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Revelo · AI Opportunity Index
We surveyed a representative sample of our vetted engineers about the 30 tasks in shipping a feature, from scoping to updating the docs. Here is what they told us.
Source: Revelo AI Opportunity Index, a representative sample of Revelo's vetted engineers at 99% confidence, ±5% margin.
Inside the AI Opportunity Index
We surveyed a representative sample of Revelo's vetted engineers about the 30 tasks in 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 is 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 did not. 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). We expected some tasks to come out negative. None did.
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.
Most of the engineers Revelo places are senior.
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, and the distinction matters. In the Revelo AI Opportunity Index, 85% of Revelo's vetted engineers use AI across the feature-shipping lifecycle, but they apply it with judgment: near-universal on building and testing (up to 95%), and deliberately lower on production-critical work like deploying and monitoring (62%). Fluency here means using AI well and knowing where it doesn't belong, not leaning on it blindly.
The data says yes. Across all 30 tasks of shipping a feature, Revelo's vetted engineers were more satisfied with their work when working with AI, with no task showing a decline, and they pull back to roughly 62% adoption on deployment and post-release monitoring, exactly where a mistake reaches production. AI acts as a multiplier on already-vetted, senior-weighted talent, not a crutch.
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 points the other way for vetted engineers. AI adoption drops to roughly 62% on production-critical tasks like deploying and monitoring. That's deliberate restraint exactly where a mistake reaches production, rather than running every task through a model.
Source: Revelo AI Opportunity Index, a representative sample of Revelo's vetted engineers at 99% confidence, ±5% margin.
