The Hidden Factors That Determine AI Success or Failure

•By Chris Pearson
Enterprise AI
Business professionals assembling large blue and orange puzzle pieces in a modern office, symbolizing the integration of overlay and embedded AI solutions for enterprise success

Enterprise AI adoption is still in its early stages. While many executives feel intense pressure to “do something with AI,” most organizations remain in exploration mode. The challenge isn’t whether to adopt AI, but how to do it in a way that creates measurable business outcomes instead of scattered pilots with little long-term value.

That’s the purpose of our upcoming report, developed in partnership with UC Berkeley Haas. We’re creating a structured way for CIOs and IT leaders to cut through the hype and evaluate AI solutions against the factors that matter most.

At a high level, the framework looks across three broad dimensions:

  • Value: Does the solution align with your top use cases? How quickly can it deliver results? Can it scale to meet enterprise demands?
  • Cost: Beyond licensing, what’s the total cost of ownership, including integration work and the organizational “activation energy” needed for adoption?
  • Risk: Are governance, security, and compliance controls in place? Will end users adopt the solution? Does it fit with your organizational structure and future roadmap?

We also examine the spectrum between overlay AI (fast-to-deploy point solutions) and embedded AI (platform-native capabilities that scale more slowly but more deeply). Most enterprises won’t choose one or the other—they’ll mix both approaches depending on use case and urgency.

Our central finding: fears of “falling behind” are often premature. The winners won’t be those who chase every AI pilot, but those who methodically balance speed, cost, and governance with clear business alignment.

Want more? Email [email protected] to reserve your copy of the report today.