AI agents are flattening corporate hierarchies as leaders navigate hybrid human-AI workforces

Learning to lead in a hybrid human-AI enterprise

To optimize AI's potential within a hybrid workforce, leaders need to adapt workplace strategies -- re-evaluating roles, skills, and culture. As adoption of AI agents looks set to surge by as much as 300% in the next two years, leadership teams are carefully considering the implications of a hybrid human-AI workforce. Unlike existing enterprise-level automation that relies on manual input, AI agents are capable of autonomously coordinating complex tasks, interacting with multiple tools and environments across an organization. In early applications that center on customer service, HR, and sales, adoption of agentic AI has led to productivity gains of 30-50%. Their autonomy positions agents more as collaborators than tools, working side-by-side with human employees in blended teams that look poised to upend traditional workplace dynamics. More than three-quarters of HR leaders believe that the deployment of AI agents will transform existing workplace norms, driving a complete reappraisal of how roles and responsibilities are distributed, how skills are prioritized, and how workplace culture is shaped. Though many admit they're in the early or preparatory phase of this shift, 86% of chief HR officers predict that navigating digital labor shaped by agentic AI will be a central component of their role in the years ahead. Fluency in the change management aspect of agentic AI adoption will be a crucial differentiator when it comes to unlocking the full potential of the technology going forward, believes Ateet Jayaswal, chief culture and employee experience officer at Wipro, a leading technology services and consulting company. This moment is one that he says, "calls for a mindset shift in how HR leaders would enable their organizations." Redeploying roles to enable higher-value work As AI agents assume ownership of more complex and integral tasks, the distribution of roles and responsibilities within an organization will undergo significant change. It's estimated that three-quarters of current roles will require redesign, reskilling, or redeployment by 2030 as a result of agentic AI. For leadership, this shift should be about reskilling employees toward higher-value work in order to optimize the potential of an agent-human hybrid workforce, says Jayaswal. For example, Wipro is a complex organization of 240,000 employees across 65 countries. It previously had multiple policies, documents, and knowledge fragmented across different systems, which delayed response to employee queries. But the company has recently integrated a custom agentic AI assistant -- an agent co-created in partnership with enterprise agentic AI platform Ema Unlimited -- that can swiftly navigate this complex system, assuming responsibility for 50 HR tasks that had previously fallen to human employees. With the help of an AI agent, average response time to queries has lowered from 48 hours to five seconds. Human employees have more time to focus on work "that requires a creative and imaginative mind and cross-functional collaboration, leveraging diverse ideas and thoughts to problem-solve," says Jayaswal. The AI agent, meanwhile, handles rote administrative tasks like sorting timesheets or helping employees navigate policies and take actions in the flow of work. When reallocating employee responsibilities, though, it is imperative that humans remain in the loop, Jayaswal caveats. When agentic AI is incorporated into enterprise technology, it must work with sensitive and personal data and therefore needs even more stringent guardrails and constraints than consumer applications. "When you expose an AI agent to organizational data, when you integrate it into multiple enterprise systems, then pathways around the AI agent become extremely important," he says. "It's an evolving space that leadership needs to have front-of-mind." Governance should include robust data privacy rules and the establishment of governance layers, such as an AI council, he suggests. At a fundamental level, the adoption of AI agents will force a re-evaluation of human roles, believes Jayaswal. Rather than employees primarily performing repetitive tasks or troubleshooting, a significant proportion of their time will shift to designing, teaching, and optimizing an AI agent that can do this work for them with far greater speed and predictability and without the agent getting bored. "The nature of your job changes from being the hero who comes in to solve the problem to designing the hero who can solve the problem," he summarizes. "The individuals who I have seen thrive in this environment are the ones who make this shift." An evolving employee skillset Just as roles and responsibilities will be reconfigured to reflect the input of AI agents, the core skills of human employees will be reprioritized. More than four in five HR leaders say they're planning to reskill workers to become more competitive in a market shaped by AI agents. Technical skills will be increasingly important. Leading employers such as Salesforce, Danone, and Walmart are already rolling out dedicated AI and digital skills programs that aim to equip everyone from frontline workers to C-suite executives with a baseline level of AI literacy in response to the pervasiveness of the technology. But desirable soft skills will also evolve, Jayaswal points out. Employees who assign tasks to an AI agent need to plainly articulate what modular steps may be needed to accomplish a task, what the desired outcome should be, and what parameters or guardrails need to be in place to ensure the agent