Can Sparker.ai sell an AI Product Decision Sprint?

The hypothesis

Mid-sized and enterprise organizations will pay for a short, structured engagement that helps them decide whether and how to pursue a specific AI opportunity.

The buyer is not purchasing more ideas. The buyer is purchasing clarity: a recommendation supported by enough product, technical, data, and market evidence to make the next investment responsibly.

Why this matters now

Organizations are under pressure to demonstrate AI progress, but pressure is a poor evaluation method. Teams can spend months on pilots that were never tied to a valuable user problem, a measurable outcome, or a production path.

If this Bet succeeds, the Decision Sprint becomes Sparker.ai’s first proven consulting method. If it fails, the evidence should point toward a more urgent buyer, problem, or outcome.

The experiment

1. Publish a one-page offer with a specific outcome.
2. Contact 10–20 warm connections.
3. Conduct at least five serious customer conversations.
4. Present the offer to qualified prospects.
5. Ask for a paid pilot.
6. Record the buyer’s language, objections, budget signals, and preferred deliverables.

Success criteria

• At least three qualified prospects recognize the problem without extensive explanation.
• At least one prospect requests a proposal.
• At least one prospect agrees to a paid engagement, or provides strong evidence of budget and near-term timing.
• The service can be explained in two sentences.

Failure criteria

• The problem is consistently viewed as low urgency.
• No identifiable buyer owns the decision.
• Prospects want general education rather than a decision.
• Buyers need a materially different outcome.
• The service remains difficult to explain after five conversations.

Current evidence

Assessment recorded September 17, 2026: this is a failed bet.

My experience was that hiring consultants comes through referrals. It also takes a long time for a prospective buyer to recognize and define the problem they need help with. That makes the path from an offer to a buying decision slower than I expected.

AI moves too fast for the cycle I was testing. By the time the problem becomes clear enough to buy help, the available tools and what they can do have moved again.

These are qualitative lessons from this bet, not a claim about every consulting business. I am not claiming conversation counts, proposals, or paid engagements that are not recorded here.

Scheduled close

The original September 30, 2026 decision date stays in place. The outcome is set to Failed, with no extension of the experiment. The ledger will apply that outcome on the scheduled date.

The next bet is Phone Computer.

Decision rule

The original rule was to promote, modify, reprice, replace, or kill the service at the decision date. The selected outcome is now Failed. Keep the record and its lessons; do not extend the deadline to rescue the premise.

Continuing without new evidence is not an available outcome.