# Execution Scorecard: Near-miss detection AI for existing warehouse CCTV

Score: 44/100

Tier: Research first

Near-miss detection AI for existing warehouse CCTV scores 44/100 for execution readiness. The recommended next step is Process two weeks of archived footage from three mid-market warehouses and present the near-miss reel to their safety managers; measure willingness to pay against their current incident-rate costs.

## Bottlenecks
- Well-funded incumbents (Voxel, Intenseye) are already selling video-AI safety to enterprise sites.
- Union and privacy concerns around worker surveillance can stall deployments.
- 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.
- Needs a crisp wedge before broad product work starts.

## 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
- **2026-08-14 / Frame the wedge**: Write the one-sentence promise and test it in the strongest channel. Proof: Process two weeks of archived footage from three mid-market warehouses and present the near-miss reel to their safety managers; measure willingness to pay against their current incident-rate costs.
- **2026-08-17 / 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.
- **2026-08-21 / 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.
- **2026-08-28 / 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.
- **2026-09-04 / 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.
- **2026-09-13 / 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 "Near-miss detection AI for existing warehouse CCTV". Keep the first milestone tied to Process two weeks of archived footage from three mid-market warehouses and present the near-miss reel to their safety managers; measure willingness to pay against their current incident-rate costs.. Use these bottlenecks: Well-funded incumbents (Voxel, Intenseye) are already selling video-AI safety to enterprise sites.; Union and privacy concerns around worker surveillance can stall deployments.; 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.; Needs a crisp wedge before broad product work starts.. 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.