Evaluating how AI delivers business value

What do Facebook (now Meta), Apple, Amazon, Netflix, and Google have in common? Well, other than being companies that make up the popular FAANG acronym, they have a heavy focus on ML. Each one relies on this technology to not just make small percentage gains in areas, but many times, it is this tech that is at the heart of what they do. And as a key point, the only reason they keep AI and ML at the heart of what they do is because of the value it creates. Always focus on delivering business value and solving problems when you look to apply AI.

Google owes much of its growth to its hugely successful ad algorithms. Responsive search ads (or RSA) have a simple goal of trying to optimize ads to achieve the best outcome. RSA does this by pulling the levers it has, such as the headlines and body copy, to get the best outcome, which is clicks. Its bidding algorithm always has a dynamic pricing model for certain keywords based on a massive number of factors such as location, gender, age group, search profiles, and many others. Alphabet's (Google's parent company) revenue in 2020 was $182.5 billion, with a b. In no other way can you create such a massive generation of cash with so few people other than software and ML.

Some of these minor adjustments are changing the price by a penny, moving a user age group to target by a year, and changing the actual ad that is shown based on the context of the web page. Google's algorithms then measure whether it was successful. Can you imagine if a developer had to code up each individual change to a pricing model after making such small changes? Even if they did do that, there would be a much lower chance that the actual adjustments being made would be the ones actually impacting the desired end value.

We can consider another example in the form of how a system can determine what shows we might like by looking at Netflix. Netflix is able to suggest what we might want to watch next by using a recommendation system that uses your past viewing history to make predictions about future viewing habits. A recommendation system is simply something that predicts with different degrees of accuracy how likely you are to like a piece of content. We make use of this technique every time we pull up our Amazon home page, get an email from Netflix that there is a new show that we might like, or doomscroll through Twitter.

There is a massive business value to each of these, as getting more eyeballs on screens helps sell more ads and products, improves click-through rates (the number of people who actually click on an ad), and increases other metrics that, at the end of the day, make that platform more valuable than if you simply got a list of the top 10 most sold items. You have your own personal robot behind the scenes trying to help you out. Feels nice, doesn't it?

Netflix does this by creating latent features for each movie and show without having to use the old style of asking users questions such as: How much do you like comedies? Action movies? Sports movies? Think about how many people didn't fill these out, and thus Netflix didn't have the retention rate of people who did.

This prediction system so valuable that Netflix offered a million-dollar prize to anyone who could improve it by 10%. This tells us two things:

• That there is a huge business value in improving the AI system
• That there is a dire shortage of people that Netflix could find to work on this problem

A latent feature is a synthetic tag generated from a weighted combination of other attributes. Did you ever describe a movie to a friend as epic? That was our mind putting together attributes of a movie, the sum total of which created the epic label. This allows for more freedom and essentially infinite ways to combine what already exists to create a system that can determine (with incredible accuracy) what someone will like and buy. This also allows a reduced number of features to be considered.

Amazon has made real-world moves based on this, shipping items to local distribution centers before people buy things in order to increase delivery speed. This allows yet another competitive advantage that increases their ability to capture and retain customers. Maybe tomorrow you'll have a drone waiting outside your house for a few minutes waiting on you to click buy now on that new phone you've been eyeing for a week.

Every example here should show you not only how AI can solve problems at scale, but also that AI and ML are not just technical fields. If you really want to make an impact, you need to make sure you keep the business problems you are trying to solve at the forefront of your mind.

These are just a few of the numerous business problems that you may want to solve, but what techniques would you even start with when looking at them for the first time? In the next section, we will dive into just that.