AI is accelerating software delivery but without strong observability and release automation it risks introducing blind spots that could undermine reliability and trust, says Tom Totenberg, Head of Release Automation and Observability, LaunchDarkly.
There’s a joke in the engineering world: If you want heavy scrutiny on your PR change 10 lines. If you want it to pass with minimal review change 1000 lines.
AI has redefined what’s possible in software delivery enabling developers to meet rising demands for speed and innovation. While still emerging and experimental AI is now embedded at every stage of the software lifecycle. It has become a powerful tool to reduce time-to-market and sharpen competitive edge.
For early adopters AI is now central to the daily work of developers dramatically shortening the path from idea to prototype. What began as a side project has quickly evolved into widespread adoption.
But with this increased speed comes risk. As teams build and ship at higher volumes there’s mounting pressure not to compromise on quality especially during testing and delivery. Without training and safeguards in place AI-driven development can introduce blind spots in stability and long-term efficiency.
While the gains are undeniable DevOps teams must ensure speed doesn’t come at the cost of reliability. When errors or outages occur AI shortcuts can be quickly exposed impacting end users and undermining trust.
The developer’s AI revolution
AI has become an incredible accelerator for software delivery teams helping move from concept to code faster than ever. New research shows that 85% of developers globally are regularly using AI for activities such as coding and nearly two-thirds are using at least one AI coding assistant agent or code editor in their daily workflows.
But this isn’t just about writing code faster. It’s about giving developers room to focus on higher level outcomes rather than staying mired in the minutiae of logic puzzles.
However the time saved generating repetitive code pushes more work down the delivery process: reviewing and testing proportionally become a larger part of the SDLC. That’s a shift in mindset which is unsatisfying for a large number of engineers.
This is where dangerous shortcuts appear without proper oversight. It’s much easier to slap ‘LGTM’ on an AI-generated PR than it is to retain the focus and critical thinking needed for consistent high-quality review. The result is that bugs often only appear once customers are affected. When an issue slips through it’s already live.
There’s a growing trust gap in AI-assisted development but this can be addressed with strong practices that deliver both speed and safety. Developers should be able to innovate rapidly with systems in place to ensure each release is observable reversible and reliable. It’s not about slowing down AI it’s about guiding it with real insight not guesswork.
As leaders it’s your job to ensure that your teams are incentivized by customer impact.
The growing risk surface in an age of outages
IT teams are operating in an era of heightened vulnerability where outages cause some of the biggest crises and impact to the bottom line for companies. The data show that no organization is immune and DevOps teams are on the frontline protecting teams in the event of systems going down.
There’s always risk when companies trade speed for scrutiny. AI-generated code can introduce subtle bugs that go unnoticed especially if testing and visibility tools aren’t keeping pace. The danger isn’t AI itself but assuming its output is always correct so without proper checks small issues can escalate into major problems once they reach production.
The worst part? Because AI generated faulty code the humans responsible for system stability don’t know that code as intimately which lengthens the remediation process.
As a result we’re simultaneously seeing teams embrace AI while also realising they need stronger safety nets. Two categories to highlight are observability for real user monitoring and release automation to ensure problems can be rolled back before they escalate.
Building resilience through observability
As AI accelerates delivery the risks increase. Issues can surface quietly and by the time they’re noticed customers are already impacted. That’s where observability and real-time monitoring matter. Observability gives teams the ability to see how systems behave in production by connecting what’s happening inside the code to what users experience outside it.
However traditional observability has a problem: it’s observing high level system performance rather than granular change-level impact.
When combined with feature delivery observability acts as the missing net catching errors early and linking them directly to code changes. Release observability monitors real impact to small end-user cohorts.
Combining observability with a runtime control layer standardizes the release provides focused context for observability and automates the remediation process.
Real-time insights paired with release automation allows teams to catch problems trace them to specific features and roll back – often before critical end users are affected.
It’s all about control at speed: automated safe and resilient releases. Developers stay confident users get a smoother experience and the business keeps moving.
Responsible AI use across the development process
As teams adopt AI more deeply the goal isn’t just faster delivery it’s responsible delivery. Observability and release automation lay the groundwork for this by making sure AI-driven changes can be seen understood and corrected in real time.
Ultimately responsible AI in software development really comes down to balance. It’s about using AI to speed things up (like coding testing even deployment) without losing sight of reliability and impact.
AI can help automate repetitive work but it still needs human oversight and strong systems around it. That’s where observability and controlled releases come in. You want to pair AI-driven speed with visibility knowing exactly how those changes behave for exposed audiences once they’re live.
By linking changes to real-time feedback and even rolling back automatically if something isn’t right teams can innovate confidently. It’s about building a partnership between AI and developers where the AI accelerates creativity but humans stay accountable for quality and trust.
Release automation platforms that understand how feature changes impact performance and user experience in real time so they can make smarter safer decisions. It’s all about turning AI from a risk into a partner without compromising reliability or user trust.
So, has AI caused shortcuts in software development? Only when speed is left unchecked.
With visibility control and human accountability AI becomes not a shortcut but a smarter safer route to innovation.
