Evaluating AI Vendors and Tools

The AI vendor landscape is crowded and confusing. Every software company now claims to be "AI-powered," and separating genuine capability from marketing hype requires a structured evaluation approach.

The Due Diligence Checklist

1. Verify the AI Is Real

Ask vendors: What specific AI technology powers your product? Where does the model come from — is it a proprietary model, a fine-tuned open-source model, or a wrapper around a third-party API? Many "AI-powered" products are thin interfaces on top of OpenAI or Anthropic APIs, which is not inherently bad but affects your risk assessment and pricing expectations.

2. Evaluate on Your Data

Never trust demo data. Any AI product looks impressive when the vendor controls the input. Insist on a proof of concept using your actual data, your real use cases, and your edge cases. Measure performance on examples that represent your hardest problems, not your easiest ones.

3. Understand the Data Flow

Where does your data go? Is it stored, used for training, or shared? Many AI services process data on external servers. For sensitive data, you need clear answers about data residency, encryption, retention policies, and whether your data improves models that serve competitors.

4. Assess Lock-In Risk

How difficult is it to switch vendors? If a product stores your prompts, configurations, and workflows in a proprietary format, migration becomes expensive. Prefer vendors that allow data export and use standard formats.

5. Check the Pricing Model

AI pricing is often complex — per token, per API call, per seat, or usage-based tiers. Model the cost at your expected scale, not at the trial scale. A product that costs $50/month during evaluation might cost $5,000/month in production.

Red Flags to Watch For

Be cautious of vendors who refuse to share technical details, will not allow testing on your data, guarantee specific accuracy percentages without seeing your use case, or cannot explain what happens to your data. Transparency correlates strongly with product quality in the AI space.