AI ISN’T JUST FOR TECH GIANTS: HOW TO SPOT YOUR FIRST HIGH-IMPACT AI OPPORTUNITY
There’s a lot of buzz and hype around AI from large tech companies, but it’s also for SMBs: the biggest AI wins for small and mid-sized businesses are often the smallest projects.
If you’re a manager suddenly responsible for “figuring out AI,” while still juggling your real job, you’re not alone. Many data, process, and AI initiatives land on the desk of already-busy SMB leaders without the budget or time to hire a full-time project manager.
The truth is you don’t need a big team or budget to get results with AI.
What AI Really Is (Without the Hype)
AI tools like ChatGPT or Claude don’t “think”—they generate one word at a time based on patterns in the data they were trained on. It’s just incredibly fast pattern-matching and really good probabilities of what the next best word is; that’s why it seems almost human in the responses.
So think of it as a very knowledgeable assistant available to you 24x7 that you can get help with regarding some common tasks, and you just have to know how to set it up.
The truth is: most “AI tools” are simply fancy front ends wrapped around public models like ChatGPT. You can access the same capabilities directly with a simple prompt and a $30 subscription to one of the models.
The trick isn’t buying AI—it’s knowing what problem to solve.
So where should you start with AI?
Start small. Get some success under your belt learning how to use it first. Think about:
· Repetitive work your team dreads
· Tasks you’d normally hand to an assistant
· Work that’s important but not strategic
Examples:
· Extracting totals from invoices and dropping them into spreadsheets
· Creating draft replies to repetitive client emails
· Predicting inventory needs using sales history
· Automatically summarizing meetings or research notes
These aren’t flashy—but they save time, reduce errors, and unlock momentum; in other words, get you comfortable working with the technology.
Think Like a Project Manager (Even If You’re Not One)
When you’re managing projects off the side of your desk, the most important skill is framing the problem clearly.
Here’s a simple 3-question guide I’ve used to launch dozens of data and AI projects:
1. What is the purpose? What problem are we solving?
Be specific. Instead of “Improve the time to respond to opportunities.” Say “Current email responses for requests for our products or services takes too long to respond and we’ve determined that it is costing us sales. A future desirable state would be a significant reduction in the response time, such as over half of what it takes now, with more detail, which we have determined would result in an increase in sales.”
2. How will we know it worked? What does done look like?
Specify the “done” state so that everyone agrees on what “done” looks like. That means than an independent observer can make a judgement call on whether the intended outcome or multiple outcomes were achieved. For example, current critical new sales request emails take 4 hours to respond. Reduce this time to 1 hour from receipt of request to send to requestor.
3. What’s our path to get there?
Lay out the approach to get from the starting point to the “done” point. You might find it works better if the steps are described in terms of completed outcomes as this helps to think through to the end state.
For example:
Step 1: Documented steps to respond to an email for new sales from the trigger (when the email has come in) to the steps to respond and then to the point at which the response is sent.
Step 2: Confirmation of time for each step and overall cumulative time for the current process for the specific type of email.
Step 3: Identification of steps most suitable for AI automation.
Step 4: Subscription service purchased and API obtained.
Step 5: Call made to API to simulate step and new time and outcome for particular step is achieved.
Step 6: Call embedded in Zapier / Make and outcome is verified.
Etc. Define all the steps to the end state.
Some Pro tips:
1. Write your purpose as two sentences: One describing today’s current problem and cost. One describing tomorrow’s improvement and value.
2. Don’t get too analytical about the done state because overly analytical minds will get mired and distracted in non-value-add over thinking.
3. Describe the steps in terms of done states, and all the way from beginning to end.
Quick Word on Privacy
Start with low-risk, internal data. Don’t feed customer or employee info into tools unless it has either been anonymized or you’re confident in how the data is handled such as on-premises only and not put out on the web.
Action Step: Your First AI Brainstorm
Take 10 minutes. Write down 3 repetitive tasks or repetitive processes in your business that:
· Burn time
· Involve simple decisions
· Could be improved with automated drafts or insights
That could be your starting point.
Ready to move from overwhelm to action?
If you’re a manager in an SMB and responsible for a data, process, or AI project—but can’t justify a PM or consulting firm—you don’t need to go it alone. I’m putting the final touches on a toolkit to help you kickstart your small AI projects with confidence—without the fluff; and I’ll make the link available here at the bottom of next Tuesday’s post.