Why 96% AI adoption at Make didn’t start with tools or training

When Sara Maldon joined Make two years ago, there was no approved AI tool. Nobody could use ChatGPT or any other model for work – there was nothing to adopt.

Make already had a culture built around automation. As a platform, automation was the product. “Do it once, do it twice, automate it, never do it again” was already how teams operated. But AI didn’t have the same structure around it yet.

Leadership knew AI would be critical but didn’t have a vision for where to take it. Sara was hired before they had a specific problem to solve. Her brief was to find the vision and build it.

Part of the logic was commercial. Make needed to understand what the market would expect from an AI-powered automation platform. Leadership believed the fastest route was to experience it internally first.

Miriwa – a Korean expression meaning “going into the future” – became the programme to make that happen. By the time it launched, organic adoption had already taken hold. People were using tools like Gemini day-to-day, but only 16% had built AI automations and 5% were using agentic automation. Make tackled this as a company-wide priority – aiming for everyone to build with AI and agents.

Most mid-market firms try to get there by approving a tool and tracking logins, but Make’s route looked nothing like that.

How Make built adoption without a big rollout

In her first week, Sara organised a voluntary four-week bootcamp. A third of the company signed up without being asked. People wanted to understand what was happening in AI but hadn’t been given a way in.

Before launching Miriwa, she spoke with over 160 team members one-on-one, almost half the company. She gathered data on individual knowledge levels, AI readiness, and where each department saw opportunities. That early discovery work meant the programme felt relevant to the people it was designed for.

Alongside that, Sara had the support of a part-time resource to target small implementation projects. The early engagement and the evidence gave Sara what she needed to ask for more. As she puts it: “Excitement gets things off the ground, but real change takes time and consistency.”

On the back of those initial results, the programme moved from what she describes as a “curiosity effort” to an intentional push to transform how departments worked. Sara built a dedicated team to make that real.

Make calls them “samurai”. Full-time people dedicated to AI adoption in every department. One in HR, one in engineering, one in product, one in marketing and so on. Each samurai operates as an embedded product manager who understands the department’s actual problems, builds solutions alongside the teams, and coaches people through using them. Internally, it’s known as a “buddy programme”. They’re embedded in a single department and report into both Sara’s team and the department VP.

But the role isn’t straightforward. One samurai put it to Sara recently: “I’m obsessed with impact, but it doesn’t depend 100% on me. I need to convince people and get them excited, and that doesn’t always directly show.” Sara hires for resilience and curiosity partly for that reason.

Why setting a high bar meant early projects failed, by design

The samurai were new in roles that hadn’t existed before, working in a field that was changing underneath them. Because each one understood their department’s specific needs, not every team got the same brief. But the approach ran across a common framework, what Sara calls AAA: automation, AI, and agents.

She deliberately doesn’t recognise time saved as a success metric. If a project saves time but doesn’t shift a core business number, it doesn’t count. Sara calls her targets “big fat hairy goals”, ambitious enough to stretch but realistic enough that teams don’t dismiss them.

Against that bar, early pilots had a high failure rate. Sara expected that, because the failed projects often produced more value than the ones that shipped. A samurai might scope out an ambitious solution when the team they were serving actually just needed help with the basics. In overreaching, they’d learn where the real friction was. A department might have no process to act on what a tool produced, or data wasn’t flowing where it needed to. Sara used those discoveries to fix fundamentals rather than let her team keep pushing against resistance and risk losing buy-in.

To balance ambition with momentum, each team now runs a tiered portfolio:

• One or two major strategic projects that could change how they work
• Three to five procedural improvements that support how teams operate
• A handful of quick wins to build confidence

That structure held as the programme’s scope expanded. When agents launched this year, the focus shifted outward. Client-facing teams needed to build literacy around a genuinely new capability, not just optimise internal workflows.

Technical teams already know how to build, so Sara asks them to pick the right solution for the problem rather than defaulting to agents.

What Miriwa delivered against the targets

When she announced Miriwa in July 2025, Sara targeted 90% AAA literacy across employees and 75% of projects moving core departmental metrics. The team cleared literacy (96% of eligible employees, 91% overall) and landed at 73% on business impact. The bar goes up again this year.

By 31st January, 96% of employees had built AI automations. Roughly half were agents, half AI workflows. That measures breadth, not depth, since a lightweight workflow counts the same as a revenue-critical agent. As Sara puts it: “Focus on the one who wants and invest the energy there, because that’s where the success will be.” The next benchmark is measurable impact from those builds.

ARR per FTE and average contract value (ACV) both improved, and time to fill in hiring came down. Sara won’t claim direct cause and effect, though the improvements tracked to the areas where Miriwa invested most.

Adoption shows across departments:

• Product built an Insights Hub that pulls themes from thousands of user research data points into summaries via Slack
• Marketing uses Make to update outdated articles, transcribe video, generate SEO fields, and monitor monthly performance
• Engineering automated SOC compliance and change management
• People & Culture launched a fully automated merch store where employees earn points for learning and redeem them for rewards

All samurai projects must already move core business metrics. Employee builds don’t have to show that yet, but the programme is heading in that direction.

Why hedging kills AI programmes before they start

Sara pushes back on leaders who hedge. When people come to her wanting to test whether an idea is viable, she’s blunt: commit to it properly or don’t bother. That doesn’t mean betting everything on day one, the samurai model came after the business case, not before it. But she has no patience for perfectionism slowing things down. She told one new samurai after seven weeks without a failed project: “I want to see a failure next week. Otherwise I will throw you into one.”

The organisational alignment matters too. IT needs to be ready because nothing starts without an approved tool. And the cultural side is harder. “If you want to do a culture transformation you need to have your HR officer on board being excited and potentially being the first person to build.”

An important caveat: Make sells automation. Their workforce is technically inclined and culturally predisposed to experimentation. A traditional mid-market firm with lower digital fluency faces a harder version of this. The principles transfer, but the timeline and starting energy will look different depending on your cultural starting point. This is something we see consistently at aibl when working with mid-market teams.

Sara frames the work as something worth doing beyond the metrics. “It is probably the most meaningful thing to be working on in an organisation right now, because you can see how that changes the way people think and operate.” Most mid-market leaders aren’t short of reasons to start. The gap is usually in how they structure what comes after.