01. Introduction
02. Key Questions for Business Leaders
03. What's Next?
04. Conclusion
05. Appendix A: Current State to Agentic AI
06. Appendix B: Leapfrogging to Agentic AI
07. Appendix C: A Brief Business History of AI
08. References
We are at a pivotal moment in business history. The rise of Agentic AI—artificial intelligence that reasons, learns, and acts autonomously—is more than just another technological advancement. It is a structural shift in how businesses operate, compete, and grow.
Unlike previous models that analyze data and generate insights, Agentic AI is different because the systems are reactive, predictive, and proactive in their decision-making and behavior. It makes decisions, executes strategies, and adapts in real-time. It does not just offer options; it acts with increasing autonomy. This is the gateway to an entirely new economic reality where speed, adaptability, and intelligence define market leadership.
An essential feature of this shift is Agentic AI’s ability to perform multiple actions concurrently. An AI agent can simultaneously perceive the environment, learn it, adapt its responses, and make decisions without human intervention. Tasks that rely on quick decision-making, pattern recognition, and dynamic response are well within the realm of this technology.
The advent of Agentic AI is not just about the design of thinking machines and large language models, it’s about digital agents acting on behalf of humans—a leap from augmentation to autonomy. Accountability, transparency, security, and ethical design then become essential considerations.
The question is not whether Agentic AI will change the world—it’s how organizations will seize the opportunity to adapt to the world it is already creating. In this white paper, we examine the key questions that business leaders are asking today and provide plain language responses that we hope will lead to a greater understanding of this ground-breaking technology.
“Agentic Artificial Intelligence is a reference to autonomous systems and models that can achieve pre- defined goals without the need for constant human monitoring. Agentic AI systems go well beyond generative AI, as they are designed to comprehend what the intended outcome of a given task is and the context of the problem that needs to be solved. ”
—Mimi Brooks
From the earliest automatons to more recent chatbots, thought leaders have envisioned a future where AI systems can work and act intelligently and independently. Tectonic advances in Agentic AI bring that a step closer to reality. In this white paper, our goal is to help leaders understand the true business impact of Agentic AI—what it is, how it evolved, its implications for commerce, its value proposition, how it changes the traditional paradigm, how it is likely to evolve, the practical steps leaders must take, and the risks that must be considered when adopting this approach.
We can achieve this through a series of questions and answers, as follows:
First, what are the implications Agentic AI holds for the business realm, with its capacity to reduce the traditional accepted degrees of separation between humans from six to just one, giving leaders and workers unprecedented access to knowledge and resources?
Second, what is the added value proposition Agentic AI brings to the business ecosystem, including the ability to rapidly identify trends, customer behaviors, and competitive movements, together with an unprecedented capacity for scenario planning and risk assessment?
Third, what are the anticipated outcomes of the Agentic AI-driven shift away from time-wasting cognitive burdens to a working environment driven by leaders who exhibit intelligence, resilience, and impartiality, while practicing mediation?
Fourth, what are the inherent business risks that must be addressed when implementing Agentic AI?
Lastly, what does a future look like in which Agentic AI systems will evolve to work alongside other agents and humans?
“By offloading cognitive burdens to AI, humanity could, in theory, focus on what makes us distinctly human. This raises a fundamental question: Is this transformation dependent on technological capability, or is it ultimately shaped by our choices?”
—Michael Carroll
Unlike basic automation tools, true Agentic AI is designed to think, adapt, and act autonomously. It doesn’t just follow commands; it anticipates needs, solves problems in real-time, and continuously improves. This is a fundamental shift from past technological revolutions, one that has the power to elevate productivity, unlock new markets, and redefine entire industries.
Figure 1. Business Implications of Agentic AI
As depicted above, Agentic AI, with its capacity to reason, learn, decide, and act autonomously, promises to collapse the traditional accepted degrees of separation between humans from six (i.e., the suggestion that any two people on the planet are, on average, six social connections apart) to just one by seamlessly connecting people, ideas, businesses, and assets. This unprecedented level of connectivity promises to transform how we live, work, and solve everyday problems.
The quest for one degree of separation entails real-time problem solving, seamless communication, access to universal knowledge, and hyper-personal interactions. This in turn opens the door to unprecedented human creativity, cooperation, and knowledge. From a business perspective, this will result in shorter innovation cycles, the merging of global markets, and disintermediation—the removal of middlemen from future interactions.
