If you’re trying to make sense of what AI can actually do for your business, you’re not alone. Every week, there’s another think piece, listicle, or research report telling entrepreneurs and business leaders how to leverage the latest wave of generative AI to scale. Most are written for the Fortune 500—not for teams still figuring out which SaaS tool will even talk to their QuickBooks.
This week, I came across a new Harvard Business Review article co-authored by one of my all-time favorite professors, Jeff Shay: How Ambitious Entrepreneurs Can Use AI to Scale Their Startups. Jeff was my professor back when I was earning my B.S. in Business Administration at the University of Montana, and we’ve stayed in touch ever since. He’s always had the right advice at the right time—like when he told me (long before it was cool) that “business plans are dead,” and pointed me toward the business model canvas instead.
His latest piece is aimed at founders and business leaders just starting to grasp the power of AI—laying out the core frameworks for where to look for automation and efficiency gains. It’s accessible, practical, and perfect for anyone new to the space. If you’re early in your AI journey, you’ll find it valuable.
But here’s where I’d build on it—especially for small and mid-sized businesses.
The real challenge most companies will face when implementing AI isn’t the technology itself. Tools and APIs keep getting easier. Multi-agent orchestration? Deep integration? These will be commoditized and packaged for the masses in no time—or the chosen AI platform will set itself up for you.
The real bottleneck is data.
If your data is scattered, unstructured, or just plain messy (think Excel files on someone’s hard drive), no amount of AI magic will save you. What most SMBs need isn’t just another AI tool—they need a new mindset around data. Data wrangling, data literacy, and an organizational culture that sees data as a business asset—these are the competitive advantages of the next decade.
I’ve seen it firsthand: from my early work at Adobe with Target and Recommendations, to wrestling Watson into commerce platforms, and now at Yottaa, the teams that win are the ones who invest in the “unsexy” stuff—cleaning, structuring, and understanding their data.
If you’re leading an SMB and thinking about how you’ll take advantage of AI, spend less time shopping for the flashiest new LLM and more time figuring out what questions you want answered and whether you have the data to get there. I’m a big fan of the Goal–Question–Metric approach: start with your business strategy, define the questions that reveal whether you’re making progress, and let the metrics fall out of that process. Those metrics will tell you what data you need—and whether it’s in a system an AI can actually use.
We’re close to seeing sophisticated prediction and attribution models made accessible to SMBs, but data science is still a field of trial and error. There’s a lot of potential math out there that could help a business leader predict outcomes or attribute effort to results—but it will require outside help. Over the next few years, that will likely come in the form of consultancies and contractors, until the discipline of data science becomes more common in SMBs. Given the typical IT adoption curve, it might take longer. In the meantime, get your Excel files organized and at least onto a shared drive.
Kudos to Jeff and the other authors for helping more entrepreneurs get on the field. The next level is building that data muscle. (And if you’re curious about Jeff’s take, check out his LinkedIn post here—)
