Over the past few years, many companies have adopted chatbots to answer customer inquiries, automation tools to send emails, and artificial intelligence capable of generating text, images, or analyses.

Now, a new concept is beginning to gain momentum: AI agents.

The difference may seem technical, but it has important implications for organizations. While many traditional tools wait for an instruction and produce a response, AI agents are designed to execute sequences of actions in pursuit of a specific goal.

Instead of asking an AI, “Which customers should I contact?”, we are moving toward systems capable of analyzing information, identifying opportunities, prioritizing them, and preparing the next step within a workflow.

Automation is beginning to shift from simply executing tasks to coordinating entire processes.

A Chatbot Responds; an AI Agent Can Act

Imagine a customer service chatbot.

A user asks about the status of a request, and the system retrieves the relevant information to provide an answer. The interaction usually ends there.

An AI agent can take the process further. Depending on the tools and permissions available to it, the agent could identify a problem, consult different sources, update information, or route the case according to predefined rules.

The key difference lies in its ability to work toward a goal by carrying out multiple actions.

This does not mean that every process needs an AI agent. Many simple automations will continue to be handled more efficiently through traditional workflows.

Marketing Is One of the Areas With the Greatest Potential

Marketing teams work every day with tasks that require information from multiple platforms.

Imagine an AI agent that periodically reviews campaign performance, detects relevant changes, and prepares a summary highlighting where the team should focus its analysis.

Another agent could classify incoming inquiries based on intent and organize information to make the sales team’s work easier. Other agents could support research, create first drafts of content, or analyze customer feedback.

The goal should not be to eliminate human involvement, but to reduce the operational work required before people can make decisions.

Greater Autonomy Also Requires Clear Boundaries

The more capable a system becomes of taking action, the more important it is to define what it can and cannot do.

Using an AI agent to prepare a report is very different from allowing it to modify budgets, send communications to customers, or update critical information without human review.

That is why implementing AI agents requires clear permissions, approval mechanisms, and criteria for determining when a person needs to intervene.

Autonomy should increase according to the level of risk.

Repetitive and reversible processes can support higher levels of automation, while important financial, reputational, or commercial decisions will likely continue to require human oversight.

Data Quality Remains Essential

An AI agent can be highly sophisticated, but if it works with incomplete or inaccurate information, it will make decisions based on a distorted view of reality.

Before implementing these solutions, many organizations therefore need to address less exciting but equally important challenges: organizing databases, connecting platforms, and establishing clear processes.

Artificial intelligence does not automatically fix poor information structures.

In fact, the more we automate, the more important the quality of the data feeding those systems becomes.

The Real Transformation Lies in the Processes

The greatest potential of AI agents is not simply doing exactly what we do today, only faster.

The real opportunity lies in rethinking how certain processes work.

If a team spends several hours every week collecting data from different sources before it can even begin analyzing it, it may not need to automate each task individually. Instead, it can redesign the entire workflow so that the information arrives ready for analysis and people can focus on interpreting it and making decisions.

That is where the real impact of the technology begins to emerge.

The Next Advantage Will Be Knowing What to Delegate

AI agents will continue to evolve and will likely take on an increasing share of operational tasks. However, implementing more automation will not automatically create a competitive advantage.

The difference will come from knowing which processes to delegate, which decisions should remain under human supervision, and where technology can free up time for higher-value activities.

At LNM, we see artificial intelligence as a tool for expanding the capabilities of teams. Because the future of automation is not simply about replacing human tasks, but about designing better ways for people and technology to work together.