The shift to one degree of separation also redefines human agency. In this new ecosystem, workers have unprecedented access to knowledge, resources, and power, which in turn demands a new level of responsibility. Collaboration, judgment, safety, and rewards driven by creative endeavor become the drivers of purposeful enterprise, dovetailing with leadership initiatives.
These developments represent a true paradigm shift—a revolution in how we conceptualize relationships, business, communication, and problem-solving. Advocates will attest that we are moving beyond the limitations of traditional networking to a future facilitated by seamless interaction, unprecedented access to knowledge, ground-breaking insights, and hitherto unthinkable capabilities.
As Agentic AI integrates with automated reasoning, where AI systems process information and derive new knowledge in a manner similar to human logical reasoning, it allows machines to act as intermediaries between people, businesses, objects, and ideas, creating new channels of connection. This creates an ecosystem where intelligent agents mediate and expand, exponentially enhancing the speed, breadth, and depth of business interactions.
Agentic AI is fundamentally changing the economic landscape by altering how businesses operate, compete, and innovate. Companies that harness AI’s capabilities for decision-making and strategy are able to operate at unprecedented speeds, outperforming those still bound by human-limited decision-making processes. The transformation from six degrees to one degree accelerates everything: ideas, products, solutions, and customer feedback.
Agentic AI can provide significant value to business leaders by virtue of its capacity to enhance decision-making, streamline operations, and drive innovation. This includes rapidly identifying trends, customer behaviors, and competitive movements. Its capacity for scenario planning and risk assessment also facilitates a focus on high-impact leadership activities.
By taking advantage of its ability to streamline workflows, reduce process bottlenecks, and improve resource allocation, Agentic AI can revolutionize how organizations connect with customers and partners alike. For example, creating highly tailored customer experiences using AI-driven insights, deploying intelligent chatbots or virtual agents that resolve issues 24/7, and constantly monitoring customer feedback and social media to understand brand perception.
Agentic AI can also act as a driver of creativity and new opportunities by identifying untapped market opportunities or product ideas based on data insights, simulating prototypes, running virtual experiments, and testing innovations faster while also monitoring competitor actions and providing actionable insights.
The provisioning of real-time advice and suggestions for decision-making through virtual advisors, together with tracking KPIs across teams and highlighting areas needing attention, is particularly useful in the context of anticipating and adapting to future challenges. Trend prediction, workforce transformation, and regulatory compliance are other areas where Agentic AI systems can augment human capabilities.
Figure 2. The Business Value of Agentic AI
In volatile situations, Agentic AI can provide stability and guidance by detecting early warning signs of crises, like supply chain disruptions or PR challenges, as well as recommending the best courses of action in high-pressure situations and dynamically shifting resources to address urgent needs.
Scalability is another key feature of this technology. As business grows, Agentic AI has the capacity to help scale operations without proportional increases in cost or complexity by automating repetitive tasks, thus reducing reliance on manual labor for scaling operations. It has the potential to adapt strategies for different regions and markets as well as build organizational resilience by ensuring systems and processes remain robust as the business expands.
While Agentic AI can be transformative, successful implementation requires not only the alignment of its required capabilities with business strategy but also a balance of automation with human oversight to build trust and effectiveness. By integrating Agentic AI into business leadership practices, leaders can leverage its power to innovate, improve operational effectiveness, and stay ahead of the competition while focusing on strategic vision.
We are already seeing an Agentic AI-driven shift away from time-wasting cognitive burdens such as complexity, distraction, and divisive business practices to one degree of separation operations.
But how is this being achieved? It helps to consider the following key characteristics of Agentic AI:
If we examine the capability of each agent separately, as shown below, there are several differences between traditional system capabilities and emerging Agentic AI functionality.
