--- title: Should a City Fund a Grocery-Access Study? AI Analysis | 3Dogs Nexus description: Before spending incentive dollars on a grocery store, a 12-model AI panel recommended testing operator appetite first - and named that as the dominant risk. url: https://3dogs.ai/case-studies/grocery-access-feasibility/ --- - Should a City Fund a Grocery-Access Study? AI Analysis | 3Dogs Nexus Start a Decision Case → 🐾3Dogs NexusStructured Decision Intelligence ← Case studies YouTube Start a Decision Case Case study · economic-development decision support # Before a city spends incentive dollars on a grocery store. By Alan Finney — Founder, 3Dogs Nexus A mid-size city wants a full-service grocer in an underserved district. The reflex move is commissioning a $50,000–$75,000 market feasibility study before committing incentives. This demonstration run put that exact decision through 3Dogs Nexus — and the panel pushed back on the reflex, recommending a cheaper partner-first sequence that tests real operator appetite before any big study is funded. Should a city fund a grocery-access feasibility study before offering incentives? The panel recommended a partner-first, two-phase approach: test whether any operator actually wants the site before committing incentive dollars to a study, because operator appetite — not demand modelling — was the dominant risk. Economic-development money is spent under public scrutiny, so the reasoning is the deliverable. 156 API calls · 12 AI models11-analyst panel: 11–0 proceed-with-conditions2m 47s end to endModerate confidence · dissent-preserving ## The call, verbatim “Partner first—test operator interest before funding any study, then co-invest in a targeted feasibility analysis with a committed bidder.” The panel didn't rubber-stamp the feasibility study — it reordered the sequence so the city spends the big money only after real grocery operators show real interest. ## The two-phase plan the panel specified ### Phase 1 — city-funded baseline ($15K–$25K) A trade-area viability assessment: demographics, income, competitor leakage, and minimum demand thresholds. Cheap, fast, and it either kills the idea early or arms the city for real operator conversations. ### Phase 2 — joint study with committed operators A feasibility study co-invested with shortlisted operators, scoped to their parameters — lease costs, capex, ROI hurdles — with third-party validation of operator projections. The dominant risk it named: operator-appetite uncertainty. If fewer than three operators commit in writing, a $50K–$75K standalone study becomes a sunk cost with no path to an actual grocer — leaving the city to either abandon the project or overpay subsidies to attract a single bidder. ## What the evidence showed The panel grounded the recommendation in comparable mid-size cities — including Edenton and Waco — that built successful downtown grocery projects by validating demand and operator interest in stages rather than commissioning a monolithic study first. The district's demographics support a store; the open question was never demand on paper, it was a committed operator. ## How does AI market-feasibility analysis help an economic-development office? The same way it helps any decision-maker facing an expensive default: it stress-tests the sequence, not just the answer. Here the adversarial panel's value wasn't “yes or no on the grocery store” — it was restructuring when each dollar gets committed, with named conditions and a documented rationale an economic-development office can put in front of a city council. ## The delivered report The complete recommendation as delivered — the call, the conditions, the panel vote, the risk analysis. Download the full PDF report → "We don't make your decisions. We make them better." 3Dogs Nexus · Structured Decision Intelligence · 3dogs.ai · Alan Finney · alan@3dogs.ai · (702) 845-2886 Internal demonstration run by 3Dogs staff on the production engine, 2026-07-22 (case 2026-9404, mode: direct / no-follow-up). Not a client engagement; no agency or city supplied data. All verdicts, vote tallies, conditions, run statistics, and quotes are drawn directly from the delivered report and run logs. The full PDF is embedded above, unedited. Terms · Privacy · Security ### Try this on your own question. Free, no card. Bring a real decision — ideally one where you already know the answer — and see what the panel does with it. Start a decision case ## Questions this case answers ### What was the dominant risk? Operator appetite. A feasibility study can establish demand and still leave you with no one willing to run the store. ### Does this apply to other economic-development decisions? The pattern does: identify the risk that actually kills the project, and test that first rather than the risk that is easiest to study. ### Try this on your own question. Free, no card. Bring a real decision — ideally one where you already know the answer — and see what the panel does with it. Start a decision case ## Related decision case studies - Would an AI Panel Have Caught Lehman's Repo 105?We ran the Lehman Brothers investment decision through a multi-cloud AI panel twice, with no hindsight in the prompt. It named Repo 105 - AI Forecasting With Probability RangesAsk one AI to forecast and you get a single confident number. 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