AI is not always the answer: The AI Feature Trap That Can Kill Early-Stage Companies – Part 2

In a recent blog titled, When Early-Stage Companies Should Actually Use AI (It’s Rarer Than You Think), I mentioned seeing a mysterious farm implement at the Shelburne Museum in Vermont. A number of readers questioned the connection to AI. Let me explain. 

Now, that museum is one of my favorite places to go. Walk into one of the buildings on the expansive grounds and you’ll find yourself staring at a collection of mysterious farm implements. Interesting, well-crafted tools that clearly served important purposes—but nobody alive remembers what those purposes were. Curators can guess based on the materials and construction, but the actual utility? Lost to time.

While there’s a pretty long leap from the bucolic scenery of rural Vermont to artificial intelligence, the notion of unclear use is exactly how many early-stage companies are approaching AI features today.

“We need AI in our product to stay competitive,” they announce, like archaeologists picking up an ancient tool and declaring it must be important because it looks sophisticated. They’re so focused on having the trendy feature that they forget the most fundamental question every early-stage company should be asking: “Will this actually help our users accomplish what they’re trying to do?” (And maybe even more importantly, “will this help us do that better than the companies we are competing against?”)

With companies spending an estimated $200 billion globally on AI initiatives in 2024, early-stage companies feel pressure to add AI features to their products—not necessarily because users are demanding them, but because they think investors, competitors, or the market expects them.

The Feature Trap That Threatens Product Focus

Here’s the uncomfortable truth most early-stage companies need to hear: Your users might not care about your AI features.

While established companies can afford to experiment with AI features for differentiation, early-stage companies operate under entirely different constraints. You’re not optimizing an existing product with proven market fit—you’re fighting to build something people actually want to use and pay for. And unless AI is integral to the functionality it may well be a waste of precious resources. 

I see this constantly in product roadmaps:

“Let’s add AI-powered recommendations to our dashboard!” (Do your users actually struggle with finding relevant content, or do you just want to sound more sophisticated?)

“We should include machine learning insights in our reports!” (Are users asking for more insights, or are they asking for simpler, clearer information?)

“Everyone’s talking about AI assistants for our industry!” (Everyone’s also talking about basic functionality your product still doesn’t have.)

This approach—what I call “AI feature theater”—leads to sophisticated-sounding capabilities that users ignore, while the core user experience problems stay unsolved.

The User Value Test Every Product Should Pass

Before you even consider AI features, ask yourself: “What problem are our users actually trying to solve, how are we solving it differently, and would AI genuinely help them solve it better?”

Can they accomplish their goal with your current features? Focus on that first.

Are they struggling with a specific task that AI could meaningfully improve? Prove it with user research.

Are they asking for AI, or are they asking for better outcomes that you think AI might deliver? (Bonus quote: “If I asked my customers what they wanted they’d have asked for faster horses” -Henry Ford.)

The fundamental principle here is that users don’t buy features — they buy outcomes (well, yes, features are fun, but outcomes are what matter). Your constraint isn’t that you lack sophisticated algorithms; it’s that you need to prove your product delivers value that people will consistently pay for.

The Real Cost of Feature Distraction

Every early-stage company has the same limiting resource: development capacity and user attention. When you choose to build AI features, you’re choosing not to build something else that might be more critical to product-market fit.

That AI feature project means:

• Your developers aren’t building core functionality that fills gaps in the market
• Your UX is getting more complex instead of simpler and more intuitive
• Your team isn’t focused on the fundamental user problems that determine adoption
• Your limited resources are funding impressive demos instead of user value

The opportunity cost isn’t just the development time — it’s also the go-to-market momentum you lose while building features that don’t drive adoption.

Investor-ready framing tip: If you’re resisting the urge to build unnecessary AI features, that’s not a weakness, it’s product discipline. Communicate how your team is validating use cases, testing AI’s ROI on user outcomes, and only building where it creates defensible value. That sends a smarter signal to investors than simply caving to pressure to AI-ify your product.

What Users Actually Want Instead

Instead of asking “How can we add AI to our product?” early-stage companies should be laser-focused on user fundamentals:

• Core Functionality: Does your product reliably do the basic job users hired it for? Can they accomplish their primary task without friction or confusion?
• User Experience: Is your product intuitive and fast? Do users get value within minutes of starting, or do they have to learn complex workflows?
• Real Problems: What are users actually complaining about? What causes them to churn? What prevents them from getting the outcome they want?
• Clear Value: Can new users immediately understand why your product is worth their time and money?

These aren’t necessarily AI problems. These are product problems that require user research, design thinking, and disciplined execution.

The “Shiny Feature” Reality Check

Here’s a framework that I use to put user (and by extension, competitive) value first:

Step 1: Identify user struggles, not feature gaps. What specific tasks do users find difficult, time-consuming, or frustrating? Where do they get stuck or give up?

Step 2: Apply the simplicity test. For each user problem, what’s the simplest possible solution? Can better design, clearer information, or streamlined workflow solve this without AI?

Step 3: Measure user impact, not feature adoption. Will solving this problem help users achieve their goals faster, cheaper, or more effectively? Can you quantify the improvement?

Step 4: Count the complexity cost. Every AI feature adds interface complexity, user education burden, and potential failure points. Is the user benefit worth the added complexity?

If you can’t clearly show that an AI feature will meaningfully improve user outcomes—not just make your product sound more impressive—you shouldn’t build it. 

The AI Features That Actually Matter

Let me be clear: if it truly makes a difference, then you have a reason to add AI. Not all AI features are vanity projects. Some genuinely solve user problems that simpler approaches can’t address:

• Automating tedious work: If users spend significant time on repetitive tasks that AI can handle better, faster, or more accurately than humans.
• Handling complexity users can’t: If users need to process large amounts of data or make decisions with many variables that overwhelm human cognitive capacity.
• Personalizing at impossible scale: If users need customized experiences that would be impractical to deliver manually.

But notice the pattern—these are all cases where AI solves a real user problem, not where AI makes the product more impressive to talk about.

When AI Features Become User Problems

Even well-intentioned AI features can backfire if they’re not implemented with user needs first:

• The “Black Box” Problem: Users don’t understand why the AI made certain recommendations, reducing their trust in the system.
• The “Almost Right” Problem: AI that’s 85% accurate feels worse to users than a simple tool they can control completely.
• The “Solution Looking for a Problem” Problem: AI features that solve problems users didn’t know they had, creating confusion instead of value.
• The “Complexity Creep” Problem: Each AI feature adds menu items, settings, and concepts that make the product harder to learn and use.

Remember: users don’t care how sophisticated your algorithms are. They care about whether your product helps them accomplish their goals efficiently and reliably.

The Bottom Line: Users First, Features Second

Remember those mysterious farm implements in the Shelburne Museum? They were undoubtedly useful tools in their time, but without understanding the specific problems they solved and having the right context for using them, they’re just expensive curiosities gathering dust.

Don’t let your AI features become digital curiosities that impress demo audiences but confuse actual users.

The goal isn’t to have sophisticated-sounding features—it’s to build a product that users love enough to pay for and recommend to others. Focus on understanding user problems first. Build the simplest solutions that actually solve those problems. Prove that people will pay for the outcomes you deliver.