The disruption caused by artificial intelligence has already reached the corporate world — and it is knocking directly on the door of anyone who manages teams and makes decisions every day.
A Deloitte survey conducted in August 2026 with 501 business and IT leaders in the United States revealed a striking number: 43% of respondents expect AI agents to significantly disrupt their teams within the next 12 to 18 months.
But here is the point no one is openly talking about: only 21% of those companies say their processes are ready for that shift.
In other words, most organizations are preparing for the arrival of AI without having decided what it should actually do. That disconnect is exactly the heart of a new policy paper from HEC Paris, which investigates how companies are adding AI to managerial routines without clearly defining what work the technology should absorb.
The problem is not with automation itself — the technology has already proven it can handle a large share of the repetitive tasks involved in day-to-day management.
The problem is the lack of clarity about what exactly should be handed over to the machine and what still needs a human touch nearby. 🤔
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That is exactly the gap this article will explore — with data, real-world examples and an honest look at what changes (and what doesn’t) when AI starts taking over the corporate routine.
Start with the work, not the job title
An interesting provocation came from Google co-founder Sergey Brin in 2025, when he described management as the easiest thing to do with AI. He explained how the technology can summarize internal communications, distribute follow-up tasks and even help surface the contributions of a quieter engineer during promotion discussions. Provocative? Absolutely. But also revealing about where things are headed.
For leaders, the most effective approach is not to look at job titles but at the tasks that make up each role. An executive position combines administrative, analytical, relational, political and symbolic activities — and artificial intelligence affects each of them quite differently. Seeing that clearly is the first step toward redesigning work intelligently, instead of simply layering technology on top of old processes.
Tasks like performance monitoring, workflow coordination, compliance, scheduling, document review and initial analysis rely heavily on standardized information, formal rules and measurable outcomes. AI can aggregate metrics, detect anomalies, model scenarios, flag potential violations, retrieve knowledge and prepare initial explanations. This is where automation really shines. But the decisions around those outputs still require human context — a performance anomaly calls for interpretation and care about metric manipulation, a compliance alert may demand sensitive judgment, and a risk model organizes evidence, but the person who decides which risks to accept remains a responsible human being.
What AI agents are already doing inside companies
When we talk about automation applied to management, a lot of people still picture those industrial factory robots or basic scheduling software. But the current landscape is very different. So-called artificial intelligence agents are systems capable of receiving objectives, planning steps, executing actions and learning from results — all autonomously, without needing a human instruction at every step. They are already being used for candidate screening in hiring processes, executive report generation, real-time goal tracking, demand prioritization in agile squads and even operational decision-making in certain business verticals. What used to take days of analytical work can now be condensed into minutes by a well-configured agent.
The impact on the daily routine of leaders is immediate and noticeable. Coordinators and managers who used to spend hours consolidating performance data now receive ready-made dashboards with automatically generated analyses and even action suggestions based on historical patterns. This is not science fiction — it is what companies like Salesforce, Microsoft and SAP are already delivering on their enterprise platforms with integrated AI layers. The volume of low-cognitive-value decisions a leader had to make each day is starting to be absorbed by the machine, which, in theory, should free up mental space for what truly matters: strategy, culture and relationships.
But there is an important catch. That time savings only happens when the company has well-defined processes, organized data and clear governance around which decisions can be delegated to AI and which need to stay with people. And that is exactly where the knot is — because most organizations still have not done that mapping. They adopt artificial intelligence tools the way someone buys a sports car without ever having learned to drive: the potential is there, but without preparation, so is the risk of a crash.
Management and leadership respond to AI in very different ways
There is a classic distinction, associated with Harvard professor John Kotter, that helps a lot in understanding this moment. Management exists to maintain stability: it reduces uncertainty, allocates work, monitors progress and keeps people and processes running as expected. Leadership, on the other hand, creates change: it sets direction, sustains identity and culture, builds bridges between departments and rallies people around a common purpose. They look similar on the surface, but they are profoundly different at their core.
And it is precisely because of that difference that artificial intelligence impacts each one in its own way. Because management is more structured, rule-based and data-rich, it is naturally more susceptible to automation. Leadership, however, leans much more heavily on social intelligence, creativity, trust and accountability — territories where the machine is still in its infancy. The conclusion from recent analyses, which combine research on task automation, dozens of interviews with executives and AI specialists, points to a consistent asymmetry: AI is increasingly capable of supporting structured managerial tasks, while leadership work rooted in judgment and human relationships still depends on people.
