Moving from a large corporation to a startup requires blending foundational knowledge of scaling processes with newfound resourcefulness and risk appetite. This transition builds a holistic business muscle, not just a product one, by forcing leaders to operate without endless resources or established brand trust.
Unlike in big tech where CPOs can be purely visionary, startup CPOs must constantly shift their focus between strategy and execution. This 'pendulum' might swing from 80% strategy in the beginning to 80% execution pre-launch, requiring hands-on leadership to be effective.
Dedicate a recurring 'Customer Day' not only for user interviews but for the team to step back from tactical work. Use this time to synthesize existing data, analyze market trends, and refocus on the core 'why' behind the product, preventing the team from getting lost in the weeds of feature development.
A product leader should actively manage development by allocating effort into three buckets: future big bets, core foundation (stability/tech debt), and growth/optimization. The resource allocation isn't fixed; it must dynamically shift based on the product's maturity and immediate business goals.
To build a useful multi-agent AI system, model the agents after your existing human team. Create specialized agents for distinct roles like 'approvals,' 'document drafting,' or 'administration' to replicate and automate a proven workflow, rather than designing a monolithic, abstract AI.
In risk-averse sectors like law, AI's ability to automate core, revenue-generating tasks (e.g., writing) acts as the primary driver for innovation. The threat of being made obsolete forces legacy players to embrace technology and new business models they would otherwise ignore or resist.
To introduce AI into a high-risk environment like legal tech, begin with tasks that don't involve sensitive data, such as automating marketing copy. This approach proves AI's value and builds internal trust, paving the way for future, higher-stakes applications like reviewing client documents.
As AI automates 'hard' product management tasks like data synthesis and spec writing, the role’s value will shift. PMs who thrive will be those who master uniquely human skills like stakeholder influence, creative problem-solving, and critical thinking, which AI cannot yet replicate.