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When a franchise headquarters with 30-200 franchise stores wants to adopt AI chatbots, friendliness analysis, etc. like Burger King, it must present quantitative evidence to the board and franchise owners on 'how much labor costs will actually be reduced and when ROI will occur.' Hiring a consulting firm costs 20-50 million KRW (approx. $15,000-$37,500), which is unaffordable for SME franchises.
Input the franchise industry (F&B/Beauty/Convenience Store), number of franchise stores, current labor cost structure, and the type of AI solution to be adopted, and automatically generate an AI adoption ROI estimate based on industry-specific benchmark data. Provide a board-ready PDF including itemized figures for labor cost savings, customer satisfaction improvement, operational efficiency, and the break-even point.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |