Decision Labs
Short, intensive labs where a real decision gets made — and defended — under realistic constraints.
Access to AI tools is no longer scarce. The scarce thing is deciding where AI belongs, how the work should change, and when to scale or stop. Each lab ends in a decision, a rationale and an artifact.
Nine decisions. Nine labs.
AI Adoption Decision Lab — Decide where AI should — and should not — enter the business. AI Pilot Design Lab — Design a pilot that produces evidence, not false confidence. AI Workflow Redesign — Redesign the work — don't bolt AI onto the old process. Build vs Buy vs Integrate — Choose between SaaS, APIs, self-hosted models and custom build — deliberately. AI Governance Decision Lab — Decide how much autonomy, oversight and control is appropriate. AI Risk & Failure Simulation — Rehearse the failure before it happens in production. AI Vendor Decision Lab — Evaluate vendors on what actually matters at scale. Scaling AI After the Pilot — Turn a successful experiment into a supportable capability. When Not to Use AI — The most valuable AI decision is often not to use it.
Explore the Institute
Executive ProgramsMulti-week pathways for the leaders who own AI adoption — from where to start to when to scale.Explore Executive Programs →AI Engineering ProgramsFor engineering organisations where AI writes more of the code — and the constraint moves from writing to deciding.Explore AI Engineering Programs →FrameworksThe reusable decision models behind every ADAAS programme.Explore Frameworks →AssessmentsDiagnostics that tell you where you stand — and where to start.Explore Assessments →About the InstituteThe decision-making mission and method behind every ADAAS Institute programme.About the Institute →
Make a better AI decision — before the bigger bet.
Bring your team to a Decision Lab, or talk to us about running one inside your organisation.