Reality-Tested Roadmaps
A reality-tested roadmap is an AI roadmap treated as a dependency-aware budget for uncertainty rather than a statement of ambition. The core mistake it corrects is treating the roadmap as a statement of intent instead of a statement of sequencing. A roadmap is not a promise. It is a bet with visible failure modes.
What it exposes
AI work fails at the edges: data access is slower than expected, model behavior is less stable than expected, review cycles take longer than expected, and vendor changes arrive earlier than expected. A roadmap that does not account for those edges is a confidence exercise, and most teams discover the missing work only after the launch date is already public. The framework also exposes overconfidence about rollback — if you cannot turn a feature off quickly, you have shipped a liability with a product label — and roadmaps that promise too many parallel AI experiments without respecting WIP. Concurrency feels productive until it multiplies rework.
How to use it
Write the dependency chain down first. Every AI feature depends on data availability, context assembly, model routing, evaluation, deployment, and fallback; if any link is not ready, the feature will not survive real use, and a roadmap that omits the chain is lying by omission. Slower conversations are cheaper than broken launches.
Make rollback a first-class requirement. Every AI initiative should answer four questions: How do we turn this off? How do we know it is hurting us? How fast can we revert? What manual path exists if the model degrades?
Set WIP rules — the more novel the work, the lower the WIP: no more than one high-risk AI launch per squad at a time, no feature ships without evaluation coverage, no vendor migration without a fallback path, and no roadmap item enters “done” until the operational notes exist.
A survivable roadmap is dependency-explicit, rollback-aware, and honest about capacity. You do not need a roadmap that impresses the room; you need one the organization can execute.
Essays
Questions
Why do AI roadmaps fail?
They fail at the edges — data access, model stability, review cycles, and vendor changes all behave worse than the plan assumed. Sequencing around ambition instead of dependencies means the hidden work surfaces only after the launch date is spoken for.
What should every AI initiative answer before launch?
Four rollback questions: how do we turn this off, how do we know it is hurting us, how fast can we revert, and what manual path exists if the model degrades. Fuzzy answers mean the roadmap is overconfident.
How many AI experiments should run in parallel?
The more novel the work, the lower the WIP should be. A strong rule is no more than one high-risk AI launch per squad at a time, because novel work punishes loose concurrency.