One Well-Built GenAI Flywheel Can Lift the Entire Business
TL;DR
- Not every GenAI feature needs its own flywheel. One well-built flywheel can power the entire product.
- The core loop is customer experience → detailed preferences → latent needs → personalized features → customer experience again.
- Do not try to add GenAI. Design a sustainable flywheel for user insight.
Introduction
While designing GenAI-based services for several recent projects, I came to one realization. I already knew that it was important to launch early, capture user insights, and create a loop in which value compounds.
But applying that idea myself revealed something deeper. Not every GenAI feature needs its own flywheel. One well-built flywheel can power every feature.
1. What Is a GenAI Flywheel?
A GenAI flywheel is a cycle like this.
Customer experience → detailed preferences → latent needs → personalized features → customer experience again
When GenAI improves the customer experience, users voluntarily share more detailed preferences. Those preferences reveal latent needs that users do not ordinarily express. When these insights are turned into personalized features, the experience improves again and the loop grows stronger.
2. GenAI Without a Flywheel Is Just a Feature
A GenAI service built without this perspective ultimately ends as “one more feature.” It may look impressive at launch, but without a sustainable loop of value, it remains an isolated experiment that only incurs costs.
3. What to Ask Before Building a GenAI Service
Through recent projects, I learned that success depends first on these questions.
First, which feature creates the most value when personalized?
Not every feature creates additional value through personalization. Product search? Absolutely. FAQ search? Probably not.
Second, how much user information must be collected for meaningful personalization?
The depth of the latent needs you can discover depends on how compelling the experience feels. Users share information only to the extent that they see their input reflected in the result.
These two questions determine whether a GenAI investment produces ROI or merely adds cost.
4. Chatbots Are the Best Tool for Gathering Insight
Traditional analytics—logs, clicks, and purchase histories—provide only indirect estimates of user behavior. A chatbot, by contrast, can ask questions in the user’s own language and receive direct answers. It can provide insight into why users search, what they value, and how they make decisions. This is a kind of information that is difficult to extract through existing methods.
Like Amazon’s Rufus, a well-placed chatbot can uncover subtle user preferences that previously amounted to little more than guesswork. A conversational interface, rather than a search bar, encourages users to share meaningful context and intent.
There is another important point. Users share more information when a chatbot appears intelligent. Before ChatGPT’s image generation improved, users rarely uploaded personal photos. Once its quality reached a certain level, however, users voluntarily provided large volumes of personal images. If a chatbot merely appears to understand detailed requests, users will readily share detailed preferences. In most cases, the level of detail users share is proportional to the system’s perceived intelligence.
5. Separate Data Collection from Value Delivery
I used to think every GenAI feature needed its own flywheel. I now know that it does not.
The two can be separated:
- Data-collection flywheel — where GenAI captures users’ latent needs
- Value-delivery features — where those insights create personalization and business impact
In other words, one well-designed GenAI flywheel can fuel the entire ecosystem. Once one loop begins turning, it can lift every other feature in the product.
One service designed around this concept from the beginning is EncBird,1 an AI English-learning service. EncBird built its entire flywheel around a single hub called the Expression Dictionary. Chatbot features such as PictoChat, which uses photo-based conversation; DiaryChat, which lets users write an English diary with an AI coach; and FreeChat, which provides business-scenario role-play, collect users’ expression data during conversations and store it in the Expression Dictionary. Flashcards and English-writing quizzes then use that data to provide personalized review. The more users converse, the richer their Expression Dictionary becomes, the more precise their review becomes, and the better the learning experience becomes, leading back to more conversation.
The important point is that each chatbot feature does not have an independent flywheel. They share one data-collection flywheel: the Expression Dictionary. Value delivery—review and quizzes—is layered separately on top of it. The separation between data collection and value delivery described above is embedded directly in the service design.
6. Launch Early and Design the Data Pipeline First
GenAI makes it possible to reach 90 percent usability very quickly. The remaining 10 percent comes from operational experience and real user data.
Many companies hesitate to launch until they reach 95 or 100 percent. But speed matters for chatbots.
- Define from the beginning which user inputs you will capture.
- Build a data pipeline that collects those inputs, cleans them, and feeds them back into the system.
- A chatbot designed to reinforce itself through user data becomes more powerful over time.
Conclusion
Let us not try to “add” GenAI. Let us design a sustainable flywheel for user insight. Let us create one loop that fuels personalization, learning, and long-term business growth.
One well-built GenAI flywheel can lift the entire business.
The chatbot you deploy today can become the competitive advantage that defines your business tomorrow.