Kene Anoliefo on turning AI anxiety into AI fluency
Learn how to transform AI anxiety into productive action with product leader Kene Anoliefo. Discover her practical framework for AI adoption—the 3 Ps (People, Process, Platform)—and why context enablement, not just technology, is the key to building AI fluency in your team. From creating psychological safety to rewarding knowledge sharing, get actionable strategies for leading product teams through AI transformation.
The past year has been a blur for product teams. New AI tools land every week, each promising to change the way we work (or make us worry we’re already behind). For Kene Anoliefo, founder of HEARD and longtime product leader at Spotify, Netflix, and Google, that mix of excitement and anxiety is the real story.
“You feel anxious because it is overwhelming,” she says. “Every week there’s a new breakthrough that could change how you do your job. The key isn’t reacting to everything — it’s starting with the work you already love and using AI to make it better.”
Kene has spent her career at the intersection of product, design, and AI. At HEARD, she helps product and research teams use AI to get customer feedback fast. Through it all, she’s seen the same pattern repeat: Teams struggle less with technology itself, and more with what it means for who they are.
In high-performing teams, people equate what they do with who they are. When that gets disrupted, it hits deep.”
– Kene Anoliefo, Founder, HEARD
1. Turning anxiety into action
Kene calls this moment the “Spider-Man stage” of AI adoption: the point right after you’ve been bitten by the radioactive spider but before you know what to do with your new skills.
“We suddenly have superpowers, but no instruction manual,” she says. “You’re sticking to walls and breaking lamps while someone’s telling you to go defeat the Green Goblin.”
That confusion is normal. The challenge for leaders is not to eliminate anxiety but to help people channel it. “Instead of feeding the constant ‘I need to catch up’ panic, focus on one part of your work you already enjoy,” she says. “If you love talking to customers, ask how AI can help you do that 20% better. You’ll build confidence from there.”
2. The 3 Ps: People, Process, Platform
Kene organizes her approach around a simple framework: people → process → platform.
- People: Start with identity. “AI is shaking people’s sense of what makes them special,” she says. “A designer might wonder, ‘Who am I if AI can design?’ Leaders have to make space for those questions. You can’t skip over them.”
- Process: Redefine what “good” means. “If AI helps you move 50% faster but drops quality to 75%, is that okay? Those trade-offs need to be explicit.”
- Platform: Only after you’ve aligned people and process should you bring in tools. “Most companies start here: They buy a bunch of AI tools and then wonder why no one uses them. Without alignment, you just end up with expensive shelfware.”
The 3 Ps build on each other: people first, process second, platform third. “If you start with the platform, you’re building on shaky ground,” Kene says.
Deeper dive: Who am I if AI can do my job?
Rather than telling people their “jobs are safe,” Kene suggests you help them see how their value is changing. As AI takes over more of the execution, the work that remains becomes about orchestration: defining taste, judgment, and purpose across the system. The craft doesn’t disappear; it moves up a level. For example:
Question: “Who am I if AI can design?”
Answer: You’re still the one who defines taste, intent, and coherence. AI can generate layouts, but it can’t decide why a product should feel a certain way or what emotional response it should create. Designers move up a layer, from pixel-pushers to curators of meaning.
Question: “Who am I if AI can write specs or code?”
Answer: You’re the person who ensures the system holds together. You connect what’s built to what customers actually need. AI can write requirements or functions, but it can’t set priorities, interpret trade-offs, or judge what “good” means for your users. You become the sense-maker, not just the spec writer.
Question: “Who am I if AI can do research?”
Answer: You’re the one who knows which questions matter. AI can summarize findings; it can’t spot what’s missing or sense the tension behind what customers say. Researchers evolve from data collectors to insight architects, designing the loops that keep human judgment in the mix.
3. From gatekeepers to architects
AI blurs role boundaries. A designer can code, a PM can prototype, an engineer can run user research. That flexibility is powerful, but it also triggers insecurity.
“When people feel their value is threatened, they protect their turf,” Kene says. “That’s how gatekeeping starts. Someone learns a powerful workflow and keeps it secret, thinking scarcity will make them indispensable.”
Her antidote is cultural, not technical. Reward “architects,” not gatekeepers. “An architect’s job is to design systems and practices that others can use,” she explains. “Their value comes from enabling ten other people to do great work.”
That shift only sticks when leaders change the incentives. Kene suggests making “enablement” a visible metric: Celebrate the person who records a Loom to show a workflow or documents a repeatable AI use case in Confluence. “If people see that sharing is recognized, they’ll do it.”
Shifting from guarding knowledge to sharing it
“Gatekeepers” protect their value by holding knowledge close: the “secret prompt,” the undocumented workflow, the file only they can find. Kene says it’s rarely malicious, and is often a way to feel safe when roles are changing. But it slows everyone down.
“Architects,” by contrast, make their knowledge reusable. They document, demo, and design systems others can build on. Their success comes from multiplying expertise, not guarding it.
Teams grow faster when the most respected move isn’t hoarding information, but enabling others to do great work.
4. Context is your new competitive edge
Kene believes the next phase of AI adoption will be less about intelligence and more about infrastructure: the systems that give AI the context it needs to perform well.
She calls this practice “context enablement”: capturing your product strategy, design principles, customer truths, and ways of working in one place so both humans and AI can use them.
AI is like a brilliant but clueless new hire. It can do great work, but only if it understands your company’s context.”
— Kene Anoliefo, Founder, HEARD
At HEARD, she’s seen how documenting that context — what she calls a “context library” — dramatically improves AI output. “Once a team externalizes what they know, the quality of AI-generated work goes up immediately. And it sends a cultural signal: We’re not gatekeeping; we’re teaching the machine and each other how we work.”
A context library doesn’t require fancy tools. Kene advises starting small:
- Collect the docs you’d share with a new hire: product principles, personas, research notes.
- Summarize them in simple language.
- Store them somewhere accessible, like Confluence.
- When you use AI to brainstorm or write, attach this context and compare results with and without it.
“The difference will be obvious,” she says.
5. Building psychological safety for change
Beneath all the frameworks, Kene’s real message is about psychological safety. Anxiety doesn’t vanish when teams adopt AI; it just shifts shape. Leaders have to normalize that discomfort so people can stay open and curious.
She encourages small, recurring forums where teams can share what they’re trying, as well as what failed. “It could be a 20-minute lunch demo where someone shows a workflow that saved them time, or even one that flopped. The point is to make experimentation normal, not heroic.”
This culture of openness also keeps the focus on progress, not perfection.
Leaders can’t shortcut AI fluency. It’s not about buying a tool. It’s about building confidence, one workflow at a time.”
— Kene Anoliefo, Founder, HEARD
6. What leaders can do today
Kene’s playbook for leading through uncertainty boils down to a few habits:
- Acknowledge the anxiety. Tell your team it’s normal to feel overwhelmed.
- Start where energy already exists. Pick one part of the work people love and explore how AI can help there.
- Reward sharing. Make documentation and demos a visible part of performance.
- Invest in context. Capture how your team works so AI can work alongside you.
- Model curiosity. As a leader, show that learning is part of the job, not an extracurricular.
Her final piece of advice is simple: “It’s not that you don’t belong at the table, it’s that no one’s handing out invitations. Just sit down.”
Keep learning
Kene’s framework — alongside perspectives from Laura Burkhauser, Ravi Mehta, Aakash Gupta, and Elena Verna — is featured in Atlassian’s new ebook, AI fluency: The new product superpower. Read it now →And you can listen to her full interview with Atlassian here.