Revelo Research · 2026 Report
We surveyed a representative sample about the 30 tasks that typically go into shipping a feature, from scoping to updating the docs. Here's what they told us.
Source: How Engineers Use AI, Revelo Research 2026. A representative sample of vetted engineers in Revelo's network at 99% confidence, ±5% margin.
Inside the report
We surveyed a representative sample of engineers in Revelo's network about the 30 tasks that typically go into shipping a feature, from scoping a problem to watching it run in production. For each task, engineers told us how important it is, how satisfied they are with it today, and whether they use AI to do it. Results hold at 99% confidence with a ±5% margin.
The 30 tasks are a map of the job an engineer does to ship one feature. Revelo's data team wrote out each step, from clarifying the requirements to updating the docs, and grouped them into 7 stages in the order the work happens. We used the jobs-to-be-done method to score them: for each step, engineers rated how important it is and how satisfied they are with how it goes today, and the gap between the two is the opportunity. Engineers rated only the steps that are part of their own work. The survey ran in late July and early August 2026.
The 30 tasks, grouped into the 7 stages of shipping a feature:
Understand & scope
Design & plan
Set up
Implement
Verify & test
Debug & fix
Review & ship
The 30 tasks in order, from scoping to docs.
Averaged across design, build, test and debug tasks, AI adoption is 89%. The highest rates are handling errors and edge cases (95%), exploring an unfamiliar codebase (95%) and writing unit tests (94%). On these tasks a wrong suggestion costs a few minutes of review.
Adoption is lower on the tasks where a mistake reaches production or other people: deploying (62%), monitoring after release (62%) and stakeholder alignment (64%). That's a 26-point gap between building a feature and releasing it.
We read this as good judgment. Engineers use AI most where its output is cheap to check and least where being wrong is expensive.
On every task, engineers reported higher satisfaction when they used AI than when they didn't. The average lift is +0.4 on a 5-point scale.
The smallest lift was +0.15 (diagnosing root cause). The largest was +0.71 (scaffolding a service). No task came out negative.
Senior engineers (6+ years) reported a +0.5 satisfaction lift from AI. Junior engineers (0 to 3 years) reported +0.3.
One likely reason: an engineer who already knows what good output looks like can check and correct a model quickly. An engineer still building that judgment has to work harder to catch mistakes.
Of 1,005 Revelo placements through May 2026, 73.1% were senior engineers and 24.6% mid-level.
AI adoption sits between 83% and 89% in every segment we tested: backend 86%, frontend 84%, senior 85%, junior 84%, engineers based in Latin America 84%, the global pool 87%, startup-focused 89% and enterprise-focused 83%. Every segment showed a positive satisfaction lift on all 30 tasks.
Four questions hiring managers ask us about AI.
Yes. In this survey, 85% of the engineers in Revelo's network use AI while shipping features. Adoption is highest on building and testing (up to 95%) and lower on deploying and monitoring after release (62%), where a mistake reaches production. Fluency here means using AI heavily where its output is cheap to check and less where being wrong is expensive.
The data says yes. Across all 30 tasks, vetted engineers in Revelo's network were more satisfied with their work when they used AI, and no task showed a decline. Adoption drops to 62% on deployment and monitoring after release, the two tasks where a mistake reaches production.
Adoption is highest on building, testing, and debugging: handling errors and edge cases (95%), exploring an unfamiliar codebase (95%), and writing unit tests (94%). It's lowest on release-risk work: deploying to production (62%) and monitoring after release (62%).
The data suggests the opposite for vetted engineers. AI adoption drops to 62% on deploying and monitoring after release, the tasks where a mistake reaches production. Engineers use AI most where its output is cheap to check.
Source: Revelo AI Opportunity Index, a representative sample of Revelo's vetted engineers at 99% confidence, ±5% margin.
