Related question
Why do AI projects spiral in scope and cost?
Short answer
Because possibility overtakes practicality. A client shares a challenge, the real issue turns out to be simple and solvable, and then the ideas start. What if we added this? Could it tie into that? Suddenly the original idea is buried under features nobody asked for.
It always starts the same way. A client shares a challenge. We talk it through to isolate the real issue, which is usually something simple, strategic, and solvable. We sketch a possible solution: a tool, a dashboard, maybe some automation.
Then the possibilities unfold. What if we added generative AI? Could this tie into predictive analytics? Why not build a full platform? Before long the original idea is buried under a mountain of features, and the client is quietly wondering whether this is what they wanted or what the vendor wanted.
Anyone who survived the software boom has seen this movie. The lure of possibility overtaking practicality is not new.
Two habits contain it. Project evolution: show a schematic of how the idea grew from the root challenge to the expanded proposal, so the client can see exactly where the scope expanded and make an informed decision about it. Project containment: acknowledge openly that not every feature is necessary today, and if the proposal exceeds budget or bandwidth, scale it back. No ego, no pressure.
Most proposals in this market are delivered take-it-or-leave-it, and that is not how trust gets built. AI work is not about building more. It is about building right, and that starts with the client's priorities rather than the builder's enthusiasm.