For quite some time in the already long series of articles I’ve been writing on what is and what should be corporate AI, I have been discussing concepts such as agents, working with ontologies and world models that represent the company, connecting them with management tools such as ERPs or CRMs, and basically, learning to optimize a company to the limit. However, now that we are talking, as Mustafa Suleyman says, about AI wiping out most white collar jobs in the next 12 months, we should try to clarify what leadership becomes once that machinery exists. 

What, in my view, is the key shift? In optimized companies, leadership will stop being mainly about making more decisions than everyone else, since many operational decisions will increasingly be made or recommended by systems that understand the company’s ontology, can simulate consequences through world models, and can learn from feedback. Essentially, the leader’s role moves up one level.

Today, executives spend enormous amounts of time resolving trade-offs, setting priorities, escalating decisions and coordinating. In an optimizable company, many of these local decisions will be handled by learning systems, so the leader’s job becomes to define the objectives, constraints, and trade-offs that these systems will be allowed to optimize. 

Think about it this way: every single metric is ultimately a governance decision , because it encodes a theory of what the company is for. Today, strategy is typically expressed in PowerPoint presentations, KPIs, budgets, annual plans, and management speeches and interviews with analysts.

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