In the initial days, apps used to compete based on feature sets. Then they began to compete on user experience and now they are being measured on the basis of how “helpful” they are to users.
This trend has been heavily influenced by artificial intelligence and will continue to evolve over time. Not the flashy chatbot on your company website or the rush to implement an “ask AI” button shortly after ChatGPT launched, but the significant ways that AI actually affects the way users navigate your product, their decision-making, and whether they return to your solution.
And for some reason, many product teams are not properly measuring the effects of this.
To illustrate, consider how applications are being used today. People don’t want to spend time figuring out how to use an application; they want guidance from the app. If an app feels confusing, slow, or mentally exhausting, that customer will disengage from your product because there is too much competition for attention.
This is exactly where AI copilots are becoming powerful.
Let’s take a look at a finance application. In the past, users would interpret charts, look through categories, and manually keep tabs on how much money they spent. We have now implemented artificial intelligence within the application that can instantly explain to users how their money is being spent (categorized), provide recommendations for better ways to save money, and respond to user queries in a conversational manner. As such, users no longer view the finance application as a transactional experience but rather as a personalized experience.
We are seeing the same transformation happening within other industries. In productivity applications, AI is generating summaries and organizing employees’ workflows. In shopping applications, AI is reducing fatigue from making decisions because there will be fewer choices to make. In software as a service (SaaS) platforms, AI is helping users complete necessary tasks without having to endure an extended onboarding process.
What is most interesting is that users do not generally think about this functionality as being provided by something called AI. Instead, they simply recognize that there has been a substantial improvement in their ability to easily use a finance application. While the distinction might seem inconsequential, it is quite significant.
Retention has typically been evaluated based on clear indicators like the daily number of users, the length of time spent using a product during a single session, the rate of churn, and the rate at which notifications are opened by users. In contrast, AI-co-pilots will begin providing another, “softer” form of retention that is based on being a reliable solution.
Users will return to the application because it decreases the amount of time and effort they must invest in accomplishing a given task.
This shift represents an entirely different behavior loop.
Users may not remain in the application for longer periods of time as a result of the introduction of co-publishing feature technologies. On the contrary, they may actually perform tasks with increasing speed. However, to the extent that the product continues to eliminate friction for users, the product can be relied upon by users over time as part of their routine. Establishing trust is much stronger than employing any of the previous methods used to encourage user engagement.
According to Gartner estimates, over 60% of digital products entered into the market in 2025 will have some form of generative AI embedded into them. However, simply incorporating AI technology into the product(s) will not guarantee increased retention rates. The most successful generative products are leveraging AI technology to support and assist users with tasks where they may feel uncertain or confused, or abandon a task midway through the process.
Article Authored by Kumar Saurav, Co-founder & Chief Strategy Officer, AdCounty Media
