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.
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Time3.6 hours
Structure5 modules · 21 lessons
What you get
$250, and exactly what that buys.
- 5 modules, 21 lessons
One module per framework criterion, about 3.6 hours of material.
- Applied practice, not just reading
Applied scenarios, Branching decisions, Card sorting and more.
- Work at your own pace
Progress is saved as you go, so you can stop and resume on any device.
- Access that does not expire
Once bought, the course stays in your account. There is no renewal to keep it.
- Counts toward the full programme
Complete all nine domains and the Institute issues your programme completion certificate.
This does not include the accreditation exam. This course builds and evidences capability. Accreditation is a separate assessment, available with the full programme for $2,000, and the credential is awarded only if you pass it.
Why this course
Know what good looks like. Practise the decisions that make it real.
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.
Capabilities
What you'll be able to do
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.
How you will learn
Learn. Practice. Prove.
Learn
Short, evidence-led lessons establish the standard and the reasoning behind it.
Practice
Interactive decisions, sorting and scenarios turn concepts into applied judgement.
Prove
A cumulative final exam checks mastery, followed by a spoken capstone.
Inside this courseApplied scenariosBranching decisionsCard sortingMatching exercisesPriority rankingConfidence checks
Curriculum
A concise view of the complete course.
AI architecture and technology stack fit
What an infrastructure assessment is actually for, why data readiness rather than compute is the binding constraint, how to choose between cloud, on-premise and hybrid deployment, and why data flows and legacy integration belong in scope.
AI asset inventory and sanctioned tooling
Establishing what AI tooling is actually in use, why the gap between belief and reality is a map of unmet demand, and how a tool earns a place on the sanctioned list: threshold criteria, exit cost, testing on your own data, and weighting by the decisions the platform must support.
Environments, access and operational readiness
Why AI development pulls production data into weakly controlled environments, what a readiness assessment must produce, the release-path gap that stalls most first deployments, and how to diagnose a bottleneck before buying capacity.
Scalability, capacity and cost-aware architecture
Why a successful pilot predicts less than it appears to, how generative unit economics behave as volume and context grow, why elasticity beats raw capacity, and the difference between scaling and flexibility.
Model and vendor supply chain
Where switching cost actually accumulates, why concentration turns provider events into enterprise events, how to respond to a deprecation notice, and what distinguishes portability that has been designed from portability that has been proven.
Ready when you are