AI poised to reshape white-collar careers from the bottom up Financial Times insight

In an insightful piece for the Financial Times, Robert Buckland, senior adviser at Engine AI and Investa and former chief global equity strategist at Citigroup, examines how artificial intelligence (AI) will transform white-collar professions, particularly in investment banking and equity research. Drawing on decades of personal experience, Buckland offers a nuanced perspective: AI is unlikely to upend the careers of senior analysts immediately, but it will dramatically alter entry-level roles, gradually reshaping the traditional career pyramid.

Buckland begins by reflecting on his early career in equity research, where he started at the bottom of the hierarchical pyramid performing tasks such as collecting information, updating models, formatting charts, preparing presentations, and even fetching coffee. These repetitive tasks, though mundane, were essential in allowing senior colleagues to focus on high-value activities like idea generation and investor engagement. Over time, as he gained skills and insight from observing his seniors, Buckland progressed to more complex analytical work, eventually reaching the upper echelons of the profession. He notes that this structure—a pyramid in which advancement depends on mastering progressively sophisticated tasks—is not unique to finance but is mirrored across consulting, law, academia, and technology.

The central argument Buckland makes is that AI will affect the lower tiers of these career pyramids first. Tasks that rely heavily on routine data processing, model updates, or presentation formatting are highly automatable. By contrast, advanced analytical work, idea origination, strategic thinking, and client relationship management—hallmarks of senior roles—remain resistant to current AI capabilities. In other words, AI is poised to become an assistant rather than a replacement for highly skilled professionals, at least in the near term. Buckland compares this dynamic to the historical automation of UK factory work in the 1980s: disruptive, but uneven, hitting the entry-level roles hardest.

Buckland also identifies specific applications where AI could improve efficiency and value in research. For instance, AI-powered content distribution could allow clients to interact with research outputs in a more intelligent way, querying complex reports and obtaining nuanced answers as if speaking to an analyst. Yet, he cautions that current large language models struggle with subtleties such as report type, relevance, and shelf life, emphasizing that human expertise remains critical.

Ultimately, Buckland argues that AI may reshape career structures from a pyramid to more of a diamond, eroding lower-level positions while leaving senior roles relatively intact. This raises strategic questions for talent development and succession planning: if entry-level positions are increasingly automated, where will the next generation of leaders gain experience and develop the skills required to manage complex, high-level responsibilities?

By Vugar Khalilov