Agentic AI Creates a Two-Speed Enterprise Landscape in 2025

In the summer of 2025, a subtle but significant divide began to emerge in the world of enterprise automation. It wasn’t driven by budgets or new markets, but by autonomy the ability for enterprises to delegate important decision-making processes to autonomous AI agents. This divide between those companies confidently adopting agentic AI and those still lagging behind is becoming a defining characteristic of today’s corporate landscape.

According to the October 2025 edition of The CAIO Report from PYMNTS Intelligence, businesses that have long embraced automation are now rapidly moving toward agentic AI, a form of artificial intelligence that can make decisions and execute actions independently, directly contributing to a company’s goals. However, companies with limited automation are finding it difficult to make the leap.

The Two-Speed Enterprise Landscape: Race Toward Autonomy

As businesses across sectors continue to adopt AI in their operations, the gap between highly automated companies and those still relying on manual systems is growing. For highly automated enterprises, the shift to agentic AI is a natural evolution. According to PYMNTS, among enterprises with the highest levels of automation:

• 25% have already adopted agentic AI by August 2025.
• Another 25% plan to adopt within a year.
• 50% are either already onboarding or preparing to integrate autonomous agents.

In contrast, companies in the medium and low automation categories are lagging behind. Adoption is either in its infancy or nonexistent. A few are experimenting with pilot projects, but none have fully integrated agentic AI into their workflows. The reason for this gap isn’t technological limitations, but rather readiness, culture, and risk tolerance.

“It’s about comfort with change,” says Zachary Edwards, CTO of a mid-tier firm in automation. “For many, handing over decision-making to an AI feels like a big leap, not a step.”

The Automation Threshold: When Does Agentic AI Become Feasible?

For enterprises to implement agentic AI, they must already have a strong foundation of automation in place. This includes the use of tools that make basic decisions or trigger actions without human intervention. These early steps in automation think of QuickBooks, Salesforce, or rules-based supply chain triggers are helpful, but they still require oversight, similar to cruise control in a car.

However, companies at the higher automation tier are already embracing autonomous systems that actively manage operations without human oversight. These enterprises could be compared to drivers using advanced driver-assistance systems (ADAS) in cars, such as self-parking or adaptive lane steering, which make the transition to fully autonomous vehicles (and autonomous business decisions) much smoother.

The Risks of a Two-Speed Economy

As the gap between the two groups grows, the two-speed enterprise model risks becoming self-reinforcing. Enterprises with high automation are quickly adopting agentic AI, accelerating their growth and innovation cycles, which, in turn, gives them more resources to invest in further automation. Meanwhile, companies in the slower lane face a growing disadvantage, unable to keep pace with new technological shifts.

The risk is that this divide could create structural advantages for the early adopters, making it harder for slower companies to ever catch up. Without sufficient infrastructure, capital, and readiness, the gap may widen to the point of no return.

“The fear is that some companies may be permanently locked out of the most valuable innovations,” warns Dr. Lila Chang, a senior consultant at a tech advisory firm.

However, some industry experts remain optimistic that agentic AI will eventually follow a path similar to cloud adoption: a period of divergence followed by standardization across industries. If this happens, mid-tier companies may still have a chance to increase their automation maturity and join the fast lane.

The Path Forward: What’s Needed for Broader Adoption?

To avoid the pitfalls of the two-speed landscape, agentic AI vendors must address two critical areas:

1. Transparency: Enterprises need clear insights into how these autonomous systems make decisions, especially when handling sensitive tasks.
2. Compliance: Businesses will need auditability and governance mechanisms to ensure AI is aligned with company values, regulations, and risk management practices.

At the same time, mid-tier enterprises must focus on increasing their automation maturity. By strengthening their operational infrastructure whether through cloud-based tools or advanced data analytics they can ensure they don’t fall further behind.

“Building a strong operational foundation now can help bridge the gap later,” says John Mitchell, COO of a medium-size tech company exploring agentic AI adoption.

Conclusion: The Rise of Agentic AI

The rise of agentic AI marks a major inflection point for modern enterprises. The two-speed landscape is real, and businesses must decide where they fit fast movers or cautious followers. The companies at the forefront of this revolution are not just adopting new tools, but redefining innovation itself.

Ultimately, the choice will come down to readiness, strategy, and a willingness to embrace autonomy a decision that could determine the future pace of business growth and competitiveness.

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