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How IT Leaders Can Rebuild Trust in AI-Generated Code
AI is helping software teams produce code faster than ever, but the gains in productivity are creating a new challenge for IT leaders; maintaining confidence in what reaches production. A Tricentis survey of more than 2,500 CEOs, CIOs, CTOs, engineering leaders, DevOps professionals, QA teams and developers found 60% of organizations are deploying untested code into production environments. While that figure is largely unchanged from the previous year, the reasoning behind it has shifted; in 2025, organizations primarily attributed quality failures to accidental oversights. Today, many acknowledge they are knowingly accepting the risk. Leadership pressure to prioritize speed was cited by 32% of respondents, while 30% said the volume of AI-generated code has become too overwhelming to test completely. The findings suggest a growing tension inside software organizations--AI is accelerating development cycles and enabling teams to deliver features more quickly, but testing, governance and
Skills Visibility Gap Is Holding Back Hiring, Retention Efforts
Many organizations are struggling to identify and deploy the skills they already have, creating a growing gap between available talent and how it is used, according to a report from TalentLMS. Half of employees and managers surveyed said their company hires externally for skills that already exist in-house, pointing to a widespread breakdown in skills visibility. The report, based on responses from more than 1,500 U.S. employees and managers, highlights a shift in workforce challenges. Rather than a lack of talent, many organizations are dealing with “invisible talent”—skills that exist but are not recognized, tracked or effectively applied. Only a small minority of respondents said their organization does not face issues with skills visibility. That gap shows up in how managers and employees view workforce capabilities. While 90% of managers said they understand their teams’ skills, only 69% of employees agreed. A similar disconnect exists around development: 90% of managers said they
Review First, Execute Later: Use AI Coding Tools Without Losing Your Edge
In the development lifecycle, the software engineer is evolving from a traditional code author to a pseudo system architect and intent director thanks to AI. The widespread adoption of Large Language Models (LLMs) in IDEs has accelerated how quickly deliverables are shipped, but has also introduced challenges to software creation. When code can be written in seconds, concepts like ownership, technical debt and accountability are often as confusing as AI’s hallucinations. Writing software has never been just about syntax. It’s also about understanding logic, predicting edge cases, and understanding true human behavioral use patterns. To help make sense of it all, we gathered insights from industry experts on integrating generative AI into your workflow responsibly, ensuring you leverage the power of automation without compromising on security, licensing or personal engineering accountability. How should developers define "original work" today? Is there a professional obligation to discl
Is it time to move from Windows to Linux?
I’ve been developing Windows apps since Windows 3.1, back in the 90’s when the first program I wrote was an energy tracking screensaver along with a DOS TSR (Terminate Stay Resident) equivalent. In those days you booted into DOS first then started Windows with a command. This was before Visual Basic and Delphi existed, when you wrote Windows apps you had to explicitly code everything including font handling, display contexts and message loops. It was quite fiddly. A friend had got into Linux, but as far as I can see he only ever used it to benchmark his hardware by measuring how long it took to build Linux from source. Then around 2011, after fifteen years of working on Windows, I found myself needing to test some C code. MS VC was fine but Microsoft was a bit behind the curve with C99 support and I learned enough Ubuntu to let me install and run gcc and clang C compilers. When I started doing web development, it was easier to setup a local test web server on Ubuntu. I still program on
The Trick to Bulletproof AI Code? Context Engineering
As software engineers, we’ve all been told that the secret to unlocking LLMs lies in the art of the perfect prompt. We’ve experimented with role-playing cues, chain-of-thought instructions, and increasingly complex prompt templates. But treating AI like a magic syntax box misses the broader reality of production engineering. The truth is, isolated prompts fail in complex, real-world systems because they lack architectural awareness. If we want predictable, production-grade output, we have to stop focusing on clever phrasing and start focusing on context engineering—structuring our codebase, metadata, and testing loops so the model actually understands the system it's contributing to. Moving beyond basic code generation requires treating AI not as an oracle, but as a junior developer who needs strict system constraints, modular task sizes, and comprehensive execution contracts to be effective. Should engineers stop worrying about "prompting" and start focusing on "context engineering"—h