AI Opportunity & Assurance Sprint
Choose the artificial-intelligence work worth doing and know what it will take to deliver responsibly.
In a bounded sprint, DDAI helps leadership identify the strongest opportunities, test feasibility, compare value and risk, and decide whether each use case should be built, bought, integrated, deferred, or stopped.
The problem
Scattered experiments do not create an operating strategy.
Many organisations have overlapping pilots, vendor demonstrations, and staff experimentation, but no shared view of intended purpose, ownership, technical feasibility, data requirements, procurement implications, or evidence needs. The sprint turns that activity into a decision-ready portfolio.
What happens
Discover
Interview the relevant business, public-service, technical, commercial, and assurance owners.
Assess
Review value, feasibility, data readiness, dependencies, human impact, operational risk, procurement, and evidence needs.
Decide
Recommend build, buy, integrate, govern first, defer, or stop.
Plan
Produce a focused ninety-day next-step plan for the selected use cases.
Deliverables
Prioritised use-case inventory
Intended-purpose and accountable-owner record
Business or public-service value assessment
Technical feasibility assessment
Data, system, supplier, and model dependency map
Build, buy, or integrate recommendation
Initial risk and human-oversight requirements
Procurement and supplier-assurance considerations
Evidence requirements for the next review
Ninety-day implementation roadmap
Leadership decision summary
Evidary-ready system and evidence baseline
Decision outputs
Every use case ends in one explicit recommendation:
Proceed to a governed pilot
Procure with defined evidence requirements
Establish governance before implementation
Defer pending data, skills, or ownership
Reject because value or control is insufficient
Best fit
Leadership teams deciding where to invest
Public bodies planning a controlled pilot
Organisations with duplicated or unmanaged experimentation
Suppliers clarifying the assurance burden of a product
Teams preparing an AI strategy with implementation decisions attached
Typical delivery
A bounded organisation or service scope can usually be completed in approximately ten working days. Larger portfolios require a separately scoped programme. The written proposal controls the actual schedule.
Evidary route
Where one prioritised system faces a real buyer, audit, assurance, or approval trigger, the next step may be an AI Evidence Passport Design Partner engagement.
Stop funding demonstrations that cannot become accountable operations.
DDAI can help you connect business value, technical implementation, human oversight, and governance evidence from day one.
Scope an opportunity sprint