“Managing products of the future” came up when I was thinking of a suitable title for a piece about products that look and feel very different to most products that we see today. Products such as driverless cars and voice assistants popped into my head as examples of products that are likely to dominate our daily lives before we know it.
However, these products are here already and I’m keen to look at if and how this does affect the role and focus of product management.
Will we manage products differently when the user interface of these products changes? Do we need to think differently about our products when data becomes the main output? Will customer needs and expectations evolve? If so, how? These and other questions I will start thinking about; considering the nature of machine learning, different product scenarios and their impact on the role of the product manager.
It’s easy to get swept up by the hype surrounding AI and products based on machine learning, and to start feeling pretty dystopian about the future. But how much will actually change from a product management point of view? People will continue to have specific needs and problems. As product managers, we’ll continue to look at best ways of solving these problems. Granted, the nature of people’s needs and problemx will evolve, as it has always done, but this won’t alter the problem solving and people centric nature of product management.
To illustrate this, let’s look at some AI-base products and the customer needs and problems that they’re aiming to solve: Google Photos, Sonos One and Eigen Technologies.
Google Photos’ strap-line is “One home for all your photos — organised and easy to find”. Over the coming months, Google Photos will roll out the following features:
- Using facial recognition, Google Photos will know who’s in a picture and will offer a one-tap option to share it with the person in question — provided that this person is in your phone’s contact list, Google Photos will have learned this person’s face. If that person appears in multiple images, Google Photos will even suggest to share all of them in one go.
- Automated image editing suggestions, Google Photos will suggest different corrections based on the look and quality of the image. For example, if there issues with the brightness of the image, Google Photos will automatically display a “Fix brightness” suggestion.
With these new features, Google Photos aim to address customer needs with regard to sharing pictures and improving image quality respectively. These needs aren’t new per se, but the ‘intelligent’ aspect of Google Photos’ approach is.
The Sons One is entirely controlled by voice. The speaker works fully with Amazon Alexa, which means that if you’ve got an Amazon Alexa compatible device, you can control your Sonos sound system through Amazon Alexa. Because Alex is a native app within the Sonos platform, you don’t even need to have an external Amazon device — i.e. Echo or the Dot — installed to control your Sonos One speaker. The installation of the Alexa mobile app will be enough.
The integration with the Amazon’s Alexa voice assistant is a logical next step within Sonos’ mission to “empower everyone to listen better” and makes it easier for people to control the music they listen to. Granted, the user interface of Sonos One is different to other product; it doesn’t have buttons, for example. However, it still is a product like any other in a sense that it delivers tangible value to customers by solving their music listening needs.
“Turn your documents into data” is London and New York based Eigen Technologies’ mission statement. The company enables the mining of documents for specific data. For example, if you work for a mortgage lender and are looking to make a decision about the credit worthiness of a home, Eigen’s data extraction technology helps to quickly pull out key ‘decision inputs’ from a number of — often very lengthy — property documents.
The way in which Eigen Technologies use machine learning algorithms, is ultimately to improve the speed and quality of decision making. Even though the underlying technology is based on machine learning, the outcome is very much like that of any other product: a clear user interface which shows the relevant document data that a user is interested in and needs to make decisions.
Main learning point: AI and machine learning based products will no doubt change the ways in which we interact with products and what we expect of them. However, curre
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