Why Building AI Products Requires Big Investment

Why Building AI Products Requires Big Investment

Application is making inroads in marketing technology

Artificial intelligence (AI) might be overhyped, but that hasn’t stopped it from invading marketing technology.

Ad agencies and tech vendors are adopting AI to restructure their data sets and increase their odds of winning programmatic auctions. The giant cloud companies have gotten in on the action, too. Salesforce, IBM, Oracle and Nielsen all have AI products. Their investment makes sense given that 83% of companies with more 1,000 employees use or plan to use AI in the next 12 months, according to an August 2017 survey of chief information officers worldwide by Dynatrace.

While tacking the term “AI” onto a press release can generate buzz, building actual AI products is a big undertaking for most companies. Kevin Lyons, senior vice president of data science and digital technology at Nielsen Marketing Cloud, estimated that it cost roughly “millions if not tens of millions” of dollars for Nielsen to build its AI platform, which automates the creation of custom audience segments for marketers.

To create the AI product, Nielsen needed to have engineers to build the tech infrastructure, product developers to implement best practices and data scientists to monitor, test and tweak the product’s models and algorithms. Four to seven people worked full time on building the platform for a few years and several others got pulled in occasionally, according to Lyons.

The promise of AI is that it will help automate everything from car driving to ad buying. But it is an uplift to get the automation flowing.

For instance, to build AI models, Nielsen researchers analyzed academic papers that discuss theoretical applications of AI. Then an engineer had to determine how to create code to make the academic’s ideas become a reality. And since AI is an emerging field with little to no standards, coders had to continuously tinker with their projects until they found the combination of code that actually worked.

Even after an AI product comes out, there’s still more work to do. To boost efficiency, algorithms have to be perpetually adjusted.

If an AI product is part of a larger platform, it also has to be embedded in a seamless way if customers are going to use it at scale. Nielsen’s AI product came out last April, but its data management platform’s interface still has to be altered to make the AI tools more smooth for customers, Lyons said.

And some marketers are not ready to bank on AI. In a Conductor survey of 500 marketing execs worldwide in November 2017, 34% said AI is the technology that they are most unprepared to deal with in 2018.

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