Imagination Doesn't Guarantee AI Product Market Fit
How startups can bridge the gap for AI products
Recently I've been working more with data and AI startups, and the topic of AI product market fit has come up frequently. This is no accident, since can be a struggle for even mature startups to figure out.
Achieving product market fit ensures that your AI product solving real needs or enhancing experiences that your customers are likely to face.
This focus is vital for your startup's survival and growth, especially as the tech landscape constantly shifts and competition intensifies. You need to secure steady revenue and carve out your own niche in this dynamic market. But beware of the trap of FOMO – Fear of Missing Out. It's easy to get distracted by what others are doing and lose sight of what's truly important: understanding your first customers.
Fear of missing out (FOMO) can blind startup founders and take the focus away from understanding their first customers. New startups need to understand them before trying to scale. As a founder you need to consider these groups if you want to build an AI product with that has a market.
"Build it and they will come" only holds true if what you're building is something the market genuinely needs and doesn't yet have. Your AI product must not just meet but align with what your customers expect and need. It's about their pain points, not just what you as a founder think is cool or innovative.
Getting this right – focusing on product market fit – separates AI products that are dreams from those that satisfy a market demand.
What is Product Market Fit?
Product Market Fit (PMF) is a crucial concept in product development. It's the magnitude of how much a product satisfies a strong market demand. A product must align with the needs and preferences of its target audience. This alignment is essential for the success and sustainability of any product in the market.
Apple's iPhone 1 is a great example of Product Market Fit (PMF). It met people's need for an easy-to-use phone that could also play music and browse the internet. It fit into three markets: mobile communication, portable music, and internet connectivity. The demand existed for a device that could seamlessly integrate these functions into one user-friendly platform.
Apple took the time to understand their first customers and their needs. They focused on prioritize quality of engagement and feedback from customers. Then built intimacy, relationships, and loyalty with their early adopters.
It holds true for startups. In the beginning, you should focus more understanding your first customers. Scaling first, then realizing you need to understand them leads to misalignment with market needs and potential failure. For startups, it's crucial to get a pulse on what your initial customers need.
Startup founders must assess whether there is an existing demand for their product. They should also consider if they are anticipating a need that may emerge. Achieving Product Market Fit is key to a long-term competitive advantage and innovation.
To understand that they need to look at their product gaps. It’s especially important if you’re creating AI products using GenAI.
What is the AI Product Gap?
The AI product gap for startups can be hard to see, since GenAI is relatively new. The gaps revolve around ideation, assumptions, and underestimating GenAI.
The First Gap: Ideation vs. Market Reality
There's a belief that imagination and innovation alone can lead to market success. Creativity is crucial for developing unique products, but it's just the start. This phase is about 'idea generation,' where balancing creativity with market realities is essential.
The risk is thinking "if you build it, they will come," without deeply understanding what customers need and want.
For your product to fit the market, align imaginative concepts with customer needs from the beginning. Startup teams at the ideation phase can fall into the certain traps.
Innovation Over Practicality: There's often an overemphasis on the novelty of the product, while its practical application in solving real customer problems might be overlooked.
Vision Without Market Insight: Sometimes, there's a tendency to rely too heavily on personal vision, neglecting the crucial step of validating these ideas with actual market research and customer data.
Complexity Over Usability: Creative ideas can lead to complex products, which might compromise user experience, making the product challenging to use for the average customer.
In this gap, the focus is on ensuring that creative ideas are not just imaginative but also relevant and desirable to your target market, blending innovation with a deep understanding of customer needs.
The 2nd Gap: Developing on Assumptions
A big pitfall I've noticed is that startup teams may assume they know they user. Then build a product on those assumptions. Even data science teams even in large companies can do this. Startups are no exception.
Several common assumptions are often made by startups developing AI products about their users:
Assuming the User's Need: Reflect on instances where you might have projected your needs onto the users. Understand that your preferences may not align with your target audience's actual requirements. Gathering user feedback is essential. Iterating on that feedback is even more essential. It helps to identify and address their needs.
Assuming Skill. Determine the gap between your and the average user’s understanding of your startups AI product. It's important to assess if your product's complexity matches the user's skill and comfort level with the technology. User experience matters - a great idea can fail because of poor usability. Dropbox is a example of a great product that struggled initially because of product complexity.
Assuming Users Are Willing to Pay. Pause to check the perceived value of your product from the user's perspective. Just because a product is innovative doesn't guarantee that users will be willing to pay for it. Reflect on your pricing strategy. Ensure the value proposition resonates with and meets your target market's needs.
Developing on assumptions can be costly. Startups can spend years building a product that has no product market fit. A product might have capabilities but lack a large market to generate revenue or a good profit margin.
It gets more complicated when Generative AI products get involved.
The 3rd Gap: Underestimating GenAI.
GenAI complicates product market fit. The biggest reason? The output of GenAI is probabilistic. It means that outputs can vary. Outputs are often probabilistic.
How viewers perceive these outputs? Can vary depending on the group. Some users might find the outputs useful and intuitive. Others may find them confusing or irrelevant. Variability means achieving product market fit with GenAI products often needs a nuanced approach.
GenAI's large models vary in accuracy too. In commercial scenarios, teams often find that it behaves like an intern. Tt has broad knowledge and great enthusiasm, and capable doing many general things. But on closer inspection, many of the outputs cannot be directly used. They need a lot of adjustments., context, and fine tuning.
