By Vin Mitty, PhD, Sr. Director of Data Science and AI at LegalShield (PPLSI)
If you look at typical AI playbooks, you’ll find the same advice everywhere:
“Centralize your data,”
“Hire data scientists early,”
“Build a proprietary roadmap.”
It sounds impressive, but most of this advice is meant for large companies with big budgets and plenty of room for mistakes. Small businesses can’t afford that. Every dollar and every hour must count right away. Following the big-company approach doesn’t just waste time; it can actually harm your business. Here’s why most AI advice doesn’t fit your needs, and what really works instead.
The Strategy Trap
Small businesses don’t need a “Grand AI Vision.” They need clarity on the one or two decisions that actually move the needle.
Starting with strategy sounds good, but it often leads to overthinking. I often see leaders arguing about which LLM to use instead of solving the real problem in front of them. Instead of asking, “How do we use AI?” try asking, “Where are we always guessing today?”
It might be:
- Which leads are actually worth a phone call?
- Which customers are about to walk out the door?
- Which marketing spend is actually hitting, and which is just noise?
If AI doesn’t help you make these decisions faster or with more confidence, hold off for now.
The “Perfect Data” Myth
This is what keeps teams stuck for years. Data quality is important, but waiting for perfect data will only hold you back.
Small businesses almost always have enough data to start learning something useful. It’s often messy and incomplete, and that’s okay. The real issue is usually not the quality of the data, but whether it’s relevant. You don’t need more data; you need the right signals connected to a decision that someone is responsible for.
Borrow the Mindset, Not the Machinery
Big tech companies build AI platforms. What you need is AI that gives you leverage. These are two very different goals.
Large companies focus on scaling up. You need to focus on what matters most. Instead of copying their playbook, take their way of thinking:
- Start narrow: Solve one nagging pain point.
- Measure fast: Did this actually save time or make money this month?
- Drop what doesn’t work: If something isn’t useful, stop doing it.
- Only expand when the value is clear.
Beware of the “Interesting” Pilot
This is the costliest mistake a small business can make. You launch an AI pilot, people say the dashboard is “interesting,” but nothing changes. Workflows stay the same, and decisions don’t improve.
That’s what’s called Innovation Theater, and small businesses can’t afford it. AI fails in these settings because no one asks the tough question at the start: “Who will use this, and what will they do differently on Monday morning?”
The “Boring” Win is the Real Win
The AI projects that last in small companies are usually not flashy. They may seem boring, but they are useful.
- Triage: Prioritizing customer outreach so you stop wasting time on dead ends.
- Flagging: Identifying high-risk accounts before they churn.
- Forecasting: Planning staffing just enough so you aren’t overpaying for idle time.
The Bottom Line
Not every problem needs AI. Sometimes a simple rule, a better process, or a clear spreadsheet is the best solution. AI should prove its value before you use it.
Small businesses succeed by being practical, not by chasing the latest trends. The goal isn’t to be “cutting-edge”; it’s to be useful. When you focus on helping people make better decisions instead of replacing them, AI becomes a tool that helps your business grow.
Editor’s note:
This article is a guest contribution by Vin Mitty, PhD, a data and AI leader with over 15 years of experience advising organizations on practical AI adoption and decision-making. The views expressed are those of the author and do not necessarily reflect the views of Industry Examiner or its editors. Guest contributions may be edited for clarity, style, and length.
