AI creates too many ideas.
That sounds like a good problem until a team starts mistaking idea volume for progress. There is always another tool to test, another prompt to share, another agent to try, another internal demo to show.
The activity feels useful because it is visible. But the business does not benefit from idea volume. It benefits from useful work that changes how something gets done.
A demo is not transformation. A hackathon is not strategy. A prompt library is not business value. A prototype is not a product.
Each can be part of the path. None of them is the path by itself.
The path is slower and less glamorous:
Spark. Problem. Prototype. Workflow. Product. Adoption. Measurement. Retirement of the old way.
Most teams stop before adoption. They build something interesting, show it around, collect polite interest, and move on to the next interesting thing. The prototype graveyard fills up quietly.
The fix is not less experimentation. It is stricter experimentation.
Every AI experiment should have an owner, a use case, a time box, a success metric, and a kill condition. If nobody can say what would change if the experiment worked, the experiment is not ready.
A useful AI experiment should answer at least one plain question:
- Does this save time on a repeated workflow?
- Does this improve quality or consistency?
- Does this reduce confusion for a customer, employee, or decision-maker?
- Does this make a decision easier to trust?
- Does this remove a step, meeting, handoff, or manual task?
- Can the pattern be reused?
If the answer is no, the team may be doing AI theater.
The hard part is not coming up with possibilities. AI will do that all day. The hard part is choosing which possibilities deserve scarce attention, then pushing them far enough that they either become part of the work or get killed.
Shiny object syndrome is what happens when motion becomes cheaper than judgment.
AI makes motion very cheap.
That means the standard for progress has to get stricter.