Sequencing an AI Roadmap Over a Year
Order AI projects so early ones build the skills, data and trust that later ones need
A taste of a lesson
Everyone wants the customer chatbot first. Should we start there?
Possibly not. A customer facing chatbot depends on things you may not have yet: an up to date knowledge base, a way to test answers, escalation rules and someone to monitor it. Starting there means building all of that under public pressure. An alternative sequence: first, an internal assistant that helps staff answer questions from the same knowledge base. You learn where the content is wrong, at low risk, and build the testing habit. Exercise: list what the chatbot needs that does not exist today.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Group AI initiatives into now, next and later horizons
- Identify enabling work such as policy, data and skills that must come first
- Check a roadmap against the real capacity of the people involved
- Set decision gates and a quarterly review routine
Lesson plan
- 1 From idea list to inventory Capture every AI idea with its owner, value, risk and prerequisites. Start
- 2 Finding the enablers Identify the foundational work several initiatives depend on. Start
- 3 Horizons and sequence Place initiatives into now, next and later based on value, risk and dependencies. Start
- 4 Capacity reality check Match the roadmap to the people and time actually available. Start
- 5 Gates and reviews Add decision points and a quarterly review routine to the roadmap. Start
- 6 Communicating the roadmap Present the roadmap so it informs without creating false promises. Start
Try asking
About this tutor
For leaders, programme managers and team heads who have a list of AI ideas and need to decide what happens first, next and later. A roadmap is not a wish list with dates: good sequencing considers dependencies such as data access, policy and skills, balances quick wins against foundational work, and leaves room to change direction as tools and evidence change. You will learn to group initiatives into horizons, identify enabling work, set review points, and communicate the plan without overpromising. It suits newcomers to planning and experienced managers alike, because we work at your level on your actual list. You finish with a one page roadmap and a quarterly review routine.
Reviews
4.3
3 ratingsSample
- Elif Y.Sample
Good on capacity. We had forgotten that the same three managers were owners of six projects. I would have liked more examples from public sector work.
- Arjun T.Sample
I am new to planning and it kept things simple without being shallow. The now, next, later format went straight into my team meeting.
- Siobhan M.Sample
The enablers lesson reshaped our plan. Policy and data clean up moved to quarter one and nobody argued once the dependencies were drawn out.
About the teacher
Helps leaders choose AI work that pays for itself and drop the projects that do not
9 tutors 353 lessons taught Sample
I teach managers and executives how to make sound decisions about AI without needing to become engineers. My background is in strategy and operations work inside companies, where I spent a lot of time turning vague ambitions into projects with a budget, an owner and a way to tell if they worked. I teach with real decisions: a use case...
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