Why Treating Your Team Like a Product Works Until It Doesnt

Treat Your Team Like a Product: A New Management Approach

A framework recently shared by Enjoy The Work, a San Francisco startup advisory firm, proposes an intriguing idea for technical founders struggling with people management: treat your team like you treat your product. Design it, instrument it, measure it, iterate on it. For engineers and product managers transitioning into leadership roles, this mental model offers an appealing bridge between their existing skills and their new responsibilities. Yet, while the framework provides useful scaffolding for novice managers, it also reveals deeper tensions about what effective leadership actually requires.

The Core Argument

Technical founders excel at building instrumented, data-driven products but often manage people through gut feelings and casual check-ins. This creates a blind spot that hampers their effectiveness as leaders. The solution, according to this framework, is to apply the same systematic thinking to people management: define what great looks like, establish core metrics, instrument the human system, and run experiments to improve team performance.

Example of Transitioning to Management

For engineers making the leap to management, I've seen this mental model provide genuine relief. A former engineering director I worked with at a Series B startup described his transition to management as "being thrown into a dark room and told to build something without knowing what tools I had." The product management framework gave him a vocabulary and structure that made leadership feel less mystifying. Within three months, he had implemented written role expectations, regular team health check-ins, and a simple dashboard tracking team velocity, satisfaction scores, and goal completion rates. His team's delivery improved by 40 percent, and attrition dropped from three departures in six months to zero over the following year.

Where the Framework Succeeds

The strongest element of the people-as-product framework addresses a real problem: the lack of intentionality in how many technical leaders approach team building. Research from CEB (now Gartner) found that 60 percent of new managers receive no training before taking on their first leadership role. For this majority, management becomes something that happens to them rather than something they actively design.

The framework's emphasis on defining what great looks like directly counters this passivity. When leaders articulate clear standards for talent, behavior, and team culture, they create the equivalent of a product requirements document for their organization. This clarity benefits everyone. A 2019 study by Leadership IQ found that employees who could clearly articulate their company's strategy were 2.3 times more likely to be engaged and 1.4 times more likely to stay with their organization.

The framework also correctly identifies that measurement can improve management. Google's Project Oxygen research, which analyzed performance reviews and employee surveys from over 10,000 managers between 2008 and 2018, demonstrated that manager effectiveness is both measurable and improvable. When Google identified eight key behaviors of effective managers and provided feedback to low-scoring managers based on these metrics, those managers improved their scores by an average of 5 percent in one year. The managers in the bottom quartile improved by 12 percent.

Moreover, the emphasis on instrumentation addresses a genuine gap. Many managers operate without basic feedback mechanisms. A Gallup study of more than 7,000 adults found that only 21 percent of employees strongly agree that their performance is managed in a way that motivates them to do outstanding work. Creating regular check-ins, establishing clear ownership, and implementing simple team health pulses, as the framework suggests, can transform this situation.

Where the Analogy Breaks Down

Despite these strengths, the product management framework for people misses critical aspects of effective leadership. The most glaring omission is agency. Products don't have feelings, motivations, or the ability to interpret your actions through cultural and personal lenses. People do.

When you instrument a product, it doesn't know it's being measured and can't change its behavior in response to that knowledge. But when you instrument people, everything changes. The Hawthorne effect, first documented in studies at Western Electric's factory in the 1920s and confirmed in hundreds of subsequent studies, shows that people modify their behavior when they know they're being observed. This isn't necessarily negative, but it means that measurement itself becomes an intervention, not just observation.

Daniel Pink's research on motivation, synthesized in his 2009 book "Drive," reveals another limitation. Pink analyzed four decades of scientific research on motivation and found that for creative, cognitive work, external metrics and rewards can actually decrease performance. People perform better when motivated by autonomy, mastery, and purpose rather than by being measured against predetermined metrics. A 2013 meta-analysis by Cerasoli, Nicklin, and Ford, published in the Journal of Applied Psychology, examined 40 years of studies involving more than 200,000 participants and confirmed that intrinsic motivation consistently predicts higher quality work than extrinsic motivators, particularly for complex tasks.

The framework's suggestion to measure "energy" with "eyes and ears" points to this tension without fully addressing it. Energy, psychological safety, trust, creativity, and cultural cohesion resist quantification. Amy Edmondson's research on psychological safety at Harvard Business School found that the teams with the highest reported error rates were actually the highest performing teams. They weren't making more mistakes; they were comfortable enough to discuss mistakes openly. A simplistic metrics-driven approach might have flagged these teams as problematic when they were actually modeling healthy behavior.

