Software engineering is entering a new era. Over three decades, organizations have
repeatedly reinvented how software is delivered: Agile changed how teams collaborate,
DevOps transformed how software moves to production, and cloud computing changed how
it is built and operated.
The Next Evolution of Software Delivery Read More »
In practice, this means organizations need to pay closer attention to how AI is presented and disclosed. Among other requirements:
people must be informed when they are directly interacting with certain AI systems, unless this is already obvious
providers of generative AI systems must enable AI-generated or manipulated content to be identified through machine-readable marking
deepfakes must be clearly disclosed and
AI-generated or manipulated text published to inform the public on matters of public interest must be labelled in certain circumstances.
The EU AI Act Keeps Moving: Is Your AI Governance Keeping Pace? Read More »
When we published the open invitation that seeded this paper, we were careful to frame it as a set of questions rather than a thesis. Six long conversations later, I am more convinced than ever that this framing was the right one. What follows is not a verdict on AI-assisted software development. It is a snapshot of a profession reasoning its way through a structural change in real time.
AI-Assisted Software Engineering: The New Delivery Paradox Read More »
When organizations deploy vector-based “Traditional” Retrieval-Augmented Generation (RAG) systems, performance issues are often attributed to model choice or hyper-parameter tuning.
Evaluating vector-based “Traditional” RAG Systems: Why Knowledge Base Quality Matters Read More »
AI-assisted coding is no longer experimental. It is reshaping how software is written, reviewed, and deployed at a pace few anticipated. For some, this is a productivity revolution or change of paradigm. For others, it is a fundamental architectural risk. For most, it is both.
AI-assisted coding is no longer a future-facing concept for software development. It is already embedded in daily engineering workflows. But despite widespread experimentation and growing adoption, the reality of how developers use AI today is far more grounded than popular narratives suggest.
How Developers Are Really Using AI Today — And What That Means for Software Quality Read More »
If 2025 was the year organizations realized something was wrong, 2026 will be the year they are forced to fix it.
The conversation around AI is shifting decisively. Hype is giving way to scrutiny. Experimentation is giving way to expectations. And organizations are being challenged to prove — not promise — that their AI systems are safe, reliable, and worth the investment.
2026 Outlook: From Experimentation to Accountability Read More »
Across the market, ambition outpaced execution. AI initiatives multiplied, budgets were approved at a rapid pace, and expectations were sky-high. But by the end of the year, a more sober reality emerged: many AI projects failed to deliver meaningful business value, not because the models were weak, but because the foundations were.
2025 Recap: When AI Hype Met Software Reality Read More »
As AI adoption accelerates across sectors, executives are increasingly looking for ways to demonstrate that their organisation builds and deploys AI systems responsibly. ISO 42001, the world’s first certifiable standard for AI management systems, is quickly becoming the reference point.
Preparing for ISO 42001: Insights from the Field Read More »
For all the complexity of modern software systems, some of the most devastating breaches still begin with something shockingly mundane: a hard-coded username, password, API token, or key hidden deep within a repository.
Hard-Coded Credentials: The Skeleton Key Hiding Inside Your Software Ecosystem Read More »