Figure 3. The Paradigm Shift to Agentic AI – Potential Outcomes
The outcome of Agentic AI from a leadership perspective necessarily includes the pursuit of deep understanding (what we have referred to as “Intelligence”). Agentic AI will present the workforce with curated knowledge and insights, synthesizing vast amounts of information into digestible and meaningful narratives. Leaders will use AI to explore the long-term impact of their decisions through simulated outcomes or individuals dedicating time to lifelong learning, enriched by AI-generated content. The potential is immense, yet it depends on a conscious choice to reflect, question, and seek understanding beyond the immediate.1
By Mitigation, we surmise that Agentic AI could help manage our desires, curating our digital experiences to minimize distractions and encourage balanced living. This is an approach that requires a conscious decision to practice restraint, set boundaries, and prioritize well-being over endless consumption while at the same time facilitating a curated experience for workers. Technology can support this but the commitment must come from business leaders.
Resilience, or the capacity to withstand or to recover quickly from difficulties, is the third leadership consideration of Agentic AI, for here we have a technology that can provide guidance and support, offering solutions and potential connections to helpful resources when businesses face adversity. The correlation to leadership resilience in the working world is the existence of specific guidance proffered by autonomous agents.
Finally, we examine the impact of Agentic AI on leadership Impartiality. Achieving true equality is often complicated by systemic barriers and deeply embedded inequities. Agentic AI holds the potential to democratize access to information, identify biases, and promote fairness on a large scale. The dissemination of targeted and relevant information to the workforce in a timely manner then becomes critical.
The business paradigm shift driven by Agentic AI is heralded by the proliferation of those four leadership strengths and their corresponding organizational attributes. This notion of a new renaissance suggests a business world where technology enables human flourishing. However, it’s critical to understand that this technology, no matter how advanced, is only an enabler. The real transformation will depend on human choices.
1Michael Carroll: “Rebooting Humanity: Will Agentic AI Lead to a New Renaissance?”
“AI agents are making waves in industrial operations, but let’s face it—not all AI is the same. Some agents are little more than automated scripts, while others are game-changers, capable of making decisions, learning from experience, and driving real value.”
—Pieter van Schalkwyk
From the outset, it is important to establish human-in-the-loop frameworks where critical decisions are reviewed and approved by humans. For example, Agentic AI decisions might be perceived as inadvertently violating ethical norms or legal regulations, especially in areas like hiring, pricing, or resource allocation.
Agentic AI systems can also exhibit emergent behavior, producing outcomes that are unforeseen or unintended, so it is important to test AI systems in controlled environments and use simulation tools to predict and refine agent behavior before deployment. Once deployed, it is crucial to allocate human resources for continuous monitoring, retraining AI models, and updating systems to ensure scalability and reliability. The use of explainable AI (XAI) tools will offset any lack of transparency of the decision-making process and apply accountability in case of unforeseen errors.
Learning science describes a practice known as “scaffolding, ” which gives workers exposure to real-world practice with safeguards which are gradually withdrawn as experience grows. This concept will be important as Agentic AI systems are applied to different tasks and business areas, with decision-makers building appropriate scaffolding based on the degree of confidence in the data used to train the models, the criticality of the decision, the consequences of error, the degree of human supervision, and the level of experience of the humans who work alongside these systems.
Finally, it is no surprise that there are a plethora of systems out there masquerading as authentic Agentic AI. This phenomena has been referred to as “agent washing, ” so it is critical that organizations emphasize the importance of distinguishing genuine agent capabilities from rebranded automation tools.
While the evolution of agentic systems promises transformative benefits, it also demands that systems maintain meaningful human oversight, thus preventing agents from developing goals contrary to human values and mitigating the risk of cascading failures or exploitation in interconnected systems.
The future of Agentic AI will depend on a blending of technological, societal, and ethical trends. As shown in the figure below, these systems are likely to exponentially increase their autonomy as they become capable of handling complex multi-step tasks across various domains. This could include rewriting and optimizing code without an overt need for human intervention, as well as enhanced adaptation to changing environments or user needs without manual intervention. By expanding their capabilities to operate effectively across tasks not explicitly pre-trained on, the systems will become increasingly effective in handling everyday business scenarios.
Agentic systems will evolve to work alongside other agents and humans by coordinating multiple AI agents for large-scale tasks like supply chain optimization. Enhanced interfaces for collaborative decision-making with humans will ensure alignment with human intentions.
Figure 4. The Probable Evolution of Agentic AI
By learning and adapting to human values through feedback loops, Agentic systems will increasingly integrate ethical reasoning to ensure their actions align with societal values. Built-in mechanisms will ensure adherence to laws and regulations while autonomous decision-making that incorporates considerations like fairness, privacy, and environmental impact will continue to evolve.
