Months into a service design and documentation process, I sat in a Teams call filled with dashboards showing user behavior, conversion funnels, and engagement metrics. We had more data than ever, yet the team kept asking the same question: “Who exactly are we building this for?”

I recently sat in a strategy session where a team spent hours debating whether to rebuild their underlying backend engine using the “latest and greatest” framework. The discussion covered algorithms, infrastructure costs, and competitive benchmarks.

I was recently called in to consult with a team that’s spent six months building a custom authentication system. It’ll work, eventually. But there are proven, battle-tested solutions that could have been integrated in two weeks.

Every week, another vendor slides into your DMs with promises of “revolutionary agentic AI solutions” that will “transform your entire business.” The marketing drumbeat is deafening: Gartner names agentic AI as the top strategic technology trend for 2025, IBM declares 2025 “the year of the agent”, and McKinsey positions agents as “the next frontier” of AI innovation.

You’ve been filing bug reports your entire digital life—you just didn’t know it.

Ever notice how the most powerful question in product management is also the favorite of three-year-olds, philosophy professors, and 90s boy bands?

In the rush to add new features and capabilities, product teams often overlook a fundamental truth: how you build and deliver is as important as what you build and deliver.

As I logged into my favorite MMORPG last weekend, something struck me: while I haven’t touched Microsoft Word’s release notes in years (or ever), I was eagerly devouring every detail of this game’s latest update.

McKinsey’s recent article “The missing data link: Five practical lessons to scale your data products” has hit on something I’ve been advocating for years: treating data as a product rather than a project or resource. While I’m typically wary of consultancy research (aren’t we all?), this piece validates much of what we’ve observed in the field about the transformative power of data product thinking.

Technical complexity is the enemy of shared understanding. When presenting complex product concepts, data analyses, or system architectures, the gap between your expertise and your audience’s comprehension can quickly become a chasm.