When we talk about AI adoption in organizations, it’s easy to imagine engineers and data scientists as the main heroes. They bring the technical expertise and build the systems that power change, and their work is crucial. But what often gets overlooked are the people who live the business problems every day, the ones who spot where things slow down or get stuck and start imagining how AI tools might help. These individuals are the real champions in weaving AI into everyday work.
I think of these AI champions as the bridge between technology and real impact. They are not necessarily the most technical people on the team, but they bring a different kind of expertise. It’s their curiosity and persistence that inspire them to try no-code and low-code platforms, experimenting with solutions that fit the unique rhythm and needs of their work. Instead of waiting for someone else to build the “perfect” AI tool, these individuals get their hands dirty. They test, learn, and adapt until something clicks.
This approach reminds me that adopting AI isn’t about simply pushing out new shiny technology with great fanfare. It’s less a top-down rollout and more an invitation to co-create. Those closest to the work are the ones who really understand the friction points—the awkward handoffs, the repetitive tasks, the data struggles—and they’re the best situated to shape how AI can make those moments better. When we design AI with people and their context at the center, rather than for them as passive recipients, the results stick.
There’s a lesson here about leadership and sustainable growth. Empowering these AI champions means trusting non-technical team members to experiment, to fail fast, and to keep iterating. It also means setting clear boundaries and support structures so these efforts align with broader goals without overwhelming the individuals involved. The technology itself is only part of the equation. The human curiosity and courage to embrace change safely and thoughtfully often make the difference between a failed pilot and lasting improvement.
We are fortunate now to have tools that make this kind of experimentation accessible. No-code and low-code platforms lower the barrier to entry. You don’t have to build everything from scratch or have a deep programming background to start shaping workflows with AI. It’s about willingness more than skill sometimes. Simply being open to trying and learning opens the door to reshaping how we work, freeing up time, and improving effectiveness.
So the real question for organizations isn’t just “How can we adopt AI?” but “How are we nurturing our everyday AI champions?” What if these folks had clear recognition and resources, instead of feeling like an unofficial part of a side project? What if leadership made their explorations a central part of the AI journey rather than peripheral tinkering?
Taking this human-centered perspective changes the story around AI adoption. It moves from a technology rollout to a collaborative effort where everyone contributes, learns, and grows together. And that, to me, feels like the path toward sustainable and meaningful change.