Does the general public systematically and repeatedly misunderstand the nature of the value AI actually creates?

Language models have dominated the current AI wave and hype cycle. Interacting with them creates an illusion of human-likeness. This thinking is monolithic, and there are far more interesting scenarios for driving real value from AI.

90–95% of AI investments are nowhere near the required level of systematic approach – or anywhere near the CEO’s and Executive Team’s agenda.

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.

The radical changes in the way we work in the past five years have left a large share of employees disengaged from their work. Up to 59% of employees globally are already quiet-quitting.

Our 4-step guide offers concrete guidance for CEO’s on how to remedy the situation. WIth the help of AI, of course.

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.