Kene Anoliefo has led product and design at Google, Spotify, and Netflix. She founded Heard, an AI-powered user research platform, and is now building tools that help teams capture and structure institutional knowledge so both people and AI can produce high-quality work.

We discuss

• The three Ps of AI adoption: people, process, and platformWhy most companies jump to platform tools whilst ignoring the people problem
• Moving from gatekeepers to architects in product organisations
• Context enablement: turning implicit knowledge into explicit AI inputs
• The coordination tax and how AI changes team collaboration
• Why AI is like a brilliant but clueless new team memberBuilding context libraries to 10x your AI output quality

Key takeaways

Start with people, not platform tools. Most companies rush to buy AI tools whilst ignoring the deeper questions: Who am I in this AI era? What makes me valuable when AI can do parts of my job? Anxiety about AI is rooted in identity, not technology. Address the people layer first before investing in new platforms.

Transform from gatekeepers to architects. Don't own user research, product strategy, or design - architect the principles and standards so anyone can do great work in those domains. With AI giving everyone superpowers, your value shifts from creating scarcity to enabling abundance.

Context is your competitive advantage, not just data. AI trained on generic internet data produces generic output. Context libraries - structured repositories of your product strategy, design principles, customer segments, and research standards - help AI understand how your company builds products.

AI is brilliant but clueless without your context. Think of AI as a talented new hire who needs onboarding. It has capabilities but no understanding of your product, customers, or how your team works. Just like you'd explain these things to a new employee, you need to provide this context to AI tools through documentation optimised for machine reading.

Don't wait for permission to engage with AI. The biggest barrier isn't technical knowledge - it's mindset. You don't need to have worked at ML-first companies or taken AI courses. The cure for AI anxiety isn't trying to learn everything; it's starting with work you already love and asking how AI can make it 10-20% better.

Chapters00:00: Introduction to Kene Anoliefo01:00: Kene's journey from Spotify and Netflix to founding Heard04:00: Early interest in AI and building AI-powered research tools07:00: Overcoming AI anxiety and intimidation10:00: The three Ps framework: people, process, and platform14:00: Why feelings matter more than tools in AI adoption15:00: The coordination tax and new team dynamics18:00: Gatekeeping vs enabling in the AI era20:00: Context enablement and knowledge architecture23:00: AI as a brilliant but clueless team member26:00: Career advice: take more shots on goal28:00: Making context enablement practical and actionable30:00: Demo: Building context libraries for AI33:00: Creating user research context libraries36:00: Using AI to generate context documentation39:00: Before and after: brainstorming without context42:00: The multiplier effect of context investment45:00: Context engineering as the next frontier48:00: Design context libraries and component repos51:00: Prototyping with V0 using pre-built contexts56:00: Democratising product ideas across the organisation59:00: Lightning round: staying sharp in the AI era01:01:00: Desert island books and dishes01:04:00: Final advice and where to find Kene

Where to find Kene Anoliefo:LinkedIn: https://www.linkedin.com/in/kene-anoliefo-68a8b714/Website: https://kene.ai

Where to find Axel:Linktree: https://linktr.ee/axelsooriahLinkedIn: https://www.linkedin.com/in/axelsooriah/

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