Why so many leaders still are not ready
The Deloitte survey leaves no room for doubt: there is a massive gap between the expectation of disruption and the actual readiness level of organizations. When only 21% of companies say they have processes prepared for the integration of AI agents, what that number is really saying is that the vast majority of leaders are navigating in the dark. They know the change is coming, they feel the pressure from the market and from boards of directors to adopt artificial intelligence, but they lack clarity on how to redesign workflows, redistribute responsibilities or measure what that adoption will actually generate in terms of real results — positive or negative.
Part of this problem comes from a very common conceptual confusion: many leaders still treat automation as a layer of operational efficiency, when in reality it represents a structural shift in how management works. Automating a task is not just removing an item from the human checklist — it means redesigning who is accountable for that deliverable, how errors are handled, how organizational learning happens and how trust is built within teams. When that redesign does not happen, what you see is silent chaos: AI tools being underused, team resistance, automated decisions that no one knows how to question and a general feeling that the technology promised more than it delivered.
There is also a human factor that cannot be ignored. Many leaders feel, even if unconsciously, that the expansion of artificial intelligence in management is a threat to their own role. And that perception is not entirely unfounded — after all, if AI can monitor performance, deliver data-driven feedback and even suggest promotions or terminations based on objective indicators, what is left for the manager to do? The answer exists, but it requires a redefinition of professional identity that few are willing to face head-on. The result is slow adoption, full of hidden resistance and implementation decisions driven more by fear than by strategy.
What actually changes in the role of those who lead
The disruption that artificial intelligence brings to leadership is not the extinction of the leader — it is the reinvention of what it means to lead. When automation takes over analysis, monitoring and routine execution tasks, the leader needs to increasingly become the architect of the environment where AI will operate. That means defining which values guide automated decisions, ensuring that the data used by agents is representative and fair, and creating bridges between what the machine delivers and what the people on the team can absorb and implement. It is a less operational and much more philosophical function, in the sense that it demands clarity about purpose, ethics and long-term direction.
Another point that transforms the leadership role is the need to develop what some experts call AI literacy — the ability to understand enough about how these systems work to ask the right questions, identify biases and interpret results with critical thinking. We are not talking about learning to code or becoming a machine learning engineer. We are talking about a manager who can look at an AI-generated report and ask: does this data cover all the groups on my team equitably? Was the model trained with contexts similar to mine? What is the margin of error here? That kind of questioning is what separates a leader who uses AI intelligently from a leader who simply outsources decisions to an algorithm.
And there is a dimension that artificial intelligence still cannot replace, not even close: the ability to create emotional context within a team. Motivating someone who is going through a tough time, noticing that a talented person feels invisible, navigating conflicts that have far more to do with power dynamics than with deliverables — all of this is still human territory. People management, in the deepest sense of the word, remains an art that depends on presence, empathy and reading signals that never show up on any dashboard. What changes is that, with AI handling the operations, the leader finally has the time and energy to dedicate to the side of leadership that has always been the most important — and the most neglected.
How companies are navigating this transition in practice
Some organizations have already moved past the talk and are, in fact, redesigning their management models with artificial intelligence at the center. A recurring example is the use of AI agents for OKR tracking — the well-known objectives and key results that guide teams in quarterly cycles. Instead of the manager needing to hold weekly status meetings with every team, the agent monitors each indicator’s progress in real time, identifies deviations before they become problems and sends contextualized alerts with adjustment suggestions. The manager only steps in when there is a decision that requires human judgment — a priority conflict, a strategy change or a conversation about realigning expectations.
Another movement gaining momentum is the creation of hybrid squads, made up of people and AI agents working together on specific projects. In this model, automation handles research, information synthesis, first-draft document generation and data analysis, while humans are responsible for curation, creative judgment and stakeholder communication. Consulting firms, fintechs and large digital retailers are already testing this format — and the productivity results, when properly implemented, are significant.
But pay attention: the success in these cases did not come from the technology itself. It came from the way leadership conducted the transition — with transparency about what was changing, with space for teams to ask questions and voice concerns, and with clear metrics to evaluate whether artificial intelligence was actually delivering value or just adding another layer of complexity. The technology is the means. Clarity about the destination is what makes the difference between a successful disruption and an expensive project that never leaves the drawing board. 🚀
