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.

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.

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).

AI has shifted from experimental use cases to a core driver of digital transformation across industries. From AI-powered chatbots in financial services to predictive maintenance in manufacturing and fraud detection in retail, businesses are embedding AI into critical workflows.

It’s already been a year since the EU AI Act’s operationalisation. We all need to realise that the Act has moved from policy to execution, and the next 12 months will separate the reactive from the ready (or proactive). Here is what’s coming next for businesses, citizens and organizations.

Introduction: A Community Contribution in the Era of AI Accountability
As organizations increasingly embed AI into critical workflows and products, a new challenge emerges: how do we know that these systems are actually doing what we intend—and doing it well?