Everyone’s under pressure to do something with AI. Boards want plans. Investors want transformation. But jumping into AI without preparing the business is a fast track to wasted investment.
In our event, AI: The Power of the POC, we brought together tech leaders Kerry Jenkin (CIO, Great Rail Journeys), Gabrielle Earnshaw (Founder, Making Mobile Simple), Ed Marshall (CTO, Enablis), and Simon Roberts (Technology Delivery Director, Enablis) to unpack what it really takes to become AI-ready, and why a proof of concept (POC) is one of the smartest ways to start.
Here are five practical tips to help you get your business ready for AI.
Know Why AI Is Different (And Treat It That Way)
AI doesn’t follow the same rules as traditional IT. It’s not a plug-and-play tool with guaranteed outcomes. You won’t always know what outcome to expect, and that’s the point. It’s experimental, data-driven, and can be unpredictable.
As Kerry Jenkin, CIO at Great Rail Journeys, puts it:
“With a lot of technology investments, you pretty much know what you’re doing. You know what the angle is going to be. But with AI, you don’t know what results you’re going to get.”
This difference matters. AI systems are powered by context, not just by code. The quality of your data, the clarity of your goals, and the way your people engage with the solution will determine success.
So before rushing into the latest tools, take a step back and ask: Is your business equipped to handle the test-and-learn nature of AI? That’s the first signal of readiness.
Assess Before You Invest
It’s easy to get caught up in the possibilities of AI: automating processes, boosting productivity, delivering insights at speed. But many businesses jump straight into planning without first checking if they’re ready to support it.
If the right foundations aren’t in place, even the smartest ideas will struggle to take off.
Knowing your capacity helps you design AI projects that are grounded in reality, not just ambition.
Here are three key areas to check:
- Data: Is it accessible, structured, and suitable for training?
- People: Does your team have the technical and governance capacity?
- Leadership: Are key people aligned on what success looks like?
Treat this as your groundwork. When you’re clear on what the business can support, you can design AI initiatives that are both realistic and scalable from the start.
It’s not about slowing down. It’s about building on solid ground so your AI delivers long-term value.
Start Small, But Be Strategic
Once the foundations are in place, don’t launch a full-scale AI project right away. Start with a low-risk, strategic test. And that’s where a Proof of Concept (POC) comes in. It helps you answer a specific question: Can this idea work in our context?
When done well, it offers evidence and clarity before investing further. But to get real value from it, you need to be intentional from the beginning.
As Gabrielle Earnshaw, Founder of Making Mobile Simple, explained:
“Different leaders will have different views. So make it very clear. What’s the concept you’re trying to prove? What are the principles that you want to stick to?”
A principles-first approach can help you design a POC that generates actionable insights:
- Define the concept clearly: What are you trying to validate? What does success look like?
- Make the code disposable: Keep it simple. The goal is to learn, not to be perfect..
- Test critical assumptions: Focus on what’s riskiest or most uncertain, not every feature.
Start small, but make it count. A well-designed POC will test the tech and show whether your business is ready to scale it.
Don’t Skip the People Element
Being AI-ready involves culture. You can have the best tools and cleanest data, but if your team isn’t on board, adoption will stall.
Kerry Jenkin shared:
“AI shouldn’t be a threat to their job. Bring people on the journey and show what’s in it for them.”
Here’s how to put that into practice:
- Involve end users early: The people using the tool day-to-day know the challenges and what’s likely to work.
- Identify internal champions: Look for team members who are curious, open to change, and ready to support others.
- Be transparent: Let people know what’s being tested, why it matters, and how decisions are being made.
When you bring your people with you, you unlock better ideas, smoother adoption, and stronger impact.
Don’t Mistake a POC for a Launch
A successful POC is a milestone, but it’s not the finish line. The real value lies in what the POC reveals and how you apply those insights to shape a smarter development plan.
Ed Marshall, CTO at Enablis, advised that businesses should be prepared for POCs to fail. After all, the goal is “to find out what worked, what didn’t, and why, so you can address the gaps before moving forward.”
That’s why being clear on your intent from the start matters. Gabrielle Earnshaw put it simply:
“In a POC, you want to fail fast, get quick feedback, and keep iterating. Keep it small, then check if it worked.”
It’s also why many teams choose to run more than one POC, especially when integration and tech choices are on the line. Simon Roberts, Technology Delivery Director at Enablis, reflected on a previous project:
“We ran the first POC, then a second one with integration. We tested tech choices, and that helped the team understand what a future roadmap might look like. How long will this take? Nobody really knew. But there was learning.”
Use what you learn to build a more realistic plan for scaling. That’s what turns a promising experiment into real momentum.
Final Thoughts
For businesses serious about integrating AI, readiness is a competitive advantage. A good POC won’t just prove a solution. It’ll prove your business is ready to adopt it well.
Need help designing your AI roadmap? Enablis helps organisations get AI ready and to run low-risk, high-impact proof of concepts that bring clarity, direction, and momentum.
Get in touch if you’d like to learn more!