I saw this play out at a fintech startup in 2021. The VP of Engineering, inspired by product management thinking, implemented detailed metrics for his team: lines of code shipped, pull requests reviewed, story points completed, and response time to Slack messages. Within two months, the metrics looked great. Code velocity increased by 30 percent, and response times dropped by 50 percent. But three senior engineers quit within four months, citing a culture of surveillance and lack of trust. Exit interviews revealed that team members had started gaming the metrics, breaking work into smaller stories to inflate completion rates and sending quick Slack responses without thoughtful engagement.

The issue wasn't that measurement is bad, but that the team experienced it as depersonalizing. The VP had optimized for what was measurable while inadvertently degrading what mattered most: trust, judgment, and genuine collaboration.

What Actually Works

The most effective approach borrows the framework's structure while adding elements it underemphasizes: context, humanity, and qualitative judgment.

Consider Stripe's approach to engineering management. They maintain a detailed engineering ladder that specifies expectations at each level, similar to a product spec. But they pair this clarity with what they call "high judgement, low ego" decision-making. Managers use frameworks and data as inputs to decisions, not as the decisions themselves. Patrick Collison, Stripe's CEO, has written about the importance of "soft" factors that resist measurement: taste, judgment, and the ability to sense what work matters most.

Microsoft's transformation under Satya Nadella offers another instructive example. Microsoft abandoned its stack ranking system in 2013, a quintessentially metrics-driven approach where managers were forced to rank employees and designate a percentage as low performers. While this system provided clear quantitative differentiation, it destroyed collaboration as employees competed against teammates rather than focusing on customer value. Nadella replaced it with a growth mindset culture that emphasized learning, collaboration, and impact. Microsoft's market capitalization increased from 300 billion dollars in 2014 to over 3 trillion dollars by 2024, suggesting that the shift away from rigid quantification toward more holistic evaluation didn't harm performance.

Netflix's approach, detailed in "No Rules Rules" by Reed Hastings and Erin Meyer, offers perhaps the most nuanced balance. Netflix maintains exceptionally high performance standards and measures results rigorously. But they pair this with radical transparency and what they call "context, not control." Managers provide clear context about what success looks like and why it matters, then trust talented people to figure out how to achieve it. They measure outcomes while preserving autonomy around process.

A Better Framework

For technical leaders transitioning to people management, the people-as-product framework works best as a starting point, not an endpoint. Here's how to apply its strengths while avoiding its limitations:

• Define success clearly but qualitatively. Instead of asking only "what are our metrics," start with "what behaviors and outcomes define a great team." At Shopify, teams create "user manuals" for working together that specify communication preferences, decision-making processes, and team values. These aren't metrics, but they're far more specific than "vibes."
• Measure what matters but acknowledge what resists measurement. Track team velocity, goal completion, and satisfaction scores. But also schedule regular qualitative discussions about team health, psychological safety, and growth. When Google's Project Aristotle studied 180 teams to understand what made teams effective, psychological safety emerged as the most important factor. They measure it through surveys, but they cultivate it through qualitative practices like managers modeling vulnerability and explicitly inviting dissent.
• Instrument feedback loops without creating surveillance. The distinction matters. Weekly one-on-ones, quarterly team health discussions, and anonymous feedback mechanisms create loops. Monitoring Slack response times or measuring lines of code creates surveillance. The former invites participation; the latter invites gaming.
• Experiment thoughtfully but recognize that people aren't A/B tests. When you experiment with meeting cadences or decision-making processes, explain what you're testing and why. Invite feedback throughout, not just at the end. Treat team members as partners in improving the system, not subjects being observed.
• Distinguish between metrics for learning and metrics for evaluation. When Google used Project Oxygen to improve manager effectiveness, they shared aggregate data about effective behaviors but didn't tie individual manager scores directly to compensation or promotion. This distinction allowed managers to engage with the data as learning rather than as judgment.

The people-as-product framework succeeds at getting technical leaders to take people management seriously. It fails if it remains the ceiling of their development rather than the floor. Use it to get started. But don't stop there.

For further insights on improving team management, explore more resources at Enjoy The Work's guide.