AI Roadmap & Prioritisation

AI Roadmap & Prioritisation treats the portfolio as a sequence of learning and investment decisions. Opportunities move through Intake, Explore, Validate, Scale and Retire when evidence and policy conditions are met. Prioritisation weighs value, readiness, dependencies, uncertainty and capacity alongside non-negotiable gates.

A transformation roadmap connects strategic choices to delivery capacity, value assumptions and readiness. Use evidence to sequence work and revisit priorities.

Why this matters

AI outcomes are uncertain and conditions change after planning. Evidence gates, work-in-progress limits and explicit dependencies keep funding and commitments responsive to what has been learned.

Questions to bring to the work

• What uncertainty must this initiative resolve before more capacity or authority is committed?
• Which shared dependency or portfolio constraint should change its sequence?

Explore this chapter

Organise the roadmap around learning and investment decisions because problems, model performance and business value evolve through evidence.

Plan how uncertainty will be reduced and decisions improved instead of treating probabilistic AI performance as a guaranteed fixed output.

Make data readiness a continuing roadmap dependency that changes what can responsibly be explored, scaled and sustained.

Combine business, data, technical and governance contributions while retaining explicit outcome accountability and shared execution commitments.

Revisit the value hypothesis as evidence, adoption and context change; route formal valuation and impact measurement to Impact.

Compare value with feasibility, readiness, risk, learning and dependencies while keeping non-negotiable constraints outside weighted trade-offs.

Use Intake, Explore, Validate, Scale and Retire as evidence-governed investment states, with visible policy and work-in-progress limits.

Show a 12–24 month investment story linking strategic bets, enabling capabilities, dependencies and future evidence-based choices.

Use decision conversations and review triggers to update the roadmap when operating evidence or enterprise priorities change.

Manage the balance, capacity, dependencies and continuing relevance of the whole portfolio rather than approving isolated projects indefinitely.

Detect when technically successful or busy AI work drifts from enterprise intent, authority, capacity or accepted autonomy, then reset it.

What useful evidence looks like

• A portfolio board showing state, owner, next evidence need, gate conditions and decision date.
• Capacity, ageing work, dependencies and recorded decisions to continue, re-sequence, stop or retire.

Move from understanding to practice

Framework source

Editorial adaptation from the supplied AIDIVE Version 1 master documents.

• AIDIVE Alignment Dimension Version-1.docx · A.3 AI Roadmap & Prioritisation; AIDIVE Alignment - Practices & Playbook Version-1.docx · AR-A07, AR-A08, AR-A09, AR-A10

Alignment

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into practice.

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Grow the practice.

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Bring AIDIVE into your work.

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