There's a moment in every transformation when you stop talking about what's possible and start looking around the room to figure out who's going to make it real.
I've been thinking about this a lot lately.
The organisations I talk to - across aviation, travel tech, and B2B data businesses more broadly - have made real progress on AI adoption. Usage is up. Tool access is up. Most teams have at least someone who's run a prompt through Claude or ChatGPT and seen something useful come back.
But the thing about most adoption metrics: they measure usage, not capability.
They tell you how many people are driving, not how many can build the car or where they are heading. And building the car to go the right direction is what matters now.
The next phase of AI transformation for those organisations that have tired of counting how many people have logged into Claude isn't about getting more people to use AI tools. It's about growing the number of people who can take a problem they understand and use AI to build something that solves it. For some, that means fundamentally rethinking how their function operates.
A Builder is someone who sees a problem, reaches for AI to solve it, and ships something that helps their team. They understand the business, know where the friction lives, and care enough to fix it. Users drive. Builders build.
This post is about recognising the builders you already have - and being honest about the builders you still need to create.
You already have more builders than you realise
One of the most consistent patterns I've noticed is that builders don't announce themselves when they enter the room. They don't appear on an all-hands agenda. They don't wait for permission. They just start solving problems.
They're the person who figured out how to use Claude to build a tool that takes 2 minutes to complete a task that used to take two days. The team member who built a workflow that turned something cumbersome and time-consuming into something fast and reliable - and then told approximately no one about it.
In almost every organisation I've encountered, these people are already there. They're in operations. They're in finance. They're in commercial teams and product teams and customer success. They didn't go through a formal AI training programme to become builders - they had curiosity, a problem worth solving, and just enough access to tools to start experimenting. And most importantly, had a deep understanding of what makes the company what it is, which for OAG is our data.
The challenge isn't that organisations lack builders. It's that most builders are invisible. They're solving problems in isolation, and their innovations aren't spreading the way they should. The workflow that transforms one team's Monday morning could benefit five other teams - but nobody knows it exists.
The gap isn't tools. It's confidence.
When I look at where most organisations are right now, the biggest barrier to creating more builders isn't access to technology. The tools are there. Claude, ChatGPT, Lovable, n8n - the ecosystem is mature enough that someone with curiosity and a weekend can build something genuinely useful.
The gap is confidence and support.
The people in most organisations are smart, experienced professionals who are very good at their jobs. But "very good at their job" was defined in a pre-AI context. The skills that made someone excellent at their role two years ago are still valuable - but they're no longer sufficient on their own. That creates a quiet anxiety that pulls people down and anxiety being what it is, it then doesn't get spoken about enough.
I've noticed that people who haven't yet become builders tend to fall into one of four camps.
The curious but cautious.
They can see the potential. They've played with AI tools a few times, but they haven't found the right problem to solve or the right moment to commit. From experience these people are just waiting for someone to tell them it's ok to try.
The overwhelmed.
They're already stretched and learning anything new, let alone new AI capabilities, feels like one more thing on an already impossible list. The irony is that AI could reduce that workload - but you have to invest time to save time, and that initial investment feels impossible when you're already running at capacity.
The sceptical.
They've seen technology hype cycles before. They've watched "transformational" tools get rolled out, adopted by a handful of enthusiasts, and then quietly forgotten. They need proof, not promises.
The Dunning-Kruger camp.
These are people who've used AI a handful of times, got some decent outputs, and now believe they've seen the ceiling. They paste something into Claude, get a passable answer, and assume that's what AI can do. But the gap between casual prompting and genuine building is enormous - like the difference between microwaving a ready meal and running a kitchen. This group is tricky because they don't know what they don't know. They need exposure to what good looks like, some inspiration that resets their sense of what's possible. Not criticism.
None of these positions is irrational. Each one is a reasonable response to uncertainty and time pressure. But the cost of staying in any of these camps is compounding. Not because AI gives everyone a free performance boost, but because every quarter that passes, the gap between builders and non-builders widens and that shows up in engagement, quality, and speed.
Building is a skill, not a talent
There's a myth that I feel like I have spent most of my adult life challenging: the idea that some people are just naturally good with technology and others aren't.
I have always found this framing is frustrating, and more importantly, it's unhelpful. It lets people off the hook and puts a ceiling on people and their organisations that don't need one.
Building with AI is a skill. Like any skill, it can be learned, practised, and improved. The person who shipped their first agentic workflow six months ago wasn't born knowing how to do it. They tried something, it broke, they tried again, and eventually it worked. That's not talent, sure it shows they had a keen interest but not in the tech and tools, in building something that solves a real problem they experience every day. That's the normal iterative learning cycle applied to a new tool not something that only people who are “naturally good with tech” are capable of.
The real advantage in AI isn't technical sophistication - it's domain knowledge. The person who understands their industry deeply, who knows the edge cases and the exceptions and the things that reliably break, that person armed with AI tools is more powerful than any generic technologist. The expertise becomes more valuable with AI, not less. The tool takes their judgement, ideas and expertise and amplifies them; it doesn't replace it.
But skill development requires investment. It requires time - not "find ten minutes between meetings" time, but real, protected space to experiment without the guilt of neglecting existing responsibilities.
It requires psychological safety, which means experiments that fail are treated as learning, not evidence that AI "doesn't work for us." The first automation or prototype someone builds will probably be clunky. The second will be better. By the fifth, they'll be helping others. But they'll never get to five if the first failure makes them feel exposed.
And it requires visible leadership. Senior people need to be building too, not just approving budgets for AI initiatives. When a director automates part of their own workflow and talks openly about what worked and what didn't, it sends a signal that no email or training programme can replicate.
What the next twelve months could look like
Imagine every function in your organisation has at least one active builder. Someone who understands both the technology and the domain, and has started connecting the two.
They've identified the workflows in their function that are ripe for change and long-standing problems that the tech team never got around to building a tool for. They've built something that saves real hours every week. And crucially, they've brought colleagues along - turning curious observers into people who are actively experimenting.
- A commercial team that uses AI not just to prepare for customer meetings, but to proactively surface patterns in data that customers haven't thought to ask about yet.
- A finance function where processes that used to take days are compressed into hours, freeing the team for the analysis that actually requires their judgment.
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A customer success team where queries arrive pre-enriched with context, so every interaction starts from understanding rather than discovery.
None of this requires science-fiction technology. It requires builders. People who see a problem, reach for AI as a tool, and iterate until something works.
And builders create more builders. When someone on your team makes a splash with something that leaves jaws on the floor - the time saved, the frustration removed, the capability unlocked - that's more persuasive than any presentation. One builder in a team of eight becomes three within six months. Not because of a mandate from high but because the results are visible, the approach learnable and - critically - ambivalence gives way to empowerment and motivation.
An invitation
If you're already building - the thing I'd ask is this: stop doing it quietly. Share what you've built. The people around you need to see it more than you need the modesty.
If you're curious but haven't started - pick one task. Just one. Something you do regularly that feels repetitive, tedious, or unnecessarily manual. You don't need a perfect plan. You need a problem worth solving and a willingness to try.
If you're sceptical - good. We need critical thinkers. But give it the chance to show you, not tell you. Watch what your peers are actually building before you decide it isn't relevant.
The builders are already there. In most organisations, they're invisible and working in isolation. The opportunity - and it is a genuine one - is to make them visible, connect them to each other, and give everyone around them the confidence to start.
That's how this compounds.
The Builders Blog is a practitioner-first publication on AI, aviation data, and building things that actually work. Subscribe to The Builders Blog LinkedIn newsletter here.
