Many organizations have prioritized AI adoption, but they actually need something harder to achieve: AI fluency.
The Access Problem
Some teams are moving fast. With increasingly capable models, they are compressing timelines, surfacing insights, and automating work that used to eat up hours. These groups are getting more productive every month.
Other functions down the hall are still waiting. They need a formal rollout, a governance approval, or someone to tell them what to do. The gap between the AI haves and have-nots in your organization is widening.
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When leaders notice this gap, their instinct is to treat it as a tooling problem. They push to get everyone access to the same platforms, provide general-use training, and hire specialists for IT departments. That approach makes sense on the surface. But access is table stakes. It is a good start, not a destination.
Why Fluency Beats Access
A Harvard Business School study found that workers using AI tools completed tasks 25% faster and produced results rated more than 40% higher in quality. Those numbers look impressive until you read the rest of the findings. Performance actually declined when people used the tools without understanding where they applied and where they did not. Knowing how to use a hammer does not mean you know when to reach for one.
Departmental leaders need guidance on applying capabilities in the context of their specific workflows. Without that knowledge, they cannot ask the right questions. They end up throwing software licenses at problems that require a different approach entirely.
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Re-Engineering, Not Just Adopting
Teams playing catch-up tend to focus on injecting new tools into existing workflows. The thinking goes: if this process works, add AI to make it faster. But that misses the point. When AI can handle tasks differently, the workflow itself may need redesigning.
Consider an SDR team that asks IT to improve sales lead routing. Completely reasonable request. But someone with visibility across the broader system might see something else. The data pipeline supporting lead routing could be unnecessarily complex. With the right support, the conversation shifts from improving one step to overhauling the entire pipeline. That opens the door to fully agentic lead follow-ups, something the original ask never mentioned.
Throwing a software license and a Slack channel at departmental leaders will not build the right kind of adoption. Technical support and strategic guidance are required to reimagine work from first principles.
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The Hub-and-Spoke Model
The typical pattern puts a centralized team in charge of taking requirements, interpreting them in isolation, and delivering capabilities to departments months later. This model cannot keep pace when AI capabilities launch weekly. By the time a solution reaches a department, the problem may have changed.
A more effective approach pairs a central hub that owns platform strategy, governance, and reusable patterns with AI engineers embedded directly inside business departments. These engineers serve as spokes, helping teams identify vertical use cases day-to-day and providing the cross-functional visibility needed to make real impact. An AI engineer who solved a problem for finance can share the pattern with someone facing the same challenge in operations.
In a department just getting started, the embedded AI engineer handles scouting, prototyping, and building. In a more mature department, they shift toward enablement, feeding patterns back to the hub and helping teams handle AI without getting buried in process. Over time, departments organically become AI-fluent as they learn from the engineers.
