Overview

Pawel Kwiatkowski managed the AI selection process for a large Polish insurance company when his technical team handed him a 12-page evaluation scorecard. Every vendor had been rated on latency, model benchmarks, API documentation quality, and 23 other dimensions. Three vendors were essentially tied. "We had a technically excellent process," he told me. "But I realized we had never asked the single most important question: will this tool actually work with the way our underwriters think and work?" The technical scores told him what the product could do. They told him nothing about whether it would fit.

Technical evaluation is a necessary but insufficient basis for AI vendor selection. You need to know whether the system performs at the required level on standard benchmarks, and whether it works in your specific context with your specific data and your specific users. Those are two different questions. Organizations that answer only the first routinely deploy technically excellent systems that nobody uses or that fail on the 20% of cases that deviate from the benchmark distribution.

This lesson covers how to run a technical evaluation that answers both questions.

Building Your Evaluation Criteria

Evaluation criteria should flow directly from your use case requirements. This sounds obvious. In practice, most organizations copy evaluation frameworks from elsewhere rather than deriving criteria from their specific situation. The result is thorough evaluation of the wrong things.

Start from your use case requirements. For each use case you are evaluating the vendor for, ask: what does this system need to do, how well does it need to do it, and under what conditions?

Requirements typically fall into four categories:

Functional requirements. What specific capabilities does the system need? "Summarize legal documents" is a functional requirement. "Identify material adverse change clauses in contracts up to 200 pages" is a more specific and therefore more testable version of the same requirement. The more precisely you can specify what you need, the more accurately you can evaluate whether a vendor delivers it.

Performance requirements. At what quality level, speed, and scale? For generative AI tools, this covers accuracy (how often is the output correct and useful?), latency (how fast does it respond?), and throughput (how many requests can it handle simultaneously?). Define your minimum acceptable thresholds before you begin evaluation, not after you have seen vendor demos.

Integration requirements. What systems does this tool need to connect to, and how? Your document management system, your CRM, your data warehouse, your authentication infrastructure. Integration complexity is frequently underestimated. A technically excellent AI system that requires six months of integration work to become functional is less valuable than a slightly less capable system that connects in four weeks.

Security and compliance requirements. What data will be processed by this system, and what controls apply to that data? Relevant certifications (SOC 2 Type II, ISO 27001, HIPAA, GDPR compliance), data residency requirements, and penetration testing standards should be specified as requirements, not evaluated as optional features.

The Proof of Concept

A proof of concept (PoC) is a time-bounded test of a vendor's system on your actual data and in your actual operational context. It is the most important stage of technical evaluation. No benchmark score, no reference customer call, and no vendor demo substitutes for a PoC.

The PoC should be designed to answer two questions: does it work technically, and does it work operationally?

Technical PoC: Run the system on a representative sample of your actual data. Not synthetic data, not public benchmark data - your data. Measure performance against the thresholds you defined in your requirements. A document summarization tool that achieves 94% user-rated quality on public legal benchmarks might achieve 78% quality on your company's specific contract formats, which are written in a particular way that reflects your legal team's drafting style. You need to know that before you commit.

Design your evaluation dataset carefully. It should include:

• Typical cases (80% of your volume)
  - Edge cases (the 20% of cases that are unusual, complex, or high-stakes)
  - Failure cases (examples where you know the expected output is difficult - these test robustness)

Pawel's team designed a 200-document evaluation set for their underwriting use case: 160 typical policies, 30 unusual or complex cases, and 10 cases that had historically caused errors in their manual process. The vendor that scored highest on the typical cases scored substantially lower on the edge cases. That gap told Pawel which vendor would fail in production most often.

Operational PoC: Have actual end users work with the system for two to four weeks. Not selected power users - a representative sample of the people who will actually use this tool. Collect structured feedback: what worked, what did not, what was confusing, what would they need to be able to use this confidently every day?

This stage surfaces the fit issues that technical evaluation misses. A tool that requires a specific prompting style that your users find unnatural will generate poor outputs and low adoption. A tool that produces output in a format your downstream systems cannot process cleanly will create integration work you did not budget for. A tool that handles English well but performs significantly worse on content in another language your team uses will create a fairness and reliability problem.

Evaluating the Vendor, Not Just the Product

The technical quality of the product is necessary but not sufficient. You are also entering a relationship with the organization that builds and maintains the product. That organization's stability, responsiveness, and roadmap alignment matter.

Key vendor-level assessment questions:

• How long has the vendor been operating, and is their financial position stable? Startups with strong products can fail. An excellent product from a failed vendor is stranded capability.
  - What is the vendor's track record on product stability? How frequently do they make breaking changes, and how much notice do they provide?
  - How do they handle security incidents? Ask for their most recent incident response report.
  - Does their product roadmap for the next 12 months align with where you expect your needs to evolve? Request the roadmap and assess the alignment explicitly.
  - How is their support structured for customers of your size? A vendor who provides dedicated technical account management to enterprise clients but routes smaller customers to a ticket queue creates different levels of operational risk.

Making the Final Decision

After completing technical evaluation and PoC, consolidate your findings into a structured decision matrix. Assign weights to each evaluation criterion based on its importance to your use case. Apply the weights to convert raw scores into a weighted ranking.

But do not let the matrix make the decision. It is a tool for organizing and communicating information, not a substitute for judgment. If the top-ranked vendor raises concerns that do not show up cleanly in a scorecard - cultural fit, unexplained inconsistency between their demo and PoC performance, concerns raised in reference calls - those concerns belong in the decision, even if the rubric does not capture them cleanly.

Document the decision rationale. In 12 months, when something goes wrong (as it will, eventually), you will want a record of what you knew when you decided. That documentation also protects the decision-maker: good decisions based on available information sometimes produce bad outcomes. A documented rationale shows the quality of the process.

Key Takeaways

• Technical evaluation must answer two questions: does the system work technically, and does it fit operationally? Most evaluations answer only the first.
  - Derive evaluation criteria from use case requirements rather than copying generic frameworks. The wrong criteria produce thorough evaluation of the wrong things.
  - A proof of concept is non-negotiable. Run it on your actual data, including edge cases. Run it with actual end users. Benchmark performance against requirements you defined before seeing the demos.
  - Evaluate the vendor, not just the product. Financial stability, product roadmap alignment, and support model all affect the long-term viability of the relationship.
  - Document the decision rationale. Good processes sometimes produce bad outcomes. Documentation shows the quality of the process and protects future accountability.
  - Use decision matrices to organize, not to decide. Quantitative rubrics are tools for communication. Final judgment requires a human who understands the context the rubric cannot capture.