If you own a small or midsize business, you've probably heard it everywhere: You should be using AI. What you're not hearing is where it actually pays off.

According to Reimagine Main Street's June, 2025 survey of nearly 1,000 businesses, over 75% are either already actively using or exploring AI implementation, and only 25% are using it to improve their day-to-day operations.

If you're evaluating AI for your business, the better question isn’t "Should we use AI?" It’s "Where is our team spending time that could be done faster, easier, or more consistently?"

Over the past few years, I’ve seen of small and midsize businesses use AI to save hours each week, improve customer response times, and get more value out of the data they already have. I’ve also seen companies invest in tools that looked impressive but didn’t solve any real problem.

The difference usually comes down to clarity. AI is powerful when applied to the right tasks, and frustrating when it’s not. Understanding where it fits into your day-to-day operations is what separates meaningful results from wasted time and budget.

What AI is actually good at

AI performs best when work follows patterns, relies on existing information, or requires processing large amounts of data much faster than a person could.

Some of the strongest business applications include:

• Summarizing lengthy reports, emails, or meetings
• Searching thousands of documents in seconds
• Drafting first versions of emails, articles, or proposals
• Categorizing customer requests
• Identifying trends across operational data
• Automating repetitive administrative work
• Generating recommendations based on historical information

Take customer service as an example. AI can instantly classify incoming requests, recommend responses, summarize conversations, and surface relevant documentation for your team. None of these tasks requires you to reinvent the wheel. They follow patterns, which is exactly where AI excels.

At WorkBetterNow, the AI implementation with our staff is showing the same lesson: AI creates the most value when it supports clear processes and helps teams use their time and information better.

The same applies to finance, operations, marketing, and sales. Anywhere your business spends hours processing information, AI can often reduce that effort to minutes.

Where AI still fails

AI has important limitations that every business should understand before relying on it for critical decisions.

AI generates responses based on the information it's given and the patterns it has learned. If your documentation is incomplete, your data is outdated, or your processes are inconsistent, its recommendations will reflect those same weaknesses.

Some common examples include:

• Making strategic decisions: AI can analyze data and present options, but it cannot determine your company's priorities or long-term goals.
• Working with incomplete information: When key details are missing, AI often fills the gaps by making educated guesses instead of recognizing what it doesn't know.
• Understanding unwritten processes: Every business has knowledge that exists only through experience. If it hasn't been documented, AI has no way of learning it.
• Resolving conflicting requirements: When different stakeholders have competing priorities, AI cannot independently determine which direction best aligns with your business objectives.
• Guaranteeing accuracy: AI hallucinates. A lot. It can confidently present incorrect information, invent facts, or cite nonexistent sources. That makes human verification essential whenever accuracy matters, especially for financial, legal, compliance, or customer-facing work.
• Adapting to entirely new situations: Unexpected events, unique customer requests, or changing business conditions often require additional judgment and context.

One mistake I often see is businesses assuming AI understands their company the way their employees do. It does not.

Unless you've provided the necessary documentation, processes, and data, AI has no understanding of how your organization operates. It can only work with the information available to it.

Where businesses see the biggest return from AI

Rather than looking at industries, it is more useful to think about business functions.

Some areas naturally produce stronger results because they involve repetitive work, structured information, and measurable outcomes.

Notice the pattern. None of these examples replaces an entire function. Instead, AI removes repetitive work that slows teams down, allowing them to complete projects faster and make better use of the information they already have.

How to decide if AI is the right solution for your business or specific workflow?

Not every task should involve AI. 

Before introducing it into a workflow, I recommend asking a few practical questions.

1. Is the task repetitive?

The more often the work follows the same steps, the more likely AI can help.

2. Does it follow clear rules?

Processes with consistent inputs and expected outputs are ideal candidates.

3. Is there enough reliable data?

AI depends on information. Poor data almost always produces poor results.

4. Can the results be reviewed?

Some business activities require an additional level of verification before action is taken.

5. Will AI solve a meaningful business problem?

Saving five minutes a month is different from eliminating hours of manual work every week.

If you answer "yes" to most of these questions, AI is probably worth exploring.

If not, improving the underlying process may deliver a greater return than introducing another technology.

What success with AI actually looks like

One misconception is that successful AI projects involve automating as much work as possible.

In practice, the most successful organizations measure AI differently.

They look for outcomes such as:

• Faster turnaround times
• Reduced manual processing
• More consistent outputs
• Better visibility into business information
• Quicker access to internal knowledge
• Improved operational efficiency
• Better decision support

AI is no longer a question of if businesses should use it. The real opportunity lies in understanding where it can make the biggest difference.

Start with one repetitive process. Measure the results. Refine the workflow. Then build from there. Small improvements made consistently often have a greater impact than trying to automate everything at once.