Did You Train Your AI Before You Put It to Work?

Imagine hiring a new employee.

On their first morning you hand them a laptop and say: “Go grow my business.”

You don’t explain what the company does. You don’t describe your customers. You don’t show them your products, pricing, competitors, successful proposals, or what a good lead looks like. Then three hours later, you’re disappointed with their work.

It sounds ridiculous. But that’s essentially how many businesses start using artificial intelligence.

They open an AI tool and type: “Write me a marketing email.” “Give me some leads.” “Create a social media post.” Then they judge AI based on the generic answer they receive.

The problem may not be the AI. The AI doesn’t know your business.

What Does It Mean to “Train” Your AI?

Technically, most small businesses aren’t retraining the underlying artificial intelligence model. A better description is configuring, instructing and providing business context to the AI so it can give you better answers.

But I still like the word training because the process is surprisingly similar to onboarding an employee. The more relevant information, examples, expectations and feedback you provide, the more useful the tool can become.

Instead of treating AI like Google, start treating it more like a new business assistant.

Start by Teaching AI Eight Things About Your Company

1. Who Are You?

Give AI basic company context: what the company does, where it operates, how long it has been in business, how large it is, and what products or services it provides. Don’t assume the AI already knows.

2. Who Is Your Best Customer?

Describe the customer you actually want. That could include geography, industry, company size, revenue, home value, age or demographic when appropriate, customer needs, buying triggers, average purchase, decision-maker and common pain points. The more accurately you define the customer, the better AI can help you think about lead generation and marketing.

3. Why Do Customers Choose You?

What makes your company different? Price, service, speed, expertise, experience, quality, technology, relationships, convenience, or a specialized product? If you don’t tell AI what makes you different, don’t be surprised when it writes marketing that sounds exactly like your competitors.

4. How Does Your Business Make Money?

AI doesn’t necessarily need every confidential financial detail. But understanding your basic economics can dramatically improve strategic recommendations. Which services are most profitable? What is your average transaction? Which customers have the highest lifetime value? Do you make money from recurring revenue or one-time transactions? Which products are you trying to grow?

There is a big difference between asking “How can I generate more customers?” and asking “How can I generate more customers for the service that produces our highest margin and strongest repeat business?”

5. How Do You Sell?

Explain your sales process. Where do leads come from? What happens after someone contacts you? How long is your sales cycle? Why do opportunities stall? What objections do customers raise? How do you define a qualified lead? What percentage of leads convert? This is where AI can become more than a writing tool and start becoming useful for revenue analysis.

6. How Should Your Company Sound?

Give AI examples: your website, articles you’ve written, successful emails, sales presentations, proposals and customer communications. Then explain what you like about them. The objective shouldn’t be having AI replace your voice. The objective is having AI help you communicate your ideas more effectively without losing your voice.

7. What Should AI Not Do?

This may be just as important as telling it what to do. Establish boundaries. Don’t invent statistics. Don’t create fake testimonials. Don’t make promises the company cannot support. Don’t disclose confidential information. Don’t automatically publish something without human review. Don’t provide pricing outside established guidelines. Don’t assume facts when information is missing.

Artificial intelligence can generate information confidently even when that information is wrong. NIST specifically identifies confidently generated false information—sometimes called hallucinations or confabulation—and data privacy as risks businesses should manage when using generative AI.

8. What Does Success Look Like?

Tell AI what you’re trying to improve: saving time, generating leads, improving conversion, producing better proposals, analyzing sales performance, finding customer trends, reducing administrative work, improving customer service or better forecasting. A tool becomes considerably more useful when it understands the objective.

Here’s the Difference

Imagine a Phoenix HVAC company asking: “Give me some marketing ideas.” AI can certainly answer.

