Generative AI is the star of the moment to improve the productivity of digital product development teams and to take software quality to a new level. So far, its use is mainly focused on code writing and testing tasks. But its potential goes much further and starts at the project’s birth, in the discovery phase.
It is the beginning of the project when our team meets with the client and starts a process of research, definition, and design that allows fine-tuning after understanding the current state of affairs, the final destination, the scope of the solution, the timeline of the project and the number of professionals needed, generating the documentation to ensure that decisions will be data-driven.
Initially, during the discovery phase, generative AI can be used to learn more about clients, the projects, or the technologies they use, as well as to automate data analysis from research, interviews, and listening to meetings to detect points of alignment between all parties involved or to quickly identify inconsistencies between the requirements of different users. Generative AI is even a fundamental ally in preparing the relevant documentation. This task can save the team many hours that they can devote to more creative activities to improve the solution or generate more added value.
Then, during the solution stage of the discovery phase, where the technological architecture design, the business definitions, and the digital product design are addressed, generative AI can, based on the analysis, suggest additional functionalities or even the removal of some of those included in the project that could generate frictions or delays.
Review by review
With the prototype underway (or even with a product’s first version already being produced), a new opportunity arises to extract value from generative AI: analyze user reviews, feedback, or other less direct sources (comments on social media, informal emails). The purpose is to identify patterns in users` needs, behaviors, and preferences and, based on that, indicate new development paths or even warn about some functionality users had been looking for without success. For example, many tried to change their password in the digital application when this feature was only available on the website.
If it is an existing product being already marketed that is being upgraded, user feedback can help enhance the final solution.
There is another phase in which generative AI can help detect new features for a digital product: the maintenance phase. Analyzing user queries allows not only to detect bugs more quickly but also, if the same type of claim, complaint, or suggestion is reiterated, to propose some features, a new screen, or a new way of doing things that improves the current operation or experience, facilitates users’ tasks or solves issues.
In short, the power of generative AI can be applied to improve digital products, not only from the point of view of speed when coding or ensuring better verification at the testing phase, but also to suggest new functionalities and enhance their performance levels, user satisfaction, and monetization.