A Basic Overview: Generative AI vs. Agentic AI

The rapid development of artificial intelligence, particularly in the area of agentic AI, has moved us closer to a future where intelligent systems can perform more advanced and proactive roles. The key challenge is how to integrate these technologies in a way that is safe, ethical, and well-informed. To do this, it is important to first understand what agentic AI is—and what it is not.

Generative AI is reactive by nature. It uses large language models to generate answers based on information it has already been trained on. Its capabilities are limited to what is contained in those models.

Agentic AI, by contrast, acts proactively. It relies on multiple AI agents, each designed to handle a specific function, to solve problems in a way that resembles human reasoning and decision-making.

Multi-color digital computer blocks shaped AI

How agentic AI changes team dynamics

Companies will need to rethink how their human workforce interacts with these AI systems. In practice, this means building cohesive teams where AI agents and human employees take on clearly defined roles that complement one another.

For example, if the objective is to improve supply chain efficiency, and a shipment delay occurs due to a natural disaster, an AI agent could independently identify alternative routes or suppliers. This kind of response mirrors how a human team member might react in the same situation.

AI is reshaping the workforce

According to research from McKinsey, up to 30 percent of jobs may be fully automated by 2030. Furthermore, around 60 percent of existing roles—including many that involve repetitive office tasks—will be significantly transformed by AI during that same time frame. For organizations, this signals a major shift in how work is defined, structured, and delivered.

However, the pace and nature of change will differ across industries. Jobs that involve repetitive, routine tasks will likely be affected first, as these functions are the easiest to automate. The speed of this transformation also means that job displacement may happen faster than new roles can be created, presenting a real challenge for business leaders.

Steps business leaders can take now

Strong leadership is essential to navigate these changes effectively. Just as with any workforce, AI systems must be given clear objectives, timelines, and expectations. Business leaders should ensure that agentic AI is developed and deployed with the following elements in place:

A well-defined structure for how the system will work alongside human teams, ensuring alignment in roles and communication.
A detailed understanding of every step involved in the tasks the system is meant to perform.
A framework that accounts for company culture, values, and context, which human employees typically understand intuitively.
Clear ethical and security guidelines, especially concerning fairness, accountability, and the protection of sensitive information.

Trust must be built into AI systems

Trust is central to the effective use of agentic AI. These systems act on behalf of the organization and must be able to make decisions that reflect the company’s values and standards.

Users need to be confident that the information they share will remain secure, that the AI will provide accurate and unbiased responses, and that the system can detect when human involvement is needed—perhaps through cues such as the tone of a person’s voice or the context of the situation.

Agentic AI requires investment and oversight

Designing and training a custom agentic AI system requires significant time and resources. It is not a one-time task but an ongoing process that involves teaching the system, monitoring its performance, and gradually assigning it more responsibility—much like onboarding a new employee. Without a structured approach and built-in checks, the system could act unpredictably.

It is also critical to understand that agentic AI is fundamentally different from traditional generative AI. While generative AI responds to specific prompts using a fixed knowledge base, agentic AI relies on advanced technologies such as machine learning, automation, and natural language processing. This allows it to interpret more complex situations and offer solutions to open-ended challenges.

The future depends on thoughtful integration

To remain competitive in this rapidly evolving environment, organizations must be prepared to invest in agentic AI and the infrastructure needed to support it. Those that fail to adapt may find themselves left behind as this technology reshapes how business is done.

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