From Chatbots to AI Agents: What the Next Phase of Enterprise AI Will Look Like 

The first phase of enterprise AI was largely about asking questions and generating answers. Chatbots could retrieve information, draft content, summarise documents and support employees with routine tasks. The next phase is beginning to look different. AI systems are moving from responding to requests towards carrying out defined tasks, interacting with business systems and, in some cases, managing parts of a workflow. 

That shift from assistance to action is at the heart of the move towards AI agents. For enterprises, however, the question is not simply what agents can do. It is how organisations can give them enough access to be useful without losing control over data, processes and decisions. 

The Enterprise Is Moving Beyond the Chat Interface 

The distinction between a chatbot and an AI agent is increasingly practical. A chatbot generally responds to a prompt. An agent can interpret an objective, determine the steps required, use connected tools and work through a process with less intervention. 

Consider a customer service request. A chatbot may explain an organisation’s returns policy. An agent could identify the customer’s order, check the relevant terms, initiate the appropriate workflow and escalate the case if it falls outside defined rules. 

The shift is already visible in enterprise adoption. According to the Liferay 2026 Agentic AI Adoption and Governance Report , 54% of companies are running AI agents in production or actively piloting them, including 28% with agents already in production. 

The shift is therefore less about replacing chatbots than extending AI into the processes behind the conversation. 

Agents Will Need Access to More Than Enterprise Data 

An agent that can only read information has limited operational value. To complete a task, it may need access to a CRM, ERP, commerce platform, knowledge base or internal workflow. 

This makes interoperability a central issue. Enterprises rarely operate on a single technology stack, and replacing established systems simply to accommodate AI is neither practical nor desirable in most cases. 

The architecture around agents therefore matters as much as the underlying model. APIs, identity management, permissions and reliable data connections determine what an agent can see and what actions it can take. 

This also changes the way organisations need to think about data. The objective is not to make every piece of enterprise information available to every AI system. It is to make the right information available in the right context, subject to the same controls that govern human access. 

Governance Moves From Policy to Practice 

The more autonomy an AI system has, the more important governance becomes. An inaccurate answer from a chatbot can create a poor customer experience. An agent that takes the wrong action can affect an order, financial transaction or internal process. 

Liferay’s 2026 report highlights the gap between adoption and readiness. While 54% of companies are running or piloting AI agents, only 24% have a company-wide AI usage policy. Security or privacy concerns are cited by 30% of companies as a barrier to getting more value from AI, followed by cost at 29% and lack of training at 27%. 

These findings are based on a Pollfish survey of 500 full-time employed US adults directly involved in their company’s AI decisions, evaluation or day-to-day use. 

For enterprises, this means governance cannot be added after deployment. Organisations need clear boundaries around what agents can access, which actions they can take, when human approval is required and how decisions can be audited. 

The Human Role Will Change, Not Disappear 

The move towards agents does not necessarily mean removing people from workflows. In many cases, the role of employees will shift from performing every individual task to setting objectives, reviewing exceptions and making decisions where judgement is required. 

The Liferay report points to the importance of preparing employees for this transition. Only 27% of companies identify lack of training as a barrier to getting more value from AI. This suggests that deploying agents is only one part of the task; employees also need to understand how to work alongside them and when their intervention is necessary. 

That model is particularly relevant to complex enterprises, where processes often involve regulatory requirements, commercial judgement and multiple stakeholders. 

The Next Enterprise AI Layer Will Be About Orchestration 

The move from chatbots to agents represents a broader change in how enterprises may use AI. Instead of treating AI as a separate interface, organisations are beginning to explore it as a layer that can connect people, information and business processes. 

The most useful agents will not necessarily be the ones with the broadest autonomy. They will be the ones that can operate reliably within a defined business context, use trusted information and know when to act and when to hand a decision back to a person. 

The Liferay report also found that only 25% of companies measure AI’s impact with clear KPIs. This underlines an important distinction between deploying AI and establishing whether it is delivering meaningful business value. 

For enterprise AI, the next stage is therefore less about making systems more conversational and more about making them operational. The organisations that can connect AI to their existing digital ecosystems while maintaining appropriate governance, preparing their employees and measuring outcomes will be better positioned to turn experimentation into repeatable business processes.

Manish Gupta is Director of Liferay India