Why Agentic AI is the Next Big Leap in Digital Transformation

When most people hear the term “artificial intelligence,” they typically think of ChatGPT, Microsoft Copilot, or other AI-powered tools that have exploded in popularity over the last couple of years. These tools have captured the imagination of the general public, as well as the attention of many businesses, thanks to their ability to generate content, automate simple tasks, and assist humans in day-to-day activities. But what if I told you AI could go much further? What if AI could make decisions and take action on its own, without humans telling it what to do?

That's exactly what is happening with a new frontier of AI, one that is less talked about but already having a significant impact on organizations around the world. This powerful new capability is called agentic AI, and it has the potential to fundamentally reshape not only how businesses operate but also how we think about digital transformation as a whole.

I am the CEO of Third Stage Consulting Group . We are an independent, technology-agnostic consulting firm that helps clients worldwide achieve digital transformation success. In recent years, we’ve seen a sharp increase in AI-related work, and more specifically, we’re starting to see the rise of agentic AI as a game-changer for businesses.

While most of us are already somewhat familiar with traditional AI through common applications like ChatGPT, agentic AI is something very different—and far more powerful. In addition to my summary below, be sure to check out my new video as well:

What is Agentic AI?

So, what exactly is agentic AI? Simply put, agentic AI refers to AI systems that are capable of setting goals, making decisions, taking action, and continuously improving—without the need for human intervention. While traditional AI might give you suggestions, generate content, or perform specific tasks when asked, agentic AI operates autonomously. It doesn’t just assist you; it acts on your behalf.

Think of the difference between using a GPS and a self-driving car. A GPS helps you navigate by providing you with directions, but you—the human—still have to steer the car, apply the brakes, and make decisions along the way. On the other hand, a self-driving car not only interprets data from its environment but also makes the actual driving decisions without human input. Agentic AI, in business terms, is like that self-driving car.

Instead of simply providing information for a human to act on, agentic AI gathers data from both internal and external sources, makes decisions based on that data, executes actions, and learns from its outcomes to continuously improve its future decision-making.

The Core Capabilities of Agentic AI

Agentic AI is built on four core capabilities:

1. Data Gathering and Processing: Agentic AI systems collect vast amounts of data—both from inside the organization and from external environments. This could include historical business data, real-time operational data, customer behavior, market trends, and even geopolitical or weather-related data.
2. Decision-Making: Once the data is gathered, agentic AI processes the information and makes decisions based on what it learns. These decisions could be as simple as ordering more inventory or as complex as rerouting global supply chains due to an impending storm.
3. Action Execution: The AI doesn’t just make recommendations—it actually executes decisions. Whether that means placing an order, adjusting a production schedule, optimizing logistics, or resolving customer service issues, the AI acts on its own.
4. Continuous Learning and Feedback: Perhaps the most fascinating part is that agentic AI learns from its own successes and mistakes. It constantly refines its actions and decisions through continuous feedback loops, making it better and more reliable over time.

Real-World Use Cases of Agentic AI

You might be wondering how this all plays out in the real world. The truth is, many organizations are already using agentic AI to automate and improve their business processes—often without fanfare. Let me share a few examples from industries that are actively exploring and benefiting from this technology.

One of the clearest applications is in supply chain management. Supply chains are notorious for being complex and prone to disruptions—everything from weather events to labor strikes to fluctuating demand can wreak havoc on supply chain operations. Traditionally, supply chain managers relied on human experience and tribal knowledge to adjust operations in response to these disruptions.

With agentic AI, however, the system can proactively monitor weather forecasts, transportation networks, and other external signals, then autonomously reroute shipments, adjust inventory levels, or trigger alternative sourcing strategies. For example, if a hurricane threatens to close a key shipping port, an agentic AI could automatically reroute ships to an alternative port without requiring human input. This kind of automation dramatically reduces response times and helps minimize disruption.

Another common use of agentic AI is in customer service, especially through conversational AI systems like advanced chatbots. Unlike simple scripted bots that follow a rigid question-and-answer tree, agentic AI-driven chatbots can handle more complex interactions without human assistance. They can process returns, answer detailed product questions, and even resolve quality complaints, all while learning and improving with each customer interaction. This significantly reduces the workload on human customer service teams and enhances the customer experience.

