2025 has us all talking about a lot of things—an inauguration, lackluster Super Bowl and polemic half-time show, intense Arctic cold snap, and everything AI always. Related to the latter, there’s been a clear shift this year in AI evolution with the rise of AI agents. While there is a lot of hype building about agentic AI, I wanted to move beyond speculation to look at what’s really going on.

Both generative AI and agentic AI fall under the broader artificial intelligence umbrella. However, their capabilities, applications and implications for business differ significantly. Understanding these differences is especially crucial for organizations looking to make smart choices about where to invest in AI.

What is generative AI?

Generative AI refers to artificial intelligence systems designed to generate text, images, code, and other outputs based on input prompts.Several years ago, while exploring new opportunities in the experiential space, it quickly became clear that landing interviews was the easy part - finding an agency truly delivering the kind of experiential work they advertised was the real challenge. In many cases, 'experiential' amounted to a single button triggering an on-screen animation. Despite bold positioning, few had true case studies to support their capabilities - yet nearly all were eager to pitch experiential as a core offering.

By now everyone has heard of generative AI. Let’s make sure we’re all talking about the same thing. Generative AI refers to artificial intelligence systems designed to generate text, images, code, and other outputs based on input prompts. These models rely on deep learning techniques to analyze patterns and produce contextually relevant content. While highly effective for content creation, data analysis, and automation, we should always remember that generative AI operates reactively, responding to inputs rather than proactively making decisions or taking actions.

What is agentic AI?

On the other hand, agentic AI goes beyond content generation by incorporating autonomy, decision-making, and long-term context management. Agentic AI can initiate actions, adapt to new information, and solve complex problems without constant human intervention—notably different from generative AI, which primarily responds to prompts.

These AI agents are able to interact with systems, execute multi-step processes, and refine their approaches based on real-time data, making them the more dynamic and capable cousins to generative AI, ideal for business applications.

Generative AI vs AI agents: The GPS vs the driver

Here’s another way to think about the difference between these two: A traditional GenAI model is like a GPS—it provides directions based on input but requires a driver to follow them. It excels at guidance but doesn’t take action on its own.

An AI agent, on the other hand, is like a driver—it not only navigates but also operates the car, adjusts routes in real time based on traffic, refuels when necessary, and ensures a smooth journey from start to finish.

How generative AI and AI agents will shape business

The evolution from traditional GenAI to AI agents represents a fundamental shift in how organizations can leverage artificial intelligence. However, success requires more than just adoption – it demands strategic thinking and careful implementation.

Instead, work towards an open, decentralized approach built around:

• Individual data ownership
• Granular permissions for information sharing
• Interoperability between systems
• Focus on user-centric solutions

Remember: The goal isn't to have the most advanced technology - it's to solve real problems in ways that meaningfully improve how people work and live.