[PODCAST] Artificial Intelligence In Times of Crisis

About This Discussion:

We know Artificial Intelligence can help with decision making, but can it help in a crisis that we’ve never seen before like what we’re experiencing with COVID-19? In this episode, we talked to Gary Saarenvirta of Daisy Intelligence, which uses machine intelligence to help retailers and insurance companies use data to make better and smarter business decisions. 

We talked about how companies can still use data when random events like COVID-19 happen. We also talked about what innovation means in the retail space, and how companies can make changes now that will set them up for a long term future, plus so much more.

About Gary Saarenvirta:

Gary Saarenvirta, founder/CEO of Daisy Intelligence, is recognized as one of North America’s preeminent authorities on artificial intelligence. Gary is a passionate evangelist for the application of A.I. and reinforcement learning in strategic merchandising decisions. He’s not afraid to challenge the status quo when it comes to the advantages of deep learning over traditional approaches to data mining and analysis, both in speed and precision. 

Gary’s 25+ years of experience includes managing the analytics practice at Loyalty Consulting Group, as well as leading IBM’s analytics and data warehousing practice areas. He entered the workforce with an aerospace engineering degree (so yes, he’s even a trained rocket scientist), and today his focus lies in making A.I. an indispensable tool for data-driven businesses.

Gary can be reached on LinkedIn: in/garysaarenvirta and Twitter: @daisyintel

Here is the Official Breakdown: 

8:10 - We learn what Daisy Intelligence does, and how their technology is transforming the retail industry

13:10 - How does AI work when it comes to a black swan event like what we’re currently seeing in our current economy? Can this be predicted, and can companies utilize data to actually thrive in a moment like this?

11:15 - Are data modeling and AI the same thing (as they are many times interchanged terms) and if not, understanding the difference between the two?

15:58 - What’s the competitive edge for Daisy Intelligence customer’s when it comes to utilizing predictive analytics, and what kind of return can they see by being able to pivot based on this data?

17:30 - As we see a world-wide spread of changes, can this same data be used to not just optimize results, but predict results.

18:50 - As things begin to settle out of this increase in retail sales, what can companies do to make sure the fall of sales isn’t so drastic?

23:40 - When it comes to being able to implement a high-tech solution like Daisy’s, can small companies consider being able to add something like this to their work flow?

25:50 - In addition to cost, what kind of man power does it take to implement something like this?

28:30 - Are un-organized companies doomed in being able to take advantage of machine intelligence?

31:50 - Is Moore’s Law dead? Can this level of power continue to grow at the rate we’ve become used to?

31:36 - Many people fear AI because they feel that it will take away jobs. Is this true? And if not, how does AI actually benefit a workforce?

35:10 - What are the risks for a company when they’re just focusing on profitability?

37:58 - How can retailers actually focus on innovating, and how can AI intelligence help them  get there?

40:20 - When it comes to AI, why is 95% accuracy not a very impressive number?

45:10 - Will events like we’ve recently seen help retailers finally wake-up to the fact that they must invest in technology?