Starting the AI Journey: How Small Businesses Can Get Microsoft Copilot Right

For many small and midsize businesses, AI feels a little like cloud computing did a decade ago. Everyone is talking about it. Some organizations are already using it, and others are worried they’ll be left behind.

The challenge is that most discussions about AI start with the technology instead of the business. Someone says “we need to implement AI,” licenses get purchased, and everyone waits for something good to happen.

That’s backwards.

The organizations seeing the most success with tools like Microsoft Copilot rarely chase the latest features. They begin with a much simpler question: what problem are we actually trying to solve? Here’s how to answer that well, and what to put in place before you spend a dollar on licenses.

Where Should a Small Business Actually Start With AI?

Start with a business problem you can name and measure, not with a license purchase. Look for the repetitive work where your team loses hours every week, because those tasks are familiar, measurable, and low risk.

A common mistake is buying licenses before identifying use cases. Instead, look at where your team is losing time every week. Identifying use cases first also lets you set measurable goals, so you can tell later whether the project actually worked.

Provisioning a few dozen licenses and telling people “use Copilot to save time now” almost always leads to failure. People either don’t use it at all, or they don’t use it on the low-effort, high-return repetitive processes where it actually pays off.

Many SMBs wear multiple hats every day. Owners step into operations, managers become project coordinators, finance teams become analysts, and salespeople become marketers. AI can help reduce that burden, but only when it’s introduced thoughtfully. Nobody actually wants to “implement AI.” What you want is to reclaim time, reduce friction, and help your people focus on work that creates value.

If you look at your business and its processes, you might discover a few familiar time sinks.

These are often the best starting points because they’re familiar, measurable, and low risk. You also already have processes for these tasks, and concrete results to compare against. AI delivers the most value when it assists with repetitive work, not when it’s expected to make important business decisions. AI initiatives are most successful when they’re easing a process that already exists. Employees get frustrated when they’re trying to make up a process on the fly for AI to understand.

Think of it as a force multiplier for your existing team, not a replacement for it. At the beginning of the AI journey, you can think of AI as an assistant or intern that’s phenomenal at handling prescribed or repetitive tasks but benefits from a more experienced employee (that’s you!) verifying and fine-tuning the end results.

As much as many tech CEOs like to paint a picture of AI agents handling every aspect of your business automatically, that’s not step one of the journey, and in many cases it shouldn’t be a step at all in your journey.

Why Do So Many AI Rollouts Fail?

Usually for strategic rather than technical reasons. MIT’s 2025 research found that about 95% of generative AI pilots produced no measurable impact on the bottom line, and the researchers pointed to a learning and integration gap rather than the quality of the models.

The MIT researchers were blunt about the cause. The problem wasn’t the quality of the AI models. It was the learning gap, for both the tools and the organizations using them. Companies bolted AI onto processes nobody had examined and then wondered why nothing changed.

The broader data tells the same story. Goldman Sachs found that while 76% of small businesses report using AI, only 14% say it’s fully embedded in how they actually operate. The gap between trying AI and running on it is where most of the disappointment lives.

There’s one more finding worth sitting with. MIT’s researchers noted that efforts pairing internal knowledge with outside expertise succeeded substantially more often than teams going it alone. Asking for help turns out to be one of the few variables that consistently moves the odds in your favor.

Is Your Data Ready for AI?

Before AI can deliver real business value, it needs access to information that is organized, correctly permissioned, and trustworthy. Gartner predicts organizations will abandon 60% of AI projects that are not supported by AI-ready data.

That means taking an honest look at your environment before you turn anything on. The questions aren’t complicated, but the answers are often uncomfortable.

Many organizations discover that preparing for AI also helps improve existing operations. Cleaning up shared drives, organizing SharePoint sites, reviewing permissions, and standardizing file storage aren’t glamorous projects, but they dramatically improve the quality of AI-generated results. They also make the business run better whether or not you ever buy a license.

A useful rule of thumb is simple.

Another way to think about it: if we hired someone today, would they be able to find critical information easily? If you hired someone and pointed them at a single folder with a million “New Document (3).doc” files and no structure or descriptions, they wouldn’t be able to find anything quickly. Copilot and other AI solutions are similar. They may be able to ingest all the content, but without proper structure (and security!), there’s no telling what may turn up.

That parenthetical about security deserves its own moment, because this is where a lot of quiet risk hides. Copilot works within each user’s existing Microsoft 365 permissions and doesn’t hand out new access. That’s reassuring, but it comes with a catch: if permissions are looser than you intended, Copilot makes it effortless for someone to surface content they could technically already reach but never would have found on their own. Microsoft’s own guidance is to review sharing settings and run data access governance reports to find and fix oversharing before rolling Copilot out broadly.

How Many People Should You Start With?

