## Agentic AI Adoption Hinges on Existing Automation Levels
Many businesses are observing the rise of agentic AI from a distance, hesitant about how – or even if – to integrate it into their operations. However, adoption isn’t happening evenly. A recent PYMNTS Intelligence report, “From Zero to Beta: How Agentic AI Just Entered the enterprise Fast Lane,” reveals a strong correlation between existing automation and the willingness to embrace agentic AI.
The report found that 25% of enterprises with high levels of automation had already implemented agentic AI as of August, with another 25% planning to do so within the next year. This means half of these highly automated companies are actively using or preparing to use autonomous agents.
Conversely, adoption is virtually nonexistent in companies with medium or low levels of automation. While some mid-automation businesses are experimenting with agentic tools, none have made a formal commitment to their use.
This disparity stems from the fundamental nature of automation within an enterprise. Many commonly used digital tools – like accounting software, CRM systems, or basic supply chain rules – still require meaningful human oversight. These systems function more like “cruise control,” offering assistance but not fundamentally changing operations.
Companies with lower automation levels face substantial upfront work. Successfully piloting agentic AI requires significant process overhauls, redesigning governance structures, and often, extensive staff retraining.
Highly automated enterprises, though, have already established systems capable of independent decision-making. These companies are more comfortable with autonomous technologies, viewing agentic AI as a natural extension of existing advanced driver-assistance features like self-parking or lane keeping. This allows for smoother integration with minimal disruption.
The report highlights a potential risk: this initial divergence could become self-perpetuating. Companies rapidly adopting agentic AI will likely accelerate their innovation cycles, fueling growth and enabling further investment in autonomous technologies. Meanwhile, those lagging behind may struggle to catch up due to a lack of both infrastructure and financial resources.
While some predict agentic AI will eventually follow a path similar to cloud adoption – initial disparity followed by widespread standardization – others fear that the structural advantages created by early adoption will be challenging to overcome, permanently widening the gap between leaders and laggards.
Two key developments will determine the future landscape. First, vendors need to prioritize transparency, auditability, and compliance features to build trust in agentic AI. Second, mid-tier enterprises must focus on increasing their overall automation maturity, raising their operational baseline to enable triumphant agentic AI integration.
Ultimately, agentic AI is no longer a distant possibility; it’s actively influencing business strategy, investment decisions, and organizational structures. The companies leading the charge aren’t simply adopting a new technology – they are fundamentally reshaping the pace and nature of innovation.
