There is a version of the AI adoption story playing out quietly in many organisations right now. The technical team builds. Everyone else watches. A two-speed organisation takes shape, not by design, but by default.
We think that is a missed opportunity.
The people with the most valuable instincts for where AI can help are not always the ones who write the code. They are the ones who know the workflows. Who live the problems daily. Who understand, better than anyone, where time and energy quietly disappear in a given process. That knowledge exists throughout any organisation, and when you give it the right tools and the right conditions, the results tend to surprise people.
So earlier this year, we created those conditions. We closed the laptops on business-as-usual for a day and ran Optimation's first company-wide AI Hackathon.
We could have run workshops, assigned online courses, or set up a lunch-and-learn series. All of those things have their place. But none of them do what a hackathon does.
A hackathon compresses the learning curve because the stakes are real and the timeline is short. You aren't exploring AI in the abstract. You are building something specific, for a real problem, that you will present to your colleagues by 2pm. That constraint changes everything. It creates conditions for genuine curiosity, for trying something you might otherwise have talked yourself out of.
There is also something that happens when people work together under a little pressure and a lot of creative licence. The conversations get better. The questions get braver. And when it's working well, people stop noticing that they are learning.
This part does not always get talked about, but it matters as much as the day itself.
Before we asked anyone to build anything, we made sure people were building in an environment that was genuinely safe, secure, and set up for success. We chose Claude as our AI platform, configured our connectors, and locked things down appropriately. People needed to know that what they were doing was encouraged, that it was secure, and that they could push and experiment without accidentally breaking something important.
That psychological safety, the sense that it is okay to try things here, is the prerequisite for everything else. Think of it like a test kitchen. You need to trust the environment before you can be free in it. We did that groundwork upfront, quietly, so that on the day itself the only thing people had to focus on was building.
We also made sure everyone had at least a baseline familiarity with the tools before they started. Enough to know where to begin. And, we invited people to share their ideas ahead of time, so teams could hit the ground running rather than spending the morning staring at a blank page. That small step made a real difference to the energy in the room from the start. The goal was to remove friction, not create it.
A few deliberate choices made the day work. We would make each of them again.
Team composition was intentional. We did not let people self-select into groups of close colleagues. Teams were mixed across two dimensions: AI proficiency and domain depth. You want people who can move fast with the tools sitting alongside people who have deep, specific knowledge of a business problem. Neither alone produces the best result. The person who knows a workflow cold but is new to AI is extraordinary at spotting where an agent would actually add value. The person who has been working with AI for months is extraordinary at making it real quickly. Together, they are formidable.
The theme was doing real work. Secret Agent was not just a fun wrapper, though it was definitely that. The theme was chosen to create a particular kind of energy. When people are having fun, they are more daring and more curious. They try things they might dismiss as too simple or too ambitious in a more formal setting. That willingness to have a go, especially at the start of an AI journey, is worth more than any amount of structured instruction.
A few deliberate choices made the day work. We would make each of them again.
Team composition was intentional. We did not let people self-select into groups of close colleagues. Teams were mixed across two dimensions: AI proficiency and domain depth. You want people who can move fast with the tools sitting alongside people who have deep, specific knowledge of a business problem. Neither alone produces the best result. The person who knows a workflow cold but is new to AI is extraordinary at spotting where an agent would actually add value. The person who has been working with AI for months is extraordinary at making it real quickly. Together, they are formidable.
The theme was doing real work. Secret Agent was not just a fun wrapper, though it was definitely that. The theme was chosen to create a particular kind of energy. When people are having fun, they are more daring and more curious. They try things they might dismiss as too simple or too ambitious in a more formal setting. That willingness to have a go, especially at the start of an AI journey, is worth more than any amount of structured instruction.
The brief had clear boundaries. This is the counterintuitive one: more creative freedom does not always produce better results. We gave teams a tight framework for what makes a good agent.
The idea here was to provide a framework that wasn't limiting, but liberating. We wanted teams to stop staring at a blank canvas and start asking the right questions about their actual work.
Kick-off at 9:30am. Teams formed. The brief went live.
What followed was one of the most energising days we have had as a business. By lunchtime, something had visibly shifted. People were no longer asking what AI can do. They were asking where in my week have I just accepted inefficiency as normal?
At 2pm, teams presented. Awards were on the line: the Speedy Gonzales Efficiency Engine for the biggest productivity boost, the Maverick for the most creative leap, and the People's Choice decided by company vote. The competitive energy was excellent. The costume commitment was variable. The quality of thinking across the board was impressive.
Some of the sharpest, most immediately usable agents came from people across every function of the business: delivery leads, client-facing roles, operations people who know their workflows with a precision that is genuinely hard to replicate. When you give domain expertise the right tools and a structured challenge, that is what you get.
By 3:30pm we were celebrating. The conversations over drinks were, in a lot of ways, more valuable than anything built during the day itself, because people were already thinking about what they would do next.
A few things stayed with us after the day.
There is no substitute for actually building something. The learning compresses dramatically when there is a deadline, a team, and a little healthy competition. Watching a demo or sitting through a presentation does not come close.
Several people came out of the day talking about their roles differently. About where their time had been going. About what it might feel like to genuinely have capacity back. That kind of shift in perspective is quieter than a product feature, but it tends to matter more in the long run.
The Secret Agent theme, the costumes, the awards, the energy of the room: these were not decoration. They were structural. When people are having fun, their guard comes down and their curiosity goes up. For many in our team, this hackathon was their first real attempt at working with AI. The fact that it happened somewhere that felt playful, safe, and collective made all the difference.
And something worth naming: the skills that matter most are changing. Knowing how to prompt, direct, and work with these tools is becoming part of everyone's job, not just the technical team's. But so is being genuinely human. Judgment, empathy, real client relationships: AI does not replace those things. It gives people the capacity to use them more.
Probably, yes. The approach is more transferable than it might look from the outside. The things that made it work for us, a secure environment to experiment in, teams mixed by skill and domain knowledge, a tight brief, and a day that felt genuinely fun, are not specific to Optimation. They are good conditions for any team taking its first serious steps with AI.
If you are somewhere in the space of knowing you need to do something about AI but not being sure where to start, we would genuinely like to talk. No pitch, no pressure. Just a real conversation about where you are and what might actually be useful for your team.
Get in touch with our team. We are building this in real time too, and are happy to share what we are learning.