Most companies already know AI is an inevitable evolution to the way we work. They’ve purchased licenses and perhaps developed governance policies. They’ve hosted training sessions. They’ve assembled steering committees. Somebody has almost certainly created a deck including the phrase “future of work”. And yet, months later, many organizations find themselves asking the same question: “Why aren’t people using it?  

Usually the assumption is that people need more training, and sometimes that’s true. But our team at Simantel had a hypothesis: AI adoption is less about the technology itself and more about helping people embrace new ways of working. In our experience, the bigger challenge was giving people time and space to play and acknowledging when they did. 

Our teams weren’t resisting AI. They weren’t protesting outside conference rooms. They were doing what professionals do when they have deadlines, meetings, revisions, approvals and approximately 47 unread emails: They defaulted to the workflows they already knew.

Habits are stubborn little things. If you’ve purchased a gym membership in January and found yourself eating queso on the couch by February, you already understand the challenge. Knowing something is valuable and building a habit around it are two very different things.

So, when we decided to prioritize the expansion of AI adoption across our agency, we stopped asking how to convince people that AI mattered and instead asked, How do we make using AI feel as natural as our other processes and tools?

TL;DR: The answer involved a trophy.

We focused on AI consistency over volume

When AI adoption programs only reward volume, they can go sideways quickly.  It’s akin to using “added contacts” as an adoption metric for CRM.

• Who generated the most prompts?
• Who used the most credits?
• Who completed the most tasks?

At first glance, these measures seem reasonable. Until the same three people win every month. The early adopters and resident enthusiasts somehow find time to test every new feature before the rest of us have finished reading the release notes. But if they’re the only people participating, you don’t have adoption. Instead of rewarding volume, we decided to reward consistency.

We turned AI usage into a fun competition

The concept was intentionally simple: Use AI 30 times in a month. Roughly 1.5 times per day. No complicated scoring systems or requirements to become a prompt engineering wizard overnight. The team names were things like “The Makers” and “The Quants”. Yes, very Harry Potter.

Departments competed based on participation rates, not total usage. A five-person team had just as much opportunity to win as a 50-person team, and managers couldn’t rely on one AI power user carrying the department across the finish line. They had to help bring everyone along. The reward? Bragging rights for the team and a trophy that serves as a display of the accomplishment.

We weren’t trying to create experts. We were developing habits.  

We recognized that different roles use AI in different ways

Another assumption we challenged was that everyone should be learning AI the same way. That’s why we organized challenges around how people actually work.

• Our creative teams focused on ideation, content transformation and concept development.
• Our analytics, media and technical teams focused on formulas, data analysis, automation and code generation.
• Our strategists focused on research, planning, audience understanding and campaign evaluation.

The competition turned into a cross-team brainstorm

To earn credit for challenges, participants shared examples of their outputs with colleagues inside of an instant message channel devoted to the challenge. Originally, this was intended to verify participation, but it became a way to crowdsource ideas and start discussions.  

• A strategist would discover how an analyst was using AI.
• A creative director would see a workflow from a developer.
• A media specialist would borrow a prompt structure from somebody in UX.

Useful ideas travel quickly once people can see them. 

The results of our competition surprised us

We expected adoption to increase, but we didn’t expect it to accelerate as quickly as it did. Following launch, advanced feature usage increased 116% immediately and reached 177% above pre-launch levels by the second month.

The percentage of users engaging with AI tools increased from 79% to 88%.

Average tool interactions per user increased 173%.

Credit consumption increased 94%.

That last metric might be our favorite. Not because we enjoy spending credits, but because higher credit consumption suggested people weren’t just checking a box to earn points but were instead tackling increasingly sophisticated tasks and integrating AI into real workflows.

People weren’t just using AI more often. They were using it more meaningfully. Right now, it’s firmly bragging rights of the Quants, which probably won’t surprise anyone who has ever met an analyst with a spreadsheet problem to solve. But the real victory was broader.

The Makers may not have claimed the trophy yet, but they were responsible for some of the most visible examples of AI in action, using AI to generate headline options, explore creative directions and transform content for different audiences and channels. Most important, they helped dispel the myth that AI is only useful for technical work.

The Strategists became some of the agency’s most active experimenters. They used AI to pressure-test campaign ideas, explore audience perspectives, and accelerate research tasks. Their work reinforced that some of the highest-value AI applications aren’t about creating content at all. They’re about creating better thinking.

Every department found practical ways to integrate AI into their work, creating a growing library of use cases that helped move the agency from isolated experimentation to shared adoption.

5 tips to use gamified AI adoption on your team

Here are a few lessons we learned along the way and how they can help you:

1. Reward consistency before expertise.

Don’t build a program that celebrates your most advanced users. Focus on building one that encourages participation from everyone else.

2. Make the goal feel achievable.

One interaction per day feels manageable while transforming your entire workflow immediately feels daunting and impossible. Make it something your people believe themselves capable of managing.  

3. Create role-specific challenges.

People engage more readily when they can see direct relevance to their work. 

4. Make success visible.

Good ideas spread faster when people can see them in action and that’s why we required “screenshots or it didn’t happen” proof points. 

5. Remember that behavior comes before mastery.

Nobody becomes an expert before they become a regular user. Build the habit before you refine it. Reward experimentation, not just outcome. 

The real lesson from AI gamification

Humans are wonderfully irrational creatures. We know what we should do, but we don’t always do it consistently. 

That’s why adoption conversations miss the mark. Because they focus on demonstrating the value of the technology instead of capitalizing on the value of rewarding experimentation. 

Creating an AI culture requires thoughtful leadership. And sometimes it requires a trophy sitting on someone’s desk reminding us that bragging rights are a surprisingly powerful motivator.