What Is an AI Agent? A Practical Example for Small Businesses

For many businesses, the first experience of artificial intelligence has been through a chatbot.

Ask ChatGPT a question. Use Copilot to summarise a document. Draft an email. Analyse some information. Brainstorm an idea.

These are useful applications of AI, but they generally have one thing in common: a person asks the AI to do something and the AI responds.

AI agents take that idea a step further.

Instead of simply answering a question, an AI agent can be given an objective, carry out a series of tasks, use different sources of information and interact with other business systems.

In other words, AI starts moving from answering questions to doing work.

I’ve been experimenting with this while building Blink Blue’s own sales process. It has given me a useful, practical example of what an AI agent can actually look like inside a small business.

What is an AI agent?

An AI agent is software that can work towards an objective by gathering information, interpreting what it finds, taking a series of actions and interacting with other tools or business systems.

A traditional chatbot might answer:

Tell me about Company X.

An AI agent can potentially be asked to:

Research Company X, identify the information I need, structure the findings and create the appropriate records in my CRM.

The difference isn’t simply that the AI produces a better answer.

It participates in a process.

That distinction is important when thinking about where AI can create value in a business.

How can a small business use an AI agent?

Small businesses can use AI agents to assist with repetitive processes involving research, information gathering, categorisation, data entry, document processing and interaction between different business systems.

The best opportunities are often not spectacular.

They’re processes that happen every day.

Someone receives some information.

They look something up.

They check another system.

They copy information into a spreadsheet.

They categorise it.

They update another application.

Someone reviews it.

A decision gets made.

Individually, these tasks may only take a few minutes.

Repeated across a business every day, they can consume a significant amount of time.

That’s where AI agents can become interesting.

A practical example: researching potential customers

Like most new businesses, Blink Blue needs to find customers.

That means identifying organisations that might be a good fit, understanding what they do, researching them, finding the right people to speak to and keeping track of that information in a CRM.

None of those tasks is particularly difficult.

But doing them properly takes time.

For each potential customer, I might need to:

• understand what the company does
• review its website and other public information
• get a sense of the size and nature of the business
• look for signs of its current technology maturity
• identify potential technology, data or AI opportunities
• identify an appropriate person to contact
• capture useful information about the organisation
• enter that information into the CRM
• determine whether the prospect is worth approaching
• decide what should happen next

Doing that for one company isn’t a problem.

Doing it properly for 50 or 100 companies creates a substantial administrative workload before you’ve had a single conversation.

So I started with a simple question:

How much of this process actually requires me?

Building an AI agent to do the groundwork

I built an AI agent to carry out much of the initial research process.

Give it a company and it works through a defined series of steps.

It researches the organisation using publicly available information.

It identifies and structures useful facts about the business.

It looks for signals that might indicate where technology, data or AI could potentially help.

It identifies an appropriate person to contact where that information is publicly available.

And it prepares the information needed for the CRM.

The result isn’t simply a long AI-generated report for me to read.

The output is structured information that can become part of the sales process.

The workflow looks something like this:

That last step matters.

Can an AI agent integrate with a CRM?

Yes. AI agents can interact with CRM and other business systems using APIs, integrations and automation tools.

That means the result of an AI process doesn’t necessarily have to be another document or piece of text that someone needs to manually process.

It can become structured business data.

In my example, information discovered during prospect research can be structured and added to the CRM so that company information, contacts, research, opportunity indicators and next actions form part of the existing sales workflow.

This is where I think AI becomes considerably more interesting.

If my process finished with an AI-generated company summary, I would still need to read it, extract the useful information, open the CRM, create the appropriate records and enter everything manually.

Some time would have been saved.

But I would also have introduced another disconnected tool into the process.

Connecting the AI to the systems where the work actually happens changes that.

The AI is no longer sitting beside the business process.

It is participating in it.

Should an AI agent make decisions automatically?

Not necessarily.

In fact, deciding where the AI should stop can be just as important as deciding what it should automate.

