AI can now think, act, and adapt, marking a pivotal organizational shift in how work gets done. As agentic AI ushers in a new era of autonomy, leaders are confronted with a strategic question: how much control should we give to intelligent systems without compromising human oversight and purpose?
The dictionary defines the word “agentic” as being able to accomplish results with autonomy. Agentic AI can automate a broader range of tasks, mimicking human decision-making to solve complex problems in real time. It doesn’t just support human work but strategically advances it.
According to Emergen Research and other industry trackers, the Agentic AI market is projected to grow from $2.9 billion in 2024 to an estimated $48.2 billion by 2030. The excitement is contagious, and I understand organizations need to stay competitive. However, a balanced approach is imperative for success.
Let’s take a closer look.
Decoding Autonomous Intelligence
How exactly does Agentic AI work? It begins, as all intelligence does, with data. Agentic AI analyzes information using natural language processing (NLP), computer vision, and other advanced AI capabilities to extract actionable insights. From there, it defines objectives based on user input or predetermined goals.
Once these goals are set, Agentic AI takes over. It operates autonomously, determining the best path forward through outcome-based decision-making and continuous feedback loops. Once complete, it uses the strategies and knowledge gained for future scenarios.
But the true power of Agentic AI emerges through AI orchestration —the coordination of multiple intelligent agents working together toward a shared goal. Functioning as a team, each is responsible for a distinct subtask to solve complex tasks.
In this multiagent system, dozens, hundreds, or even thousands of Agentic AI agents collaborate seamlessly. With the right architecture and governance, these systems can achieve remarkable levels of collective efficiency and scalability.
Building Upon Traditional AI
Generative AI focuses on creating. Agentic AI builds on this by applying these generative outputs to specific goals using external tools.
Think of a Generative AI model like OpenAI’s ChatGPT as a line cook in a restaurant kitchen. Now, think of Agentic AI as the executive chef. The cook is responsible for specific tasks, such as dicing onions, mincing garlic, and peeling and slicing vegetables, to make the kitchen more efficient. Meanwhile, the chef develops menus and combines the ingredients to create perfectly plated dishes.
Without the cook, the chef would be less productive and creative. Conversely, without the chef’s direction and vision, the cook’s efforts alone would not result in delicious meals for the restaurant’s patrons.
Putting It In Motion
Here is an example of Agentic AI not just automating but orchestrating intelligence at scale. Within retail, Agentic AI can function as autonomous customer service and sales assistants, achieved through integration with customer support platforms, inventory management systems, and behavior analysis.
Let’s say a customer is shopping online for hiking boots from an outdoor sporting goods store. The customer is greeted by an Agentic AI assistant that uses browsing and purchase data to suggest styles, sizes, and prices. At the same time, live inventory updates are sent to the customer through their preferred channels.
An Agentic AI assistant manages orders, applies loyalty points, tracks shipments, and handles returns. Meanwhile, another suggests cross-selling, such as socks or hiking gear, to boost not only sales but also customer experience.
At the retailer’s head office, the marketing team can also benefit. Gen AI can create emails or social media posts. Then, an Agentic AI system can take it a step further by scheduling the posts, analyzing audience engagement, and automatically adjusting future content.
The FOMO is Real
After reading all of that, you might be thinking, “Sign me up!” I know there is a sense of FOMO out there, but let’s look at some statistics.
According to the EY US AI Report from Ernest & Young Global Limited, 34% of leaders have implemented AI in organizations. Yet only 14% have taken the next steps into a full rollout. The reasons cited include a gap between technology and strategy, with significant concerns around cybersecurity and data privacy.
In my opinion, leaders won’t win by adapting the fastest, but by acting with intention and clarity.
Giving Technology Full Autonomy
The autonomous nature of agentic AI raises a host of concerns. Some of the challenges I noted in my article From Sci-Fi to Sigh: The Bumpy Rollout of Agentic AI in Customer Service include controls and reliability issues in delegating essential tasks, complications in ethics and bias affecting brand representation and security, and data governance.
Something that must really be emphasized is data. Agentic agents rely on well-structured, quality data. Every action, decision, and recommendation stems from it, helping to ensure the insights generated are relevant and trustworthy.
When the input data is inaccurate, incomplete, or biased, the flaws are magnified as the system scales. In other words, poor quality data doesn’t just produce bad results. It multiplies it across every automated process.
Keeping Humans in the Loop
I’m often asked, “Can Agentic AI fully replace people and operate completely on its own?” The short answer is yes, but not to the extent many imagine.
Relying too heavily on AI can strip away human oversight, creating blind spots and disconnecting organizations from their customers’ real needs. Execution matters. Without thoughtful implementation, we risk losing the very insight that drives great customer experience.
There’s also a human element to consider. When every interaction is automated, the personal connection fades. The best results come from balance, where AI handles efficiency, and people bring empathy and understanding.
At DATAMARK, we manage this risk by focusing on actionable insights generated alongside every automation. Rather than letting AI “do everything” for us, we design it to automate what truly adds value, while simultaneously delivering insights into customer preferences and behaviors. This helps us better serve our clients, enabling them to shape future product designs, refine offerings, anticipate customer needs, and identify market opportunities.
In Conclusion
Like any transformative technology, Agentic AI comes with both opportunities and challenges. When mismanaged, it can amplify complexity, driving systems to act without adequate human oversight or ethical consideration. At its best, it enables progress by empowering humans to focus on creativity, strategy, and innovation while machines handle execution.
As leaders, we need to take a thoughtful, outcome-driven approach to utilizing this technology. We must continuously assess where it creates real value by aligning use cases with strategic impact, ROI, and customer experience. This isn’t about adopting agentic AI for its own sake. It’s about purposeful deployment where it makes sense.
