The first month of AI adoption on a creative team is when scattered individual experimentation turns into a shared starting point. For Mekanism, Superside's GenAI consulting team spent two weeks on a baseline diagnostic and two weeks on leadership work covering positioning, upskilling and change management. The hardest part was getting leadership to one shared position. From there Mekanism did most of the work itself, reaching 85 to 90 percent adoption within about eight months.
Most creative teams don't start from zero. People are already using AI on their own, some heavily and some not at all, and the first month of AI adoption on a creative team is about turning that uneven picture into something the whole team shares.
What follows is one real engagement, told in the order it happened: how Superside ran that first month with the creative agency Mekanism.
First comes the starting point and what the month covered, then what any team can do alone and why leadership alignment was the hardest part. The outcome itself is in our published Mekanism story, so the focus stays on the mechanics of the month and the months after it.
Where Mekanism started
Before any plan exists, a team needs an honest picture of where people actually are. Mekanism's starting point will look familiar to most creative leaders, so it is the right place to begin.
Constanza Lima, GenAI Consulting Associate at Superside, described the agency this way when the consulting team first spoke with its people.
Mekanism was AI-curious, but not yet AI-enabled at scale.
People were experimenting and saw the potential. Adoption, though, depended heavily on individual initiative, and nobody had a shared view of where to start or which use cases to prioritize.
Mekanism had already taken its own first step before bringing in outside help. Ian Kovalik, Chief Creative Officer, AI Superstudio, Mekanism, explained that the agency ran an informal internal survey and found three groups:
- People who were all in on AI
- People who wanted nothing to do with it
- People who were curious but had no idea where to start
That split came from Mekanism's own survey, and it was the starting state rather than a finding from the engagement. Jason Harris, CEO and Co-founder of Mekanism, described attitudes before the work as mixed, ranging from people using AI every day to strong skeptics on the creative side.
What the first month of AI adoption on a creative team covered
The month ran in two halves, and our consulting team’s account stays at the level of workstreams rather than week-by-week detail. That limit is deliberate, because the first 30 days of AI adoption are easy to over-dramatize and the useful part is knowing what each half was for.
Javier Mena, GenAI Consulting Manager at Superside, was one of the AI-native experts on our team for the engagement. The first two weeks were a baseline diagnostic covering three areas:
- AI readiness across the organization, assessed through a survey and one-on-one interviews
- Where AI could have the most impact in existing workflows
- Tooling recommendations, both off-the-shelf and custom
The published case study adds that the analysis surfaced high-impact opportunities in ideation, copywriting and proof-of-concept creation. Javier Mena singled out how the recommendations were built step by step, starting from the baseline findings and moving into more specific deep-dive conversations.
They did a lot of surveying, a lot of research of our staff, and then they came back to us with a plan.
The second two weeks shifted from findings to decisions. We supported Mekanism’s leadership on three fronts:
- Product offering and positioning
- Upskilling across the organization
- Change management
| Phase | Focus | Outputs |
|---|---|---|
| Weeks 1-2 | Baseline diagnostic | Readiness survey and interviews, high-impact workflow areas, tooling recommendations |
| Weeks 3-4 | Leadership work | Positioning, upskilling, change management |
| Months 2-8 | Mekanism’s own rollout | AI team, approved tools list, client agreement process, adoption from ~20% to 85-90% |
What a team can do on its own before anyone is engaged
You can start this week by picking one or two repetitive tasks, running a small creative AI pilot with a few curious people and measuring a simple before-and-after. The steps below come from our consulting team’s own advice and from what Mekanism built for itself, credited to whoever did it.
Start with one or two repetitive tasks
Javier Mena's advice is to start small and close to the work. Pick one or two high-volume, repetitive tasks, such as resizing, versioning, or first drafts of decks, because those give the fastest wins.
Run a short pilot with a few curious people, then measure a simple before-and-after, like time per asset or the number of revision rounds. Share what worked with the wider team, because, in Javier Mena's words, "real examples from your own work build more momentum than any training program."
Find out where people actually stand
The diagnostic at Mekanism paired a readiness survey with one-on-one interviews, and a team can run a lighter version of both itself. The survey shows the spread across the team, and the interviews show the reasons behind it.
Constanza Lima found that people were most unsure about where and how AI could be used safely, particularly across client accounts, and how to use it well in day-to-day workflows. For a structured way to gauge your team's starting point, our AI readiness assessment goes deeper.
Write down the approved tools and restrictions
Uncertainty about what is allowed holds people back. Constanza Lima's advice is to clarify approved tools and restrictions and to write client-specific guidelines wherever the rules differ by account.
