On March 19th in London, 21 team members stepped into a serious game designed to accelerate their understanding of AI Agents and automated workflows—not through a lecture or a deck, but through hands-on decision-making, team competition, and high-speed collaboration.
The team was eager to explore how AI could support their work and unlock new ideas. What followed was a multi-level experience that gave participants the skills and confidence to begin using AI with real purpose.
Seeing Opportunities
At the start of the game, teams were challenged to compete without the help of AI. Divided into four teams, each group included three sub-teams: Race Control, Pit Team, and Driver Performance. They had to operate as a cohesive race crew, relying entirely on human analysis and decision-making.
This first phase helped players sharpen their instincts and start identifying where AI could make a difference—which tasks slowed them down, where decisions piled up, and where greater visibility could have improved performance. The game laid the groundwork for strategic thinking and showed them how to spot the value of AI in the flow of work.
Augmented Intelligence
Enter RaceAI—a smart teammate—capable of answering questions, offering insights, and helping players make better informed decisions more quickly. Teams learned to work with this AI assistant through basic prompting, refining their questions, and sharing useful insights across sub-teams.
Here, the game shifted. Teams moved faster. Decisions became more confident. Challenged to develop AI use cases for their sub-team, one group developed an idea for an AI agent to automatically populate the racing strategy card, freeing up human teammates to focus on validation and adjustments. The dynamic changed from simply getting through the race to strategically optimizing it.
As one player shared: “It’s important to ensure that we have a human in the loop when working with AI.” That is exactly what this level was all about—blending human oversight with AI assistance to make better, faster choices.
Automated Execution
During the final phase, players were introduced to a new layer of complexity: agentic workflows. A Weather Agent (AI) replaced the Weather Analyst (human) in Race Control, freeing up an individual to complete a separate task. Teams had to adjust their roles and responsibilities in real time and develop a plan to capitalize on spare capacity. Players then had access to more advanced AI capabilities—context-aware dialogue, agentic workflows, and reasoning that helped them anticipate and adapt their racing strategy to changing conditions.
Rather than micromanaging every detail, teams had to decide what to delegate to AI and when to take control themselves. The result? Smarter resource allocation, sharper teamwork, and faster pivots. This phase pushed the group to think beyond assistance and toward orchestrating AI as part of their execution engine.
It’s no surprise that performance improved. Endurance Elite emerged as the overall AI Racer champion
Value Realization
As the game closed, attention shifted from competition to application. Teams reflected on their learning and translated it into action by creating and prioritizing AI use cases—24 in total—each mapped against effort and impact. This wasn't theoretical. It was grounded in the real dynamics they had just experienced: speed, complexity, tradeoffs, and the power of well-placed AI.
“This was an eye-opening experience to what we can do with AI,” one participant said. And that clarity is now fueling what's next.
The client is rolling out a formal AI training plan, using the momentum from AI Racer to guide deeper exploration. They’re not just curious about AI—they now have the tools and shared language to identify, shape, and apply AI use cases that align with their business objectives.
From seeing opportunities to driving transformation, AI Racer helps move teams from chat to automation—and lays the foundation for long-term impact.