The Unexpected Challenge of AI Adoption
When we set out to explore the potential of artificial intelligence, we had high expectations. We imagined that giving 5,000 smart and motivated individuals access to the best AI tools would lead to significant transformation. However, what we discovered was more complex than anticipated — and humbling.
The real challenge wasn’t with the technology itself, but with the people using it. Many employees struggled to give themselves permission to experiment. They felt guilty about stepping away from their daily tasks, such as checking emails. Instead of exploring new possibilities, they often defaulted to familiar use cases, even if those didn’t offer the most efficient or innovative solutions.
This realization shaped our approach moving forward. It became clear that the issue wasn’t a lack of capability, but rather the absence of an environment that encouraged genuine exploration. This insight is not unique to Canva. I see similar patterns in conversations with customers across industries. Companies often invest in AI tools, roll out policies, and mandate usage, yet after several months, little change is observed. Adoption remains stagnant, and teams revert to old habits.
The Problem Most Organizations Are Facing
The common frustration among customers is consistent: putting a tool in front of every employee doesn’t automatically lead to meaningful change. Deployment isn’t the same as enabling. Whether it’s a small marketing team or a global enterprise, the pattern remains the same. People need time to experiment and find the right use case for their specific role. This doesn’t happen during a single lunch-and-learn session or in the margins of an already packed workday.
As AI evolves from simple question-and-answer prompts to agentic systems capable of running entire workflows autonomously, the learning curve becomes steeper. The missing ingredient is often the same: people need actual time to experiment without feeling like they’re falling behind on their real job. In today’s fast-paced work environment, carving out space for learning feels like a luxury. Yet, ironically, it might be the highest-leverage investment a company can make.
What We Tried
This insight led us to create AI Discovery Week. Internally, this initiative helped accelerate adoption across the company, with over 90% of Canva employees now using AI assistants weekly or daily. Externally, it shaped how we help customers navigate the same transition.
The program was designed around one core idea: meet people where they are. For go-to-market teams, this meant three key areas:
- Deeply learning Canva AI to better support customers
- Improving internal use of the broader AI tool stack
- Creating space for play-and-build sessions to prototype apps, content systems, and workflows that enhance their work
We also introduced guided sessions, role-specific workshops, and leadership panels. Teams from across Canva, Anthropic, OpenAI, and Google were brought in to provide hands-on experience with industry-leading tools. The week culminated in a two-day hackathon where teams submitted ideas.
The Results Were Striking
One B2B growth marketer developed a 7-agent workflow that pulls data from Slack, sales calls, customer reviews, and online forums to generate digital ad formats. This saved 60 working days and allowed the creative team to focus on higher-impact formats like motion and video. My Chief of Staff built a full end-to-end app for Cannes Lions that coordinates scheduling across 20+ executive diaries, manages the Canva Cabana program, and integrates lead interactions into the CRM.
By the end of the initiative, the company logged 26,000 hours of hands-on exploration.
What We Learned About AI Training That Actually Works
Three key insights emerged quickly:
- Most AI training fails because it’s too centralized. Use cases vary widely across functions. A designer, marketer, salesperson, and engineer will all use these tools differently. A one-size-fits-all approach won’t drive behavior change.
- Community accelerates adoption faster than formal enablement. The hackathon operated as a friendly competition, with the best ideas celebrated in team meetings and shared across the company. Some of the most interesting projects came from unlikely collaborations — engineers pairing with marketers, salespeople working with designers to solve shared problems.
- The moment someone realizes “this actually works” is critical. Once that moment happens, adoption becomes self-sustaining. People stop seeing AI as a trend and start treating it as part of their workflow.
What This Means for How You Hire
Alongside training, many companies ask: “How do we hire for this?” Most approaches are flawed. The instinct is to screen entry-level candidates on AI familiarity, but this is the wrong question at the wrong level. Asking a recent graduate whether they use AI is akin to asking if they know how to use Microsoft Word — the answer tells you almost nothing.
The gap that matters is at the leadership level. When hiring senior leaders, we look for individuals who can articulate how they would redesign workflows, rethink team output, and deploy emerging agentic tools practically. This is the kind of leadership that drives real change.
Sustaining Momentum Is the Harder Work
A learning week is a catalyst, but sustaining momentum is the real test. We built ongoing infrastructure to support this: an AI Hub with self-paced courses, toolkits, and templates; fortnightly AI Forums to share practical use cases; a network of AI Exemplars leading roadshows on emerging tools; and an AI Show & Tell where product and research teams showcase the latest developments.
For other leaders, the takeaway isn’t to replicate the exact model. The format — the week, the hackathon, the hub — isn’t the point. The real goal is making AI adoption part of your culture, not just part of mandatory training.
The Behavior Gap
The conversation around AI is still overly focused on tools. Most organizations no longer face a technology gap; they have a behavior gap. The challenge now is helping teams build enough confidence and fluency to change how they work. This requires more than access or a week of training. It demands an honest reckoning with human habits, anxieties, and organizational pressures that make real behavior change so difficult.
The AI was never the hard part. We are.
