Executive AI Roadmap
Convert everything learned into an executable enterprise AI transformation roadmap.
A roadmap is where the earlier modules become a plan someone can execute. It starts with readiness, which means something only against a specific ambition: assess the capabilities that ambition needs, with evidence, and find the weakest link before scaling. Maturity is the ability to run and repeat governed, measured AI value, and the target is the level the strategy requires, not the highest one available.
Prioritizing the portfolio is capital allocation rather than ranking ideas: choose the combination the organization's people can finish, stage the money behind evidence and stop what fails. Quick wins, strategic bets and transformation initiatives buy different things, so each needs its own funding, gates and measures, and the mix is chosen on purpose. A pilot proves possibility under borrowed conditions; production proves the organization can run the system; scale proves the result repeats elsewhere at a sensible cost. The first quarter is a contract with a few owned outcomes and a production decision at its end, while later years are a direction held up by named assumptions. Transformation governance settles trade-offs between functions by a date, and a sponsor brings the mandate, money and time to do it. The work is done only when the work itself has changed and results have moved.
AI Economics tested whether each capability can pay for itself. This module sequences everything into one management loop in which each part hands the next something usable and evidence from execution changes the plan. Future of AI then asks how to keep that loop working as the technology moves.
Questions this module answers
- Is the organization ready for its chosen AI ambition, and where is the weakest link?
- How should the AI portfolio be prioritized, funded and balanced?
- What evidence should move an initiative from pilot to production to scale?
- What belongs in the first quarter's plan, and what in the multi-year roadmap?
- How do sponsorship, change management and operating rhythm turn the plan into changed work?
The chapters
Readiness
Assesses readiness against a specific ambition and sets the maturity level the strategy requires.
- 001 Assessing Enterprise AI Readiness Readiness only means something against a specific ambition. Assess the capabilities it needs, with evidence, and find the weakest link before you scale. 12 min read
- 002 The AI Maturity Model AI maturity is the ability to run and repeat governed, measured AI value. Climb five levels to the one your strategy requires, assessed with evidence. 12 min read
Portfolio and prioritization
Allocates capital across the portfolio, gives each investment type its own gates and sets the evidence for pilot, production and scale.
- 001 Prioritizing the AI Portfolio Prioritizing AI is capital allocation, not ranking ideas. Choose the combination your people can finish, stage the money behind evidence, and stop what fails. 13 min read
- 002 Quick Wins, Strategic Bets and Transformation Initiatives Quick wins buy value now, strategic bets buy options, transformation buys structural change. Give each its own money, gates and measures, and decide the mix on purpose. 12 min read
- 003 From AI Pilot to Production to Scale Four proofs, four gates. A pilot proves it is feasible under borrowed conditions; validation proves it is valuable; production proves it is operable; scale proves it is repeatable elsewhere at a sensible cost. Demand evidence for each. 12 min read
The roadmap
Builds the first-quarter plan and the multi-year roadmap and sets up the governance and sponsorship that carry them.
- 001 Building the 90-Day AI Plan A 90-day AI plan is a contract for one quarter. A few owned outcomes, long-lead work started first, a written not-yet list, and a production go-or-no-go on Day 90. 12 min read
- 002 Building the Multi-Year AI Roadmap A multi-year AI roadmap sequences decisions under falling certainty. Only the quarter is a contract; later years are a direction held up by named assumptions. 12 min read
- 003 AI Transformation Governance and Executive Sponsorship Transformation governance settles trade-offs between functions, on evidence and by a date. A sponsor brings the mandate, money and time to do it; a senior name is not enough. 11 min read
Execution
Drives change, measurement and operating rhythm, and joins every part into one loop from strategy to execution.
- 001 AI Change Management, KPIs and Operating Rhythm AI transformation is done when the work has changed and results have moved. Change makes it possible, a short scorecard shows it, and a fixed rhythm turns evidence into decisions. 12 min read
- 002 Module 09 Synthesis — From Strategy to Execution An AI roadmap creates value only as one loop. Each part hands the next something usable, and evidence from execution changes the plan. 9 min read
After this module you can
- Assess readiness against your ambition with evidence and name the weakest link.
- Prioritize an AI portfolio as capital allocation, with staged funding and stop rules.
- Set the evidence an initiative must show to move from pilot to production to scale.
- Write a first-quarter AI plan with owned outcomes, a not-yet list and a production decision at its end.
- Run a review rhythm in which evidence from execution changes the plan.