Most business problems are really about making good decisions. When you’re deciding which project to prioritize next, how much inventory to hold or which ingredients to use in a new product, you’re constantly trading off goals with constraints.

The concept of agency in artificial intelligence has become a focal point in both technical and public discourse. At Renessai, we have strongly argued that AI does not have agency at all, but, a closer look at both research and practical experience reveals a more nuanced reality. One could ponder even what is even the thinking we humans do.

As a business leader, you don’t need to understand the inner workings of LLMs, but understanding a few basic fundamentals is enough to steer your AI initiatives towards reliable outcomes, rather than exposing your organisation to unnecessary business risks from hallucinations.

Effective AI requires more than technical excellence. AI is at its best when designed for a global scale, but the different local regulatory rules hinder this utilisation and result in many missed opportunities.

We at Renessai call for value-sensitive global standards. This helps innovation and alignment, and ensures AI works for everyone, everywhere. And as we have already many standardisations in place, it should not be a hurdle to build one for AI either.

Everyone has their own mental models about what AI means. To add to that, the meanings of words change over time. For example, in relation to AI, many have recently started to talk about “agents”. Not too long ago, AI was synonymous with the Large Language Model (LLM). And a few years back, the synonym was machine learning. But do not worry, this article is not here to provide a new, definitive definition of AI.