In manufacturing and warehouse operations, agentic AI becomes even more powerful when combined with robotics and Industry 4.0 technologies. For example, AI-powered robots on the shop floor can automatically optimize production runs based on shifting demand, redirect materials within the warehouse, or detect and resolve bottlenecks before they become serious problems. It’s not just about automating repetitive tasks—it’s about optimizing and adapting operations dynamically based on real-time data.

These are just a few examples, but agentic AI is expanding into nearly every industry, from financial services to healthcare to logistics and beyond.

The Risks and Ethical Considerations

Of course, with great power comes great responsibility—and risk. As exciting as agentic AI is, it also raises serious questions that organizations must address before fully embracing it.

One of the most pressing concerns is the question of human oversight. How much control should humans retain when it comes to decision-making? At what point do we trust the AI enough to let it act autonomously? And how do we design governance models to ensure agentic AI operates within the parameters of the organization’s values, ethics, and legal obligations?

Bias and errors are another major concern. Just like traditional AI, agentic AI is only as good as the data it learns from. If it is fed biased, incomplete, or flawed data, it will make biased, incomplete, or flawed decisions. For example, an AI-powered hiring system could inadvertently discriminate against certain candidates if trained on biased historical hiring data. Worse still, because agentic AI is making decisions without human intervention, these biases could scale quickly without immediate detection.

Cybersecurity risks also escalate when introducing agentic AI into your ecosystem. Each AI bot represents a new potential vulnerability. Even if your core systems are secure, AI agents often require integrations with those systems to function effectively. If one of those AI agents is compromised, it could provide attackers with a backdoor into your broader IT environment.

Finally, there are significant regulatory and legal challenges. Governments worldwide are still in the early stages of figuring out how to regulate AI—let alone agentic AI. Will governments limit how much decision-making power organizations can hand over to AI? Will certain industries be restricted from using autonomous AI for sensitive tasks? These are open questions that will shape how organizations adopt and scale agentic AI in the coming years.

The Future of Agentic AI in Digital Transformation

So, what does all of this mean for the future of digital transformation? From where I sit, agentic AI is set to redefine how we think about enterprise technology.

Historically, digital transformation has largely been about implementing off-the-shelf software—ERP, CRM, supply chain management, HCM systems, and so on. These systems are typically configured during implementation and then used in more or less the same way for the next 10-20 years. Agentic AI completely breaks that mold. It introduces a fluid, adaptive, and continuously learning component into the technology landscape.

In the future, I believe agentic AI will not replace digital transformation—but it will become a critical part of it. Organizations will likely use agentic AI to complement traditional systems, adding a layer of intelligence that drives decisions, automates processes, and adapts in ways that static software never could.

Some experts, including Elon Musk, have even suggested that we may eventually have more AI bots in the world than humans. Whether or not that prediction holds true, it is clear that agentic AI will soon become a type of coworker for many of us, augmenting human capabilities rather than replacing them entirely.

Will Agentic AI Replace Our Jobs?

This brings me to the million-dollar question: should we, as human workers, be worried? Are we on the verge of being replaced by AI?

The answer, in my view, is: it depends. While agentic AI will likely automate many tasks and functions, I don’t believe it will replace humans entirely—at least not anytime soon. Instead, it will change the nature of our jobs. We will spend less time on repetitive, rules-based tasks and more time on creative problem-solving, strategic thinking, and overseeing the AI itself.

In many cases, agentic AI could make our jobs easier, more impactful, and more engaging. It will help us make better decisions faster and free us from many of the time-consuming tasks that bog us down today. Of course, this transition will require reskilling and adaptation, but it’s a challenge I believe we can—and must—rise to meet.

Where Do We Go From Here?

As organizations continue to experiment with and deploy agentic AI, we will need to navigate not only technical challenges but also complex ethical, cultural, and regulatory considerations. The good news is that with thoughtful planning, organizations can harness the power of agentic AI to drive innovation while minimizing the risks.

If you want to learn more about how to define an AI strategy for your organization or explore how agentic AI might fit into your digital transformation, we’ve developed a detailed AI Strategy Guide, available for free on our website. It covers best practices, key considerations, and a step-by-step approach to defining an AI roadmap.

And of course, I’d love to hear your thoughts. Are you already experimenting with agentic AI in your organization? Are you excited or concerned about its potential? Share your comments—I’d love to hear your perspective.

Be sure to also download our new Guide to AI Strategy to get started on your AI journey. It's a free resource available to digital transformation project teams across the world!