Agentic AI promises to develop a capacity to allocate resources and proliferate systems that can self-diagnose and resolve operational issues while also minimizing energy usage. Improvements in reasoning capabilities will enable agentic systems to handle more abstract and creative tasks, such as commonsense reasoning through an enhanced understanding of the physical and social world. Given the ability to autonomously explore new domains and generate novel insights, systems will simulate potential future outcomes to inform current actions.
To ensure their reliability, agentic systems will adopt more robust fail-safes, such as better transparency in decision-making processes to enhance trust, safeguards to prevent agents from being repurposed for harmful goals, and the prevention of unintended consequences in complex environments.
As agentic AI becomes more widespread, its interaction with social systems will also mature to include the possible recognition of agentic AI entities in certain legal contexts, cultural adaptation, and balancing job displacement with the creation of new industries driven by autonomous agents.
Primarily, Agentic AI is likely to evolve as a collaborative tool, emphasizing alignment, safety, and integration while expanding its role in solving global challenges. Its trajectory will be shaped not only by technological breakthroughs but also by the ethical and regulatory frameworks developed to guide its growth.
Several core business shifts are likely as Agentic AI gains momentum:
1. From Predictive Strategy to Dynamic Adaptation
For decades, businesses have relied on predictive strategy—analyzing historical data, forecasting trends, and setting long-term plans. In an environment where AI can anticipate shifts and adapt strategies in real-time, static business plans will become liabilities.
It is imperative that strategic planning shifts from the typical “here-to-there” model to a “there-to-here” approach that involves constant re-evaluation based on shifting conditions. Scenario-based thinking that is focused on preparing for multiple possible futures is of paramount importance.
Key considerations here include the potential formulation of AI-augmented teams that blend human judgment with AI intelligence, striving to ensure that AI is enhancing collaboration rather than replacing human roles, and consideration of organizational structures that will allow for faster decision cycles and adaptability.
2. From Decision Authority to Decision Orchestration
As AI analyzes more data, identifies patterns, and makes optimal decisions faster, the role of human leadership will shift from decision-making to decision-orchestration. By defining the values, risks, and constraints within which Agentic AI operates, leaders will guide the decision- making process, intervening only where human judgment is essential. A key question will be how best to govern AI-driven decisions to align with business values while ensuring AI enhances human judgment rather than replacing it.
3. From Business Efficiency to Business Intelligence
Traditional AI focused on efficiency—automating routine tasks to save time and costs. Agentic AI, however, focuses on intelligence by learning from past outcomes and improving decision- making over time. This means that businesses no longer think of AI as a tool for efficiency and are now seeing it as an active business operator.
Agentic AI can be embedded into core business decision-making, not just support functions, while also harnessing AI-generated intelligence for competitive advantage. The next logical step, therefore, is to determine how an organization can integrate AI into strategic decision-making beyond automation and how AI will learn from every business cycle to improve future outcomes. A focus on AI-driven insights and intelligence will augment the measurement of success beyond efficiency gains.
4. From Rigid Organizations to AI-Augmented Ecosystems
Most organizations are structured around fixed hierarchies and processes. Agentic AI’s ability to act and learn autonomously means that businesses will become more fluid, more networked, and more adaptive. Leaders are rethinking organizational design and considering a movement from rigid departments to AI-augmented teams that work dynamically. On a macro level, businesses are already building ecosystem-based strategies that leverage AI-powered partnerships, supplier networks, and customer engagement in real time.
Key considerations here include the potential formulation of AI-augmented teams that blend human judgment with AI intelligence, striving to ensure that AI is enhancing collaboration rather than replacing human roles, and consideration of organizational structures that will allow for faster decision cycles and adaptability.
5. From Risk Avoidance to AI-Enabled Strategic Foresight
This paradigm shift enables leaders to determine how Agentic AI can help predict and mitigate risks before they materialize as well as how to build resilience and agility into the business model. The challenge is to determine any new leadership capabilities that will be required to lead in a world where AI co-pilots decision-making.
A Call to Action
Agentic AI is not a distant future—it is unfolding now. Business leaders can prepare their organizations for this shift in the following ways:
Figure 5. Implementing Agentic AI
The deployment of Agentic AI systems capable of independent reasoning and decision-making will give rise to entirely new industries and services. This paradigm shift promises to highlight human potential by enabling workers to focus on complex tasks that require creativity, strategy, and innovation.
