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Applying the Hawthorne effect to modern engineering teams. ![](/assets/HVLC1SLAU7LLZivIFC7EidDQgWw.CBxjrRUl_Z2qq592.webp) ![](/assets/aHHJTe8rMRUw3vhronp54jURux4.B4FF3eqv_zv7yV.webp)Bernardo Hernández·Co-founder·Mar 24, 2026 In the 1920s, researchers conducted a series of experiments at the Western Electric Hawthorne Works plant in Chicago. Their original goal was simple: understand what workplace conditions improved productivity. They adjusted lighting levels, modified breaks, and changed working hours. Output increased. Then they reversed some of the changes, including reducing the lighting. Output increased again. The conclusion was unexpected: **Productivity** had not improved primarily because of physical conditions, it **improved because workers knew they were being observed**. The attention itself changed behavior. This became known as the Hawthorne Effect: **individuals modify their actions, often improving performance, simply because they are aware that their work is visible.** That insight is almost a century old, yet it feels remarkably current in modern engineering organizations. ## Engineering: The Least Visible Critical Function In most technology companies, engineering represents the largest single investment on the P&L. It defines product differentiation, speed of execution, and long-term margin structure. In many businesses, engineering is the business. And yet, for years, it has operated with limited structural visibility. When it comes to engineering, performance discussions often rely on qualitative narratives. Story points are debated. Lines of code are dismissed as meaningless. Operational metrics describe system health but rarely capture individual contribution or learning velocity. Across every organization I have worked with, I have seen the same pattern: When performance lacks a shared, objective reference point, conversations become interpretative. **When visibility is missing, improvement slows down because reality is unclear.** ## With Pensero Visibility Becomes Structural Pensero introduces a different kind of observation: Instead of relying on self-reported metrics or planning estimates, it analyzes real delivery artifacts after the fact by connecting to GitHub, Jira, CI/CD systems, and collaboration tools. Engineers do not need to change how they work. There is no additional reporting burden. The work happens as usual, and its impact becomes measurable. At ClosedLoop, this dynamic became clear as the company scaled. Andrew Eye began hearing from the board that engineering appeared slower-shipping than other portfolio companies, yet he had no concrete way to validate or contextualize that perception. Moreover, as AI tools became embedded in the workflow, attribution mattered less than outcomes, and the core question became simple: are we actually going faster? That moment illustrates the essence of the Hawthorne Effect in modern engineering: **once performance becomes visible in a credible way, behavior adjusts accordingly.** Within the first week, ClosedLoop had meaningful signals. For the first time, engineering performance became a shared, trusted reference point. Early data revealed variation across the team. Leadership used that information for coaching rather than punishment. Best practices spread. Tool adoption became more intentional. Conversations shifted from opinion to evidence. Over time, performance converged upward. Andrew described the outcome directly. The entire team now operates above the 80th percentile. Delivery accelerated significantly. In his words, the company is moving four times faster than before, and the improvement is visible in the charts. This is the Hawthorne Effect applied correctly. Not through pressure, but through structured clarity. ## Transparency as a Cultural Signal What is particularly telling is how engineers responded. **Transparency did not create resistance, it created ambition.** Engineers valued having objective proof of impact. They appreciated being able to benchmark themselves globally. The system felt consistent and contextual, which made it credible. Andrew put it in terms that go beyond productivity: “*Pensero is why you want to work here.”* That statement reflects a deeper shift. When performance is observable in a fair and trusted way, excellence becomes part of the culture. Visibility stops being about control and becomes about growth. Leaders can invest aggressively in AI tools and coaching because they can see whether those investments translate into acceleration. Engineers can experiment and improve because feedback is grounded in reality. The Hawthorne researchers discovered that attention changes behavior. Modern engineering organizations are rediscovering that structured, evidence-based visibility changes culture. In a world where engineering is the largest capital allocation decision many companies make, operating without clear visibility is no longer sustainable. When reality is measurable, performance improves naturally because engineering teams understand how they are actually performing. When clarity becomes structural, excellence compounds. Next step ## Find your level of AI in engineering See the 5 levels, from human-only to fully autonomous agents, and what moving up is worth. [See the 5 levels →](/how-it-works#levels) Next step ## Scope your audit in 30 minutes We look at your tools, AI agents and vendors and tell you what we would measure first. [Book a scoping session](/book-demo)[Start free](/auth/signup/) © 2026 Product shots use illustrative data. 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