Most business owners I talk to in Aroostook County and across Maine have the exact same reaction when I bring up artificial intelligence. They think it is something built for Google, Microsoft, or massive corporations with bottomless budgets. They look at their 20-person operation and think they are simply too small for it.

That is a mistake. The truth is that small and mid-sized businesses have the most to gain from this technology right now. You do not need a team of data scientists to make it work. You just need a clear understanding of where your bottlenecks are.

I spent eight years in the Army Signal Corps and another 23 years building software. I have seen a lot of technology trends come and go. Most of them are just noise. But what we are seeing right now is fundamentally different. It is not about replacing your workforce or building a sentient robot. It is about taking the repetitive, time-consuming tasks off your plate so your team can focus on the work that actually matters.

The "Too Small" Myth

When you run a company with 15 or 30 employees, every single hour counts. You do not have the luxury of excess capacity. If your top salesperson is spending four hours a day digging through emails, that is four hours they are not selling. If your operations manager is manually updating spreadsheets to guess what inventory you need next month, that is a massive vulnerability.

People assume this technology requires a massive upfront investment and months of integration. That used to be true. It is not true anymore. Today, we can build targeted, specific solutions in a matter of weeks. The goal is not to overhaul your entire business. The goal is to find one specific problem that costs you time and money, and fix it.

Let me give you a few examples of what this actually looks like in practice for companies your size.

Real-World Examples

Think about a 15-person distributor. Every day, they get hundreds of emails from customers. Some are asking for quotes, some are checking on order status, and some are reporting issues. Right now, they have one person whose entire job is to read those emails, figure out what they are about, and forward them to the right department. It is slow, it is prone to errors, and it is a terrible use of human intelligence.

We can set up a system that reads every incoming email, understands the context, categorizes it, and automatically routes it to the correct person. If it is a routine question about business hours, the system can even draft a reply for review. That employee who used to spend all day playing traffic cop can now focus on actual customer service and solving complex problems.

Or consider a 25-person manufacturer. They have been in business for a decade and have years of sales data sitting in a database. But when it comes time to order raw materials for the busy season, they are basically guessing based on what they remember from last year. Sometimes they over-order and tie up cash in inventory. Sometimes they under-order and miss out on sales.

Instead of guessing, they can use their historical data to build a forecasting model. The system looks at past sales, seasonal trends, and even external factors to predict exactly what they will need and when. It takes the emotion and the guesswork out of the equation. They order what they need, when they need it.

Then there is the 20-person professional services firm. They live and die by responding to Requests for Proposals. Writing a good proposal takes eight to ten hours of focused work. You have to read a massive document, pull relevant case studies, and format everything perfectly. It is exhausting.

An AI tool can read the RFP, search the firm's past proposals and marketing materials, and draft a complete first pass in minutes. It will not be perfect, and a human still needs to review and refine it. But instead of starting from a blank page and spending eight hours writing, the team spends two hours editing. They can bid on four times as many projects with the exact same headcount.

Finally, look at a regional retailer with a solid website. They get dozens of inquiries a week. Some are ready to buy today. Some are just kicking the tires. Right now, their small sales team treats every lead exactly the same, calling them in the order they came in.

We can implement a scoring system that analyzes the language in the inquiry, the pages the person visited on the website, and their company size. It automatically flags the high-priority leads and sends them straight to the top closer. The tire-kickers get an automated nurture sequence. The sales team spends their time talking to people who actually want to buy.

The Bottom Line

None of these examples involve building a custom supercomputer. They are practical, targeted solutions to common business problems. They take existing tools and apply them to specific workflows.

The biggest risk for a 20-person company right now is not that a computer will take your jobs. The biggest risk is that your competitor will figure out how to do the same work in half the time, and you will be left trying to compete with a manual process.

Inertia is a powerful force. It is easy to keep doing things the way you have always done them. But the companies that figure out how to apply these tools to their daily operations are going to have a massive advantage over the next few years.

If any of this sounds like your business, I would be happy to talk through what is realistic for your situation. No pressure, just a conversation about where your bottlenecks are and whether this technology can help clear them out.