But now give it this context: We are a 15-person residential HVAC business serving North Phoenix, Scottsdale and Cave Creek. Our average replacement job is approximately $12,000. Our strongest replacement customers tend to own higher-value homes with systems over 10 years old. We compete on responsiveness, technician quality and service rather than being the cheapest provider. Most of our business currently comes from referrals and Google. Our biggest revenue opportunity is increasing replacement leads during slower periods.

Then ask: “Give me five lead-generation strategies. For each one, identify the expected customer, approximate cost category, difficulty of implementation, how we should measure success and what additional information you need from me before making a recommendation.”

Now you’re having a business conversation. That’s very different from asking AI to “give me marketing ideas.”

Your Employees Are Already Moving This Direction

This matters because AI isn’t only a leadership tool. Employees are increasingly incorporating it into daily work.

The U.S. Census Bureau reported in August 2026 that about 56% of U.S. workers said they had used AI on the job for at least one of the work activities the survey measured. Among workers who had used AI during the previous week, 31% said it allowed them to complete work one to two hours faster, while another 25% estimated saving less than an hour.

A separate field experiment involving 7,137 knowledge workers across 66 companies found that workers who actually used an AI tool integrated into their existing applications spent about two fewer hours per week on email during the latter part of the experiment.

Those aren’t theoretical productivity benefits. But there is an important difference between employees independently experimenting with AI and a company developing a thoughtful approach to using it.

Don’t Just Give Employees AI. Give Them a Framework.

A small business doesn’t necessarily need a 100-page AI policy. But employees should understand a few basic rules.

What can be uploaded?

Customer information? Contracts? Financial information? Internal reports? Employee information? Proprietary data? The answer may depend on the AI platform and the company’s policies, but it shouldn’t be left entirely to individual employees to decide.

What requires human verification?

If AI is producing financial analysis, research, customer communications, legal or regulatory information, pricing recommendations, statistics or competitive information, someone should still be accountable for verifying the work.

What tasks should we actually use AI for?

Start with repetitive or information-heavy work where there is a measurable benefit.

Sales: researching accounts, preparing for meetings, organizing CRM notes, developing follow-up ideas and identifying stalled opportunities.

Marketing: content development, customer research, campaign ideas, website FAQs, competitive reviews and analyzing performance.

Operations: meeting summaries, process documentation, workflow analysis and identifying repetitive administrative tasks.

Management: analyzing reports, preparing planning documents, examining trends and exploring different business scenarios.

The objective isn’t using AI everywhere. It’s identifying where it creates value.

Build a Business AI Playbook

One of the most valuable things a small business can create now is a simple internal AI playbook. It doesn’t need to be complicated.

Document your company information, ideal customers, products and services, competitive advantages, preferred tone and communication style, sales process, key metrics, approved AI tools, information employees should not upload, tasks where AI is encouraged and tasks requiring human review.

Then give employees examples of prompts that have produced good results. Instead of 15 employees individually trying to figure out how to use AI, the organization starts building institutional knowledge around it. That’s when the tool begins becoming a business capability.

You Don’t Need to Become an AI Expert

Small-business owners have companies to run. They shouldn’t need to spend every morning researching new AI models, applications and software companies. But ignoring AI isn’t a particularly good strategy either.

The opportunity is finding the practical middle ground: understand what AI can do, understand what it shouldn’t do, give it enough information to become useful, establish rules for employees, measure whether it is actually saving time or improving results, and only then decide where deeper technology investment makes sense.

This is also an area where an outside advisor can provide value—not because every business needs an “AI consultant,” but because someone who understands revenue, sales, marketing and business operations can help identify where AI solves a real business problem—and where it doesn’t.

At Borgwardt Revenue Advisory, the goal isn’t to replace people with technology. It’s to help businesses use technology more intelligently so their people can spend more time on the work that actually creates value.

Before asking what AI can do for your business, start by making sure your AI understands your business.

Sources

National Institute of Standards and Technology (NIST), Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile.

U.S. Census Bureau, AI Use at Work, August 2026.

National Bureau of Economic Research, field experiment on generative AI and knowledge-worker productivity, 2025.