When someone says, “We need to use AI,” what comes to mind?
For many people, it is a conversation with ChatGPT, Gemini, or Microsoft Copilot, which many companies use in the workplace: help me write a better email, summarize a document, or work through a spreadsheet. Those are useful starting points. They give us a way to explore ideas and experience what the technology can do.
In this article, an “AI tool” means software you use that has artificial intelligence built into it. ChatGPT and Gemini are familiar examples. Your company might also provide an internal AI assistant—a tool employees use to ask questions or get help with their work. You may hear these tools called “AI applications”; application is simply another word for a software program.
These chat tools are only part of the picture.
Before an organization develops an AI strategy or governance plan—the rules about how AI may be used and who is responsible—its people need a shared understanding of what they are discussing. Otherwise, one person is imagining a writing assistant while another is imagining technology that can carry out parts of a business process.
You do not need to become a developer to participate. A few basic concepts can make the conversation much more useful.
Three pieces that work together
The words can sound more complicated than the idea. Think of a model, a connector, and an agent as three different parts of a software helper.
The model interprets information and generates responses. Examples include OpenAI’s GPT models, such as GPT-4, and Google’s Gemini models. Think of the model as the engine behind the conversation: it helps make sense of your request, summarize information, or draft an answer. ChatGPT is the tool you interact with; GPT-4 is the name of one model that has powered it. Google uses the name Gemini for both its AI tool and its family of models. The names can be confusing, but the distinction is simple: the tool is what you use, and the model generates the response. It can make mistakes, so its output still needs appropriate checking.
The connector provides access to another system. Think of it as a doorway to a document library, calendar, or client system. Permissions are the access rules that determine what the software can read or change through that doorway.
The agent is the software helper carrying out a job. It uses a model to interpret information and work out steps, and tools or connectors to perform permitted tasks. You give it a purpose and boundaries: what it may access, what it may do, and when it must ask a person for approval.
The model and the agent are not the same thing. A model can draft a response; an agent uses that capability as part of getting a job done.
A simple example: Helping employees keep promises
Imagine you finish a client meeting and add a note to your customer relationship management system (CRM)—the system your company uses to keep client information and a record of interactions. Your note says: “Send this client information about our account options by Friday.”
You could set up a software helper to read these notes in your approved CRM and turn the recorded commitments into follow-up actions, with a draft message ready for your review.
Here is how the pieces might work together:
- A connector gives the helper access to the relevant client meeting notes in your CRM, within its approved permissions.
- The model interprets your note: which client needs the information, what you promised to send, and that it is due by Friday.
- The agent turns that commitment into an action. If configured and authorized to do so, it creates a follow-up task in the CRM with you as the owner and Friday as the due date. It retrieves the current account information from an approved source, asks the model to prepare a draft message, and places the draft with the task for your review.
- You check the information and approve the message. In this example, the agent cannot send anything without your approval. If the notes are unclear or the information is missing, it asks for help.
The connector provides access. The model helps interpret and draft. The agent coordinates the steps. You remain responsible for reviewing what goes to the client.
That is a step beyond asking AI to write a better email. The helper is supporting a defined part of your process: turning a note in your CRM into an assigned follow-up task and a draft message ready for review.
It does not need to handle the entire client relationship to be useful. A small, clearly defined job is a good way to help people understand the possibilities.
Where does the information go?
This is often where the conversation becomes confusing.
“Does the information go onto the internet?” can mean several different things. Does it leave the organization’s own systems? Is it sent to the company providing the AI tool so its computers can work with it? Is it stored? Is it used to improve a model? Can other people see it?
Those are separate questions.
Sending information to an online service does not automatically make it public. It can, however, mean that another organization processes or stores it. The answers depend on the particular service, account, settings, agreements, and how the connection has been set up.
An AI tool, such as ChatGPT, Gemini, or your company’s internal assistant, might use public websites, your organization’s internal documents, or both to answer a question, depending on its features and permitted access. Even if a document is stored inside your organization, the tool may send some of its contents to an outside AI provider’s computers to prepare the answer. This is what “processing” means here. It does not automatically make the information public, but it does mean that where a document is stored and where its contents are used can be different places.
Before using sensitive business or client information, understand what the tool can access, where that information is processed, and how it may be used. These details depend on the specific tool and how it is configured.