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 »
In our previous article, How Leaders Should Measure Success in AI-Driven Software Engineering, We argued that optimizing for the percentage of AI-generated code misses the point — what really matters is how fast and reliably organizations turn ideas into value. But saying “measure time-to-market” is easy. Doing it systematically is harder. Too often, teams chase a single KPI — like lead time or deployment frequency — and call it a success. The truth is that time-to-market isn’t a single number. It’s the result of balance: between how fast you deliver, how well you manage quality and risk, how efficiently work flows through your system, and how effectively you learn from customer feedback.
A Practical Framework for Measuring Success in AI-Driven Software Engineering Read More »
In the previous blog celebrating Cybersecurity Awareness Month, we explored how AI is transforming cybersecurity — empowering both attackers and defenders — and why trustworthy, transparent, and responsible AI adoption is essential for building resilient defenses.
From Awareness to AI Literacy: Enabling Human Oversight in the Age of Agentic AI Read More »
October marks Cybersecurity Awareness Month. The theme “Secure Our World” invites businesses (and individuals) to reflect on how digital risks are evolving and what it takes to stay ahead. It is an open invitation toward resilience in the face of attackers enhancing their playbooks by leveraging cutting-edge technologies and tactics.
AI in Cybersecurity: Striking the Balance Between Innovation and Trust Read More »
Imagine celebrating that 60% of your code is written by AI — only to realize your product still ships late.
That’s the trap many organizations are likely to fall into. Ambitious targets like “By 2026, half of our code will come from AI” sound bold, look measurable, and they can even make the case for strong boardroom slides.
How Leaders Should Measure Success in AI-Driven Software Engineering Read More »
From GitHub Copilot to ChatGPT plugins and IDE-integrated assistants, AI fundamentally changes how developers write and ship code. What once required hours of manual configuration and debugging can now be scaffolded in seconds by a smart prompt. But while productivity is skyrocketing, the rise of AI-assisted development introduces new challenges in code security, quality, and accountability.
Code, Comply, Repeat: Why Human Oversight is Essential in AI-Assisted Code Development Read More »
Computer Vision AI systems are much more complicated than AI systems using tabular datasets (typically extracted from databases) as the features they employ to make a decision (millions of pixels) are orders of magnitude more than what tabular datasets usually entail (tens to hundreds of database columns).
Is there an (easy) way of detecting bias and fairness in Computer Vision AI systems? Read More »
