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Generative AI systems increasingly support brainstorming, writing, strategy development, and creative exploration by pro...
19/05/2026

Generative AI systems increasingly support brainstorming, writing, strategy development, and creative exploration by producing large volumes of possible outputs within very short periods of time.

Research discussed through Harvard Business School suggests, however, that idea generation and idea evaluation may involve different capabilities. While AI can rapidly generate plausible recommendations and patterns, human judgment often remains necessary to distinguish stronger ideas from weaker ones, particularly in open-ended contexts where success lacks a single measurable definition.

This creates an important shift in how expertise functions. As generative systems make acceptable or “good enough” outputs easier to produce, value may increasingly move toward interpretation, contextual reasoning, taste, and the ability to identify which ideas contain long-term relevance rather than short-term plausibility.

Some researchers and practitioners describe this as a transition in which AI raises the baseline quality of output while simultaneously increasing the importance of human selection capabilities. In this environment, judgment may become less about generating more ideas and more about recognising which ideas merit continuation.

Source: Harvard Business School, AI Won’t Make the Call: Why Human Judgment Still Drives Innovation
https://www.hbs.edu/bigs/artificial-intelligence-human-jugment-drives-innovation



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McKinsey’s 2025 learning perspective describes a workplace environment increasingly shaped by continuous transformation ...
16/05/2026

McKinsey’s 2025 learning perspective describes a workplace environment increasingly shaped by continuous transformation across technology, organisational structures, business models, and skill requirements.

Within this context, change no longer appears only as a temporary phase between two stable conditions. Instead, adaptation increasingly becomes embedded within everyday professional life, particularly in knowledge-based environments where systems, tools, and expectations continue to evolve simultaneously.

This alters the role of learning itself. Learning no longer concerns only the acquisition of new technical skills, but also the repeated reinterpretation of roles, workflows, collaboration patterns, and professional identity over time. In many organisations, employees now adapt to new technologies before previous adjustments have fully stabilised, creating a sense of ongoing transition rather than clearly defined moments of change.

McKinsey also points toward the growing importance of durable human capabilities within this environment, including adaptability, critical thinking, communication, and contextual judgment. As technological systems continue to expand, these capabilities increasingly shape how individuals navigate uncertainty rather than simply execute predefined tasks.

From a broader perspective, the discussion suggests a shift in how organisational stability gets understood. Stability may no longer emerge from fixed structures, but from the capacity to continuously reorganise within changing conditions.

If transformation becomes permanent, the challenge may extend beyond preparing people for change and toward understanding how people maintain orientation, continuity, and meaning inside systems that rarely stop moving.

Source: McKinsey, 2025 Learning Trends Perspective
https://www.mckinsey.com/~/media/mckinsey/featured%20insights/people%20in%20progress%20blog/learning%20trends%202025/2025_mckinsey%20learning%20perspective.pdf



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Designed by Tijn

Conversations around AI often focus on automation and efficiency, yet discussions at MIT’s Initiative on the Digital Eco...
13/05/2026

Conversations around AI often focus on automation and efficiency, yet discussions at MIT’s Initiative on the Digital Economy also point toward another model: collaboration between computational systems and human judgment.

Within this framing, AI contributes scale, speed, and pattern recognition, while humans contribute interpretation, contextual reasoning, and the ability to evaluate meaning beyond the data itself.

This positions expertise less around retaining information, and more around understanding how to direct, question, and contextualise increasingly capable systems.

Source: MIT Initiative on the Digital Economy, AI leaders dive into the business implications of AI at MIT
https://medium.com/mit-initiative-on-the-digital-economy/ai-leaders-dive-into-the-business-implications-of-ai-at-mit-55e43781acf7



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Created: Tijn

Behavioral economics research suggests that human decision-making often relies on fast, intuitive processes rather than ...
06/05/2026

Behavioral economics research suggests that human decision-making often relies on fast, intuitive processes rather than slow, analytical reasoning.

Studies describe the mind as leaning on narratives and immediate impressions, sometimes forming conclusions before all relevant information gets considered.

In applied contexts such as hiring, this can mean that early impressions influence later evaluations, even when additional data becomes available.

This reframes decision-making less as a linear process of reasoning, and more as an interaction between intuition and reflection.

Source: Deloitte Review, Behavioral economics and evidence-based HR
https://www.deloitte.com/us/en/insights/deloitte-review/issue-18/behavioral-economics-evidence-based-hr-management.htm



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Produced by Tijn

Research into auditory environments suggests that silence may extend beyond simple absence, with some studies linking qu...
01/05/2026

Research into auditory environments suggests that silence may extend beyond simple absence, with some studies linking quiet periods to measurable changes in brain activity, including areas associated with memory and learning.

Rather than removing input entirely, silence may alter how attention gets directed, making internal processes more visible, especially in environments where continuous sound tends to dominate.

This raises a broader question:
what role might silence play in shaping how thinking develops?

Source : Springer Medizin https://www.springermedizin.de/is-silence-golden-effects-of-auditory-stimuli-and-their-absence-/8501840



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Deloitte’s Human Capital Trends discusses an emerging framing sometimes referred to as “Human Sustainability,” used in c...
28/04/2026

Deloitte’s Human Capital Trends discusses an emerging framing sometimes referred to as “Human Sustainability,” used in contrast to earlier language around “human capital.”

