Depth and breadth
There is an understandable tendency in professional life to value specialisation. We develop expertise, build experience and become known for knowing something particularly well. Depth matters. It gives us credibility, judgement and the ability to see nuances that are often invisible from the outside.
But depth on its own may not be enough. The idea of the T-shaped professional offers a useful way of thinking about this. The vertical stroke of the T represents depth: significant knowledge and capability in a particular discipline. The horizontal stroke represents breadth: an ability to understand, connect and work across other disciplines, perspectives and areas of expertise.
The idea is not new. In their 2001 Harvard Business Review article, Introducing T-Shaped Managers: Knowledge Management’s Next Generation, Morten Hansen and Bolko von Oetinger described T-shaped managers as people able to share knowledge and ideas across an organisation while remaining deeply committed to their own area of responsibility.
More than 25 years later, the idea feels increasingly relevant. Many of the problems we encounter at work no longer sit neatly within one discipline. Technology decisions are also questions about people and culture. Design decisions intersect with operations. Strategy encounters finance, capability, communication and implementation. The ability to move between these perspectives, without necessarily being an expert in all of them, matters.
AI adds another dimension.
In its 2026 article AI-powered software development: How technology is rewriting the rules, McKinsey describes how AI is beginning to blur some of the traditional boundaries between specialist technology roles. As individuals become capable of working across a broader range of activities, roles can become increasingly T-shaped: retaining disciplinary depth while extending across previously separate areas of work.
McKinsey makes a similar observation in Why AI will elevate instead of replace commercial teams, arguing that the T-shaped model of broad capabilities combined with deep expertise may become even more pronounced as AI changes how work is organised. Deep domain knowledge remains valuable, but increasingly sits alongside capabilities such as data literacy, digital fluency and the ability to work effectively with AI.
In some ways, AI makes the horizontal stroke of the T easier to extend. We can explore unfamiliar disciplines more quickly. We can analyse data, prototype ideas, write code, create visual concepts or interrogate bodies of knowledge without necessarily possessing years of specialist training in each. But, perhaps that makes the vertical stroke more important, not less.
Expertise is not simply the ability to produce something. It is the accumulated judgement that helps us recognise whether something is good. It allows us to notice what is missing, question what appears plausible and understand nuances that may not be immediately obvious.
And between depth and breadth sits something else: curiosity.
Curiosity is what encourages us to step beyond the boundaries of our expertise. To ask enough questions to understand another discipline without pretending to be an expert in it. To become interested in how other people see a problem, and to recognise connections between ideas that might otherwise remain separate. Perhaps this is what makes the T-shaped model worth revisiting now.
Depth gives us judgement. Breadth gives us perspective. Curiosity connects the two.
As AI makes it increasingly possible to know a little about a great many things, the challenge may not be simply to become broader. It may be to remain deeply knowledgeable about something, while staying curious enough to see how it connects to everything else.