The biggest barrier to AI adoption isn’t awareness. It’s the gap between talking about AI and building with it.

Professionals whose primary role isn’t writing code, but whose daily work increasingly depends on AI solutions, don’t need engineering backgrounds to start building AI capability. They need the right tools, structured support, and permission to fail. Here’s how we proved it, and how you can replicate it.

Your teams are already in AI conversations, whether they’re fielding questions from customers, evaluating vendor solutions, or identifying automation opportunities in their day-to-day responsibilities. Given the pace at which agentic AI tools have become generally available, most of them have never built anything with the tools they’re discussing.

These teams, regardless of whether they sit in sales, operations, finance, or product, have watched the demos and completed the certifications. But when someone asks, “how does this actually work?”, they don’t have the answer that comes from having built with it themselves.

The cost is real: slower adoption, delayed productivity gains, missed opportunities to identify use cases, and a growing disconnect between what your teams know about and what they can implement.

We asked business professionals, people who work with AI tools daily but aren’t writing production code, what held them back with AI adoption. ” 90 percent wanted hands-on experience building AI agents. They were missing the connective tissue and scaffolding to build safely. The following chart illustrates the most common barriers business teams face when attempting to build with AI.

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