Last week, I wrote about the AI feature trap—how early-stage companies add AI to their products not because users need it, but because it sounds sophisticated. The farm implement metaphor from the Shelburne Museum struck a nerve: too many companies are picking up impressive-looking tools without understanding what problems they actually solve.
But based on some emails I got (such language!), I feel compelled to make this statement: if you are building a native AI application, then you are not the companies I am talking about! AI is its own thing and there are myriad applications than should and are being built to take advantage of the incredible promise that AI represents. I was referring to companies who are seeking to add AI to existing applications without a clear reason to do so.
But here’s the thing: there are times when early-stage companies should embrace AI. Not just for product features that do make sense, but also for their operations. Two very specific scenarios where avoiding AI could actually hurt your competitive position.
The difference between smart AI adoption and expensive distraction comes down to one question: Are you solving a business constraint that threatens your ability to compete and survive, or are you trying to make your operations sound more impressive than they actually need to be?
Exception #1: AI Is Your Core Value Proposition (aka “duh!”)
If you’re building an AI company—where machine learning isn’t just a feature but the fundamental reason customers pay you—then obviously AI isn’t optional. It’s your entire business model.
But let’s be honest about whether you’re actually in this category. Slapping “AI-powered” on your marketing materials doesn’t make you an AI company. Using a chatbot for customer service doesn’t make you an AI company. Even incorporating some machine learning for internal optimization doesn’t necessarily make you an AI company.
You’re an AI company if removing the AI component would eliminate the primary reason customers choose you over competitors. If you stripped away all the algorithms and machine learning, would customers still have a compelling reason to pay you instead of using alternatives?
If the answer is no—if your competitive advantage disappears without AI—then you should be investing heavily in it. If the answer is yes, then you’re probably not really an AI company, and you should be very careful about where else you deploy AI resources.
Exception #2: You Have an Operational Constraint That Could Kill You
This is where things get interesting for most early-stage companies. Sometimes you face specific operational problems that threaten your ability to reach profitability or compete effectively, and those problems genuinely require AI to solve.
Notice I said “threaten your ability to compete.” Not “would be nice to optimize” or “could make us 10% more efficient.” We’re talking about constraints that put you at such a disadvantage that customers will choose competitors, or costs will spiral beyond what your unit economics can handle. Let’s dive into this a bit more.
Supply Chain: When Manual Processes Can’t Keep Up
The numbers from established companies tell a compelling story. Early adopters of AI-enabled supply chain management have reduced logistics costs by 15%, improved inventory levels by 35%, and enhanced service levels by 65%. But these results come from companies that already had the scale and complexity to justify the investment.
For early-stage companies, AI in supply chain makes sense only when:
You’re in a business where inventory mistakes or delivery delays directly cost you customers who won’t give you a second chance. Maybe you’re competing against much larger players who can afford stockouts, but you can’t.
You’ve already optimized everything simple—seasonal planning, supplier relationships, basic inventory management—but you’re still losing customers or burning cash because manual processes can’t handle the variability in your business.
You have enough clean historical data (usually 12-18 months minimum) to actually train useful models. Most early-stage companies discover their data is messier and less predictive than they assumed.
Healthcare: When Administrative Chaos Blocks Growth
The healthcare AI market has grown 3,000% from 2016 to 2024, with 94% of healthcare companies now using AI somewhere in their operations. But this growth is primarily among established organizations with existing patient volumes and operational complexity.
For early-stage healthcare companies, AI makes sense when:
Manual scheduling and administrative processes are creating patient experience problems that directly impact retention and word-of-mouth growth. If no-shows and scheduling conflicts are killing your unit economics, and basic reminder systems aren’t solving it.
The administrative burden is preventing your clinical staff from focusing on patient care, limiting your ability to scale without proportionally increasing overhead costs.
You’re competing against larger practices that can absorb inefficiencies you can’t afford. If manual processes put you at a competitive disadvantage in patient experience or cost structure.
Healthcare organizations implementing AI-powered scheduling have achieved up to 50% reductions in no-show rates, but only after reaching sufficient scale to justify the complexity and cost.
And of course there are other real applications for AI in healthcare: live AI scribing. Procedure coding. Billing. And there are also many clinical applications that are making our lives safer and healthier. They’re all awesome uses of AI.
