For decades, technology has been largely constructed as tools, extensions of human intention and control, helping us lift, compute, store, move, and more. But these tools, even the most revolutionary ones, are always waiting for us to “use” them, to help us get work done—whether it’s building a car, sending an email, or dynamically managing inventory—rather than doing it ourselves.
However, with recent advances in artificial intelligence, this basic logic is changing. “For the first time, technology can now do work,” NVIDIA CEO Jensen Huang recently observed. “(For example), every robot taxi has an invisible AI driver inside it. That driver is doing the job; the tool it’s using is the car.”
This idea embodies the transformation underway today. Artificial intelligence is no longer just a tool for humans to use: instead, it is becoming a Active operator and coordinator The “work” itself is capable not only of predicting and generating, but also of planning, acting and learning. This emerging category—“agent” AI—represents the next wave of artificial intelligence. Agents can coordinate across workflows, make decisions and adjust based on experience. In doing so, they also blur the line between machine and teammate.
For business leaders, this means agent AI upends the basic management calculations around technology deployment. Their job is no longer to simply install smarter tools, but to guide organizations toward a holistic synthesis, distribution, and evolving workforce. With agents, companies must rethink what they are made of: how they design jobs, how they make decisions, and how they create value when AI can perform on its own. How organizations redesign themselves around these agent capabilities will determine whether AI becomes not just a more efficient technology but a new basis for strategic differentiation.
To better understand how executives are responding to this shift, BCG and MIT Sloan Management Review A global study of more than 2,000 leaders from more than 100 countries was conducted. Survey results show While organizations are rapidly exploring agent AI, most still need to define the overarching strategy and operating model required to integrate AI agents into daily operations.
Organizational Challenge: Redesigning the Enterprise
Intelligent AI’s dual role—as both machine and teammate—creates tensions that traditional management frameworks cannot easily resolve. Leaders cannot completely eliminate these tensions; instead, they must learn to manage them. There are four prominent organizational pressures:
- Scalability and adaptability. Machines scale predictably, while people adapt dynamically. Agent AI can do both, requiring new organizational design principles that can balance efficiency and flexibility in workflows.
- Experience and expediency. Leaders must weigh the trade-offs between building long-term capabilities and moving quickly to seize short-term opportunities in a rapidly changing technology environment.
- Oversight and autonomy. Agent AI needs to oversee not only output but also actions; organizations must decide when to involve humans and when agents act independently, and develop clear accountability structures for everyone.
- Transformation and reimagining. Leaders must choose when to layer AI into existing processes for immediate benefit, and when to rebuild end-to-end workflows around agent potential.
Leading companies have not fully resolved these tensions. Instead, they are embracing them—redesigning systems, governance, and roles to turn friction into forward momentum. They view the complexity of agent AI as a feature to be exploited rather than a flaw to be fixed.
What leaders should do now
For CEOs, the challenge now is figuring out how to lead an organization that makes technology and people work together. Managing such new systems requires a different framework than previous waves of artificial intelligence. While predictive AI helps organizations analyze Faster and better, generative AI helps create Faster and better, agent AI now enables them to operate Be faster and better by planning, executing and improving on your own. This shift upends traditional management approaches and requires a new leadership playbook.
Reimagine work, not just workflow. In predictive or generative AI, the leadership task is to insert models into the workflow. But agent AI requires something different: It doesn’t just execute a process—it Reimagine it dynamically. As agents plan, act, and learn iteratively, they can discover new, and often better, ways to achieve the same goals.
Historically, many workflows were designed for humans to imitate machine-like precision and predictability: each step was standardized so work could be reliably replicated. However, agent systems invert this logic: the leader only needs to define the inputs and desired outcomes. The work that occurs between these start and end points is organica living system that optimizes itself in real time.
But most organizations still view AI as a layer on top of existing workflows—essentially a tool. To harness the true potential of agent AI, leaders should first identify a few high-value end-to-end processes (where speed of decision-making, cross-functional coordination, and learning feedback loops are most important) and redesign them around how humans and agents learn and act together. The opportunity lies in creating systems that scale predictably and adapt dynamically, not both.
