a broader shift towards more plural, non-linear professional lives. The implications are significant for individuals who must make sense of identity, income stability, personal brand, time allocation, wellbeing and long-term career coherence across multiple roles. They are equally significant for organisations, many of which are still not designed to engage such talent effectively. Legal structures, HR policies, reporting lines, compensation systems, conflict‑of‑interest rules and assumptions about loyalty and hierarchy are still largely built for full‑time, single-employer careers. In practice, that can mean something as simple as repeating almost identical compliance and workplace-conduct training separately for multiple organisations every year. More fundamentally, it can also mean that individual roles taken sit awkwardly across systems built for only one employer at a time. Business schools, therefore, face an uncomfortable question: if a growing share of talent no longer fits the career model around which our programmes, services and assumptions are built, what exactly are we preparing people for? Put differently, how can we better support individuals facing rising career complexity while also helping organisations that remain poorly equipped to engage and manage this kind of talent? As AI accelerates change in the job market, this issue is unlikely to fade. More likely, it will intensify. The shifting landscape Alongside ethics and sustainability, AI has become one of the defining lenses through which many business schools now think about relevance. That makes sense in today’s climate and leaders need to understand how emerging technologies reshape workflows, decision-making, organisational design, service models and strategy. Curriculum designers and professors who ignore these crucial threads of AI will clearly be missing something important. The risk for others, however, is twofold. Firstly, some schools are treating AI too narrowly, as an assistant tool or a technology topic, rather than as a catalyst for broader redesign. Secondly, while many are modernising programme content, they are leaving a deeper structural assumption untouched: that leadership development is still mainly about preparing people to enter, rise within, or run relatively stable organisations. Yet, this ignores the fact that AI is not only changing work itself but also
indirectly encouraging more fragmented, flexible and portfolio-like career structures. That matters because career structures shape what learners need from education. Many aspiring students still imagine a relatively linear path and a singular kind of preparation would make sense if that remained the dominant reality. Increasingly, however, careers after graduation do not unfold so neatly. Even before AI, professionals were being pushed across organisations, projects, sectors and role types by restructuring, flexibility, shifting ambitions and a less stable employment landscape. AI is now adding fresh momentum to that trend. As firms rethink workflows, unbundle tasks, automate parts of roles and reassess which capabilities truly need to sit in full-time positions, more leadership and specialist work is likely to be configured in partial, project-based, advisory, or fractional ways. In this way, AI is becoming a powerful enabler of new forms of collaboration that play out between individuals and multiple organisations. That demands a different kind of preparation: not only competence but also career architecture; not only promotion but also sequencing; not only leadership within one
“Career structures shape what learners need from education”
12 Business Impact • ISSUE 3 • 2026
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