Humans in the loop: how software teams are learning to trust AI

Date:
Thu, 23 Jul 2026 13:45:21 +0000

Description:
Treating AI like a virtual teammate is bearing fruit for enterprise teams.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter Engineering discipline in software development has been under the spotlight since AI started being used by devs to generate code, with many teams worried its causing unnecessary risk. Headlines have been full of cautionary tales about bugs created by hastily shipped code or senior executives taking vibe coding into their own hands.

Nevertheless, teams are finding ways to build trust in AI-generated code. Google s 2025 DORA survey concluded that AI has an amplifying effect on organizations' processes - quality processes enhanced by AI lead to higher quality outputs, and more of them. Our own research shows that most teams arent being haphazard with safeguards or accepting AI outputs at face value. Most teams are being more rigorous and giving their outputs the same scrutiny as the work of a teammate. That tells us a lot about how the best-performing teams are learning how to work with AI. Latest Videos From Watch full video here: Kevin Boyle Social Links Navigation

CEO at Gearset. Seventy-six percent of enterprise teams are reviewing AI-generated work at least as rigorously as human-written work. Teams using
AI to accelerate their work are seeing results from using proven engineering practices to ensure code is reviewed and tested sufficiently.

Rather than being inherently unstable, the rise of AI-generated code has emphasized that guardrails are the bedrock of consistently reliable software. You may like AI has slashed coding time in 2026, but its sacrificed software stability The rise of comprehension debt in the age of AI coding AI code security risk: The need for a smarter layer between detection and remediation Treat AI as a virtual teammate Teams treating AI-generated code as if it was produced by a human tells us something about how mature teams are using AI. The engineers using AI most effectively treat it as a virtual teammate. They stay in charge of the decisions and they use AI to accelerate the execution.

To make AI adoption an iterative process that improves over time, starting with low-stakes, repeatable tasks and applying stringent checks to outputs gives the tech a chance to work properly without expecting immediate results. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.

As MITs Computer Science and Artificial Intelligence Laboratory reported, AI codings widespread adoption doesnt mean it can handle all aspects of large-scale software engineering by itself. Releasing to production is higher stakes than writing code , for example. Scrutiny from real humans is vital to prevent errors and hallucinations from impacting the business.

At this early stage of AI being used in workflows, its positive that 43% of teams are treating AI code like human-written code, with 33% applying even stricter checks. Even the best developers on a team arent above the processes and guardrails that guarantee reliable software deployment at speed. AI
should be approached in the same way. Trust in AI follows the risk curve As with any new technology, introducing AI to the software lifecycle is a learning process. Our data shows trust in AI varies greatly depending on the stage of the software lifecycle, indicating that teams are building
confidence with appropriate caution. What to read next The next challenge is trust, not speed of code generation AI is making everyone web app builders - but leaving teams exposed GitLab study reveals AI code generation is
outpacing controls

A pragmatic approach is being taken at the best-performing enterprises. Thought leaders from Gartner, Forrester and Googles DORA team all highlight that organizations seeing success from AI-assisted development are strengthening their engineering controls and building up success over time.

This tallies with where AI is trusted to perform. The vast majority (82%) now use AI during the build stage, dropping to 58% at release where production risk is highest. Using AI primarily for earlier stages of software
development is a rational step to make sure failures dont impact the wider business.

This doesnt betray a lack of confidence in the technology. Almost half (46%) of teams are confident in the performance of AI-generated code, indicating that its more cautious use in production is a measured business decision rather than skepticism.

Outputs are much easier to review and refine before the release stage. With many businesses still lacking full observability, teams often only hear about mistakes in live code once users notify them. Despite claims that software development could eventually be fully automated, teams are showing a clear awareness of where human oversight is essential.

Rather than making blanket judgments about AIs capabilities, they are taking
a more nuanced view, which bodes well for the future of AI-assisted software delivery. Combine speed with discipline to win There is continuity in how mature teams are making sure AI-generated code is fit for purpose, but its still having a seismic impact on the role of software engineers. When I speak with developers , their feedback is unanimous: the time AI saves is
invaluable for focusing on neglected parts of their process.

AI moves the cognitive load from writing code and building configuration to reviewing and directing it. With time freed up, teams have more scope to prioritize observability, test coverage or disaster recovery scenarios - crucial elements of software hygiene that too often get overlooked.

AI adoption is increasing documentation quality according to the DORA report, suggesting that many teams are taking the opportunity to improve the broader engineering practices that support reliable software delivery.

As it takes on more of the mechanical aspects of software development, engineers become increasingly responsible for the work that matters most: validating outputs, understanding risk, and ensuring outputs align with business objectives. If someone asks why did we build it this way? its a problem if no one can answer the question without asking AI. Humans must be
in the loop.

The organizations that benefit most from AI will not be those with more automation, they will be the ones that combine AI-driven speed with engineering discipline and a strict adherence to repeatable processes. AI adoption is a DevOps challenge The debate around AI-generated code often focuses on whether the technology can be trusted in DevOps . In most cases, teams have been building this trust iteratively without throwing away human judgment to get the best results.

As AI capabilities continue to improve, the gap between high-performing teams and everyone else is unlikely to be determined by access to the latest model.

The engineers and teams that will thrive are the ones who pair AIs capabilities with their own judgement. We list the 10 best vibe coding tools
. This article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

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Link to news story: https://www.techradar.com/pro/humans-in-the-loop-how-software-teams-are-learni ng-to-trust-ai


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