The Emerging Agentic Enterprise: How Leaders Must Navigate a New Age of AI

Source: MIT Sloan Management Review and BCG (Sam Ransbotham, David Kiron, Shervin Khodabandeh, Sesh Iyer, Amartya Das); MIT SMR-BCG AI and Business Strategy report (40 pp.) · 2025-11-18

Introduction. Executives have long relied on simple categories to frame how technology fits into organizations: Tools automate tasks, people make decisions, and strategy determines how the two work together. That framing is no longer sufficient. A new class of systems — agentic AI — complicates these boundaries. These systems can plan, act, and learn on their own. They are not just tools to be operated or assistants waiting for instructions. Increasingly, they behave like autonomous teammates, capable of executing multistep processes and adapting as they go. Notably, 76% of respondents to our global executive survey say they view agentic AI as more like a coworker than a tool.

For strategists, agentic AI’s dual nature as both a tool and coworker creates new dilemmas. A single agent might take over a routine step, support a human expert with analysis, and collaborate across workflows in ways that shift decision-making authority. This tool-coworker duality breaks down traditional management logic, which assumes that technology either substitutes or complements, automates or augments, is labor or capital, or is a tool or a worker, but not all at once. Organizations now face an unprecedented challenge: managing a single system that demands both human resource approaches and asset management techniques.

The separation of technology and strategy inside most organizations exacerbates this challenge. Technology executives focus on technology issues, making pilot, vendor, or infrastructure decisions. Strategic executives focus on markets, competition, and people. But agentic AI makes that separation untenable. It simultaneously influences the design of processes, the structure of roles, the allocation of decision rights, and the culture of accountability.

This report presents findings from the ninth annual global research study on artificial intelligence and business strategy by MIT Sloan Management Review and Boston Consulting Group. In spring 2025, we fielded a global survey and subsequently analyzed records from 2,102 respondents representing more than 21 industries and 116 countries. We also interviewed 11 executives leading AI initiatives in a broad range of companies and industries, including financial services, technology, retail, energy, and health care.

Our research examines the speed of agentic AI adoption inside organizations, which has outpaced the adoption of traditional and generative AI. Exploring how agentic AI relates to capital and labor, this report outlines a series of resultant tensions and offers suggestions for organizations seeking to resolve them. to a global survey and interviews with leading executives, finds that organizations have multiple options for obtaining value from agentic AI. It offers possibilities not only to improve cost efficiency but also to expand revenue, accelerate innovation, compress learning curves, and restructure organizations. Without a strategic approach that aligns these objectives, organizations risk limiting returns on their investments.

Method. ABOUT THE RESEARCH This report presents findings from the ninth annual global research study on artificial intelligence and business strategy by MIT Sloan Management Review and Boston Consulting Group. In spring 2025, we fielded a global survey and subsequently analyzed records from 2,102 respondents representing more than 21 industries and 116 countries. We also interviewed 11 executives leading AI initiatives in a broad range of companies and industries, including financial services, technology, retail, energy, and health care.

Our research examines the speed of agentic AI adoption inside organizations, which has outpaced the adoption of traditional and generative AI. Exploring how agentic AI relates to capital and labor, this report outlines a series of resultant tensions and offers suggestions for organizations seeking to resolve them.

Discussion. A Tidal Wave of Adoption, a Trickle of Strategy Despite the technology’s wide-ranging implications, organizations are rapidly adopting agentic AI, well before they have a strategy in place. Their adoption of traditional AI has climbed to 72% over the past eight years, according to our survey. (see figure 1.) Generative AI achieved 70% adoption in just three years. In just two years, agentic AI has already reached 35% adoption, with another 44% of organizations planning to deploy it soon. Vendors are accelerating this trend by embedding agentic capabilities as features in their offerings, causing organizations to implement agentic AI before they have developed a strategic management framework.

Agentic AI’s dual nature as both a tool and coworker creates competing organizational pressures that management frameworks cannot resolve. IT leaders want predictable, scalable systems with clear technical specifications. CFOs need investment models with measurable returns and depreciation schedules. HR executives require The Emerging Agentic Enterprise: How Leaders Must Navigate a New Age of AI 3 performance management frameworks and supervision protocols. Business leaders demand both efficiency and adaptability from the same system.

These competing demands aren’t implementation challenges. They’re strategic imperatives that expose new sources of organizational differentiation. Among organizations with extensive agentic AI use, we find that 73% believe using AI fundamentally increases their ability to stand out, while 76% of their employees believe it changes how individuals differentiate themselves from coworkers.

Organizations and individuals can use agentic AI to differentiate themselves because they can now take advantage of the ways that agentic AI does not fit traditional management frameworks. The same system that offers cost reductions through tool-like automation also enables revenue expansion through workerlike adaptability, innovation acceleration through continuous learning, and organizational learning through human-agent interactions. But accessing these multiple sources of value requires navigating operational tensions that no existing framework addresses.

The path forward requires understanding four operational tensions that expose the inadequacy of traditional management approaches and then redesigning fundamental processes — work design, governance, workforce planning, learning, and investment — to work with rather than against agentic AI’s inherent duality. This report offers evidence-based recommendations on how to proceed.

Strategic Tensions When Adopting Agentic AI The competing pressures executives face aren’t abstract theoretical problems. They manifest as specific, irreconcilable conflicts in day-to-day operations. Our research identified four distinct tensions that emerge when organizations try to integrate agentic AI into existing workflows. Each tension represents a fundamental clash between established management principles, forcing leaders to choose to either apply incompatible approaches or develop entirely new, hybrid frameworks. Successfully navigating this challenge requires that leaders manage the following tensions:

1. Scalability versus adaptability. Tools scale predictably; workers adapt dynamically. Agentic AI’s ability to do both simultaneously requires new organizational design principles.

2. Experience versus expediency. When is the right time to invest in agentic systems and how should those investments be made? Leaders are faced with balancing long-term capability building with short-term returns.

Conclusion. With agentic AI, leaders are managing a new entity without historical precedent. It is a tool that learns, a worker that is owned, and an investment that behaves like both tool and worker. Agentic AI does not fit neatly within the substitute-or-complement framework because it demands to be managed as both a worker and a tool at once. The substitute-complement framework asks, “Is AI replacing coworkers or augmenting them?” But agentic AI forces a more unsettling question: “How do we manage artificial colleagues that we own like equipment but must supervise like people, and that depreciate like machinery but learn like humans?” This new technology creates critical design challenges.

Companies beginning to use agentic AI are already grappling with that question. Some are treating it primarily as a way to reduce costs whereas others are using it to expand capacity, speed up experimentation, or elevate the baseline performance of their people. Leaders need to be explicit about what they are optimizing for and design their processes, structures, and governance accordingly.

Treating an agentic AI deployment like a traditional tool misses its flexibility advantages, but treating it like a newly hired staff member underestimates the infrastructure requirements. This dual risk creates unavoidable tensions between flexibility and efficiency, control and autonomy, and upfront investment and continuous learning. Success will be defined not by eliminating these conflicts but by mastering them.

The challenge of agentic AI is organizational, not technological. Many organizations are adopting this technology at a breakneck pace, often before they have a coherent strategy in place. But the speed of adoption is not a measure of progress. Advantage will not come from having the best AI injected into the organization as quickly as possible but from having the best answer to the question, “How do we reorganize our organization around it?” The “adopt now, strategize later” approach is a high-stakes gamble that avoids the hard work of identifying a purpose for agentic AI that fits well with the organization’s strategy.