Mekanism took this a step further on its own, building an internal AI site that links to its approved tools and legal guidance from every page. Our piece on AI governance for creative teams covers the policy side in full.
Make time for hands-on learning
Constanza Lima recommends role-specific training, plus time and incentives for people to explore AI on their own. She also recommends recognizing and rewarding that initiative, while showing how AI can enhance creativity rather than replace it.
Leadership buy-in, hands-on demonstrations, and peer champions were seen as key to building confidence and making adoption feel relevant and safe.
Mekanism also runs its own bi-weekly session, called the prompt room, where people join a call to work through the topics of the week together. For building a full training program, see our article on gen AI training for creative teams.
Put it on paper
Agreement that lives only in meetings tends to fade. Ian Kovalik said the best way to build consensus is to write the plan down as an actual document, because that is when people start paying attention to it.
Measure without pointing fingers
Mekanism tracks two things: efficiency gains and how much of the approved tooling each person uses daily. Jason Harris was clear that the aim is to understand how the team as a whole uses AI, not to single anyone out.
That framing matters if you want people to keep experimenting openly. Our piece on how to measure AI ROI for creative teams goes deeper on measurement.
The hard part was getting leadership to one position
A lot of AI adoption advice focuses on skeptical practitioners. At Mekanism, the most demanding part of the month sat higher up, with leadership itself.
Javier Mena said the hardest part was shaping a shared strategic position on AI. Leadership brought a wide range of strong views on how Mekanism should position AI, which made for rich discussions and a lot of work to reconcile.
Much of our time went into bringing those perspectives together into one structured answer the whole team could stand behind.
Before that position could form, we worked with Mekanism's leadership to define the key questions to answer first. None of them requires buying anything, so any leadership team can work through them now:
| Question to settle | What it shapes |
|---|---|
| What level of AI expertise should the organization reach? | Upskilling |
| Should the rollout start with specialist AI pods or go to the whole organization at once? | Upskilling |
| How involved should creatives be in designing the new AI-enhanced workflows? | Change management |
| What should the internal messaging to teams be? | Change management |
Writing the answers down, as Ian Kovalik recommended for building consensus, gives the rest of the team something concrete to react to.
Resistance from individual creatives is a separate problem with its own fixes. Our piece on barriers to AI adoption in creative teams covers that side in depth.
What happened over the months that followed
The engagement with Superside ended after one month, and the larger change came afterward. Crediting each part to the right party matters, because it sets an honest expectation for your own team.
When the month closed, Constanza Lima said our team left Mekanism with practical AI use cases for everyday productivity and creative exploration:
- Research
- Brainstorming
- Copywriting
- Mood boards
- Storyboarding
It also left recommendations for role-specific training, clearer client-specific AI guidelines and sharing expertise beyond the few people already confident with AI.
Then Mekanism did the work. Jason Harris puts adoption at roughly 20 percent before the engagement and 85%-90% today, within about 8 months and mostly through Mekanism’s own work after the first month.
Jason Harris described the shift as a move from an AI free-for-all to centralized thinking. Mekanism now has a team focused on AI, a clear list of tools approved for client work and a process for amending client agreements to cover certain AI tools.
The reception of AI at Mekanism has grown almost exponentially in the last eight months.
Through all of it, the ideas and the craft stayed with people. We work the same way at Superside, with real creatives behind every output.
Jason Harris put it plainly:
We're really an AI-enabled organization. The way we look at it is that it's all human generated. The ideas are human led. Strategies are human led. Execution's human led.
Once the starting point is set, the next question is what the following quarters look like. Our AI roadmap for creative teams picks up where month one leaves off.
This is one agency's story, told as it happened. For enterprise in-house examples, see how one Fortune 500 company doubled its AI adoption and how another structured its AI rollout.
Run your first month of AI adoption with Superside
Superside is an AI-native creative agency. We pair AI-native experts with AI systems, so the creative we make keeps its craft and performs at scale.
Our GenAI consulting team works with creative and marketing teams at the stage Mekanism was at, with lots of experimentation and no shared approach yet. We don't stop at advice, because we combine strategy, hands-on upskilling and AI solutions built into the processes and systems you already use.
Every engagement starts with an honest read of where your team stands. From there we help your leadership agree on one position and give your people the training and guidelines to act on it, with real creatives and consultants alongside them.
Mekanism's month showed what that can set in motion when a team keeps building on it. If your team is ready for the same kind of shared starting point, book a call with our AI consulting team and we'll talk through where to begin.
Prefer to start on your own first? The AI Reset, our resource on bringing AI to your creative team, is a good place to begin.
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