A significant maturity gap in large organizations is that internal platform PMs don't treat their users (e.g., developers, finance) as customers. Applying customer-centric practices like problem framing and journey mapping to these stakeholders can dramatically improve outcomes.

A platform's immediate user is the developer. However, to demonstrate true value, you must also understand and solve for the developer's end customer. This "two-hop" thinking is essential for connecting platform work to tangible business outcomes, not just internal technical improvements.

Experienced product leaders avoid relying on muscle memory or applying a standard playbook. Each company, product space, and problem is unique. The most effective approach is to first understand the specific context and then select or create the right tools and frameworks for that unique situation.

Most users don't want abstract tools like 'agents' or 'connectors.' Successful AI products for the mainstream must solve specific, acute pain points and provide a 'golden path' to a solution. Selling a general platform to non-technical users often fails because it requires them to imagine the use case.

The failed ComponentScript framework insisted on using GraphQL for data consistency, adding significant friction. A competing server-driven UI approach succeeded by sacrificing consistency, which 80% of products didn't need. Prioritizing technical ideals over pragmatic user needs can be fatal.

While you gain deep empathy for one user (yourself), you risk creating a product so tailored to your expert needs that it alienates the broader market. This "market of one" paradox can lead to building powerful but commercially unviable tools for a niche group of power users.

By creating a "thin wrapper" UI over a technical tool like Claude Code, new products can fall into a trap. They may be too restrictive for power users who prefer the terminal, yet still too complex or unguided for mainstream users, failing to effectively serve either audience without significant optimization for one.

A critical mistake in enterprise product management is to treat the user and the buyer as the same person. The daily user (e.g., an SRE) cares about features and usability, while the economic buyer (e.g., a CIO) cares about ROI and strategic value. A successful product must deliver distinct value to both, and the PM must treat them as separate personas.

Without a strong foundation in customer problem definition, AI tools simply accelerate bad practices. Teams that habitually jump to solutions without a clear "why" will find themselves building rudderless products at an even faster pace. AI makes foundational product discipline more critical, not less.