The importance of product-market fit

Why the AI ​​boom is not going according to plan

The AI ​​boom is not living up to expectations as companies struggle to convert their AI investments into reliable revenue streams. Despite initial excitement and high expectations, companies are finding it harder than expected to adopt generative AI. In addition, AI startups are overvalued and consumers are losing interest. Even McKinsey, which originally predicted $25.6 trillion in economic benefits from AI, now admits that companies need to conduct an “organizational operation” to realize the full value of this technology.

The importance of product-market fit

Before leaders rush to transform their organizations, they should first go back to basics. Like any other endeavor, creating value with AI starts with product-market fit. That means understanding the demand that needs to be met and making sure the right tools are used for the job.

Baking pancakes with hammers

In the current AI landscape, everything is getting hammered. At CES 2024, attendees marveled at AI toothbrushes, AI dog collars, AI shoes, and even an AI button on a computer mouse. In the business world, 97% of executives expect generative AI to add value to their company, with three-quarters of them handing over customer interactions to chatbots. However, this rush to apply AI to every possible problem has produced many products that are only marginally useful, and some are even destructive. For example, a government chatbot incorrectly advised New York business owners to fire employees who complained about harassment. Turbotax and HR Block also had issues with their bots giving bad advice half the time.

The Furby Fallacy

AI is particularly prone to disrupting companies’ existing processes for establishing product-market fit. When using tools like ChatGPT, it’s easy to be fooled by their human-likeness and assume they know our needs intimately. This is similar to the Furby fallacy, where people believed the toys were learning from their users, when in reality they were just executing pre-programmed behavior changes. The tendency to humanize AI models can lead to overestimating their sophistication. The alignment problem, which refers to the challenge of giving precise instructions to AI models, makes establishing product-market fit even more important for AI applications.

Back to basics

Since AI systems don’t find the right path to market on their own, it’s up to leaders and engineers to meet customer needs. This requires four key steps: understanding the problem, defining product success, choosing the right technology, and testing the solution. Many companies make the mistake of thinking their main problem is a lack of AI and add AI without considering the end user’s actual needs. By first clearly articulating the problem, it’s easier to determine if AI is the right solution and what types of AI are appropriate for the use case. It’s also important to define what makes the solution effective, as there are always trade-offs to consider. Once the goals are clear, collaborating with engineers, designers, and partners is essential to select the right technology and address potential limitations early in the process. Finally, testing and iterating the solution ensures that it meets actual needs and adds real value.

Hit the bullseye and achieve goals

The temptation to deploy every AI application in every environment often leads companies to “innovate” without a clear plan. This approach results in firing many arrows at random and marking the spots where they land with targets. While some arrows will hit useful spots, the majority will bring little value to businesses or end users. To unlock the true potential of AI, it is necessary to first draw the targets and then focus all efforts on hitting them. This may involve developing solutions without AI or using simpler and more targeted AI implementations. Regardless of the type of AI product being developed, creating product-market fit and meeting customers’ wants and needs is the only way to create value. Companies that prioritize this will emerge as winners of the AI ​​era.

In summary, the AI ​​boom faces challenges in delivering on its promises. It is crucial for companies to focus on product-market fit and resist the temptation to deploy AI without a clear understanding of the problem and end-user needs. The Furby fallacy highlights the danger of overestimating the capabilities of AI models and underscores the need for precise instructions. By taking a back-to-basics approach and prioritizing product-market fit, companies can unlock the true potential of AI and create real value in the AI ​​era.