How to Successfully Implement AI Into a Business Strategy

On Monday I had the pleasure of speaking on an Advertising Week panel alongside Julia Huang, MD at AMEX Ventures, and Jaya Kolhatkar, CDO at Hulu. The talk, moderated by Forbes reporter Marty Swant, discussed what it means to implement AI into a business strategy from the perspective of people who build or manage AI for some of the biggest companies in the world. The three of us shared our thoughts on data, AI, and where we're heading. While our backgrounds and experiences vary, we were in agreement on a few key points throughout the discussion:

1. It’s all about the data.

Everyone is collecting data in some form, but it’s how you use that data that is most important. Julia said AMEX is always trying to test the effectiveness of data. And Jaya said that Hulu collects data on every second of use — every search, every show that’s watched, every time those shows are paused, and so forth. In all, that adds up to approximately 15 billion interactions every day. IV.AI manage similar data for all of our customers from internal customer data to external inputs from social, websites, and articles.

But it’s crucial to remember that not all data is good data. Quality controls built around testing can help ensure that the data you receive is ultimately useful in the long run. Jaya pointed out that it's crucial to put in the time to ensure you have a good foundation and can trust your data. And as I've learned, even unsupervised learning is somewhat supervised because you are choosing the data that goes in, which can open up your work to bias.

And just remember: It’s better to use less data that is of higher quality than to use more data that is questionable. Data needs to be strong and lead to a straightforward experience that empowers the consumer.

2. Identify the problems you want AI to solve.

Successful AI implementations boil down to solving a central issue that can’t otherwise be solved. If you’re working on a problem that a team of a few people can tackle, are you really solving something? In that kind of situation, people bring creativity and human ingenuity that transform how the space works when given that freedom. 

This is something that we see in relation to AI companies that are the most successful versus the ones that are doing it just to say they do AI. Ask yourself whether you’re truly increasing knowledge that can’t be increased in any other way. Jaya said that her company has teams that analyze the data; then, data scientists look at what problems can be solved and leverage AI to solve them.

Altogether, diving into the whole problem-solving part of AI can be an interesting experience. At my company, IV.AI, we often find ourselves looking at all the problems we’re trying to resolve with machine learning holistically. Our core focus is to figure out which parts of the problem solving models are the most scalable. When we examine each problem and see which ones overlap, we can scale the solution so that it solves multiple problems instead of merely one narrow one.

3. Be deliberate about where AI makes the most sense.

As AI has become more mainstream — and the education process has evolved along with it — it has become a hot-button topic. There are so many companies that want to find ways to leverage AI, but that’s not always possible. On our end, it’s important to help customers understand AI’s value and how a use case for one team can be totally different from that of another team.

It’s all part of the challenges companies wrestle with each day as they examine the technology and identify ways to implement it. With all of the data that’s now available to enterprises across the world, AI possibilities are greater than ever. As businesses become increasingly receptive to AI, it will be crucial for them to identify a strategy that works best for their specific case while also considering how their data can be harnessed to most effectively help the consumers they serve.