Most enterprise AI purchasing decisions are driven by demos. Demos are designed to impress. What enterprises need instead is an evidence-based evaluation framework that assesses whether a solution will actually work in their specific context.
The Problem with Demo-Driven Decisions
I have observed a consistent pattern in enterprise AI procurement. A vendor delivers a compelling demonstration. The audience is impressed. A pilot is approved. Three months later, the solution underperforms against expectations, and the organisation concludes that "AI doesn't work for us."
The AI works. The evaluation process didn't.
Demos use curated data, controlled conditions, and optimised examples. They demonstrate capability in ideal circumstances. Enterprise operations are not ideal circumstances. Data is messy. Edge cases are frequent. Users are busy and sceptical. The distance between a successful demo and a successful deployment is significant, and it is traversed through rigorous evaluation - not enthusiasm.
An Evidence-Based Framework
The framework below draws on evaluation methodology from health sciences, education research, and programme evaluation. These disciplines have decades of experience assessing whether interventions work in practice, not just in controlled conditions. The principles translate directly to AI evaluation.
1. Define Success Criteria Before Evaluation
Before evaluating any solution, define what "good" looks like. Specifically:
- Accuracy threshold. What level of accuracy is required for this use case? Not "high accuracy" - a number. 90%? 95%? 99%? The threshold should be determined by the business impact of errors, not by what the vendor claims.
- Performance requirements. Response time, throughput, concurrent users. What does production look like?
- Integration requirements. What systems does this need to connect to? What data formats does it need to handle?
- Governance requirements. Data residency, audit trails, explainability, compliance with relevant regulations.
Document these before you see any vendor demos. The criteria should be determined by your needs, not influenced by what the vendor shows you.
2. Test with Your Data
This is the most important step and the one most frequently skipped.
Vendor demos use vendor data. Your data is different. It is messier, more varied, more domain-specific, and more representative of real conditions. Any evaluation that does not include testing with your actual data is incomplete.
Request a trial period where you can test the solution against your own documents, your own queries, and your own workflows. If the vendor won't provide this, that tells you something.
74%
of enterprise technology buyers made purchasing decisions primarily based on vendor demonstrations
Source: Forrester, Enterprise Technology Buying Behaviour, 2023
3. Evaluate Against Edge Cases
AI systems perform well on common cases. That is expected and unremarkable. The evaluation that matters is performance on edge cases - the unusual, the ambiguous, the complex.
In every domain, there are cases that are straightforward and cases that require expertise. Your evaluation should include both, with particular attention to:
- Ambiguous inputs where the correct answer is unclear
- Rare cases that are infrequent but high-impact
- Multi-step reasoning that requires synthesising information from multiple sources
- Adversarial inputs that test the system's robustness
4. Assess Explainability
An AI system that provides correct answers without explanation is less useful than one that provides correct answers with reasoning. In enterprise contexts, users need to understand why the AI reached a conclusion, not just what the conclusion was.
Evaluate:
- Does the system provide source attribution?
- Can it explain its reasoning in a way domain experts can verify?
- When it is uncertain, does it communicate that uncertainty?
- When it is wrong, is the error identifiable from the explanation?
5. Evaluate the Failure Mode
Every AI system will fail. The question is how.
- Graceful failure: The system recognises it cannot answer confidently and escalates to a human.
- Silent failure: The system provides an incorrect answer with high confidence.
The second is far more dangerous than the first. An AI system that knows when it doesn't know is dramatically more trustworthy than one that always provides an answer.
6. Assess Vendor Maturity
Beyond the product, evaluate the organisation behind it:
- How long have they been working in AI? (Not how long since they "pivoted to AI")
- Do they have production customers in your sector?
- How do they handle model updates that change behaviour?
- What is their data handling and privacy posture?
- What does their roadmap look like, and how realistic is it?
The Evaluation Scorecard
| Criterion | Weight | Score (1-5) | Notes |
|---|---|---|---|
| Accuracy on your data | High | ||
| Edge case handling | High | ||
| Explainability | Medium | ||
| Failure mode | High | ||
| Integration capability | Medium | ||
| Data governance | High | ||
| Vendor maturity | Medium | ||
| Cost-effectiveness | Medium |
Weight the criteria according to your specific context. A health AI system will weight data governance and failure modes more heavily than a marketing content tool.
The Principle
Evidence over impression. Every AI vendor will give you an impressive demo. The vendors worth buying from are the ones who welcome rigorous evaluation - because they know their product performs under real conditions, not just controlled ones.