It’s no secret that AI’s consumption of water, energy, and land, is raising serious concerns in many quarters with some questioning whether we really need to use vast LLMs trained on vast, often “polluted” datasets, to perform relatively simple tasks.

For senior leaders, the challenge is clear: trust is the missing ingredient in AI adoption. The organisations that will thrive are those embedding Transparency, Explainability, and Accountability into their AI strategies

Image created in Canva / Alt-text: A wireframe digital head with overlaid text about AI limitations. AI is everywhere, from boardroom strategy to operational efficiency. At the moment, AI seems to be flavour of the month (every month), despite scepticism and lack of trust among many users. It’s also true that many C-suite leaders are under pressure to “do something with AI.” This often leads to organisations leaping into (AI) implementation without fully understanding what th

Image/Canva Alt-text: A woman interacts with a digital interface displaying weather, diet, and news information in a kitchen What Is AI? AI systems  (namely Gen-AI), use historical data to predict future outcomes. These systems learn by example, not by understanding Large Language Models  (LLMs) are designed for probability, not for inclusion Data matters : AI learns patterns from data. If the data is flawed, the outcomes will be too What Is Responsible AI? Responsible AI mea