Managers and AI: Will There Truly Be Only One Left Standing?

🤖🚀 Managers vs. AI: Will There Really Be Only One Left?
Are you already weary of doomsday predictions proclaiming, “AI is going to replace us all!”? 😱 And amidst the digital revolution, have you stopped to wonder: “Hang on, what about all those managers?”
If you’ve pondered this conundrum while sipping your morning cuppa (or found yourself side-eyed by an algorithm lurking near the biscuit tin 🍪☕), my latest article is precisely what you need!
I’ve tried to clarify (and poke a bit of fun at) how Artificial Intelligence may impact the various “types” of managers: from those who merely carry out orders (yawn!) to those who thrive on strategic vision and cunning brilliance. 💡
Keen to find out which category you fit into—and whether your job is perched perilously close to the digital scrapheap?
👉 Click through for a chuckle (but not too much) at AI’s corporate invasion—and do let me know your thoughts!
So, tell me: AI🔮 or humanity🤝—which side are you on?

These days, one hears a great deal of chatter—often somewhat misguided—about Artificial Intelligence (AI). Among the most popular doom-and-gloom refrains, especially from those who haven’t truly grasped what AI is (yet still fervently decry it), is the notion that AI is poised to make vast swathes of human jobs vanish overnight. Naturally, discerning exactly how much truth lurks behind such apocalyptic visions is tricky, if not akin to reading tea leaves (though we Brits are quite fond of tea in any context). Still, it’s undoubtedly the case that AI will have a significant role in reducing positions where human competence—particularly of a higher-order variety—isn’t strongly required. Put another way: the less crucial the distinctively human skill set, the greater the likelihood that some clever AI algorithm might swoop in and do the job instead (possibly wearing a tiny digital bowler hat while it’s at it).

History teaches us that automation often shrinks the arena for roles involving less specialised skills (Brynjolfsson & McAfee, 2014). Yet, it has rarely led to a net disappearance of work as such. Rather, it has typically brought about job transformation and upskilling. As such, the main risk associated with AI isn’t that the entire global workforce will suddenly end up in a queue for the dole—but that those jobs requiring less human expertise (or offering minimal added value) may be replaced by a more efficient, cost-effective machine-based solution.

Now, amidst all this talk about AI’s looming effects on employment, there’s a curious gap in our discussions: we rarely speak of how these changes might affect the managerial echelon. It’s possible that we avoid the subject out of deference, flattery, or sheer inertia; but the reality is that the manager—whether occupying a junior, middle, or top-level “C-suite” role—might well find themselves impacted by the rising wave of AI adoption.

So, is the manager at risk of being replaced by a chatbot or an algorithmic overlord? Or might a certain type of manager find a route to co-exist (maybe even flourish) alongside these digital wonders? Before we attempt to address those questions, we need to clarify what exactly we mean by “manager.” In the paragraphs below, you’ll discover a rather tongue-in-cheek classification of managers into three groups: the “functional” manager, the “good” (proactive) manager, and the “great” (visionary) manager. We shall then examine, with a spot of British humour, how each group might stand up—stiff upper lip and all—against the onslaught of AI.

Defining the Manager

A manager, in broad terms, is someone charged with coordinating and directing people, resources, and processes within an organisation in order to achieve specific goals (Fayol, 1949; Mintzberg, 1973). This typically involves planning, organising, leading, and controlling activities and projects, to ensure that everything remains congruent with the firm’s overarching strategy.

“I made that slide myself—and then I wonder why they never asked me to run another manager training session.”

From time immemorial, management courses have hammered home the point that a manager shouldn’t merely “command and control” but act as a facilitator and guide. Yet, there’s a crucial distinction among managerial types, easily distilled into three categories:

“A Manager does what the company tells him/her to do”

This is what we might call the functional manager.
He or she straightforwardly follows orders: the boss says “Jump,” and they say “How high?” (and if the boss forgets to say “Jump,” they stand perfectly still).
They tend to be diligent at adhering to established procedures but typically lack any real spark of initiative or strategic insight.

“A Good Manager does what the company wants him/her to do”

This manager is proactive.
They understand the organisation’s goals on a deeper level, align themselves with the firm’s values, and anticipate the needs of their team.
They don’t just wait for instructions but go beyond them, showing initiative and effectively motivating those under their charge.