doesn't access or share confidential data. As HR executives adapt to a blended workforce, three skills are emerging as top priorities during recruitment, according to a recent survey: relationship building, like forging constructive partnerships and account management; collaboration; and adaptability. Maintaining a healthy workplace culture In freeing up human employees to focus on higher-value tasks, the hope is that AI agents can elevate the employee experience, deepening fulfilment and satisfaction in the workplace. "At Wipro, our vision is to improve the life of Wiproites," says Jayaswal. "We are taking away non-value added work by embracing modern ways of collaborating, engaging, and transacting, leaving associates with higher order work content." But leadership teams embracing agentic AI will also need to plan for the new pressures and stressors that the technology can place on a workforce. There is already confusion and knowledge gaps, with 73% of HR leaders reporting their employees don't yet understand how digital labor will impact their work. Many organizations have opted to define AI agents as teammates or colleagues on org charts, but new research says this could erode trust and a sense of professional identity. It also raises new questions around accountability and ownership. The role of management in addressing these concerns is critical, says Jayaswal. To maintain healthy dynamics, managers need to become skilled at orchestrating blended systems, splitting their focus between supervising AI agents and motivating human employees as they also build and supervise AI agents. Upgrading employee well-being programs will be a core part of maintaining a robust workplace culture. "As there are more interactions with AI agents, you are losing some of the human touch that was provided by service delivery partners or leaders, or often even by colleagues and peers," Jayaswal says. Employee services that encourage social connection and empathetic communication may help teams navigate this. A breakneck transformation Agentic AI looks set to scale at breakneck speed across many enterprises, and it will significantly transform how these organizations operate. Carefully considering and deciding how to adapt to this newly blended workforce is now a top priority for leadership teams. Reviewing and refining organizational strategies is essential for optimizing both technological gains and the employee experience. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review's editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

The AI-related leadership that's only five years away

There's a talent problem hiding in plain sight. It doesn't show up in this quarter's earnings call. It won't surface in next year's workforce plan. But if organizations don't start treating it like a design flaw now, they'll run into a leadership wall within the next five years. For all its power to make us more productive, AI is systematically reshaping the entry-level experience that traditionally trained the next generation of leaders. We're already seeing the market signals. Harvard University research indicates junior employment has fallen 9%, with entry-level hiring dipping 80% per quarter since 2023, at organizations adopting generative AI. ZipRecruiter's 2026 Graduate Report found the share of entry-level jobs dropped to 38.6% at the start of 2026, down from over 44% three years ago. Starter tasks are being automated or pushed upward, creating workload strain in the layers above that will become a clog in the leadership pipeline. Entry-level roles have never been only about output. They are, in a very real sense, structured learning environments. The junior analyst who builds the model manually develops an intuition for when the model is wrong. The new hire who drafts the memo and gets it marked up learns judgement by seeing their thinking challenged. The new college graduate who handles the messy client situation builds emotional resilience that no training course can replicate. Yet addressing AI's impact on early careers, the World Economic Forum paints a picture of a generation now entering careers where structured, repetitive tasks that once built confidence and competence are being automated away. From day one, young professionals now are expected to contribute analysis, insight and adaptability, often without the scaffolding that makes it possible. Cornerstone's recent survey of 2,000 workers found Gen Z reporting the highest rates of AI-driven role transformation, with 38% saying AI has fundamentally changed what their job requires. Yet they're least likely to have received formal training to navigate that change. Most Gen Z workers using AI at work (59%) say their organization has never provided formal training for it. This runs directly counter to the assumption that younger workers are better served by their organizations on AI. They're forced to be self-reliant, and that's where shadow AI becomes a risk multiplier, as employees use powerful tools outside approved guardrails because official enablement hasn't kept pace. What's disappearing isn't just work. It's practice. Entry-level roles traditionally provided structured reps: learning how to prioritize, recover from mistakes, communicate uncertainty and build credibility. When AI absorbs the repetitive work, organizations may see efficiency but lose the developmental pathway that produces competent managers and grounded leaders. AI is now table-stakes operating infrastructure. A few simple steps can ensure leadership development is designed, measured and continuously improved to become its foundation: When AI takes tasks, don't just delete them. Convert them into judgement loops that review outputs, validate assumptions, pressure-test recommendations and escalate edge cases. AI can be amazing or disastrously wrong, and someone has to build the muscle to tell the difference. Use AI to generate drafts, options and scenarios. But require humans (especially fresh talent) to critique, refine and justify decisions. The learning is in the evaluation. The market is shifting toward giving entry-level hires bigger responsibilities earlier. That can accelerate growth or create avoidable failure. Build a progression ranging from low stakes and supervised decisions to independent ownership, with clear standards for what "good" looks like at each stage. AI agents can coach in the flow of work, suggesting resources, examples and next-step guidance at the moment of need, so development doesn't rely on formal training employees rarely have time to take. If early-career work is changing, managers must evolve with it. They need to explicitly teach how to think, not just what to do. That includes creating norms for responsible AI use, when to escalate, and how to communicate uncertainty. Continuous workforce intelligence generated by AI models also can help managers see capability growth (or gaps) earlier, and trigger timely coaching, projects or learning before performance issues become attrition. In fast-changing environments, a vehicle for building leaders is a stretch assignment like a rotation or gig. These cross-functional projects build judgement skills with practice; they create broader context and judgement faster than static roles do. Within our organization, for example, our Cornerstone Gigs programme creates short-term opportunities for employees to apply to assignments beyond their core team. It helps people stretch into new work that expands skills and builds new ones, while giving the business fresh perspectives and access to capabilities it might not otherwise surface. AI can surface internal opportunities and match people to them based on evolving skills, so mobility becomes a system, not an informal spreadsheet or favour network. Young professionals need permission and boundaries that disclose AI assistance where appropriate, validate outputs and discourage the outsourcing of thought. If every idea gets filtered through AI, people risk never developing their own instincts. Embed lightweight governance like approved tools, safe-use prompts and checklists to guide good practice. The real competitive divide won't be how AI is adopted, but whether organizations build the human development engine that keeps pace. Automation without human judgement doesn't scale performance; it scales mistakes. Think of this shift as moving from doing the homework to grading it. Entry-level workers in an AI-augmented environment are increasingly positioned as evaluators and decision-makers, not just producers. Grading requires a standard, enough domain knowledge and critical thinking to catch when something feels off, and confidence to override a system that sounds authoritative but isn't. The World Economic Forum's Future of Jobs research finds that 39% of core skills will change by 2030. As AI rewires work, it also revamps capability. Organizations that replace lost entry-level reps with deliberate judgement-building will create leadership capacity that compounds with their technology. The rest may scale efficiency today - but discover the leadership bill comes due tomorrow.

AI agents are flattening corporate hierarchies. Here's how companies -- and managers -- can develop a new playbook | Fortune

Signs of "the Great Flattening" are emerging across corporate America. As companies increasingly deploy AI agents to handle workflow orchestration, task coordination, reporting, and information sharing, they are beginning to rethink one of the most enduring features of modern organizations: layers of middle management. The traditional corporate pyramid -- where information flows up and down through multiple tiers of management -- is starting to compress as AI systems take over much of the coordination work that once justified those layers. Around 41% of employees say their companies trimmed management layers last year, according to Korn Ferry's survey of 15,000 professionals worldwide. And recent restructuring efforts at companies including Meta, Citigroup, CrowdStrike, and GitLab have fueled debate about whether AI could accelerate a broader shift toward flatter organizations. Cloudflare CEO Matthew Prince recently offered one of the clearest articulations of this view. After cutting roughly 20% of the company's workforce while posting record revenue, Prince said: "The vast majority of those we laid off last week were measurers," he wrote. He defined "measurers" as those in middle management, finance, legal, internal auditing, and revenue recognition. Prince said the company kept what he called the "builders," such as engineers. That claim stands in contrast to the belief that software engineers are among the most vulnerable to AI, as the technology can code quite well, especially following the release of Anthropic's Claude Code. He said "sellers" are also relatively safe from automation. Prince added that the layoffs weren't about reducing headcount, but about shifting the nature of work. The company has a record number of open positions, according to Prince, in "areas that drive growth." Bret Greenstein, chief AI officer at consulting firm West Monroe, argues that the role of management itself is changing. In an AI-enabled organization, managers will increasingly be expected to contribute measurable business outcomes rather than simply serve as conduits of information. "Thanks to AI at everyone's fingertips, CEOs know