Code of Conduct

Managing in the age of AI

The release of ChatGPT in late 2022 opened the public’s eyes to the rapid pace of AI development, set off a wave of excitement and apprehension, and ramped up competition among tech giants and upstarts to deploy new AI technologies.

Professor Zsolt Katona, who holds PhDs in both computer science and marketing, began using generative AI in 2019, when he developed and began teaching business of AI classes to MBA students and through Berkeley Executive Education. He has recently focused his marketing research on AI as well.

We talked with Katona, the Cheryl and Christian Valentine Professor, about business applications and what skills marketing professionals need to thrive.

Berkeley Haas: What are the biggest misconceptions about AI?

Zsolt Katona: One is just the word “generative,” because people are applying it to everything.

People use generative to mean that the application generates something. But many of the uses people are familiar with are more like a kind of search. Also, most of the applications that are most lucrative are not generative in nature. I read an article that said Mastercard uses generative AI for fraud detection. But it really was just a tool that detects outliers, suspicious transactions.

Otherwise, the biggest misconception is that these things can work fully autonomously. That’s essentially non-existent in most applications. What you must do for pretty much every application is figure out how to make the AI portion and the humans work together.

Are we still in the hype phase while companies figure out lucrative commercial applications?

They’re figuring it out, and it’s just a matter of time. Some of the fancy stuff is not there yet because companies are having problems getting their data in a format that allows them to easily use simple AI applications—or they might not even own the data. But the non-flashy stuff is the most lucrative. For example, using cameras in a factory to detect manufacturing defects. It’s just looking for little differences on those pictures.

In your business of AI class, what skills do you say managers working with AI technology need?

The number one thing is to understand the fundamentals of how it works. It’s even better if they have some coding skills, and I do make my students go through an exercise with code so they have at least a feeling for the building blocks. Other than that, they need to understand how to manage technology, which is not that specific to AI.

“Translating the ‘objective function’ of a model…to the business objective is a critical task that somebody has to do, and it’s not going to be a data scientist. It’s rarely going to be the engineer.”

Can nontechnical people learn enough to be effective?

My colleague who teaches marketing analytics likes to say that it’s easier to teach managers analytics than to teach data scientists to be good managers. I share that thought, and again, they don’t have to be as technically advanced as the engineers. But they should understand how the data goes in and how it results in a desired outcome.

Managers should learn enough about how it works to talk to the people who make these things, especially with respect to data needs. Translating the “objective function” of a model (i.e. what it should do) to the business objective is a critical task that somebody has to do, and it’s not going to be a data scientist. It’s rarely going to be the engineer.

Will there be jobs for marketing managers without engineering backgrounds?

I think there will be. Marketing is such a subjective topic that it’s hard to evaluate all the things AI needs to do. It comes down to a lot of human judgment. If AI can do every job in the world, then yes, marketing people will be replaced as well. But it’s a very complex type of work and it’s hard to show that a machine can do it better than humans.