AI Transformation Roadmap: Plan Dependencies & Delivery
AI Transformation
An AI roadmap is more useful when it makes dependencies visible than when it presents a confident list of launch dates.
By MAST Consulting Group · Updated · Implementation guidance
A practical approach
Separate business initiatives from shared enablers such as data access, platform capability and governance.
Identify which enablers must be ready before a pilot can produce valid evidence.
Use decision gates to distinguish experimentation from operational release.
Review the portfolio when costs, benefits or dependencies change.
- 01Question
- 02Evidence
- 03Decision
- 04Review
Put the idea to work
Bring the business owner, technical lead and relevant risk specialists into the same discussion. Start with one real use case and document the information available today. Record missing evidence as an action with an owner, rather than allowing uncertainty to disappear in a summary score.
A useful review output distinguishes facts, assumptions and decisions. It also states what would trigger a reassessment: a new purpose, a changed model, a different data source or an incident. This makes the review useful after the initial meeting.
Make your next AI decision count.
Discuss your ambitions, constraints and next practical steps with MAST.
Apply this to your AI programme
Use this guide with the people who own the business outcome, technical delivery and risk decisions. Define the system boundary, record the evidence available and assign actions for the gaps before approving the next stage.
Plan around your operating location
For regional or cross-border programmes, identify the entities, data flows and accountable owners before deciding which governance requirements apply.