A small pilot group, not the whole company. Choose a varied handful of people from different departments and levels who have clearly defined workflows.

One of the biggest misconceptions about AI adoption is that it requires a company-wide rollout. In reality, the opposite is often true.

The most successful organizations start with a small pilot group. You want a varied group of people from across departments and levels who have clearly defined workflows. If you go with a single team, they may optimize their processes beautifully, and that success won’t translate to the rest of the company. If you hand licenses to everyone at once, you can’t guide the journey effectively, and many people will just assume someone else will figure out how to use it.

The objective is not to prove that AI can do everything. The objective is to identify a few areas where it can make a meaningful difference.

After a handful of people can speak to how much time it saved them drafting emails, how it accelerated report creation from complex data, or how it made meeting follow-up 100x simpler, it becomes much easier to get the rest of the company to buy in and be engaged.

How Do You Know If AI Is Actually Working?

Define success before you begin, and measure business outcomes rather than AI activity. Time saved, friction removed, and faster response times tell you something. Prompt counts do not.

This may be the most important step in the entire journey. Too many organizations invest in AI without deciding how they’ll measure success.

If success isn’t defined upfront, it becomes impossible to know whether the initiative is working. People may say they’re using it, and you may see people using it, but how do you know if it’s making an impact, or whether your team needs more guidance, without knowing the expected result?

Instead, establish simple business-focused metrics. The ones worth tracking are business metrics, and the ones that mislead tend to be the easiest to pull from a dashboard.

Activity metrics measure motion. Business metrics measure whether the motion was worth anything.

Notice there’s no mention of prompts generated, conversations completed, or AI usage statistics. Activity metrics like that only serve to generate more cost and drag down efficiency across your organization. Focusing on the wrong numbers can lead to employees spending two hours getting AI to generate a result they could have done themselves, in their own voice, in ten minutes. Sometimes automating a process takes more time than doing it manually, and you want to be able to see that clearly.

What leadership should care about is the overall outcome. Did people work more efficiently? Did friction decrease? Did the business benefit?

These are the measurements that matter.

Will AI Replace My Employees?

No, and treating it that way is one of the faster routes to a failed rollout. AI works best as a force multiplier for the team you already have, handling the repetitive work while your people focus on context, strategy, and judgment.

Technology adoption is ultimately a people project. The organizations that extract the greatest value from AI don’t simply enable the software and hope for the best.

They provide practical examples and opportunities to share success. Your company should provide clear expectations about responsible use, and guidance on reviewing (and being responsible for) AI output. You don’t give a carpenter a nail gun for the first time and just send him on his way.

The demand for that support is well documented. In a 2026 survey of US small business decision-makers, 70% said they need more training to use AI effectively, even though 86% described themselves as comfortable with it. Comfort and capability aren’t the same thing, and the gap between them is where good guidance pays off.

It’s also worth saying plainly that the hesitation many owners feel is reasonable. When small businesses are asked what holds them back, the leading answers are data security concerns and uncertainty about AI’s accuracy. Those are the instincts of someone who understands their name is on the work, not the signs of a business that’s behind the curve. The right response is to build a process that addresses those concerns rather than waving them off.

Employees need to understand that AI is a starting point, not a final answer. Human judgment, industry expertise, customer relationships, and critical thinking remain essential. The best outcomes occur when AI handles the repetitive work while humans focus on context, strategy, and decision-making.

And AI will not replace the relationship with your customers. That’s not what they do business with you for, and that’s a context and connection that can’t be easily replicated in an AI model.

Should You Treat AI as a Project or a Journey?

Treat it as a journey. There’s no finish line with AI adoption, because it’s a capability that grows over time rather than a box you check.

The businesses that succeed are the ones that remain focused on solving problems, improving workflows, and helping their people work more effectively. They don’t try to transform everything overnight. They start with a few practical wins.

The organizations that begin now, even modestly, will develop the experience, confidence, and internal knowledge needed to take advantage of future opportunities as AI continues to evolve. Once you’ve solved some reporting and analysis tasks and gotten more familiar with prompting, you can take that experience and thought process to other parts of your business that eat up tons of time with repetitive, tedious tasks.

The question isn’t whether AI will become part of business operations. The question is whether your organization approaches it intentionally or reacts to it later. Starting small, measuring outcomes, and focusing on your people is often the best path forward.

If any of the groundwork sounds like more than you want to take on alone, that’s a reasonable place to ask for help. Bennett/Porter has spent more than 35 years helping Portland-area businesses get their technology in order, and much of the AI readiness work is the unglamorous kind we do every day: organizing information, reviewing Microsoft 365 permissions, protecting sensitive files, and making sure the foundation is solid before anything gets built on top of it.

Common Questions About Getting Started With AI