RDEL #150: How is AI adoption affecting team motivation?

41% of engineering leaders say their teams are less motivated than a year ago, often because creative work is being displaced by the review of AI-generated code.

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.

Ask an engineer why they got into the field, and you’ll likely hear about building things, untangling hard problems, the specific satisfaction of shipping something they designed. But as the tools take over more of the building, the day-to-day shape of the job is shifting in ways that may cut in the opposite direction. This week we ask: how is AI adoption affecting team motivation, and what is actually driving the decline?

The context

With AI tools now mainstream and internal adoption the top engineering priority, most leaders are measuring the rollout in terms of productivity. What gets measured far less often is its impact on motivation, which is itself a large driver of productivity. Motivated engineers sustain focus on hard problems, take ownership of quality instead of doing the minimum, and stay long enough to build the deep context that reliable delivery depends on. When motivation erodes, the cost surfaces later as slower throughput, more defects slipping past tired reviewers, and the attrition of exactly the people you most wanted to keep.

What makes this moment tricky is that AI doesn’t just speed up the job; it changes its shape. The center of gravity shifts from authoring to reviewing, from creating to verifying, from “what should we build?” to “is what the model built correct and safe?” These are real, valuable activities, but they are not what most engineers signed up for. This might impact motivation, and lead to consequences on overall engineering delivery.

The research

The findings come from LeadDev’s 2026 Engineering Leadership Report, a survey of 600 engineering leaders spanning roles from software engineers to CTOs across North America, Europe, Asia, and beyond. A recurring thread runs through the data on team health.

• A large share of teams are less motivated than they were a year ago. 41% of respondents say their team feels less motivated than 12 months ago. That is a slight improvement on the 44% reported in 2025, but it still describes roughly two in five teams losing energy.

  

  Motivation of engineers, 2025 vs 2026

• The displacement of creative work is the explanation leaders keep giving. In free-text responses, leaders repeatedly described rewarding creative work being replaced by the maintenance and review of AI-generated code.

  • One response characterized a “TikTok-ification” of problem solving that turned engineering into “a babysitting chore, rather than doing interesting work.”

• The human cost is climbing fastest at the top. CTOs reporting that they feel emotionally drained at least once a week jumped from 24% in 2025 to 54% in 2026, a 30-point swing in a single year.

  

  Level of emotional drain, by role

The application

The motivation problem the data describes is not really an AI problem. Rather, teams are reacting to losing the parts of the job that gave it meaning, with no clear story for what replaces them. That makes this a leadership narrative gap as much as a technology shift, and it is one leaders can close.

• Protect creative work. Don’t let review-and-fix quietly consume the whole week. Reserve explicit capacity each cycle for design, greenfield, and exploratory work so that “interesting” doesn’t become the first thing optimized away.

• Never wire AI adoption to fear. Retire token-usage leaderboards and “use it or be laid off” framing. When adoption is enforced through pressure, engineers come to resent the tools rather than embrace them. Measure team outcomes, and use it to resolve bottlenecks that might be adding toil to engineering work.

• Give the role evolution an explicit narrative. Don’t leave people to infer what “valuable work” means now. Spell out how reviewing, orchestrating, and validating AI output is itself a developing skill and a real career path, so the shift reads as growth rather than loss.

Protect the work your engineers find meaningful, and the productivity tends to follow.

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Wishing you a happy Research Tuesday,

Lizzie