AI IRL Podcast Episode 25: What Executives Need to Know About AI
She had just given an excellent presentation, so I wanted to invite her onto the AI:IRL podcast to talk a little bit more about AI. Specifically, about what executives need to know about machine learning.
Mariya gave us three steps leaders can start with when thinking about AI.
Here’s what she had to say.
Step One: Look at How AI Is Impacting Your Industry
“AI leads to disproportionate benefit if you get it right.”
The first thing to understand is that AI is impacting different industries in different ways.
When it comes to AI, there are certain industries that are ahead and a lot of industries that are behind.
So, if you’re thinking that AI is essential to invest in, study how it’s currently impacting your industry.
Look at what your competitors are doing.
Analyze what’s going on, where is there ROI in the market, and if there are areas where you must adopt AI to stay competitive or if you still have some time.
The thing about AI is that it leads to disproportionate benefit if you get it right, which is exponential growth versus linear growth.
Because of this, if you hesitate, you might get to the point where you are no longer able to catch up to your competitors.
So, understand the core challenge you’re trying to solve in your market. And understand whether AI is table stakes or if it could be a key differentiator for you.
Step Two: Educate Your Executive Team
“AI isn’t just magic — it’s hard math.”
If you do identify that there is a competitive thrust in your industry and you have to catch the AI wave, then it’s very important to educate your executive team.
There’s a famous quote by Shivon Zilis, a well-known, early stage AI investor.
She basically said, “The biggest predictor of whether a company will successfully apply AI is whether or not they have a C-suite executive with an advanced math degree.”
This is because these executives understand that AI isn’t just magic — it’s hard math.
Right now, it’s very difficult for non-technical business leaders to understand how to map business problems to machine learning algorithms and vice versa.
And vendors tend to have a skewed incentive to only share with you positive use cases, not negative use cases. Yet every single vendor has cases where their software did not work or made a particular function worse.
So, make sure you have an understanding of what AI is and know the conditions under which it works and does not work — then be able to use that information to create a viable AI strategy.
Step Three: Keep Your Priorities Straight
“A key thing for executives to remember is that what you care about is not how advanced your AI is. What you care about is whether or not you’re driving business results.”
Whatever you do, make sure you’re keeping your focus on improving the business — not on chasing after shiny objects.
As executive, remember that what you care about is not how advanced your AI is.
What you care about is whether or not you’re driving business results.
And you’re not always going to drive better business results with AI. Sometimes you drive better results with better UX design or better process workflows. Other times, all it takes is superior training for your employees.
So, consider your technology holistically.
Rather than just thinking about what you can do in AI for a splashy press releases, think about all of the solutions you could use to tackle your business problems.
Then, focus on the ones you truly believe will work.
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