Chapter 1: Confronting the Thinking Gap

The landscape of work is shifting dramatically, shaped largely by the advancement of artificial intelligence.

We are no longer just talking about factories. The automation driven by AI and related technologies increasingly targets roles traditionally held by analysts, planners, and coordinators. These are the positions that our business students are being trained to fill. McKinsey predicts that by 2030, up to 30 percent of work could be automated, especially routine office tasks [1]. This seismic shift forces a critical question upon us as educators:

How do we equip our business students to offer unique value that complement, and does not duplicate what AI can do?

More tools or more content aren’t the answer.

The Need To Cultivate Meta-Thinkers

What our students really need is the ability to think more deeply: ways of reasoning, framing, and deciding that machines cannot easily replicate. I call this meta-thinking. While ordinary thinking solves problems within a given frame, meta-thinkers challenge the frame itself. For example:

“Why are we trying to solve this problem?”,

“What assumptions are baked into this solution?”,

“Is there another way to see this?”,

“How does the tool I am using shape what I believe is possible?”

These kinds of questions are the bedrock of effective thinking in today’s complex and uncertain world.

In an AI-shaped world, the concept of meta-thinking consists of several distinct capabilities:

1. Critical problem definition: The ability to frame and reframe problems, spot the reasoning gaps, and craft questions to uncover what’s missing.
2. Resilience to cognitive and emotional friction: The capacity to persist with difficult problems, tolerate ambiguity, contradiction, and resist rushing towards easy but superficial resolutions.
3. Synthesis over curation: The skill of integrating disparate, messy inputs to generate original insights, moving beyond simply summarising or selecting information that fits a preconceived notion.
4. Human–AI collaboration: Leveraging AI tools to extend one’s own thought processes while always retaining full accountability for the underlying thinking.
5. Process-based identity: Understanding that one’s true value comes from how they think, not just what they produce. It is cultivated through deliberate reflection and independent of external validation.

These are cognitive capabilities, which can only be sharpened through rigorous practice. They cannot be developed within neat and pre-structured classroom settings.

Why Current Efforts Fall Short

Universities are already aware that classroom teaching alone is insufficient to impart these skills. Therefore, they have responded by attempting to embed ambiguity into learning through real-world, industry-oriented approaches such as case-based learning, internships, and client-based project work. These have become commonplace over the last two decades, all in the hope of simulating the real world enough to foster deeper thinking.

However, each of these methods has its own limitations:

• In case-based learning, students apply known tools to defined scenarios and problem statements. The decision process and final output are thus bounded. While valuable, the ambiguity remains limited.
• Internships and industrial attachments offer exposure and practical skills, but their value may vary significantly due to a lack of consistent structure and meaningful reflection. Some students gain meaningful work, while others are limited to routine tasks with few learning opportunities.
• Client-based project work comes closest to real complexity, allowing students to work on actual problems with stakeholder feedback. Yet, many of these projects conclude at the ideation stage. This prevents students from testing their recommendations in real-world conditions where they would encounter constraints, feedback, and unforeseen challenges. Furthermore, students often uncritically accept client feedback, even when the client's own understanding may be unclear or contradictory.

While each of these techniques moves in the right direction, none of them reliably creates the precise conditions for meta-thinking to form. They are necessary but not sufficient. It is tempting to assume that students will naturally learn to think deeply if simply exposed to enough real-world "messiness". But uncritically accepting this assumption risks under-preparing them for the very conditions they will face.

Cost of Ignoring the Gap

If this gap between education and actual complexity remains unaddressed, we face significant consequences in the short term and long term:

• In the short term, university graduates remain under-prepared, lacking the scaffolding to think through real workplace friction. Institutions may continue to invest in superficial industry partnerships, believing that they are sufficient, while the deeper learning challenge persists.
• In the long term, students considering university might begin to question the fundamental value of tertiary education, leading some to opt out if they perceive the learning to be disconnected from real work. On the other hand, employers might see little distinction between graduates and non-graduates, posing an existential risk to higher education itself.

This brings us to the central question: if we accept the urgency of this gap, what would it take to bridge it? How can we design a course that prepares students to think clearly and confidently in the midst of complexity?

This became my work, known as “The Tide Pool”.

Next Chapter: An Accidental Journey To The Tide Pool