The businesses that will derive the most benefit from sophisticated AI technologies are not those that are adopting them fastest. Rather, they are the ones willing to slow down.
The effective implementation and use of AI business solutions requires defining what the business actually needs, organizing its data appropriately to start taking on these tasks, and then carefully vetting the results over time. In other words, clarity, planning, and testing must come first.
For small business owners, this is good news. While it might look like major multinational corporations that are forging ahead with agentic AI have the advantage, it’s actually smaller entities like theirs that do. Since they are closer to their customers and have a better overall understanding of their operations, they can get clarity faster. Once they have that clarity, they can move quickly and effectively to incorporate AI.
For a detailed explanation of how today’s AI resources can benefit small businesses, as well as the practical steps to getting the most out of these tools, read on.
What AI can do for small businesses
In my experience, when most small business owners hear the word “AI,” their minds immediately go to complex automations or predictive analytics. In truth, however, the real benefits that AI brings are much smaller and quieter.
AI can handle scheduling and send follow-up emails. AI can take notes and provide meeting summaries so participants can focus their attention on the actual people involved. These are the sorts of tasks that AI handles well that would otherwise consume hours of your time each week.
Here’s an example from my own personal experience. While I have years of writing, podcast transcripts, and notes, I would probably never revisit that material ever again if I had to do it all by myself. There’s just too much. That’s why I built a custom Generative Pre-Trained Transformer (GPT) and trained it on this content. Now all I have to do is ask it what I’ve said about a given topic, and it will synthesize an answer from this reservoir of my previous work, leveraging my past effort and compounding the value I can derive from it.
For another example, one of my clients was burning hours writing, revising, and updating job descriptions whenever they had a role to fill, which not only slowed down the hiring process considerably but also introduced inconsistent language into the brand’s voice. We used AI to clean things up in minutes. It standardized the job descriptions according to the company’s official voice and generated new ones based on role requirements.
In short, AI can make your business leaner and more effective, but only if it is incorporated the right way. Here’s how.
Step #1: Define the problem
In practice, when many business leaders hear about a new AI solution, they sign up for it and figure out what to do with it only after the fact. This is backwards and wastes time and money.
Instead, the first step is to identify the pain points your business faces on a routine basis. Pick something specific to improve. For instance, many companies can benefit from real-time inventory capabilities or the ability to change prices on the fly in response to changing conditions on the ground. Another common area for improvement is tracking consumer behavior, which can help determine when and how to reach out to them most effectively.
The question should not be: “Look at this cool tool! What can it do?” The question should be: “What decision keeps coming up, and what would make it better?”
Step #2: Organize your data
Before you deploy an AI tool, it’s vital to organize your data. AI is particularly good at pattern recognition. If your data is messy, incomplete, or inconsistent, however, then it will find the wrong ones. Worse, analyses based on bad data can mislead you, steering you to make subpar decisions.
Many businesses, in their rush to implement AI, skip this step. But as the saying goes, “Garbage in, garbage out.” With AI, if you haven’t organized your data, then the garbage just comes out faster.
That’s why it pays to spend substantial time reviewing the current state of your databases and conducting an honest appraisal. Is the information accurate? Is it current? Is it structured in a way that AI can interpret?
You don’t need a flawless dataset to get started with AI. But correcting any obvious weaknesses will yield better results.
Step #3: Start small and validate before expanding
Another step many business leaders skip in their haste to adopt AI is the slow, gradual process of vetting AI’s performance, which can mean they don’t catch problems with the rollout early or at all. It also means that problems can multiply at the speed of AI.
Therefore, don’t try to automate everything at once. Pick just one workflow. Have the AI try out a task while the usual human staff member does as well. Compare the two, noting where the AI helps and where it falls short. Use your judgement and make the necessary adjustments. Try again.
Only start weaning human staff off the system when the AI’s performance becomes highly accurate and reliable. Even then, conduct continuous oversight and audits to ensure the AI continues to perform properly. Move on to the next thing only when you are confident that the first AI initiative is operating correctly.
Finally, embrace AI in the spirit of learning. This technology doesn’t require perfection to be useful. Indeed, if you wait for perfection, you’ll probably never get started using AI. All you truly need, however, is a specific use case and a willingness to learn as you go.
Get the most out of AI
Small business owners can derive many advantages from incorporating these next-generation assets into their operations. But don’t start with the technology. Start with a clear understanding of the business processes you want to improve and work backwards from there.
The companies that succeed with AI are not necessarily the ones with the biggest budgets. They are often the ones willing to slow the implementation process down.
Get clear. Be honest. Prepare as well as you can. And then just try.
Chris Hutchins is the Founder & CEO of Hutchins Data Strategy Consulting, and a nationally recognized leader in healthcare analytics and AI strategy, with more than 30 years of experience helping hospitals and healthcare organizations leverage data and technology to improve patient care. Over his career, he has held leadership roles at some of the nation’s most prestigious health systems, including Mass General and Northwell Health, where he guided large-scale analytics initiatives, data governance programs, and AI adoption strategies. Chris combines deep technical expertise with a human-centered approach, ensuring that technology serves patients, providers, and administrators alike.
Photo courtesy Getty Images for Unsplash+
