By now, you’ve likely heard the term “agentic AI” or “agentic AI workflows” or simply “AI agents,” which are all phrases that are referring to a type of AI system that’s able to work through complex, multi-step workflows autonomously.

Whereas current LLM capabilities are indeed stunning and game-changing in their own right, they’re limited in what they can do, because what they can do is contingent on what we tell them to do.

Right now, we’re prompting LLMs to do very specific things for very specific use cases, but soon, it seems, we’ll be entering the early innings of AI agents. Agentic systems have been around for years, but as McKinsey writes in a recent piece called “Why agents are the next frontier of generative AI,” they’ve been fairly “difficult to implement, requiring laborious, rule-based programming or highly specific training of machine-learning models.”

What they mean is that there were often so many variables in a particular workflow that it became difficult to write rules for every scenario. You had to be able to see around corners, and it was incredibly difficult to do.

However, now that LLMs have the ability to perform complex reasoning, and since frontier models have become increasingly powerful and prolific, agentic AI is now a real possibility.

“When agentic systems are built using foundation models (which have been trained on extremely large and varied unstructured data sets) rather than predefined rules,” the authors write, “they have the potential to adapt to different scenarios in the same way that LLMs can respond intelligibly to prompts on which they have not been explicitly trained.”

This is an incredible breakthrough with potentially industry-changing consequences, and it's worth exploring a bit more comprehensively.

The Potential of Agentic AI

An easy way to understand how an agentic AI system might work is to view it through the lens of booking travel.

Imagine using a natural language prompt—“Check my schedule for September and October and book me a trip to Europe. Include dinner reservations at top restaurants and some cultural experiences.”— and watching while an AI assistant seamlessly integrates with your calendar, assesses your availability, and autonomously designs an itinerary.

From selecting destinations and booking flights to securing dinner reservations at renowned restaurants, the AI handles every detail. The agentic AI consults with you at critical junctures, too, ensuring that the proposed plans align with your preferences, and uses your feedback to make adjustments as needed.

It's not hard to see how incredibly convenient and useful this would be for scenarios like this one, how an agentic AI focused on travel would not only simplify the planning process, but also elevate the overall experience, giving you a more personalized and hassle-free journey. Now imagine what it could do for your business.

Whether you’re a corporate accountant or a marketer inside of an industrial company or a CEO, on a fundamental level, your job is a bundle of individual tasks that you complete each and every day. Many of those tasks are repetitive and time-consuming and keeping you from engaging deeply with the work that truly matters, the work that moves both you and the organization forward.

But what if you had a virtual coworker that could complete complex workflows autonomously? What if you had an agentic AI that could handle a good portion of that less valuable work for you? What would it mean for your business if you could spend the vast majority of your time on strategic, high-value work?

My guess is it would unlock a series of insights that would set the direction for your organization’s future.

A Marketing Use Case

McKinsey highlights marketing as one of the most immediate opportunities for agentic AI. Running a campaign today requires stitching together research, creative development, testing, analytics, and distribution—often across a disconnected ecosystem of tools.

An agentic system could unify that workflow. A marketer could describe the audience, goals, and channels in natural language. From there, agents could:

• Draft campaign concepts.

• Generate tailored copy and design assets.

• Test with target segments across platforms.

• Refine based on analytics.

• Reallocate spend dynamically to maximize ROI.

The marketer stays in the loop, providing direction and oversight, but the tedious orchestration disappears. The result: campaigns that move faster, scale wider, and adapt in real time.

Beyond Marketing

The implications extend well beyond campaigns. Imagine:

• Manufacturing supply chains managed end-to-end by agents that adapt instantly to supplier delays, demand fluctuations, or geopolitical risks.

• Industrial marketing ecosystems orchestrated autonomously across trade shows, industry publications, email, and social—continuously reallocating budget and adjusting messaging based on real-time performance data.

• Corporate operations where agents handle repetitive reporting, compliance checks, or onboarding workflows, freeing leaders and employees to focus on strategic, high-value work.

In each case, the principle is the same: agentic AI systems connect workflows that previously required constant human management, enabling people to focus on the decisions and insights that matter most.

Work Has Already Changed

The shift to agents isn’t theoretical. Even if regulation, energy costs, or technical limits slow things down, LLMs have already transformed how we work. They’ve proven that automation can extend well beyond single prompts into complex, multi-step processes.

The real risk now isn’t moving too quickly. It’s waiting too long.

If you haven’t yet:

• Stand up an AI Council.

• Draft an AI Charter.

• Create an AI Policy.

• Identify and test AI use cases with small pilot projects.

You don’t need to adopt everything at once. But you do need to start building your AI muscle now. Organizations that hesitate will soon feel the financial effects of their inaction.

The Imperative for Leaders

Agentic AI is coming—likely faster than most expect. The organizations that thrive won’t be the ones who simply watch; they’ll be the ones who experiment early, build governance, and prepare their people to work alongside AI systems.

It’s time to stop waiting. Start with small steps. Build discipline through governance. Test use cases. Scale what works.

Because the future of work isn’t just AI-augmented. It’s agentic.