#it-project-failures

#it-project-failures

Lydia noticed the machine's battery was running low and told two other team members. The more senior went to fetch the backup battery, while the junior team member suggested a quicker method that Lydia firmly rejected.

Most of these companies start the journey from a functional standpoint, avoiding extra layers that may "divert users' attention", such as refined flows, potential edge cases, and, sometimes, proper visual design foundations and user experience. Here, the goal is to ship the product first to validate its value, then address other considerations.

Marketing organizations are racing to adopt AI while simultaneously trying to contain it. About 76.6% of marketers now have AI policies in place, up from 55.3% just a year earlier, per the Association of National Advertisers' January 2026 survey (registration required). Investment is also surging. Nearly 89% plan to increase AI spending, and two-thirds would maintain that investment even during an economic downturn.

Kemi Badenoch's recent ridiculing of the prime minister over a supposed U-turn on digital ID plans (Keir Starmer denies change to digital ID plan is yet another U-turn, 14 January) is the latest example of a frustratingly narrow view of leadership. To the Conservative leader, adapting a policy is a sign of no sense of direction; to those of us who work in product management, it looks like necessary iteration of the process.

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"I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue."

Your AI pilot showed 94% accuracy improvements. The LLM is yielding solid results. You're getting defunded anyway. The reason? You solved a problem AI can solve. Your budget-holder needed you to solve theirs. Companies launch AI pilots that produce results, then stall at scale. The team's diagnosis: "They don't get it." What's really going on: These projects never earned budget-holder buy-in.

Scrum has a bad reputation in some organizations. In many cases, this is because teams did something they called Scrum, it didn't work, and Scrum took the blame. To counter this, when working with organizations, we like to define a small set of rules a team must follow if they want to say they're doing Scrum. Enforcing this policy helps prevent Scrum from being blamed for Scrum-like failures.

To find the typical example, just observe an average stand-up meeting. The ones who talk more get all the attention. In her article, software engineer Priyanka Jain tells the story of two colleagues assigned the same task. One posted updates, asked questions, and collaborated loudly. The other stayed silent and shipped clean code. Both delivered. Yet only one was praised as a "great team player."

Much of the conversation about how to work effectively with generative AI has focused on prompt engineering or, more recently, context engineering: the semi-technical skill of crafting inputs so that large language models produce useful outputs. These skills are helpful, but they are only part of the story.

This extends to the software development community, which is seeing a near-ubiquitous presence of AI-coding assistants as teams face pressures to generate more output in less time. While the huge spike in efficiencies greatly helps them, these teams too often fail to incorporate adequate safety controls and practices into AI deployments. The resulting risks leave their organizations exposed, and developers will struggle to backtrack in tracing and identifying where - and how - a security gap occurred.

Olimpiu Pop: Hello everybody. I'm Olimpiu Pop, an InfoQ editor, and I have in front of me Erica Pisani, one of the track hosts of QCon London 2025, and a very important track in my opinion. One that is important in general, but even more important these days. And the name of the track was performance and sustainability, which seems to be two opposing words. So, Erica, please introduce yourself.

A secure software development life cycle means baking security into plan, design, build, test, and maintenance, rather than sprinkling it on at the end, Sara Martinez said in her talk Ensuring Software Security at Online TestConf. Testers aren't bug finders but early defenders, building security and quality in from the first sprint. Culture first, automation second, continuous testing and monitoring all the way; that's how you make security a habit instead of a fire drill, she argued.