When you adopt a one-size-fits-all approach, it might not work well with the diverse needs of your users. This strategy can lead to increased development costs and technical complexities.
GenAI must be tailored to specific contexts and industries. It must meet the varied and complex requirements of users. You must narrow it to defined use cases and customer groups. You must think about the context of their data - then effectively model it.
Without this customization, even the most advanced LLM or GenAI based product can fall short. They cannot address the nuanced and specific challenges of real-world scenarios.
Your entrepreneurial data teams need to be strategic. You need to create good product that fits the market and has people willing to pay for it. They need to be strategic to monetize their work and the product.
How do we bridge the gap?
Product market fit doesn’t have to be a one-size-fits-all solution. Depending on your startup's focus and the needs of your users, you might find one of these two paths more suitable.
Go broad with copilots/agents. This path is about developing an AI application that acts as an assistant or co-pilot for your users. You're focusing on a wide range of scenarios. The AI can be helpful without being specific. Set clear boundaries and limitations for what the AI can do. Make sure your users understand that the AI is there to help, not to make decisions for them. Provide a tool that augments your users' decision-making process, not replace it. This approach is great if you want to create a versatile, user-friendly AI product. It can adapt to various situations. An example here is GitHub Copilot, which assists programmers in writing out code.
Go Narrow. If you're targeting a particular industry or process, this path might be more suitable for you. Focus on using AI to transform specific, often tedious tasks in your chosen field. Find routine operations or existing workflows to augment. Use AI to make them more efficient. Your aim isn’t for the AI to be perfect. Instead, aim for it to be accurate enough (about 70% usability). This will make a significant impact when implemented on a large scale. This path is excellent for you if you want to deeply understand a particular area. You can use AI to make major improvements in efficiency and workflow.
Narrowing down is often an easier path. But it's possible to create a broad-application AI product that finds its niche. The key is in understanding the unique challenges and opportunities each path presents.
Both paths need a deep understanding of your target market. Both paths need a deep understanding of the data that you are using to train and put into the model. Embeddings, context, and data must be in line with the users understanding.
Find business domain experts who know the business context. Also look for technical expects who know how to model these contextual relationships. Contextual modeling of relationships can make or break an AI product. It can sink a GenAI one. Otherwise, you end up with a very shiny product that fits what you think users want, not what they actually want.
But there’s also a larger strategic picture that these fit into, that you need to take into consideration.
Strategic Paths to Bridge the Gap
Strategic paths need to focus on your market engagement and competition. You need to understand and connect with users to have an effective product. You also need to know your unique selling points to stand out.
Market engagement revolves how your product connects with your target audience. It's about understanding what your customers need and want, and then finding the best ways to reach and appeal to them.
Tailor to Industry: Concentrate on a niche industry with specific needs that aren't being met by mainstream offerings. This involves identifying a distinct customer segment and tailoring your products or services to address their unique challenges and requirements. This approach not only enhances your market engagement but also ensures your product stands out by offering specialized value that general market offerings might miss.
Think Monetization: When monetizing your generative AI product, align your pricing strategy with the insights you gain from market engagement. Understand and respond to what your customers value and are willing to pay for, taking into account the data overhead costs in generative AI. Your goal is to link your product's value to your customers' expectations, ensuring it is attractive to your market while being financially viable for your business.
Think about unique and unconventional markets: Direct your efforts towards a narrow or unconventional market segment that larger companies may have ignored. This could be a specific industry, demographic, or unique use cases that have the data and problem space defined.
Market engagement can help you build a strong, loyal customer base and differentiate your product in a crowded market.
But you still need to think about your competition. Consider your understanding of our company's big picture. Think about your role in driving its overarching goals.
They key here is opportunity cost, time, and resource maximization for AI products you make. Every data or AI product must deal with friction between its features and the reality of the market.
Target Lower-End Markets or Content: Concentrate on areas that larger firms might overlook because they seem less prestigious or profitable. Margins might be small for them, but not for you. By targeting these overlooked areas, you can find markets with fewer competitors where your product can really stand out. This strategy not only opens doors to untapped markets but also allows you to build a strong presence and brand loyalty in a space that's been neglected by the giants of the industry.
Harnessing High-Effort Markets: Commit to areas that require extensive effort which might deter larger companies. This does require much more specialization that average, so be careful. While this path might be challenging, it allows your startup to excel in fields where the barrier to entry is the effort required, not just capital.This approach can lead to the development of highly specialized and unique products or services that larger companies can't replicate easily, giving you a competitive edge.
Embrace riskier ventures: With measured risk, there may be greater reward. Explore entering markets with higher regulatory or operational risks that might not appeal to larger, more risk-averse companies. Venturing into areas with greater regulatory or operational risks can position you as a pioneer by developing products to these specialized use cases and datasets.
Startups can gain a competitive edge by targeting these areas. Larger companies can't address all product needs - that's an opportunity for startups.
Final Thoughts
Taking a product market approach to AI products isn’t easy. Startups and big organizations can struggle with it. But if you can rely on basic product market fit fundamentals, it really helps.
Start by evaluating if you have any of the three gaps here. These are by no means a complete list of gaps. But they are the most common that I’ve seen with startups. Bridging at least these will help ground your AI product with the market you are targeting.
Building a product hard, and product market fit is harder. Its especially hard for startups trying to roll out their first AI product. As your product evolves, so will your product market fit.
Don't just make a model—elevate it into a living, breathing product.
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