The False Exception: Making Working Operations “Sexier” (aka Lipstick on a Pig)
Here’s where most early-stage companies get tricked. This is when your operations are already working fine, but you want to add AI to make them sound more sophisticated, scalable, or fundable.
I see this a lot:
“Our inventory management works with spreadsheets and experience, but machine learning sounds more professional for investors.”
“We handle customer service well with our team, but an AI system would make us seem more scalable.”
“Our scheduling works fine, but AI optimization would look better in our pitch deck.”
Here’s the brutal test: If you removed the AI tomorrow and went back to your previous processes, would your business performance actually suffer, or would operations continue just fine?
If operations would continue just fine, you’re not solving a business constraint—you’re solving an ego problem. And for early-stage companies, ego problems are expensive distractions from the real work of building competitive advantages that customers actually care about.
The “Operational Theater” Test:
- Are you adding AI because it meaningfully improves your competitive position, or because it makes your operations sound more impressive?
- Is this solving a constraint that limits your ability to serve customers or compete on cost, or are you hoping to impress stakeholders?
- Would customers notice if you went back to manual processes, or would they get the same outcomes either way?
Most early-stage companies discover they’re using AI to solve the wrong operational problems. Instead of making working processes “sexier,” they should focus on improving customer acquisition, perfecting their core service delivery, or optimizing the fundamentals that actually drive profitability.
Working operations don’t need AI. They need customers, revenue, and competitive advantages that matter to users.
The Operational AI Framework (Use Sparingly)
If you think you might actually need AI for operations, here’s how to approach it without getting distracted from building your core business:
Step 1: Prove the constraint is real and costly. Can you quantify exactly how this operational problem is limiting growth, increasing costs, or hurting competitiveness? “Better insights would be nice” doesn’t qualify.
Step 2: Exhaust the simple solutions first. What’s the most straightforward way to address this constraint? Can you hire someone? Implement a basic process? Use existing tools? Only move to AI if simpler approaches genuinely won’t work.
Step 3: Check your data reality. Do you have enough clean, relevant operational data to train useful models? Be brutally honest—most early-stage companies overestimate both data quality and the predictive value of their historical information.
Step 4: Calculate total cost of complexity. Include implementation time, ongoing maintenance, team distraction, and the opportunity cost of not working on customer-facing improvements. What else could your team accomplish with that energy?
Step 5: Define success in competitive terms. How will you know the AI is working? What operational metrics need to improve, and by how much, to give you a real competitive advantage?
Step 6: Plan for the maintenance reality. AI systems need constant care. Do you have the organizational capacity to maintain and optimize these systems while also building your core business and serving customers?
When Not to Do It (Most of the Time)
Even if you meet the criteria above, there are still situations where early-stage companies should avoid operational AI:
If you’re less than 12 months from needing to hit profitability or raise funding, focus on proven fundamentals instead. AI projects are inherently unpredictable and could distract from more reliable paths to your milestones.
If implementing AI would consume more than 20% of your team’s capacity for more than three months, the opportunity cost is probably too high.
If you can’t explain the business case to a skeptical customer (not just an investor) in under two minutes, you’re probably solving the wrong problem.
The Bottom Line: Operations Follow Strategy (aka avoid Ready-Fire-Aim)
AI can be a powerful operational tool for early-stage companies—but only in very specific circumstances. The key is being brutally honest about whether you’re solving a constraint that affects your ability to compete and serve customers, or chasing a solution that makes your operations sound more sophisticated than they need to be.
Most early-stage companies find their real operational constraints are much simpler: they need better customer development processes, clearer value propositions, more efficient customer acquisition, or streamlined service delivery. These aren’t AI problems—they’re execution problems that require focus, discipline, and customer insight.
But for the rare early-stage company facing a genuine operational constraint that threatens competitiveness, and where simpler solutions won’t work, AI can be transformative. The trick is knowing the difference between operational necessity and operational vanity.
Remember those mysterious farm implements? They were useful because they solved specific, important problems for the people who used them. Your operational AI should do the same—solve real constraints that matter to your ability to compete and grow.
Everything else is just expensive curiosity.