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McKinsey and Wharton
AI is supposed to augment human intelligence. But what happens when the pursuit of efficiency begins to weaken the people using it? McKinsey describes “brain capital” as the combination of brain health and the cognitive, interpersonal, self-leadership and technological skills people need to adapt and contribute. In an AI economy, they are strategic assets that determine resilience, productivity, innovation and decision quality.
At the same time, Wharton warns of agency decay: the gradual erosion of human judgement through chronic delegation to machines. Eventually, making staff less capable of thinking independently.
That creates a dangerous contradiction at the heart of digital transformation. Companies are investing billions in artificial intelligence while potentially disinvesting in human intelligence. Curiosity, confidence, critical thinking, memory, judgement and the willingness to take responsibility may gradually decline.
The competitive advantage will not belong to the organisation that delegates the most thinking to AI. It will belong to the organisation that uses AI while continuing to strengthen the judgement, imagination and agency of its people. Organisational wisdom.
The leadership question
Is AI making your people more capable thinkers, or just more dependent operators?
The future fitness move
Measure where AI is building organisational wisdom — or quietly replacing it.
The other prompts:
Free downloads
12 free books on strategy, innovation, intrapreneurship, AI, future trends and leadership. Each one distils the best thinking from dozens of business books into a single, actionable read. Every book is also available as a BookBuzz session, board briefing or keynote.
AI economics for dummies
As AI companies get ready to go public and we get a deeper look at their inner workings, it’s only natural to have questions about their finances, like “Do they make money?” and “How?” Here are a few examples to help the average layperson understand the business side of AI.
https://www.mcsweeneys.net/articles/ai-economics-for-dummies
You Outsourced the AI—but You Still Own the Risk
To minimize risk, companies need to understand four under‑managed exposures: opacity in upstream models, liability triggered by customization, dependence on hard‑to‑replace vendors, and fragmented regulatory demands. T
https://hbr.org/2026/07/you-outsourced-the-ai-but-you-still-own-the-risk
Is recursive self‑improvement the dawning of AI superintelligence?
The threat posed by “recursive self-improvement.” This is the point at which AI systems can improve themselves, potentially leading to “superintelligence” far beyond human control.
https://techxplore.com/news/2026-07-recursive-selfimprovement-dawning-ai-superintelligence.html
Why retrieval quality is becoming the defining challenge in AI agent architecture
Many failures that look like LLM problems start in the context-building step. The answer the LLM gives is limited by the context it was given, or it finds through tool calls. If the agent model cannot find the right sources, then improving the generation model will not improve the overall system.
https://thenewstack.io/retrieval-ai-agent-architecture/
Data Mesh Isn’t a Technology Decision, It’s an Organizational One
Data Mesh, coined by Zhamak Dehghani in 2019, is not primarily a technology architecture. It is an operating model that changes how data ownership is distributed across the organization.
https://atrium.ai/resources/data-mesh-vs-centralized-data-teams/
The agentic marketing stack starts with the data layer
Why modernizing the data foundation is a critical first step in any AI strategy
https://www.databricks.com/blog/agentic-marketing-stack-starts-data-layer
The human advantage: Stronger brains in the age of AI
