## Agenticโค AI Adoption Hinges on Existing Automation Levels
Many businesses are observing the rise of agentic โขAI from a distance, hesitant about how – or even if – to integrate it into their operations. However, adoption isn’tโ 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 โfound that 25%โ of enterprises with high levels of โคautomation had already implemented agenticโค AI as of August, with another 25% planning to do so withinโ the next year. This โขmeans half of these highly automated companies are actively using or preparing to useโ autonomousโฃ agents.
Conversely, adoption is virtually nonexistent in companies โฃwith medium or low levels of automation. While some mid-automation businesses are experimenting with agentic tools, โฃnone have made a formal commitment to their use.
This disparity stems from โขthe fundamental nature of automation within an enterprise. Manyโ commonly used digital tools – like accounting software, โCRM systems, or basic supply โขchain โrules – still require meaningful humanโฃ oversight. These systems function more like “cruise control,” offering assistanceโ but not fundamentally changing operations.
Companies with lower automation levels face substantial upfront โฃwork.โค Successfullyโ piloting agentic AI requires significant process โoverhauls, redesigning governance structures, and often, extensiveโ staff retraining.
Highly automated enterprises, though, have already established systems capable of independent โฃdecision-making. These companies are more comfortable with autonomous technologies, viewing agentic AI as a natural extension of existing advanced driver-assistance features like self-parkingโ or lane keeping. This allows for smoother integration with โminimal disruption.
The report highlightsโค a potential risk: โthis 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 โฃto cloud โขadoption – initial disparity followed by widespread standardization – others fear that the structural advantages created by early adoption will be challenging to overcome, permanently widening โฃthe gap between leaders and laggards.
Two key developments will determine the future landscape.โข First, vendors need toโ prioritize transparency, auditability, and compliance features to โฃbuild trust in agentic AI. Second,โข mid-tier enterprises โmust focus on increasingโค their overall automation maturity,โฃ raising their operational baseline to enable triumphant agentic AI integration.
Ultimately, agentic โAI is โno longer a distant possibility; โit’s actively influencing business strategy, โinvestment 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.
