Over the past few years, I’ve worked with professionals and educators to support AI adoption. In these conversations, the questions that come up are rarely about the technology itself. Instead, they reflect a mix of overwhelm, excitement, and hesitation, and very rarely resistance.
- Where do I even start?”
- “Am I supposed to be using this already?”
- “Is this going to replace parts of my role?”
A key thing to rememeber is that AI adoption isn’t just about access to tools. It’s about how people make sense of change.
Starting Small
One of the most common patterns I see is people trying to “learn AI” in the abstract. They’ll ask about prompt engineering, or what tools they should be using, or how to stay ahead. But those questions can sometimes make the starting point feel even further away.
What tends to work better is something much simpler: starting with the work itself. Instead of asking “How do I learn AI?“, the more useful question is: “Where is there friction in my day?“
That shift changes everything, because AI becomes less about exploration and more about application. Less about keeping up, and more about making something easier.
Some of the most effective entry points I’ve seen are surprisingly small:
- Drafting a first version of something you would normally start from scratch
- Summarizing notes after a long meeting
- Reframing an idea when you’re stuck
None of these feel transformative on their own, and they don’t need to be because adoption starts to stick when it’s useful, not when it’s impressive.
Building Confidence Before Capability
Another pattern that comes up often isn’t about skill, it’s about confidence. There’s an assumption that using AI well requires a certain level of technical expertise, so when people don’t see themselves that way, they hesitate to engage with it at all.
What’s really interesting is that the people who build the most momentum with AI aren’t necessarily the most technical but the ones who are willing to try, adjust, and try again. A key skill is the enjoyment of tinkering, trying, and trial and error.
That’s why, in my personal experience, one of the most important strategies isn’t technical training but creating space for low-stakes experimentation.
What that can look like in practice:
- Encouraging people to use AI for drafts, not final outputs
- Sharing examples of imperfect use (not just polished final products)
- Normalizing iteration, instead of expecting precision (which can come naturally through prompt engineering)
Before people build capability, they need to feel comfortable enough to begin.
The Gap Between Trying and Actually Adopting
The gap between curiosity and habit is where adoption often breaks down. Most people have tried AI at least once, but far fewer are using it consistently. Trying something new doesn’t automatically change how we work because the tool needs to be relevant and applicable to our every day flows, whether that’s in the work place or at home. This is something that customer success managers and onboarding specialists think deeply about – how do we make the tool relevant to the user’s every day and changing needs?
One of the simplest ways to bridge this is by anchoring AI to repeatable moments in the day:
- Starting a task
- Switching contexts
- Wrapping something up
When AI becomes part of those moments, it stops feeling like an extra step and starts feeling like a natural one. Consistency drives adoption more than intensity.
When Organizations Try to Scale 2 Fast 2 Furious (or at 0)
There’s often a push to “adopt AI” broadly so that organizations don’t fall behind, without a clear sense of where it actually creates value. Or, on the other end, a tendency to overthink the strategy to the point where nothing really moves forward.
What I’ve seen work better is a more focused approach. Instead of trying to transform everything at once, organizations can start by identifying a few areas where AI can make a noticeable difference.
That might look like:
- Improving internal workflows (documentation, knowledge sharing)
- Supporting content creation or communication tasks
- Enhancing how teams analyze or synthesize information
From there, the goal isn’t scale immediately, it’s learning. Thinking about what works. What doesn’t. What feels intuitive. What needs more support.
Why Training Alone Isn’t Enough
Workshops are valuable and often the first step organizations take. They create awareness, spark ideas, and give people a starting point, but on their own, they rarely lead to sustained change.
I’ve seen sessions where engagement is high, conversations are strong, and people leave excited… only for that momentum to fade within a few weeks. Not because people didn’t care, but because there wasn’t a structure to support what came next. Again, this is something customer success managers and learning experience designers roadmap.
Sustained adoption usually requires a shift from one time learning to ongoing enablement:
- Creating space for teams to share how they’re using AI in their work
- Building simple, practical guides around common use cases
- Embedding AI into existing tools and workflows where possible
The Role of Culture
Some of the biggest blockers to AI adoption aren’t technical or strategic but instead rooted in organizational culture, or based in misunderstanding. They show up in subtle ways:
- Unclear expectations around what’s allowed
- Hesitation to use AI openly
- Concerns about how it might be perceived
When those signals are present, adoption slows down. Not because people don’t see the value, but because they’re unsure how to engage with it safely. This is where leadership matters more than tooling.
Clarity and engagement becomes more obvious when leadership:
- Share how they’re using AI in their own work
- Acknowledge both the opportunities, uncertainties and challenges
- Provide clear guidance on responsible use
And that permission, and the parameters placed, are often what people need to move from observing to actually participating.
Designing for More Inclusive Adoption
One thing that’s easy to overlook is that AI adoption isn’t experienced equally. Access to tools, time to explore, and exposure to training can vary widely across roles, teams and communities.
Without intention, it’s easy for adoption to concentrate among a small group of early adopters, while others are left trying to catch up.
Designing for more inclusive adoption doesn’t have to be complex, but it does need to be intentional:
- Making tools accessible across different roles (not just technical ones)
- Offering learning in different formats and entry points
- Recognizing that people will adopt at different paces
If AI is shaping how we work, then access to it should feel just as distributed.
From Awareness to Integration
At this point, most people don’t need to be convinced that AI matters. The challenge is figuring out how it fits. And that’s where the real challenge is: not in understanding what AI can do, but in integrating it into how work actually happens.
Not all at once (small steps). Not perfectly (trial and error). But gradually. Through small shifts. Repeated moments. Shared learning.
A Final Thought
The most meaningful AI adoption I’ve seen didn’t come from having the most advanced tools or the most defined strategies. It came from curious people who are willing to engage with uncertainty, try something new, question how things work, and slowly build new habits
As AI continues to evolve, that mindset becomes even more important… Because adoption isn’t something we do once, it’s something we practice.
ABBEY RAMDEO 