An AI Agent Is a Means, Not an End
An AI agent is a software tool that uses artificial intelligence to retrieve information, answer questions, or perform tasks. In organizations, its value depends on solving a real business need, accessing reliable knowledge, having appropriate system permissions, and validating benefits through short pilots and user feedback before wider implementation.
On genuine need, choosing the right solution, and the conditions for the success of AI agents in the organization
In recent years, AI agents have become one of the most talked-about topics in organizations. They can locate information, answer questions by drawing on organizational knowledge sources, perform actions in systems, and support work processes. The possibilities are many, and the technology is developing rapidly.
But before rushing to set up an agent, it is worth asking a more basic question: What problem are we trying to solve, and is an AI agent the best solution?
From my experience accompanying and testing AI agents in organizations, I have found that the technological ability to set up an agent is only a small part of the story. For an agent to succeed, several components must come together: a genuine need, a suitable solution, supporting organizational infrastructure, users who see value, and the ability to implement it at the right time.
Start with the Need, Not the Agent
It is easy to get excited about the possibilities the technology offers and to start from the solution: “Let’s set up an agent.” But the first question should be what problem we are trying to solve.
Do employees struggle to find information? Do they invest a great deal of time in performing repetitive actions? Is there a work process that could be made more efficient? Is this a significant problem that recurs and affects their work?
Even when a genuine need is identified, choose the solution that fits it. Not every problem requires a complex agent that can plan and act. Sometimes a simple agent that answers questions from a knowledge base is enough, and sometimes a general AI tool or an existing technological solution can provide an equally good answer. The goal is not to set up the most advanced agent, but to choose the solution that delivers the greatest value relative to the need.
If the need is not significant enough, it will be hard to get employees to adopt another tool - even if it works well.
Sometimes this can already be identified at the pilot stage. When it is hard to recruit users for testing, to get real questions from them, or to get them to invest time in giving feedback, it is worth asking not only how we can increase participation. It is also worth checking whether the difficulty itself is telling us something: perhaps the solution simply does not solve a sufficiently significant problem for the users.
Organizational Infrastructure Is a Condition for Success
Even when there is a genuine need, and the technological solution is suitable, we must ensure the infrastructure exists to enable it to work and deliver value.
An agent whose purpose is to make knowledge accessible requires high-quality, up-to-date, and accessible information sources. An agent that performs actions needs system connections, appropriate permissions, and clear workflows. Without the required infrastructure, even a technologically advanced agent will struggle to deliver the expected result.
For example, if an agent is connected to an organizational portal but a significant portion of the knowledge employees need isn't in it, the agent’s ability to deliver value will be limited from the outset.
The challenge is particularly pronounced in organizations where the portal is open to a wide audience of employees. Sometimes professional, sensitive, or high-value knowledge remains in restricted areas, in folders, in other systems, or even with employees.
So before asking, “To which site should we connect the agent?”, ask: Where is the knowledge the user needs really located, and will the agent have access to it?
Also, remember that not every inaccurate answer indicates a problem with the technology. In the course of testing AI agents, it often turns out that the problem stems from missing or unclear content, a broken link, permissions, or information that was not included in the sources defined for the agent.
This also reminds us that adopting AI does not replace knowledge management work. On the contrary - it makes the quality of the content, its currency, its structure, and the permissions to it even more important.
And While We Are Testing - AI Keeps Advancing
Here’s another challenge, one that may be unique to this period.
In large organizations, processes take time. Specification, stakeholder coordination, approvals, setup, user recruitment, a pilot, corrections, and repeated testing are all required. This is natural.
But the pace of progress in the world of AI is entirely different.
While an organization sets up and tests a dedicated solution, general AI tools keep evolving. A capability that did not exist, or was not good enough, at the start of the project can become a real alternative within a few months.
Therefore, the organization’s ability to progress at an appropriate pace is a significant part of a project’s success. An AI project that takes too long may reach the implementation stage after the need has changed, or after a simpler, better alternative already exists.
The implication is that choosing a suitable solution at the outset is not enough. Work in short cycles, test early with users, and examine throughout the project whether it still provides a significant advantage over existing alternatives.
This is especially evident when comparing a general AI chat with a dedicated agent. A general tool offers a wide range of capabilities and develops rapidly. In contrast, a dedicated agent is usually built around a defined need and sometimes around defined information sources.
That said, choosing a general AI tool doesn’t solve every dilemma. A dedicated agent based on defined knowledge sources may offer greater control over the information it relies on. A general tool may work with a wider range of sources, so check where the answer came from, what permissions apply to the information, and how much you can rely on the source.
That is, the choice is not necessarily between a “good” solution and a “not good” one, but between different solutions that suit different needs.
Even a Decision to Stop Can Be a Success
We are accustomed to thinking that a successful project is one at whose end a solution goes live. But in AI projects, especially as the technology changes rapidly, it is worth viewing success more broadly.
An AI project can succeed technologically and still not deliver value to the organization. If there is no significant need, the infrastructure is unsuitable, users do not see value in the solution, or the organization fails to implement it in time, the agent's work is not enough.
In such cases, changing direction, choosing a simpler solution, or even stopping the project can be a sign of a professional, sound process.
A pilot is intended not only to prove that the solution works. It is intended to help us learn and make a decision.
Therefore, before starting the next AI agent project, it is important to make sure that there is a genuine need, that the suitable solution has been chosen, that the required infrastructure exists, and that the organization can implement it in a way that will deliver value to users.
And in a world where technology changes almost every day, it is worth returning to examine these conditions along the way.
Because the important question in an AI project is not only “Did we manage to make it work?”, but “Are we solving a real problem - and is this still the right solution?”
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