If you were to spend a week reading AI startup announcements and you start to notice something. A lot of them are building the same things. Another sales copilot. Another coding assistant. Another support chatbot.
I get why. Those markets are big, easy to explain to investors and full of customers who already understand that kind of software. The money is there too. Stanford’s 2025 AI Index found that U.S. private AI investment reached $109.1 billion in 2024, and 78% of organizations said they were using AI in some form.
But what I kept coming back to is that some of the best AI opportunities right now aren’t in the crowded lanes. They’re in industries most founders never think about, where important work still runs on spreadsheets, PDFs and very long email threads.
Look for the Spreadsheet Industries
If you want to find these markets, start with one question: where does valuable expertise live inside terrible software?
Once you start looking, you’ll see it everywhere. Environmental groups, agricultural extension programs, community health organizations, local governments, professional associations, small research labs. The people in these fields know an enormous amount. Their tools just never caught up.
The tell is usually a workflow that only works because one person holds it together. The member list lives in one spreadsheet, events live on another platform, field data sits in a shared drive and reports get rebuilt from scratch every grant cycle.
How to Spot a Problem AI Can Actually Fix
Not every messy workflow needs AI. Some just need a decent database. Be honest about that, because customers in these industries have been oversold before, and they remember.
The problems worth chasing tend to share three traits. The work is repetitive but still needs judgment, like sorting field reports or matching people to opportunities. The information already exists but is scattered across formats and tools. And the cost of the mess is real: missed grants, duplicated effort, staff giving up their evenings to reporting.
When all three are true, AI can do something genuinely useful. Better yet, the people using it will feel the difference in the first week.
Start With the Workflow, Not the Model
The most common mistake I see is founders picking the technology first. They fall for a model, then go hunting for a problem it can solve. In specialized industries, that’s backwards.
Start by learning how the work actually happens. Who touches the data? What does someone do on a Monday morning? What gets checked twice because it went wrong once? Which reports do funders read, and which ones does nobody read at all?
Once you know that, picking the model gets a lot easier. Honestly, it also matters less than most people think.
Spend Real Time With Domain Experts
You can’t learn an industry from a market map. You learn it by sitting with the people who do the work and letting them tell you where your idea is wrong.
My co-founder, Dr. Tiara Moore, is an environmental ecologist who founded Black In Marine Science. A lot of what we built came from that world: scientists and community leaders who knew exactly what was broken and weren’t shy about saying so. That kind of input shapes a product in ways no survey ever will.
Experts also keep you honest about what matters. Engineers love features. Experts care whether the data is right, whether it’s credited to the right people and whether it’ll hold up in front of a funder.
Support Judgment, Don’t Replace It
In specialized fields, people’s judgment is the product. Think of an ecologist reading a water sample, a program officer reviewing a grant or a planner weighing a community’s concerns. AI that tries to take over those calls usually gets rejected, and frankly, it should.
The better role is support. Let AI organize the information, pull scattered pieces together, flag patterns and write the first draft. Then let the expert decide.
That approach is safer. It’s also easier to sell, because you’re making skilled people faster instead of telling them they’re replaceable.
Build for Small Budgets and Small Teams
Underserved industries are underserved for a reason. Budgets are tight and there’s often no IT department. The person evaluating your product might also be running payroll and setting up an event this weekend.
That forces discipline, which turns out to be a gift. Make onboarding take an afternoon, not a quarter. Price it so a small organization can say yes without a board vote, and replace tools they already pay for instead of adding one more login.
If your product works for a five-person nonprofit, it’ll work for a lot of other customers too.
Trust Is the Real Onboarding
Here’s what founders coming from crowded markets sometimes miss. In tight-knit industries, people talk. One bad experience travels fast, and one trusted referral can open dozens of doors.
So you have to build trust on purpose. Be clear about what the AI does and doesn’t do. Show where information comes from, and let customers keep control of their own data.
It’s slower than a growth hack. But it compounds.
Environmental Organizations Show What’s Possible
Environmental work is a good example of the wider pattern. According to Cause IQ, there are more than 34,000 environmental organizations in the U.S., bringing in over $29 billion a year and employing nearly 140,000 people. That’s a serious market, and most of it runs on fragmented tools.
These groups track erosion, water quality, wildlife, restoration and community engagement. They hold knowledge that governments, businesses and the public need. Yet a lot of it sits in reports, spreadsheets and disconnected systems that make it hard to share or act on.
That gap is why we started OJAI: one place for an organization’s members, maps, resources and impact, built around how these groups actually work. And environmental organizations aren’t unique. Swap in agriculture, public health, local government or trade associations and you’ll hear a very similar story.
The Model Isn’t the Moat
Models keep getting better and cheaper every few months. If your only edge is which model you picked, someone will match it soon.
What’s much harder to copy is everything around the model. Knowing the workflow inside and out. Having real relationships with the experts who shape the field. Earning trust from customers who don’t hand it out easily, in a market most founders and investors haven’t bothered to learn.
That’s the opportunity. The next great AI companies may not come from the flashiest models. They’ll come from founders willing to go where the work is messy, listen longer than feels comfortable and build something people in overlooked industries actually want to use.