“A Great Manager does what the company needs him/her to do”

This is the manager who’s strategic and visionary.
They’re not content merely to tick boxes or deliver on short-term expectations; rather, they identify what the firm truly requires (and sometimes that means going against the grain of explicit demands).
They promote innovation, spot impending market challenges, nurture talented individuals, and forge sustainable value over time (Kotter, 1990; Bass & Avolio, 1993).

In essence, the distinction between these three sorts of managers lies in moving from a reactive stance (merely doing what one is told) to a proactive one (interpreting expectations and acting effectively), eventually reaching a strategic and visionary level (discovering and creating what the firm really needs).

Managers vs. Artificial Intelligence

The “Functional” Manager: The Most Exposed

The manager who does nothing but exactly what they’re told seems particularly vulnerable to being replaced by an AI system. Let’s be honest: if your day-to-day job is simply to pass on instructions (something like “If Boss says A, do A; if Boss says B, do B”), then a basic decision-tree algorithm could quite easily replicate that. Indeed, it might do so without getting miffed about having to work extra hours, or popping out for coffee breaks, or complaining that someone else ate all the biscuits (that’s what we are here for!).

Such roles are quite common in the broad swathe of lower to mid-level management, where the manager’s function is largely to forward messages and ensure compliance with company policy. Just imagine turning up to work one day and discovering that your new line manager is an AI bot with precisely zero sense of humour, though it’ll never scold you for being five minutes late if it’s not programmed to notice timekeeping. On the bright side, you’ll at least be spared the dreaded Monday morning pep talk.

The “Good” (Proactive) Manager: Not Entirely Safe

Now, you might suppose that the second category of manager—the helpful, proactive one—would be safe, but that might be a tad optimistic. Many managerial tasks, after all, are about interpreting corporate strategy and sifting through signals, some explicit and some implied, to glean what people truly want. As we know, corporate politics is a labyrinthine world brimming with interest groups, subtle alliances, hidden agendas, and that beloved “corporate jargon” that often says one thing while hinting at another.

Modern Large Language Models (LLMs)—like GPT-4, Bard, and others—are already pretty good at textual interpretation and context understanding (Brown et al., 2020). If the company’s policies and strategic goals are fed into a well-trained AI system, it might interpret them with surprising dexterity, thereby taking over a significant chunk of that “translational” labour once performed by the manager who reads between the lines of the board’s pronouncements.

Consequently, a manager whose main skill is “just” interpreting instructions and turning them into action points might see their advantage erode. Some might devolve into little more than advanced “press this button when told” operators. Of course, a thoroughly incompetent manager might be replaced by AI entirely (though at least the AI probably wouldn’t pinch your favourite stapler).

The “Great” (Visionary) Manager: Facing Some Risks, Too

At first glance, you might think the third category—the truly visionary, strategic manager—would be the most secure. Indeed, such a manager’s value is found in intuition, creativity, forging novel connections, and shaping new paths forward that no one else might see. AI, meanwhile, is primarily about pattern recognition, working from existing data sets, or using trained models to extrapolate from known parameters (Agrawal, Gans & Goldfarb, 2018).

That said, even visionary managers aren’t entirely immune to the potential pitfalls. Often, the manager and the business owner might not be the same person: in some companies, top managers do not own the enterprise but are hired to lead it. If the owners decide to rely on an AI system—trusting it to churn out “unimpeachable” solutions based on advanced analytics—they might become suspicious of any maverick manager who proposes a direction that diverges from the AI’s projections (especially if they’ve invested half the annual budget in that newfangled system and feel the pressure to prove it was money well spent).

Nevertheless, in a scenario of wise and balanced implementation, AI can serve as the Great Manager’s best friend, augmenting their capacity to research data quickly and verify strategic hypotheses at breakneck speeds (Shrestha, Ben-Menahem & von Krogh, 2019). The synergy lies in letting the AI handle the computational heavy lifting while the manager focuses on empathy, leadership, big-picture thinking, and persuading everyone else to get on board (possibly with well-placed references to the wonders of a good cuppa in the break room).

Automation: Threat or Opportunity?

