Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarios

Researchers propose a standardized methodology for evaluating AI systems by transforming real-world use cases into detailed evaluation scenarios, addressing inconsistencies in AI measurement across industries. The work demonstrates this framework in financial services, generating 107 scenarios from six key use cases through structured worksheets and iterative human review.

The fragmentation of AI evaluation methodologies represents a significant challenge for the emerging AI industry. Organizations currently lack a unified framework for comparing AI system performance, making it difficult to assess which solutions genuinely outperform alternatives or meet operational requirements. This research addresses a critical gap by proposing a systematic approach that grounds AI evaluations in actual business contexts rather than abstract benchmarks.

The methodology centers on translating high-level business use cases into granular evaluation scenarios through structured elicitation from subject matter experts. By documenting six key elements—use case, sector, user types, intended outcomes, impacts, and KPIs—the framework creates reproducible evaluation templates. The researchers demonstrate this process within financial services, identifying practical applications including cyber defense, developer productivity, and suspicious activity reporting. The three-stage expansion pipeline combining large language models with human validation ensures scenarios remain operationally relevant rather than theoretical.

This approach carries implications for AI procurement and adoption across enterprises. Financial institutions and other sectors can now evaluate AI solutions against standardized scenarios reflecting their actual operational needs, reducing procurement risk and ensuring vendors compete on functionality rather than marketing claims. The emphasis on human-centered design principles acknowledges that AI systems ultimately serve human users whose needs extend beyond raw performance metrics.

The research signals a maturation phase in AI evaluation science, moving from isolated benchmarking toward industry-specific frameworks. Organizations in regulated sectors like finance may increasingly demand vendors demonstrate performance against these standardized scenario sets, potentially establishing de facto evaluation standards that influence product development priorities.

• →Standardized AI evaluation frameworks using real-world use cases improve comparability across different AI systems and vendors.
• →The methodology integrates human expert review at multiple stages to ensure scenarios remain grounded in operational reality rather than theoretical benchmarks.
• →Financial services sector identified six key AI use cases, providing a template for similar frameworks in other regulated industries.
• →Structured scenario development with defined KPIs and metrics enables more objective vendor selection and risk assessment for enterprise AI procurement.
• →Human-centered design principles embedded throughout the evaluation process address the gap between technical performance metrics and actual user needs.