I don’t want an autonomous AI salesperson deciding that every company it discovers should immediately receive an email.

The agent can do the groundwork.

It can research.

It can organise information.

It can identify potential opportunities.

It can reduce repetitive data entry.

It can prepare a potential next step.

But I still want to decide whether there is genuinely a reason to contact that organisation and what that conversation should look like.

That’s not a limitation of the system.

It’s a deliberate control.

The most useful automation isn’t necessarily the one that removes the human entirely.

Often, it’s the one that removes the repetitive work surrounding a decision, allowing the person to spend more time making that decision well.

What is the difference between automation and an AI agent?

Traditional automation generally follows predefined rules: when something happens, perform a particular action.

For example:

That kind of automation remains extremely useful, and many businesses could benefit from doing considerably more of it.

AI agents become useful when the process contains information that needs to be interpreted rather than simply moved from one place to another.

Researching a company is a good example.

Different websites are structured differently. Relevant information appears in different places. The significance of that information depends on context.

An AI agent can interpret that unstructured information and turn it into something a business process can use.

In practice, some of the most useful solutions will combine both approaches:

This isn’t really a story about sales

Prospect research happens to be the process I was working on.

But that’s not the reason I think the experiment is interesting.

Most organisations have processes with similar characteristics.

A service business might receive an enquiry, understand what the customer needs, classify the request, gather relevant information and prepare it for someone to respond.

A finance team might receive documents, extract information, check it against existing records, identify anomalies and prepare transactions for approval.

A property organisation might receive a maintenance request, categorise the problem, identify the relevant property information, check previous issues and prepare the case for someone to review.

A manager might spend every Friday gathering information from several systems simply to produce the same weekly report.

These aren’t necessarily “AI problems”.

They’re business processes.

And that’s an important distinction.

Where should a business start with AI agents?

Start with the process, not the AI.

It’s tempting to begin with:

How can we use AI in our business?

I think there is a better question:

Where are our people spending time on work that doesn’t really require their expertise?

Look for places where people repeatedly:

• search for information
• copy information between systems
• categorise emails, documents or requests
• re-enter information
• reconcile information from different sources
• prepare routine reports
• gather information before making a decision
• use spreadsheets to bridge gaps between applications

Those are useful places to investigate.

Sometimes the answer will be an AI agent.

Sometimes it will be straightforward automation.

Sometimes an existing piece of software already solves the problem.

And sometimes the process itself simply needs to be redesigned.

That’s why the starting point shouldn’t be deploying AI.

The starting point should be understanding how the business works today and where unnecessary effort exists.

Do AI agents replace employees?

They don’t have to.

There is understandably a lot of discussion about whether AI agents will eventually replace particular jobs.

For many small businesses, however, there is a much more immediate opportunity:

Give people back time.

Take away some of the searching, copying, categorising, re-keying and administration surrounding their actual work.

Connect information currently spread across different systems.

Prepare decisions rather than automatically making them.

My prospecting agent isn’t going to build a relationship with a potential customer for me.

I still have to do that.

It isn’t going to understand every nuance of whether Blink Blue can genuinely help that business.

I still have to make that judgement.

What it can do is reduce the amount of time I spend researching, structuring information and updating systems before I can make that decision.

And that’s a useful outcome.

The bigger opportunity for small businesses

The exciting thing about AI agents isn’t necessarily the technology itself.

It’s that they make us reconsider where people spend their time.

Every business has processes that have accumulated over the years.

A spreadsheet was added because two systems didn’t communicate.

Someone started producing a manual report because the information wasn’t available in one place.

An administrator copies information between applications because that’s simply how the process evolved.

Individually, none of these problems may justify a major technology project.

Collectively, they can create enormous amounts of friction.

AI, automation and better integration give smaller businesses another set of tools for addressing that friction.

But the technology should remain secondary to the business problem.

The question I think businesses should be asking isn’t:

How can we use AI?

It’s:

What work are our people doing today that technology could prepare, simplify or remove?

That’s often where the genuinely useful AI opportunities start.