DOP Deep: What AI features are product teams building?
A closer look at how companies are deploying AI with some inspiration for your product
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Hi product people 👋,
Over the past few weeks in the weekly briefing, you’ve probably noticed that many of the new product launches and features are AI-related.
Today, if your product’s roadmap doesn’t include some sort of AI feature, it can feel like you’re getting left behind. The truth is though, that AI product features are still new to the market. Nobody has quite figured out how widely used many of these will be - and to what extent users actually want these new products. And in a recent study, 66% of interactions with a chatbot were rated a score of 1 / 5 which suggests either some of the new ways of interacting aren't what people want after all, or that the technology hasn’t quite caught up to where it needs to be yet.
That’s why I thought it could be helpful to take a step back to understand how modern, product-led companies are deploying new AI technologies so that you can decide what might work best for yours.
In this DOP Deep dive we’ll explore the main ways companies are deploying AI technologies and we’ll take a deeper look at some examples from product-led companies like Google, Spotify, Amazon, Arc and more.
The most popular types of AI features product teams are building
How 10 product led companies including Shopify, Amazon, Grammarly and more are deploying AI features
Lessons and inspiration for your own product strategy
The most popular AI feature categories
For this deep dive, we’re going to use a sample of 10 leading product-led companies who have recently either released AI features or standalone products including Zoom, Google, Spotify, Grammarly, Snapchat and more.
When we started to look deeper into the types of AI features and products these companies were building, the AI features fell into one of these five distinct categories:
Embedded assistants - an AI powered assistant that is embedded inside the core product and is designed to complement the value proposition in some way - we’ll take a look at some examples together in a bit.
New standalone products - AI powered, standalone products that are separate to the core product offering
UX enhancers - AI powered features designed to make it easier to use an existing product or improve the overall user experience in some way
Productivity boosters / time savers - AI-powered features designed to boost the productivity of users
ML intelligence / data analysis - AI powered machine learning models deployed to add value or uncover insights
Using these categories, let’s explore how each of the companies we’ve chosen are deploying AI into their products.
A deeper look at how companies are deploying AI features
Here’s a snapshot of how each of these companies compares are deploying AI features in their products and the categories for each: