For decades, technologies have largely been built as tools, extensions of human intent and control that have helped us lift, calculate, store, move, and much more. But those tools, even the most revolutionary ones, have always waited for us to ‘use’ them, assisting us in doing the work—whether manufacturing a car, sending an email, or dynamically managing inventory—rather than doing it on their own.
With recent advances in AI, however, that underlying logic is shifting. “For the very first time, technology is now able to do work,” Nvidia CEO Jensen Huang recently observed. “[For example], inside every robotaxi is an invisible AI chauffeur. That chauffeur is doing the work; the tool it uses is the car.”
This idea captures the transition underway today. AI is no longer just an instrument for human use: Rather, it is becoming an active operator and orchestrator of “the work” itself, not only capable of predicting and generating, but also planning, acting, and learning. This emerging class—“agentic” AI—represents the next wave of artificial intelligence. Agents can coordinate across workflows, make decisions, and adapt with experience. In doing so, they also blur the line between machine and teammate.
For business leaders, that means agentic AI upends the fundamental management calculation around technology deployment. Their job is no longer simply installing smarter tools but guiding organizations where entire portions of the workforce are synthetic, distributed, and continuously evolving. With agents on board, companies must rethink their very makeup: how work is designed, how decisions are made, and how value is created when AI can execute on its own. How organizations redesign themselves around these agentic capabilities will determine whether AI becomes not just a more efficient technology, but a new basis for strategic differentiation altogether.
To better understand how executives are navigating this shift, BCG and MIT Sloan Management Review conducted a global study of more than 2,000 leaders from 100+ countries. The findings show that while organizations are rapidly exploring agentic AI, most enterprises still need to define the overall strategies and operating models needed to integrate AI agents into their daily operations.
The organizational challenge: Redesigning the enterprise
Agentic AI’s perceived dual identity—as both machine and teammate—creates tensions that traditional management frameworks cannot easily resolve. Leaders can’t eliminate these tensions altogether; they must instead learn to manage them. There are four organizational tensions that stand out:
- Scalability versus adaptability. Machines scale predictably, while people adapt dynamically. Agentic AI can do both, requiring new organizational design principles capable of balancing efficiency with flexibility across workflows.
- Experience versus expediency. Leaders must weigh building long-term capabilities against moving fast enough to capture near-term opportunities in a technology landscape that changes rapidly.
- Supervision versus autonomy. Agentic AI requires oversight not just of outputs but of actions; organizations must decide when humans stay in the loop and when agents act independently, with clear accountability structures for each.
- Retrofitting versus reimagining. Leaders must choose when to layer AI onto existing processes for immediate benefit and when to rebuild end-to-end workflows around agentic potential.
The companies furthest ahead aren’t resolving these tensions outright. Instead, they’re embracing them—redesigning systems, governance, and roles to turn the frictions into forward momentum. They see agentic AI’s complexity as a feature to harness, not a flaw to fix.
What leaders should be doing now
For CEOs, the challenge now is figuring out how to lead an organization where technology acts alongside people. Managing this new class of systems requires different frameworks than previous waves of AI. While predictive AI helped organizations analyze faster and better and generative AI helped create faster and better, agentic AI now enables them to operate faster and better, by planning, executing, and improving on its own. That shift upends traditional management approaches, requiring a new playbook for leadership.
Reimagine the work, not just the workflow. In predictive or generative AI, the leadership task is to insert models into workflows. But agentic AI demands something different: It doesn’t just execute a process—it reimagines it dynamically. Because agents plan, act, and learn iteratively, they can discover new, often better ways of achieving the same goal.
Historically, many work processes were designed to make humans mimic machine-like precision and predictability: Each step was standardized so work could be replicated reliably. Agentic systems, however, invert that logic: Leaders only need to define the inputs and desired outcomes. The work that happens in between those starting and ending points is then organic, a living system that optimizes itself in real time.
