Posted by: Adam Weston
17 October 2025
The AI pilot problem
Everyone's got an AI story. Someone built a chatbot that argued with customers. Someone else automated half a workflow before realising the other half was still on paper. Another team generated a hundred ideas and implemented none.
We've all dabbled, tested, prodded and, quite often, paused. That's the thing about pilots. They're exciting to start and easy to abandon. AI in most businesses still feels like a side project. Interesting enough to talk about, but not quite serious enough to plan around. The result is a lot of enthusiasm and not much direction.
The adoption gap
McKinsey says only 1% of companies consider their AI programmes mature (McKinsey 2025). Forrester believes one in seven will give up altogether this year (Forrester, 2025). You can see why. Trying AI is easy. Integrating it is hard.
The sticking points are rarely technical. They're human. Nobody's sure who owns the project, what success looks like, or whether it's even working. Teams get nervous about being replaced, or they dismiss the whole thing as a gimmick. Somewhere between fear and fatigue, the momentum fades.
What adoption really takes
In our work with SMEs, we've seen plenty of ideas but very few plans. Leaders know AI matters but struggle to turn experiments into habits. There's a reason for that. Adoption is about behaviour. It's the slow process of changing how people work, not what software they use.
The companies that make AI stick start small and pay attention. They treat it less like a revolution and more like a renovation: quiet, deliberate, slightly unglamorous. They look for things that make work easier: a task that takes ten minutes instead of sixty, a report that writes itself, a process that finally flows. They get the basics right before they scale.
Proof in the details
One Growcreate client used AI to clean up compliance data. Hardly headline material, but it saved hours of repetitive work and gave the team more time to think.
That's what adoption looks like. Not shiny dashboards or corporate slogans, just everyday improvements that make work feel human again.
Adoption that lasts
AI is a tool. And like any tool, it's only as useful as the hands that hold it. The real challenge is deciding what kind of work you want it to make possible.
The teams that succeed treat adoption as an ongoing practice: learn, adjust, repeat. They share what works, retire what doesn't, and make change visible one small win at a time.
What this means for SMEs
| Area | Challenge | Outcome |
|---|---|---|
| Leadership | AI seen as a side project | Define ownership and long-term goals |
| Operations | Disconnected pilots | Integrate small wins into daily workflows |
| Culture | Fear of replacement | Reframe AI as a collaborator, not a threat |
| Measurement | No clear ROI | Track saved time, not hype metrics |
| Growth | Stalled experimentation | Build shared learning into every project |
Start small. Learn out loud.
AI adoption happens every day you try again. If you're ready to move from pilots to progress, we'll help you get there.
Adam Weston
Adam Weston, Co-Founder and CMO of Growcreate and Invessed, brings energy and creativity to AI consulting. With cross-sector experience, he helps organisations amplify brand visibility, spark client engagement, and accelerate digital transformation.
Connect on LinkedInFAQs
AI adoption often slows when projects begin without clear ownership or defined goals. Many teams launch pilots quickly but lack the structure to sustain them. Without coordination, regular feedback, and shared measures of progress, enthusiasm fades before results appear.
Adoption works best when it becomes part of everyday practice. Small, focused projects that solve visible problems create momentum and confidence. Sharing lessons across teams helps everyone learn faster, while a steady approach to change turns short-term pilots into long-term progress.
Success shows up in small but consistent improvements. Workflows run more smoothly, reports take less time, and data remains accurate. People notice that the tools support them rather than slow them down. Over time, these quiet wins add up to lasting change.
Measure what makes a difference to your team: track time saved, quality improvements, faster responses, or clearer decisions. Good measurement focuses on outcomes that make work easier and more effective, not just the volume of output.
Scaling works when teams understand the value AI adds and can repeat it with confidence. A business is ready when results are visible, responsibilities are clear, and people feel comfortable improving the process together. That steady confidence is the signal to grow.