Enterprise AI8 min read
Enterprise AI Adoption: From Pilot to Production
Most enterprise AI projects fail not in the pilot phase — but in the transition from pilot to production. Here's what actually happens and how to navigate it.
Read →A structured framework for enterprise technology leaders to assess AI partners, platforms, and build-vs-buy decisions
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.
We recommend evaluating AI vendors across seven dimensions that predict long-term success:
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 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.
See the production AI systems behind these insights.