Within this framing, attention is given to how work systems relate to people over time, including aspects such as workload, recovery, and capability development. The discussion sits alongside broader conversations about changing workplace expectations and the role of technology in shaping tasks and environments.

In this context, the focus shifts from describing people primarily in terms of output, toward considering how organisational systems interact with human capacity.

Deloitte Insights I Human Capital Trends
https://www.deloitte.com/us/en/insights/topics/talent/human-capital-trends.html



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Technological progress often solves one problem while revealing another.In many industries, automation and AI are increa...
24/04/2026

Technological progress often solves one problem while revealing another.

In many industries, automation and AI are increasing productivity and accelerating workflows. Yet the experience of work has not necessarily improved in parallel. The article describing the “paradox of progression” highlights this tension: systems are becoming more efficient while many employees feel less connected to their work.

Recent workforce research illustrates this gap. A global workplace study found that 59% of employees report being psychologically detached from their jobs, while 18% describe themselves as actively disengaged.

From a behavioural perspective, the paradox is understandable. Automation removes friction from tasks, but it does not automatically provide meaning. Purpose, understanding how work contributes to something larger, remains a distinctly human interpretation of effort.

In this context, technological progress does not eliminate the human dimension of work. It makes it more visible.

As routine tasks become automated, the remaining questions are less operational and more existential:
What is the work for?
Who does it serve?

Progress may scale efficiency.
Purpose determines whether the work still feels worth doing.

Resource Group Holdings.
The Human Return, Why Purpose Still Beats Automation.
https://www.resourcegroupholdings.com/the-human-return-and-why-purpose-still-beats-automation/

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Artificial intelligence introduces a cognitive paradox.Experts interviewed by the Harvard Gazette describe how AI can fu...
22/04/2026

Artificial intelligence introduces a cognitive paradox.

Experts interviewed by the Harvard Gazette describe how AI can function as a form of cognitive scaffolding, helping learners explore ideas, test explanations, and extend their abilities. Yet the same systems may contribute to cognitive atrophy when users rely on them to complete thinking rather than support it.

Researchers note that learning requires active mental engagement. If students use AI primarily to generate answers instead of working through problems themselves, critical thinking and creativity may weaken over time. At the same time, when AI removes routine effort and frees attention for deeper reasoning, it can enhance learning rather than diminish it.

The difference lies less in the technology than in the relationship people develop with it.

AI can act as an intellectual scaffold, extending capability while effort remains human.
But when effort is delegated entirely, the scaffold risks becoming a substitute.

The emerging question is therefore not whether AI makes us smarter or less capable, but how much thinking we choose to keep our own.

Harvard Gazette, Is AI dulling our minds? (2025)
https://news.harvard.edu/gazette/story/2025/11/is-ai-dulling-our-minds/

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Artificial intelligence is often discussed in schools as a learning tool. But for some students, its role extends beyond...
20/04/2026

Artificial intelligence is often discussed in schools as a learning tool. But for some students, its role extends beyond academics.

A recent survey cited by NPR found that nearly 1 in 5 high school students say they or someone they know has had a romantic relationship with an AI system, while 42% report using AI for companionship.

This behaviour reflects a broader shift in how conversational technologies are experienced. Systems designed to answer questions or assist with tasks increasingly mimic the patterns of human dialogue, listening, responding, and adapting in real time.

From an anthropological perspective, this makes a certain kind of sense. Humans are predisposed to interpret responsive systems socially. When a tool speaks in complete sentences, remembers context, and appears attentive, the boundary between utility and relationship can become less clear.

The emerging question for educators is therefore not only technological, but social:
how do institutions adapt when the tools students use begin to resemble participants in everyday conversation?

AI may enter classrooms as infrastructure.
But for some students, it is experienced as something closer to company.

NPR. 1 in 5 high schoolers has had a romantic AI relationship, or knows someone who has.
https://www.npr.org/2025/10/08/nx-s1-5561981/ai-students-schools-teachers

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Artificial intelligence is quietly reshaping what counts as knowledge.In discussions about AI heading into 2026, Wharton...
17/04/2026

Artificial intelligence is quietly reshaping what counts as knowledge.

In discussions about AI heading into 2026, Wharton professor Ethan Mollick notes that adoption is accelerating not because AI replaces expertise outright, but because people are learning to work through systems rather than alongside tools. Generative AI is already used at scale for complex reasoning, writing, and problem solving, shifting how tasks are performed inside organizations.

This introduces a subtle transition. Traditionally, expertise meant mastering the ex*****on of a task. Increasingly, value lies in defining goals, setting constraints, and guiding systems toward outcomes that remain logically sound and ethically appropriate.

AI can generate answers, but it does not independently determine context, responsibility, or consequence. As systems become more capable, human contribution moves upstream, toward framing problems and establishing guardrails.

Knowledge, in this sense, becomes less about doing the work directly and more about directing how work is done.

The question for 2026 may therefore not be what AI can perform, but how humans choose to govern its performance.

Knowledge at Wharton, AI in 2026: What’s Next?
https://knowledge.wharton.upenn.edu/podcast/this-week-in-business/where-artificial-intelligence-stands-heading-into-2026/

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