As the traditional layers between data, decisions, and action converge, Agentic AI will facilitate real-time orchestration of business operations and promote dynamic, transparent marketplaces where companies can buy and sell production capacity in real time. Real-time Agentic AI systems will collapse long- standing barriers between people, processes, and assets by creating instant transparency and responsiveness to shifts in demand, supply, and operational capacity.
Agentic AI is dramatically reshaping the business landscape, delivering intelligent, ultra-efficient, and hyper-personalized solutions that are driving unprecedented business value. This is not just another technological transformation. It is a leadership revolution. The question is not whether Agentic AI will transform business—it already is. The question is: Will you lead the transformation, or will you be left reacting to it?
Below are “from-to” examples of how the paradigm shift from the current state to Agentic AI might be experienced.
Here are examples of what is happening as the radical innovations powered by Agentic AI are enabling companies to leapfrog over more traditional approaches.
Agentic AI has evolved from the broader field of artificial intelligence, with its roots in several key areas of research and development. It builds on decades of progress in both AI theory and practical applications.
In early business applications of AI, systems were designed to perform tasks based on predefined rules and logic. These rule-based applications—commonly referred to as “expert” or “case-based reasoning” systems—made decisions based on expert knowledge domains, usually with a narrow focus. They followed rigid pre-defined paths and lacked the ability to adapt or learn from experience.
The shift from rule-based systems toward machine learning (ML) and neural networks during the 1990’s opened up new possibilities for business. ML algorithms allowed AI to learn from data and improve over time, which laid the foundation for more adaptive systems. Neural networks, inspired by the brain's structure, enabled pattern recognition and complex decision-making, empowering AI systems to tackle more dynamic tasks.
Figure 6. Technological Evolution to Agentic AI
A major paradigm shift came with reinforcement learning—an area of AI where automated agents learned to take actions in an environment to maximize cumulative reward. This capability is key to Agentic AI because it allows the system to learn from its own experiences, make decisions based on outcomes, and autonomously adjust its behavior. The concept of non- human agents making decisions and learning through trial and error is core to the next iteration of artificial intelligence.
As AI became more capable of real-time decision-making, research in autonomous systems and robotics began to intersect with the emergence of Agentic AI. Robots and other autonomous agents, such as drones and self-driving cars, demonstrate the ability to act independently in dynamic environments. These systems take agentic AI principles and apply them to the physical world, needing to navigate complexities and make real-time decisions.
Generative AI’s increasing ability to understand, generate, and respond to human language with context and nuance is another layer that paves the way for more sophisticated autonomous agents. Collaborative agents in a business context, working autonomously but sharing information and optimizing for larger organizational goals, are continuing to evolve.
As AI has evolved, so has its application to highly complex, interconnected legacy systems. The development of Agentic AI is building on this by integrating decision-making processes, learning, and adapting to rapidly changing environments. Key influences in this regard include autonomy, intelligent agents, and human-computer collaboration.
Overall, Agentic AI has evolved through a convergence of advanced learning algorithms, real- time decision-making, autonomy, and adaptability, which has elevated AI systems to a point where they can function as proactive, self-managing agents in real-world business scenarios.
Van Schalkwyk, Peter. January 2025. The Carroll Industrial AI Agent Framework: Evaluating True AI Agency
Hoffman, Reid & Greg Beato. January 2025. Superagency: What Could Possibly Go Right with Our AI Future. Simon & Schuster.
Carroll, Michael. January 2025. “America’s Next Economic Superpower: How Agentic AI Will Redefine Global Leadership”
Webb, Amy & Sam Jordan. December 2024. The Era of Living Intelligence. Future Today Institute.
Carroll, Michael. January 2025. “Is 2025 the Year of Agent Washing?”
Carroll, Michael. November 2024. “Rebooting Humanity: Will Agentic AI Lead to a New Renaissance?”
Carroll, Michael. October 2024. “What Do Agents Mean for You, Your Business, and Your Life? Connecting Everyone and Everything—All at One Degree of Separation”
Purdy, Mark. December 12, 2024. “What Is Agentic AI, and How Will It Change Work?” Harvard Business Review.
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