Most companies use AI, but few achieve real impact. In part I of this series I explore why people—not tools—determine AI maturity, and why the next serious organisations will dominate the space between pilots and profit.
AI models mirror WEIRD cultural norms while claiming universality. Alignment methods reinforce this bias, projecting one community’s “common sense” as global truth. Recognising plurality through Wittgenstein’s language-games and Popper’s falsification reframes alignment as provisional and adaptive,
AI models often mirror our beliefs, rewarding us with agreeable but shallow answers. This sycophancy flatters rather than challenges, eroding judgment and candour. To gain true value, leaders must set incentives that favour truth over comfort, design prompts that demand trade-offs, and treat AI as a
Clicks are dying, and with them provenance, nuance, and the economics of ideas. AI digests and social updates reward skim over substance, shrinking attention to ringtone length while starving original work. I argue for friction, attribution, and long-form habits, treating summaries as aperitifs, not
Advances in LLMs show promise, but token bias undermines their logical reliability. Small input shifts can distort outputs, posing risks in fields like medicine, law, and policy. Their dependence on pattern recognition over true reasoning demands closer scrutiny and better design.
By employing leaders capable of creating an AI framework — because they are awake and aware to the unintended effects of AI on social well-being, data integrity and privacy, diversity, and governance — organisations seeking to transform into being AI-first are well positioned to engage in trustworth
There is the case to be made that this is not only the cost of doing business in the digital age but that, like environmental protections, there are great rewards to be reaped in improved quality of life.
The hope is much, for having gotten this far is to be forewarned and thus forearmed. In that we do well to employ scepticism when listening to a human interlocutor. Because even the best of us are filling in the blanks in our memory.
Ultimately, I do not think the problem is that we are building machines so ‘smart’ that their output is indistinguishable from human compositions. The problem is that we are educating humans whose output is so illiterate, and devoid of experience, it is indistinguishable from computer generated text
In science, a paradigm is a model or pattern — a typical instance or exemplar. But in rhetoric, my chosen field, it is an example or guide as to how one should behave.
