Product Market Fit in AI Products

You are an AI founder. You’ve poured your heart and soul into building an AI-powered tool. The tool seamlessly transforms complex scientific papers into comprehensible summaries in seconds. You are quite confident your product is going to revolutionize how scientific papers are read by researchers all over the world. You have a real shot at becoming the next big startup unicorn in the AI space. But after launching the product, crickets. Researchers aren’t flocking to use your product. No downloads, no paying customers, nothing but a deafening silence.

And oh, you have three sign-ups—you, your co-founder, and that friend from college you convinced to try it. So, what went wrong? How did your million-dollar idea suddenly turn into a deserted island no one wants to be a part of?

The answer: the monster called Product Market Fit (PMF).

Achieving Product Market Fit is very important for any product you wish to put in the market. It's not just important, it's necessary. Just so we are on the same page, product market fit can entail slightly different things depending on the nature of the product. However, while the specifics might differ, achieving PMF essentially boils down to this: your product solves a real problem for a well-defined customer base in a way that's both valuable and sets you apart from the competition.

Unfortunately, when it comes to AI applications, entrepreneurs often find themselves caught in the allure of the "dazzle and wow factor" rather than focusing on delivering real value and achieving product-market fit. No,  I’m not accusing AI entrepreneurs of being naive about the difference. In AI applications, the line between gimmicky technological showcase and PMF is often blurred by AI hype. This results in products that might impress with technical feats but fail to provide a concrete value proposition for a defined market, ultimately fizzling out after the wow factor loses its novelty.

When Can You Say You Have Product Market Fit?

As a founder, you've probably have to deal with PMF a few times, but if you haven't, let's get a healthy dose of what PMF is all about. So, before you can say an AI product has achieved product-market fit, there are a few variables that need to be in near-perfect synergy:

1. Real problem: The product must solve a problem that's both real and painful enough for the target audience. AI products, despite their sophistication, can miss the mark by addressing problems that either don't exist (a solution in search of a problem) or aren't painful enough to warrant an AI solution. Cue: Building an AI that flawlessly translates legal documents into another language. Sounds impressive on paper, but is it a solution to a real problem? Is the problem painful enough? Lawyers might be comfortable with existing free translation tools because they’d have to get human oversight anyway. Don't fall for the trap of building an AI product to solve a problem just because the current AI stack makes it easy to do so.
2. Well-defined customer base: A generic AI for everyone is unlikely to resonate. The product must address a well-defined and sizable market segment.
3. Valuable: Your AI needs to deliver demonstrable value to the customer. Does it save them time, and money, or improve efficiency? Does it do something they couldn't do on their own? Is the value worth the price?
4. Sets you apart: The AI landscape is crowded. What makes your product unique? Does it solve the problem better than existing solutions (human or otherwise)? Is it cheaper? Faster? Easier to use?

Ensuring your AI product ticks all the boxes is critical to success, but it isn’t exactly as easy to get right as it seems. This leads us to an incredibly important part of ensuring your product is market fit: discovery. This is the murky waters of product development where miscalculations can be very costly.

Embrace the Discovery Phase

Do you ever have that sinking feeling when you realize you've been watching a movie for hours and... you have absolutely no idea what’s going on? The plot meandered, the characters felt hollow, and the whole thing felt like a spectacular waste of two hours. There were pretty explosions, and the characters looked good in their costumes but there was zero emotional payoff!

That's the kind of risk you run when you launch an AI product without a solid discovery phase. You pour your heart and soul into building this impressive tool, but if you haven't taken the time to ensure that all key PMF variables align, you end up with a product that's all flash and no substance.

The discovery phase is where you get a good understanding of your target market, their pain points, challenges, and expectations. By thoroughly exploring the market and its needs, you can better assess whether your million-dollar AI solution truly addresses a genuine problem or is a solution in search of a problem.

The discovery phase includes activities like market research, user interviews, competitive analysis, and data gathering. It is basically where you gather all the insights to validate your assumptions, refine your product vision, and if necessary take the unfortunate but bold step of abandoning the idea.

Without doing this for your AI product, you risk creating a solution that misses the mark entirely, wasting resources on something nobody wants (developing an AI product is expensive), and failing to stand out in a crowded market. Discovery is the essential groundwork that ensures your AI product is built to solve real problems and deliver lasting value. Talking about lasting value, it's very important to build AI products with longevity in mind.

