Agentic AI: the next stage of automation or a new model of work?
For most of the past decade, business automation has been associated primarily with robotic process automation (RPA) and simple chatbots designed to handle repetitive customer inquiries. These technologies delivered measurable operational benefits. They accelerated processes, reduced costs, and improved consistency across repetitive tasks. However, their scope remained relatively narrow.
Each bot, workflow, or script was designed to solve a single, well-defined problem. Today, a new phase of automation is emerging. Agentic AI refers to systems capable of not only responding to requests but also independently completing tasks, making operational decisions, and collaborating with humans as part of broader business processes.
According to McKinsey, productivity gains in customer operations could reach 15–20% through advanced AI adoption, illustrating the scale of transformation organisations are beginning to explore. The question is no longer whether AI can automate individual tasks. The question is whether Agentic AI represents the next generation of automation, or the beginning of an entirely new model of work.
From Task Automation to Process Automation
Automation solutions used to be designed to perform a single repetitive activity.
A chatbot answered basic customer questions. A machine-learning model classified documents or customer requests.
Agentic AI goes further. Rather than automating individual steps, it can execute entire sequences of activities that previously required human involvement.
For example, an AI agent may:
receive a customer request,
interpret its content and intent,
retrieve information from multiple systems,
determine the next appropriate action,
escalate the case to a specialist when necessary.
This represents a shift from automating tasks to automating portions of end-to-end business processes.
Some analysts suggest that Agentic AI systems may eventually resolve the majority of routine customer inquiries without human intervention.
However, the future of Agentic AI is unlikely to be defined by individual agents operating independently. Increasingly, attention is shifting toward multi-agent systems.
In this model, specialised agents collaborate within a shared workflow. One agent may classify requests, another may analyse data, while a third may generate recommendations or communicate with customers.
The critical capability becomes orchestration - the coordination of agents, tasks, data flows, and decision points across the process.
AI as an Operational Co-Worker
Research consistently shows that organisations achieve the best results when AI is used to augment human capabilities rather than eliminate them.
This perspective is increasingly reinforced not only by business realities but also by regulation.
In Europe, the AI Act introduces requirements for human oversight in many high risk applications, including sectors such as e-commerce, financial services, insurance, and healthcare. Human supervision is becoming a design requirement rather than an optional safeguard.
Within customer service, back-office operations, and operational analytics, AI can effectively handle activities such as:
request classification,
document analysis,
information retrieval,
knowledge management,
workflow execution.
Humans, meanwhile, remain responsible for areas requiring judgment, risk assessment, empathy, contextual understanding, and business accountability.
The result is not workforce replacement but a new operational model in which AI functions as a digital co-worker embedded within business processes.
Technology Should Follow Operations
One of the greatest challenges in Agentic AI adoption is aligning technology with operational reality.
Many organisations continue to approach AI as a standalone technology initiative rather than as an operational transformation effort. This often explains why AI pilots fail to generate meaningful business outcomes.
Another challenge is the growing phenomenon sometimes referred to as "agent washing" - marketing conventional automation tools as Agentic AI despite limited autonomous capabilities.
At Axendi, we see this consistently across, for example, the e-commerce customer support projects. The most successful AI implementations rarely begin with a technology-first approach. Instead, they start with specific operational challenges such as managing seasonal demand peaks and automating repetitive inquiries.
Automation and Accountability
The rise of Agentic AI also introduces important questions regarding accountability, governance, and control.
In highly regulated industries, operational decisions must remain auditable and compliant with regulatory requirements.
For this reason, many Agentic AI deployments are expected to operate within a human-in-the-loop model. AI systems may perform tasks, generate recommendations, or execute portions of a workflow, while critical decisions remain under human supervision.
Many organisations are adopting operational models that combine autonomous AI execution, governance mechanisms, and human decision-making at key control points.
This approach allows businesses to benefit from increased efficiency while maintaining transparency, accountability, and risk management.
The European regulatory environment further reinforces this direction.
Requirements related to data protection, transparency, explainability, and auditability continue to shape how Agentic AI systems are designed and deployed.
A New Stage in the Organization of Work
Agentic AI has the potential to reshape how work itself is organised.
Operational teams will increasingly operate where some activities are performed by people, others by AI systems, and the primary challenge becomes designing effective collaboration between the two.
As this transformation unfolds, skills will extend beyond technology adoption alone. Organisations will increasingly need expertise in process design, human-AI collaboration, operational governance, and data driven decision-making.
