The CTO's Guide to Evaluating AI Vendors

The Build vs. Buy vs. Partner Decision

Every enterprise AI initiative starts with a fundamental question: build internally, buy an off-the-shelf platform, or partner with a specialized AI studio? The answer depends on three factors: how core the AI capability is to your competitive advantage, how much internal AI talent you have, and how quickly you need to move. For most enterprises, a partnership model - where you own the IP but leverage external expertise for speed - offers the best tradeoff.

The 7-Point Vendor Evaluation Framework

We recommend evaluating AI vendors across seven dimensions that predict long-term success:

• Technical depth: Can they explain their architecture decisions, not just their features?
• Domain expertise: Have they deployed in your industry before?
• IP ownership: Who owns the code, models, and data at the end of the engagement?
• Deployment flexibility: Can they deploy on-premise, in your cloud, or air-gapped?
• Pilot speed: Can they deliver a working prototype in weeks, not quarters?
• Reference clients: Can they connect you with real clients who went to production?
• Model agnosticism: Are they locked to one LLM vendor, or can they switch?

Red Flags to Watch For

In our experience working with enterprise clients who previously engaged other vendors, several patterns consistently predict failure: vendors who cannot explain their architecture, vendors who require long-term contracts before any pilot, vendors who retain IP rights to models trained on your data, and vendors whose "AI" is actually a rules engine with an LLM wrapper for marketing purposes.

The Pilot-First Approach

The most reliable way to evaluate an AI vendor is to run a time-boxed pilot on real data with clear success criteria defined upfront. A good vendor will agree to a 14-day pilot with no long-term commitment, deliver a working system on your actual data, and let the results speak for themselves. If a vendor resists this, it tells you something important.