In a recent Forbes analysis, Tomas Chamorro-Premuzic maps how AI is reshaping the mechanics of organisational decision-making. It is worth reading in full. What it describes is not a future risk — it is the structural condition most organisations are already operating inside, whether they have named it or not.
Reference: 'How AI Is Changing Decision Making In Organizations', Tomas Chamorro-Premuzic, Forbes, May 27, 2026.
The argument reaches a precise conclusion: 'when machines do the thinking, humans must do more of the judging.' That conclusion describes exactly what Hunome is built to enable.
The front end is now fast. The back end is the bottleneck.
Chamorro-Premuzic draws a useful distinction between the stages of a decision. The front end — gathering information, identifying patterns, structuring data — can now be executed by AI almost instantly. What used to require hours of analysis can be reduced to seconds of prompting.
This looks like efficiency. But the structural implication is deeper. When the front end of decision-making becomes faster and cheaper, the relative importance of the back end increases. Interpretation, judgment, and the capacity to challenge assumptions do not just remain human — they become the bottleneck through which organisational quality is now determined.
The organisations that benefit most from AI will not be those that eliminate human involvement, but those that deliberately reallocate it — away from low-value processing and toward high-value interpretation.
The competitive edge has shifted from access to answers to the quality of questions, the rigour of interpretation, and the ability to translate insight into action. Differentiation becomes a function of judgment.
Hunome operates precisely at this bottleneck. It is not a faster way to generate outputs. It is the structured environment in which genuine collective interpretation can happen — where the reasoning behind consequential decisions is built, made visible, and made to last.
The risk of artificial certainty
Chamorro-Premuzic names a specific failure mode that deserves attention: artificial certainty. AI systems deliver answers that are coherent, structured, and assured — even when they are wrong. Because the output is polished and immediate, it feels authoritative. Because the user did not struggle through the intermediate steps, they are less equipped to question it.
The result is a subtle but consequential shift in where error originates. Decisions may be wrong, but the decision-maker lacks the capacity to recognise it — and AI, rather than correcting the error, often reinforces it by generating credible post hoc rationalisations.
This is not a problem AI literacy alone solves. The deeper structural issue is that organisations have no mechanism for the kind of collective scrutiny that surfaces what sounds right but is not. No environment where contested perspectives are brought into genuine contact. No way to make the reasoning visible enough to be challenged.
That is a deliberative intelligence problem. And it is getting worse as AI use increases.
The most consequential error: misclassifying decisions
The Forbes analysis identifies the largest organisational mistake as not underusing AI, but misclassifying decisions — applying automation to domains that require judgment. Hiring, strategy, leadership, culture. These are not optimisation problems. They are defined by shifting goals, contested criteria, and the kind of human interpretation that cannot be encoded in a model trained on past data.
The result, as the article describes, is 'algorithmic mediocrity': decisions that are defensible but uninspired. Organisations converge on similar profiles, similar strategies, similar conclusions — not because they are optimal, but because they are statistically safe. What is lost is not nuance only, but the capacity to make bold, context-sensitive decisions that depart from the average.
This is precisely the pull to the status quo that Hunome is designed to resist. Genuine collective sensemaking surfaces the epistemically diverse perspectives — the contrarian, the tacit, the locally grounded — that algorithmic processes structurally exclude.
The organisations that benefit most from AI are not those that automate the most. They are those that build the human layer that decides what to do with everything AI produces.
Accountability requires a visible reasoning process
A further point in the Forbes analysis concerns accountability. When decisions are informed by algorithms, the question of who decided becomes less straightforward. Responsibility diffuses across humans, models, and data.
Chamorro-Premuzic's answer is clear: 'AI should function as an advisor or thought-partner, not a decision-maker. Human accountability must remain intact.'
Human accountability cannot remain intact if the reasoning process is invisible. If a decision is traceable only to an AI output, and the human deliberation that preceded or interrogated that output left no record, accountability is nominal rather than real. It is a signature on a document no one can explain.
Hunome makes the reasoning visible. The perspectives that shaped a decision, the assumptions that were challenged, the understanding that was built — all of it is documented, connected, and preserved. Not as a summary. As a living record of how the organisation understood the question and what it decided to do about it.
The premium on human intelligence has increased, not decreased
Tomas Chamorro-Premuzic’s analysis closes with a claim we hold to be accurate: 'the age of intelligent machines places a greater premium on human intelligence than ever before.' Not because humans are better at computation. Because humans bring qualities that remain essential and that the pressure of AI adoption is actively eroding: curiosity, judgment, the ability to navigate genuine ambiguity.
The challenge for organisations is to cultivate these capabilities rather than allow them to atrophy — and to build the structural conditions in which they can operate. That is what deliberative intelligence infrastructure does. It is the investment that makes every other AI investment work.
Related: 'Generative AI has made your people faster. It has not made your organisation smarter.' at hunome.com/perspectives/generative-ai-faster-not-smarter