While a tight product-research link is beneficial, it creates a management challenge where teams get so excited about implementation they neglect the next big research question. The research leader's role includes making the difficult judgment call to shift focus back toward long-term discovery, even amid product success.
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To balance AI hype with reality, leaders should create two distinct teams. One focuses on generating measurable ROI this quarter using current AI capabilities. A separate "tiger team" incubates high-risk, experimental projects that operate at startup speed to prevent long-term disruption.
The old product leadership model was a "rat race" of adding features and specs. The new model prioritizes deep user understanding and data to solve the core problem, even if it results in fewer features on the box.
Instead of a linear handoff, Google fosters a continuous loop where real-world problems inspire research, which is then applied to products. This application, in turn, generates the next set of research questions, creating a self-reinforcing cycle that accelerates breakthroughs.
As companies scale, the "delivery" mindset (efficiency, spreadsheets) naturally pushes out the "discovery" mindset (creativity, poetry). A CEO's crucial role is to act as "discoverer-in-chief," protecting the innovation function from being suffocated by operational demands, which prevents the company from becoming obsolete.
Managing innovative teams requires a balancing act. While sharing resources like software improves efficiency, it creates blind spots. Leaders should intentionally foster independent 'splinter groups' to work on the same problem, ensuring critical comparisons can be made to uncover hidden errors.
The new, siloed AI team at Meta is clashing with established leadership. The research team wants to pursue pure AGI, while existing business units want to apply AI to improve core products. This conflict between disruptive research and incremental improvement is a classic innovator's dilemma.
To avoid choosing between deep research and product development, ElevenLabs organizes teams into problem-focused "labs." Each lab, a mix of researchers, engineers, and operators, tackles a specific problem (e.g., voice or agents), sequencing deep research first before building a product layer on top. This structure allows for both foundational breakthroughs and market-facing execution.
Siphoning off cutting-edge work to a separate 'labs' group demotivates core teams and disconnects innovation from those who own the customer. Instead, foster 'innovating teams' by making innovation the responsibility of the core product teams themselves.
When pursuing a long-term strategic solution, dedicate product management time to high-level discovery and partner alignment first. This doesn't consume engineering resources, allowing the dev team to remain focused on mitigating the immediate, more visceral aspects of the problem.
Rewarding successful outcomes incentivizes employees to choose less risky, less innovative projects they know they can complete. To foster true moonshots, Alphabet's X rewards behaviors like humility and curiosity, trusting that these habits are the leading indicators of long-term breakthroughs.
