Prioritizing AI Use Cases: Classify by Type First, Measure Data Distance Next; Model Cost Barely Affects Priority
Johnny Mai, Director of Generative AI Product Strategy at Qwen, states that while 89% of organizations use AI, only 37% report an impact on EBIT. The gap lies not in the model but in backlog prioritization. He proposes a six-type classification, an Impact × Ease 2x2 matrix, and a six-factor weighted scoring system. The core insight is that 'data distance' predicts workload better than model selection—a project to extract invoices costing only $27 per month in tokens may still take two quarters to launch.
89% of organizations use AI in at least one function, yet only 37% report an impact on enterprise EBIT. Johnny Mai, Director of Generative AI Product Strategy and Go-To-Market at Qwen, attributes this gap not to model capability but to backlog prioritization—proposals often rank based on who presented last rather than value. His proposed solution follows four steps: classify by type, plot on a quadrant, score with weights, and then allocate funding.
Six types—Drafters, Answerers, Extractors, Routers, Analysts, and Operators—cover nearly all enterprise AI requests, and the type itself predicts effort before any code is written. Drafters and Routers typically require only a model and a prompt; Operators demand permission management, system integration, and error-handling protocols. A more critical structural insight is that many high-value Operators are composed of lower-tier types. For instance, a procurement approval agent begins as an Extractor (reading the request), becomes a Router (selecting the approval path), acts as an Analyst (checking against budget), and finally operates as an Operator. Funding the component parts first reduces the overall cost of the agent.
Mai identifies 'data distance' as the hidden variable: how far a use case sits from data that is already clean, accessible, and permissioned. He claims this metric predicts effort more reliably than model choice. In the Impact × Ease 2x2 matrix, the vertical axis represents Impact, and the horizontal axis represents Ease (not Effort, so the top-right quadrant signifies 'better'). Each quadrant dictates a specific funding action: High Impact/High Ease projects ship immediately with Q1 metrics; Low Impact/High Ease items receive self-serve access and usage policies to observe organic adoption; High Impact/Low Ease items receive funding to reduce their data distance, with gate review dates set instead of launch dates; Low Impact/Low Ease items are parked with documented reasons for revisit when data or models change. A common error is treating 'Park it' like 'Ship now' by setting a launch date for a project whose data does not yet exist.