# Execution Scorecard: Exhibit-based learning games for small museums

Score: 56/100

Tier: Research first

Exhibit-based learning games for small museums scores 56/100 for execution readiness. The recommended next step is Build quests for three small museums at cost. Track school-group bookings, family dwell time and repeat visits over one term against the same term last year, and test whether two of the three would put a paid refresh into their next grant application.

## Bottlenecks
- The buyer's budget is thin and partly grant-dependent, and federal museum funding is under repeated budget pressure.
- Museums may decide a free tier of GooseChase or Actionbound plus a volunteer is good enough.
- Errors in AI-drafted content about the collection would damage trust, so every quest needs expert review, which limits margins.
- A broad AI assistant can flatten differentiation unless the wedge is painfully specific.
- The first release can become a generic dashboard if the job is not named tightly.
- Needs real buyer access, not only desk research.
- Needs proof of budget or repeated urgency.

## Accelerators
- Can talk to the buyer before writing much code.
- Can ship a narrow first-win demo quickly.
- Can use local-first research artifacts to keep validation moving without a large team.
- Use specificity as the wedge: one buyer, one workflow, one measurable result.
- Show proof earlier than broad competitors with before-and-after examples and small pilot data.
- Keep implementation lighter than incumbent suites or generic AI assistants.
- Concierge review or paid template

## Dated Launch Plan
- **2026-11-12 / Frame the wedge**: Write the one-sentence promise and test it in the strongest channel. Proof: Build quests for three small museums at cost. Track school-group bookings, family dwell time and repeat visits over one term against the same term last year, and test whether two of the three would put a paid refresh into their next grant application.
- **2026-11-15 / Interview 10 people who match the buyer persona.**: Create the lead magnet and use it to recruit interviews. Proof: Problem resonance: 5+ calls or 10+ detailed replies.
- **2026-11-19 / Ship a clickable demo or concierge workflow that produces the first useful artifact.**: Build the smallest demo that proves the first win. Proof: Activation: 25% of demo visitors complete the first-win path.
- **2026-11-26 / Run one paid pilot or collect explicit pricing objections before automating the rest.**: Delete any report section that feels generic before building. Proof: Commercial pull: 3 paid pilots, LOIs, or concrete procurement next steps.
- **2026-12-03 / Promote to a deeper build plan only after the wedge survives validation.**: Run the lead magnet and first-win demo tests. Proof: Fewer than five qualified buyers agree to discuss the workflow after targeted outreach.
- **2026-12-12 / Execution checkpoint 6**: Promote to deeper implementation only once the wedge survives interviews or paid-pilot outreach. Proof: Promote to a deeper build plan only after the wedge survives validation.

## Builder Prompt
Create a dated execution plan for "Exhibit-based learning games for small museums". Keep the first milestone tied to Build quests for three small museums at cost. Track school-group bookings, family dwell time and repeat visits over one term against the same term last year, and test whether two of the three would put a paid refresh into their next grant application.. Use these bottlenecks: The buyer's budget is thin and partly grant-dependent, and federal museum funding is under repeated budget pressure.; Museums may decide a free tier of GooseChase or Actionbound plus a volunteer is good enough.; Errors in AI-drafted content about the collection would damage trust, so every quest needs expert review, which limits margins.; A broad AI assistant can flatten differentiation unless the wedge is painfully specific.; The first release can become a generic dashboard if the job is not named tightly.; Needs real buyer access, not only desk research.; Needs proof of budget or repeated urgency.. Use these accelerators: Can talk to the buyer before writing much code.; Can ship a narrow first-win demo quickly.; Can use local-first research artifacts to keep validation moving without a large team.; Use specificity as the wedge: one buyer, one workflow, one measurable result.; Show proof earlier than broad competitors with before-and-after examples and small pilot data.; Keep implementation lighter than incumbent suites or generic AI assistants.; Concierge review or paid template. Link the output to the Idea Builder prompt and do not expand beyond the first validated workflow.