things as fast as anyone in the team," Greenstein said. "You don't really need a translator." In addition, a significant portion of management work has traditionally involved gathering information, communicating updates, scheduling meetings, tracking progress, and keeping teams aligned. AI agents can now perform many of those functions continuously and at scale. "A mid-level manager can spend something like a third of the week in meetings, most of it keeping people in sync," said Andy Williamson, CEO of ONLC Training. "That's exactly the work the software can handle now." But Max Martina, president of Cambridge Leadership Associates, emphasized that this doesn't mean management layers will fully disappear. Instead, AI will supplement management work and support decision-making. "The activity of management will be supported with new tools, greater efficiency, and an opportunity to move from task-focused execution to leadership behavior," he said. "It will open doors to the domain of real leadership, not management." The Great Flattening is far from mainstream -- for now Yet the Great Flattening remains, for now, far from mainstream. Williamson notes that the most significant organizational changes are still concentrated among technology-forward companies with sophisticated digital infrastructure. Most organizations are still in the early stages of AI adoption, and many lack the systems needed to automate coordination at scale. Even so, experts increasingly believe the era of the "safe middle" is ending. "The companies flattening their org charts are not just cutting costs," said Mark Vena, CEO and principal analyst at SmartTech Research. "They are admitting that a lot of management became workflow babysitting, and AI agents are very good babysitters." At the same time, remaining managers are increasingly expected to become effective supervisors of AI systems. The emerging skill is not simply knowing how to write prompts, Williamson said, but understanding how to direct multiple AI agents toward the right work, evaluate their outputs, and integrate those results into business decisions. "The real skill is pointing a handful of agents at the right work and checking what they send back," he said. The human side of change But none of this change is easy, no matter where you sit on the corporate org chart. "There is a lot of fear of possible job loss and changing responsibilities in general," Williamson said. "Leading people through that gets more valuable, not less." That points to one of the paradoxes of the AI era: The more work becomes automated, the more valuable distinctly human leadership skills become. Organizations still need people who can build trust, navigate uncertainty, resolve conflicts, mentor employees, and help teams adapt to change. Managers themselves may struggle with the transition. "If you haven't produced deliverables in a long time, the idea that now you should do it is scary," said Greenstein. "Managers may also feel their identity is threatened if their value was defined by how many people reported to them rather than how much impact they had on the business." Greenstein argues that companies should resist viewing AI purely as a cost-cutting tool. Instead, they should focus first on automating routine work so employees can spend more time on higher-value activities. "What I tell companies is to use AI to automate the routine, low-risk work off people's plates to free up time," he said. "Then build systems that help people and AI work together to solve problems neither could solve alone." Martina agreed, saying that AI will ultimately supplement management work and support decision-making. "I don't see management layers disappearing, rather, I see management layers increasingly efficient and capable of building new capacity in their teams more quickly," he said. Leadership, not management What emerges from a flatter organization is not necessarily less leadership, but a different kind of leadership, Martina argues. For decades, many managers built careers by coordinating work, approving decisions, and serving as information brokers between executives and frontline employees. As AI takes over more of those responsibilities, the value shifts toward skills that are harder to automate: exercising judgment, navigating ambiguity, building trust, developing talent, and creating alignment around a shared vision. "The activity of management will be supported with new tools and greater efficiency," Martina said. "What remains is leadership." Yet flattening organizations creates a new problem that few companies have solved. Historically, many professionals -- from lawyers and accountants to engineers and analysts -- developed expertise through entry-level work before advancing into management and senior leadership roles. If AI automates a significant portion of that early-career work, companies may find themselves with a shrinking pipeline of future experts. "The biggest risk isn't necessarily fewer middle managers today," Martina said. "It's what happens 10 years from now if fewer people are getting the experience they need to become senior leaders and experts." Ultimately, the managers who succeed will be the ones who are hungry to learn, said Greenstein, noting that AI agent tools like Codex and Claude Code have hit tipping points in the past couple of months, moving from software developer use cases to ones suitable for any knowledge worker. "The next three months are going to be wild as leaders figure out how to adopt it, or they are pressured by people who have learned how," he said. "The same way that webmasters who could code in HTML became CMOs, people who use agents to do work are the leaders of tomorrow."