The discourse around AI’s potential to displace managerial roles is part of a broader conversation on the automation of work. Historically, each new wave of technological innovation has rendered certain tasks obsolete, yes, but it has also created brand-new professions that were previously unthinkable (Autor, 2015). According to the World Economic Forum (2020), AI and automation could produce millions of new roles in the medium term (particularly in highly knowledge-intensive fields like data science or advanced project management) while making an equivalent number of simple, repetitive tasks redundant.

For managers, this translates into a pressing need for reskilling and upskilling—in other words, training oneself in areas that AI can’t easily replicate, at least for the foreseeable future. Some examples of these skills include:

Empathetic Leadership: The ability to interact sensitively and supportively with people, understanding emotional dynamics within a team (Goleman, Boyatzis & McKee, 2002).
Creativity and Strategic Innovation: The knack for thinking outside the box, embracing bold new ideas, and sniffing out emergent opportunities in the market.
Critical Thinking and Complex Problem-Solving: Handling fluid, ambiguous situations that call for nuanced approaches, far beyond the scope of a formulaic solution.
Change Management: Orchestrating organisational transitions and transformations, aligning diverse teams and stakeholders, providing a compelling vision, and dealing with the inevitable pockets of resistance (Kotter, 1996).

Conclusions

The rise of AI in the corporate world poses questions that go far beyond the potential elimination of repetitive tasks. On one hand, the so-called “functional” manager (the one who does nothing but re-transmit instructions) might indeed be at high risk of obsolescence. On the other hand, “good” and “great” managers—those with proactive mindsets and visionary leadership styles—retain a clear advantage, at least for the time being. Yet they, too, might see their roles reshaped if AI systems become ever more adept at internal decision-making processes.

As always, technology in itself is neither angelic nor demonic; its ultimate impact depends on how thoughtfully it’s introduced and how it’s used. We can confidently predict that the “least competent” strata of the workforce may bear the brunt of these changes, while those offering real human value—particularly in interpersonal relations and creative thinking—will still be in demand. Because let’s face it: no matter how sophisticated an AI might be, it won’t be able to replicate the intangible spark of human empathy, humour, or the delightfully awkward small talk at the water cooler (AI might well generate water cooler discussion points, but can it truly cringe at the boss’s pun the way we do?).

In the end, perhaps we ought not to ask: “Will it be the manager or the AI who’s left standing?” but rather: “How can the manager and the AI fruitfully collaborate?” The best hope for a successful future likely lies in synergy, with humans harnessing the computational muscle of AI while deploying our unique capacity for imagination, moral judgement, and that subtle emotional intelligence that algorithms haven’t quite cracked.

Finally, let’s not forget:

“For the manager who wants solutions, not extra headaches.”

“A manager that is not a proactive part of the solution is part of the problem.”

In other words, best not stand idly by. After all, you wouldn’t want a chatbot wearing your name badge next quarter, now would you?

Bibliographical References

Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.
Autor, D. H. (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives, 29(3), 3–30.
Bass, B. M., & Avolio, B. J. (1993). Transformational Leadership and Organizational Culture. Public Administration Quarterly, 17(1), 112–121.
Brown, T. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (NeurIPS).
Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
Davenport, T. H., & Kirby, J. (2016). Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. Harper Business.
Fayol, H. (1949). General and Industrial Management. Sir Isaac Pitman & Sons.
Goleman, D., Boyatzis, R., & McKee, A. (2002). Primal Leadership: Realizing the Power of Emotional Intelligence. Harvard Business Press.
Harvard Business Review (2019). Artificial Intelligence for the Real World. Harvard Business Review, 97(1), 108–116.
Kotter, J. P. (1990). A Force for Change: How Leadership Differs from Management. Free Press.
Kotter, J. P. (1996). Leading Change. Harvard Business Review Press.
Mintzberg, H. (1973). The Nature of Managerial Work. Harper & Row.
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational Decision-Making Structures in the Age of Artificial Intelligence. California Management Review, 61(4), 66–83.
van der Aalst, W. M. P., Bichler, M., & Heinzl, A. (2018). Robotic Process Automation. Business & Information Systems Engineering, 60(4), 269–272.
World Economic Forum (2020). The Future of Jobs Report. Geneva: WEF.