RDEL #90: How does the gap between actual and ideal workweek impact developer productivity and satisfaction?
Bigger misalignments predict lower satisfaction and productivity. Developers overwhelmingly want AI to reduce toil from documentation, testing, and repetitive setup tasks.
Welcome back to Research-Driven Engineering Leadership. Each week, we pose an interesting topic in engineering leadership, and apply the latest research in the field to drive to an answer.
AI is changing how engineers work—what tasks they spend time on, what skills they need to deepen, and how they define a productive week. But as AI reshapes workflows, it raises an important question: are developers actually spending their time the way they want to? This week we ask: How does the gap between a developer’s ideal and actual workweek influence their productivity and satisfaction—and what can engineering leaders do about it?
The context
Software engineers juggle a complex mix of responsibilities—from coding and system design to compliance, onboarding, and constant communication. But how developers actually spend their time often differs sharply from how they want to spend it. These mismatches might seem like a normal part of the job, but over time, they quietly erode satisfaction and performance.
At the same time, AI tools are beginning to reshape how engineering work gets done. Tasks that once consumed hours—writing tests, documenting code, even coding itself—are now increasingly assisted or automated. In theory, this shift creates space for developers to focus on more creative, high-leverage work. But the reality is more complicated. Without understanding where the biggest mismatches between ideal and actual work exist—and what’s getting in the way—leaders risk applying automation to the wrong problems.
Closing the gap between actual and ideal workweeks isn’t just a question of tooling. It’s a critical step toward building high-performing, resilient teams in the age of AI.
The research
Researchers surveyed 484 software developers at Microsoft to compare their “ideal” versus “actual” workweeks. Participants reported how much time they spent on 16 core activities and how they wished they could allocate their time. They also rated their satisfaction, productivity, and frequency of AI tool usage. Statistical analysis, including Spearman’s correlation and OLS regression, was used to identify patterns.
Key findings:
The greater the mismatch between actual and ideal workweeks, the lower a developer’s productivity and satisfaction. Developers whose schedules closely matched their ideal week reported much higher satisfaction.
Developers want to spend more time coding and designing systems, and much less on communication and meetings.
Developers want to spend more time on core development activities such as coding, designing new systems, learning new technologies, and knowledge sharing, while wanting to reduce the time spent on dealing with tasks related to security & compliance, communication, debugging, addressing incident/support tickets, and task management.
Specific tasks predicted satisfaction and productivity. Spending more time coding, documenting, refactoring, and learning was associated with higher productivity; spending more time on environment setup, security, and communication was linked to lower satisfaction and productivity.
Developers who used AI tools daily reported higher productivity and higher satisfaction. The less frequently developers used AI, the lower their reported outcomes.
Effects of AI usage on productivity 
Effects of AI usage on satisfaction level The top areas that engineers wanted automated was documentation, environment setup, writing and maintaining tests, task tracking, and security/compliance.
“Developers who experience a sense of productivity, yet feel dissatisfied with their workweek present an excellent opportunity for task automation. Our analysis reveals that this group invests significant time in activities such as ‘PR/Code Reviews’, ‘Monitoring & Dashboards’, ‘Communication’, and ‘Mentoring/Onboarding’. Consequently, these tasks emerge as ideal candidates for automation.” - Kumar et al, 2025.
The application
The research underscores a powerful but often overlooked insight: developers perform best when their time aligns with how they want to work. As an engineering leader, even small shifts toward aligning the real and ideal workweek could drive measurable gains in productivity, satisfaction, and retention.
Key actions for engineering leaders:
Take stock of how engineers spend their time—and where friction slows them down. Beyond broad time allocation, it's critical to understand the bottlenecks, repetitive tasks, and interruptions that quietly erode productivity. Small misalignments—like excessive meetings, slow environment setup, or fragmented communication—accumulate into major drains on energy and performance over time. Regularly tracking and addressing these sources of friction is critical to maintaining team health and velocity. (Tools can automate this process)
Invest in AI tooling where it matters most. Prioritize automation efforts on areas developers most want to delegate—documentation, task tracking, testing infrastructure—rather than focusing solely on code generation. Using AI in the areas of high friction will increase adoption and increase the team’s overall productivity
Minimize low-leverage tasks. Target activities like repetitive compliance work, administrative overhead, and operational toil for automation, streamlining, or reallocation.
By deliberately closing the gap between the real and ideal workweek—and eliminating the friction that quietly compounds—you can build teams that are not only more productive, but also more motivated, creative, and resilient over the long term.
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Happy Research Tuesday!
Lizzie