Building for Longevity: AI Products That Stand the Test of Time

Let’s explore things from the Generative AI perspective. The world of Generative AI is a fast-paced one. Take GPT-4, released in March 2023, followed by Claude 2 in July and Gemini in December. Now, in April 2024, Claude 3 shakes things up again, and the cycle continues. Each release brings significant advancements, but for developers who built on earlier versions of alternate models, these upgrades can be disruptive. Imagine pouring your efforts into a GPT-4 powered product, only to see the open-source Llama 3 emerge just weeks later. Llama 3 might achieve similar results for a fraction of the cost, putting your product's market fit – and bottom line – at risk. Can you truly say your product is well-positioned when competitors can offer practically the same functionality at a significantly lower price? This highlights the challenge of building on rapidly evolving AI models. When building AI apps, you embrace the reality that AI tech is moving at a breakneck pace. This is what ensures that your product doesn't turn out to be a flash in the pan. You need to:

Focus on Core Functionality

I know this has been repeated a bit too often, but seriously, don't get bogged down in trendy features. The temptation of doing this is very strong when it comes to AI. Instead, prioritize core functionalities that solve a fundamental need for your users and would likely keep doing so for a long time. For instance, a fraud detection AI tool for businesses should probably focus more on identifying fraudulent patterns in financial transactions, a need that will persist as long as commerce exists. This can not and will not future-proof your product, but it is one of the best ways to ensure that it is relevant for a long time. People often use the music analogy when trying to drive the point about focusing on the core functionality of your AI product. They are like: just like a musician a musician focuses on music, focus on the core of your product.  In music, the core functionality – the melody, rhythm, and lyrics – can exist independently of the instruments used to create it. A song written for piano can be easily translated to a guitar or violin. However, in Gen AI applications, the core functionality and the underlying technology platform are inextricably linked. The way an application leverages a Gen AI model's capabilities defines its core functionality—period.

Continuous Learning and Improvement

AI tools shouldn't be static. By all means, try to build in mechanisms for your AI product to learn and improve over time. This could include making it possible for the tool to regularly restrain itself.  A spam filter AI, for instance, should constantly learn new spam tactics to maintain effectiveness or spammers will learn to bypass it eventually making your product unfit to solve the target problem.

Openness, Scalability, and Adaptability

Design your AI solution with scalability and modularity in mind. This allows you to easily integrate future advancements and adapt to changing needs. Say you built an image recognition tool for identifying different dog breeds. What happens when your user base wants additional functionality? With an open architecture, you could easily add functionality to recognize cat breeds if the need arises along the way. Llama 3 is the new darling of the community? An open architecture should allow you to swap things up.

Ethical Considerations and Responsible AI

this might not seem like a key factor for an AI product when seen from a longevity lens. However, it is. This isn't just an ethical concern; it's a business risk. You don’t want your AI product to get the sledgehammer down the road especially if you get in the crosshairs of the law. Lots of popular tech products have gone down in flames due to unethical practices, you don’t want to be the next one. For instance, an AI tool for hiring could run into pitfalls in the future if it was trained with historical data from your company, which might contain unconscious biases against certain genders, ethnicities, or educational backgrounds. If building a dedicated responsible AI team isn't feasible, consider hiring an advisor with this specific expertise. Their guidance will be invaluable in navigating the complexities of ethical AI development.

Mindset for AI Founders: Don't Be a Flash in the Pan

The AI revolution is in full swing. Here's the winning mindset for AI founders who want to avoid becoming a forgotten fad:

1. Prioritize Problem-Solving Over Hype:

• Resist the allure of flashy features that impress but don't solve real problems. Focus on genuine utility. Be the painkiller, not the party trick.
• You love your product idea? Nice. Unless you want to be the only customer for your product, potential users should be your obsession. Talk to them relentlessly. Understand their workflows, frustrations, and deepest needs. Does your AI truly make their lives easier? Rinse and repeat.

2. Embrace Agility in a Rapidly Evolving Landscape:

• The AI landscape shifts like desert sands. Build with an open architecture. This allows you to seamlessly integrate future advancements and adapt to changing user needs.
• Don't build a static tool. Integrate mechanisms for your AI to learn and improve organically.

3. Build for Longevity, Not Just the Next Headline:

• Don't chase trends. Prioritize features that address fundamental user needs and have lasting relevance.

4. Champion Responsible AI:

• Ethics Matter (and Can Make or Break You): AI comes with ethical baggage. Responsible AI isn't just about having a clear conscience–it's good for business.

By adopting this winning mindset, you can avoid becoming a forgotten fad. Remember, the AI gold rush rewards those who solve real problems, not just those with the flashiest tools.

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