But most organizations are still treating AI as a layer on top of existing workflows—in essence, as a tool. To take advantage of agentic AI’s true potential, leaders should start by identifying a few high-value, end-to-end processes—where decision speed, cross-functional coordination, and learning feedback loops matter most—and redesign them around how humans and agents can learn and act together. The opportunity is to create systems that can both scale predictably and adapt dynamically, not one or the other.
Guide the actions, not just the decisions. Earlier AI waves required oversight of outputs; agentic AI requires oversight of actions. These systems can act autonomously, but not all actions carry the same risk. That makes the leadership challenge broader than determining decision rights. It’s defining how agents operate within an organization: what data they can see, which systems they can trigger, and how and to what extent their choices ripple through an organization. While leaders will need to decide which categories of decisions remain human-only, which can be delegated to agents, and which require collaboration between the two, the overall focus should be around setting boundaries for agent behaviors.
Governance can therefore no longer be a static policy; it must flex with context and risk. And just as leaders coach people, they will also need to coach agents—deciding what information they need, which goals they optimize for, and when to escalate uncertainty to human judgment. Companies that embrace these new approaches to governance will be able to build trust, both internally and with regulators, by making accountability transparent even when machines may be executing.
Rethink structures and talent. Generative AI changed how individuals work; agentic AI changes how organizations are structured. When agents can coordinate work and information flow, the traditional middle layer built for supervision will shrink. That’s not a story of replacement—it’s a redesign. The next generation of leaders will be orchestrators, not overseers: people who can combine business judgment, technical fluency, and ethical awareness to guide hybrid teams of humans and agents. Companies should start planning now for flatter hierarchies, fewer routine roles, and new career paths that reward orchestration and innovation over task execution.
Institutionalize learning for humans and agents. Like people, agents drift, learn, and—most critically—improve with feedback. Every action, interaction and correction makes them more capable. But that improvement depends on people staying engaged, not to control every step, but to help systems learn faster and better.
To make that happen, leaders should create continuous learning loops connecting humans and agents. Employees must learn how to work with agents—how to improve them, critique them, and adapt to their evolving capabilities—while agents improve through those same interactions, across onboarding, monitoring, retraining, and even “retirement.”
Organizations that treat this as a shared development process—where people shape how agents learn and agents elevate how people work—will see the biggest gains. Managing this loop requires viewing both humans and agents as learners, and creating structures for ongoing training, retraining, and knowledge exchange. When this process is done right, the organization itself becomes a continuously improving system, one that gets smarter every time its humans and agents interact.
Build for radical adaptability. Traditional transformation programs were designed for predictability. Agentic AI, however, moves too fast for those to keep up. Leaders need organizations that can adapt continuously—financially, operationally, and culturally. But adaptability in the agentic era isn’t just about keeping up with a faster technology cycle, it’s about being ready to evolve as your organization learns alongside its agents. Each new capability can reshape responsibilities, decision flows, and even what “good performance” looks like.
Leaders will need to treat adaptability not as crisis management but as an organizing principle. That means budgeting for constant reinvestment, building modular structures that allow functions to reconfigure as agents take on new roles, and cultivating cultures where experimentation is routine rather than exceptional. Agentic AI rewards organizations that can lean into continuous, radical change. This kind of “agent-centricity” means reassigning talent, updating processes, and refreshing governance in response to what the system itself learns. The most resilient companies will see adaptability not as a defensive reflex, but as a defining source of advantage.
The agentic enterprise
For years, the story of AI has been one of automation—doing the same work faster, cheaper, and with fewer people. But that era is coming to an end. Agentic AI changes the nature of value because it can reshape the organization itself: how it learns, collaborates, and evolves. The next frontier is radical redesign, not repetition.
The real opportunity is to set up an enterprise that can reinvent itself continuously, where agentic AI becomes the connective tissue—linking knowledge, decision-making, and adaptation into one living system. This is the foundation of what we call the Agentic Enterprise Operating System: a model where human creativity and machine initiative evolve together, dynamically redesigning how the company works. Companies that embrace this shift will outgrow those still chasing efficiency—they will be the ones defining how value, capability, and competition work in the age of AI.
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