Guide actions, not just decisions. Early waves of AI require oversight of output; agent AI requires oversight action. These systems can act autonomously, but not all actions pose the same risks. This makes the leadership challenge broader than determining decision-making authority. It defines how agents operate within an organization: what data they can see, which systems they can trigger, and how and to what extent their choices affect the entire organization. While leaders need to decide which categories of decisions are still left to humans, which can be delegated to agents, and which require collaboration between the two, the overall focus should be around setting boundaries for agents Behavior.
Governance is therefore no longer a static policy; it must be flexible and adaptable to circumstances and risks. Just as leaders guide people, they also need to guide agents—deciding what information they need, which goals they optimize for, and when to escalate uncertainty to human judgment. Companies that adopt these new governance methods will be able to build trust internally and with regulators, making accountability transparent even when machines are performing.
Rethink structure and talent. Generative AI changes the way individuals work; agent AI changes the structure of organizations. Traditional middle layers built for oversight will shrink when agents are able to coordinate work and information flow. This is not a story of replacement, but of redesign. The next generation of leaders will be facilitators, 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, less routine roles, and new career paths that reward coordination and innovation rather than task execution.
Institutionalizing human and agent learning. Like people, agents change, learn, and most importantly improve in response to feedback. Every action, interaction, and correction makes them more capable. But this improvement depends on people staying engaged, not controlling every step but helping the system learn faster and better.
To achieve this, leaders should create continuous learning loops that connect 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 these same interactions, including onboarding, monitoring, retraining, and even “retirement.”
Organizations will gain the most if they view it as a shared development process (where people shape how agents learn, and agents enhance how people work). Managing this cycle requires treating humans and agents as learners and creating structures for ongoing training, retraining, and knowledge exchange. When this process is done correctly, the organization itself becomes an ever-improving system that becomes smarter every time its people and agents interact.
Create radical adaptability. Traditional transformation programs are designed for predictability. However, agent AI is moving too fast to keep up. Leaders need organizations that can continuously adapt financially, operationally and culturally. But adaptability in the agent era isn’t just about keeping up with faster technology cycles, it’s about being ready to evolve as your organization learns alongside its agents. Each new feature can reshape responsibilities, decision-making processes, and even the meaning of “good performance.”
Leaders need to think of adaptability as an organizational principle rather than crisis management. This means budgeting for ongoing reinvestment, establishing modular structures that allow functions to be reconfigured as agents take on new roles, and cultivating a culture where experimentation becomes the rule rather than the exception. Agent AI rewards organizations that are capable of sustained, radical change. This “agent-centricity” means reallocating talent, updating processes, and updating governance based on learning from the system itself. The most resilient companies see adaptability not as a defensive reflex but as a decisive source of advantage.
Agency company
For years, the story of artificial intelligence has been the story of automation—faster, cheaper, using fewer people to do the same job. But that era is coming to an end. Agent 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 duplication.
The real opportunity is to build a business that can continually reinvent itself, allowing agential AI to become the connective tissue—linking knowledge, decision-making, and adaptation into a living system. This is what we call the basis Agent Enterprise Operating System: A model in which human creativity and machine initiative evolve together to dynamically redesign how companies operate. Companies that embrace this shift will transcend those still striving for efficiency—they will be the companies that define value, capabilities, and how competition works in the age of AI.
read other wealth Column by François Candelon.
François CandelonPartner at private equity firm Seven2 and former global director of BCG Henderson Institute.
Amartya Das is the president of BCG and an ambassador for the BCG Henderson Institute.
Sesh Iyer is a managing director and senior partner at BCG. He is the Chairman of BCG North America X and Insights Leader for the Artificial Intelligence and Technology Lab at BCG Henderson Institute.
Sherwin Khodaband is a managing director and senior partner at BCG.
Sam Lansbotham is Professor of Analytics at Boston College’s Carroll School of Management.