A sample roadmap, built by graduates using the course framework. Click any item to see the reasoning behind it, why it's there, who decided, and what it was measured against.
This is an illustrative example. Every real roadmap built this way carries the same level of documented reasoning, not just the confirmed items, the "not yet" decisions too.
Why it's here
Before anyone commits to a pilot, someone needs to prove workflow visibility is even achievable. This tests one method, in one team, before it's asked of anyone else.
Added by
AI Enablement Lead, following the initial mandate and budget approval.
What it produced
A working example other teams could see and question, not just a promise that "this will work."
Where it fed forward
Directly shaped how Team A, B, and C approached their own first pilot proposals.
Why it's here
Every proposal below this point depends on this group learning the same evaluation framework, not different versions of it.
Added by
AI Enablement Lead, in parallel with the proof of concept.
What it produced
A shared vocabulary and a shared scoring method, the thing that makes every pilot below comparable to every other one.
Where it fed forward
Feeds every pilot proposal that follows.
Why it's here
Scored well across all three dimensions at once, not just the biggest number. That balance is what earned it the first launch slot.
Added by
Team A lead, scored using the shared framework, signed off by the AI Enablement Lead.
Framework applied
Value, risk, data boundaries, and adoption impact, the same four checks used for every proposal on this roadmap.
What happens next
Runs for three months, then moves into Stage 3 evaluation against efficiency, quality, adoption, and risk.
Why it's here
Lower ROI than some other candidates, but the safest risk profile and the highest adoption impact in the group. Chosen deliberately over a higher-scoring, higher-risk alternative.
Added by
Team C lead, scored using the shared framework, signed off by the AI Enablement Lead.
Framework applied
Value, risk, data boundaries, and adoption impact.
What happens next
Runs for three months, then moves into Stage 3 evaluation.
Why it's here, and why it's later than the others
This had the highest ROI of any proposal, but the initial design had no human checkpoint before AI-assisted output proceeded. It was held, not declined, until a control point was designed in.
Added by
Team B lead, revised proposal signed off by the AI Enablement Lead after two weeks of redesign.
Framework applied
The same evaluation, reapplied after the redesign. The highest score in the group still had to clear the same bar as everyone else.
What this demonstrates
The framework isn't a rubber stamp. The strongest numbers don't automatically win if the risk profile isn't ready.
Why it's here, unscored
This block is placed on the roadmap so stakeholders know when a decision will be made, not because a decision has already been reached.
Added by
AI Enablement Lead, as a placeholder tied to the Stage 3 evaluation checkpoint.
What decides this
Whichever pilots clear Stage 3 evaluation cleanly become the evidence base for what gets scaled, to more teams, more departments, or held for another round.
What this demonstrates
Honesty about what isn't known yet is part of the framework, not a gap in it.
This is what graduates build: not a roadmap that looks confident, a roadmap that can answer questions.
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