This week on High Output, Adil Ajmal, CTO of Fandom, tackles the question every engineering leader is wrestling with: How do you get an entire organization to adopt AI without simply mandating it?
While many engineering leaders are navigating AI adoption with a mix of top-down encouragement and bottom-up experimentation, Adil took a more structured path. He set a specific, measurable goal: 80% AI adoption across Fandom's engineering organization this year. Not 100%. Not "eventually." Exactly 80%—because he learned that successful technology adoption is about deliberate change management, not just wishful thinking.
Managing AI strategy for 350 million monthly users across 250,000 communities, Adil has discovered that the challenge isn't the technology itself—it's getting distributed teams to embrace it while maintaining the stability that massive scale demands.
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What's inside (34 min):
→ The 80% strategy. Why Fandom set a specific AI adoption goal rather than hoping it happens: "We've set a goal of AI adoption for our teams... we measure our usage." The framework includes enterprise licenses, internal champions, and treating it as deliberate change management, not a tech rollout.
→ Measuring what matters. How Fandom tracks adoption across different tools and teams: "We also bring in the right tool for the right thing. So if you're doing more front end development, you know, we have cursor that you can use... copilot is better for a bunch of other things."
→ The champion strategy. Instead of mandating from the top, finding internal advocates who can show others what works: "We try to find champions within our team who've had good experiences so that they can be the promoters of it for other team members."
→ Experimenting safely at scale. How Fandom balances AI adoption with stability for 350 million users: "You don't want to skip the code review part. You don't want to skip the automated test suites." The key is knowing what AI is good at—and what it's not.
→ The global content challenge. Why AI translation works perfectly for Shogun but breaks for Expedition 33: "Your translation may be factually correct, but if it doesn't actually match with how people are using it, it's not going to work out." Human oversight becomes critical at scale.
Why it matters
We're past the point of debating whether to adopt AI—the question now is how to do it effectively across entire engineering organizations. Most companies are taking one of two approaches: mandate it from the top or hope engineers adopt it naturally. Both strategies fail.
Adil's 80% goal reveals a third way: set specific, measurable targets and treat AI adoption like any other major organizational change. It requires champions, metrics, enterprise-grade tools, and deliberate change management.
His experience managing multiple company acquisitions (Twitter, Amazon, Intuit) taught him that successful technology adoption isn't about the technology—it's about people. The same principles that work for integrating acquired teams work for AI adoption: alignment, context-setting, and giving people time to internalize change.
At Fandom's scale, the stakes are higher. With 350 million users depending on platform stability, they can't afford to experiment recklessly. But they also can't afford to fall behind on AI capabilities. Adil's approach shows how to thread that needle.
Your turn
Adil's framework challenges us to be more deliberate. Here are two questions to consider:
Can you actually measure your team's AI adoption, or are you flying blind? What would change if you could see exactly how AI tools are impacting your team's velocity and output?
As AI creates more efficiency, where are you reinvesting that time—into your product, your platform, or your people?
If you're wrestling with AI adoption strategy for your engineering team, we'd love to hear your story. Schedule a chat with us → https://cal.com/team/maestro-ai/chat-with-maestro
High Output is brought to you by Maestro AI. As your teams adopt AI tools to ship faster, staying aligned on what you're actually building becomes the critical challenge. Maestro cuts through the noise with narrative status updates that digest every ticket, code change, and team discussion—because in a world where you can build anything, you need clarity on what you should build.
Visit https://getmaestro.ai to see how we help engineering leaders maintain alignment in the age of acceleration.
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