The evolution of apps: from reactive to pre-emptiveData / AI / ML
Last week I wrote about how the next wave of enterprise apps will stand out in two ways: they’ll be smart and make experiences as effortless as possible for the end user.
As machine learning and predictive modelling become more of a mainstream reality, apps will shift from being reactive “sense and respond” to more predictive and pre-emptive solutions.
They’ll evolve (or should evolve) along the following framework: Reactive > Proactive > Predictive > Pre-emptive:
1. Reactive apps: These are web-based tools that give users a way to create the outcomes they need. For example, a web app that makes it possible to book flights or hotels on the web. In these cases, the app just follows the user’s direction; the user is in the driver’s seat.
2. Proactive apps: The next stage adds a little more intelligence to proactively notify end users of changes in outcome or other information they should know. Staying with the travel theme, an example of a proactive app is a mobile app that notifies you of a flight delay for your upcoming flight.
3. Predictive apps: Here’s where machine learning kicks in. These tools use machine learning/predictive modeling to predict outcomes or potential changes to expected outcomes. For example, an app that tells you that the price of a flight you’re looking at will most likely increase in the next week.
4. Pre-emptive apps: The last stage in app evolution is artificial intelligence. Here, the app doesn’t just notify customers of changes in outcomes, but can also take the actions needed based on those changes. For example, an app rebooks you on the next available flight when it detects a problem (e.g. flight cancellation) with your current itinerary.
For app startups, it’s in this last stage where you can provide the most value and delight your users. But, this is also the stage where you need the most amount of data to make sure your app is taking the right pre-emptive action.