The tough reality around AI adoption and what to do to actually succeed

This podcast interview focuses on what’s real and not real in the world of AI, and my guest is Daniel Faggella, founder and CEO of Emerj AI Research Called upon by organizations like the World Bank, the United Nations, INTERPOL, and global pharmaceutical and banking companies, Emerj CEO Daniel Faggella helps business and government leaders navigate the competitive landscape of AI capabilities, and build strategies that win. His company, Emerj, helps governments and enterprises reduce risk and maximize the bottom-line impact of artificial intelligence capabilities. They map the capability-space of AI across major sectors (banking, pharma, retail, etcetera), helping leaders see what's possible, what's working i.e., where’s real ROI and traction, and what to do about it. Being an active reader of his weekly update on the world of AI and got inspired by Daniels down to earth and challenger view on the topic. This is why I invited him to my podcast. We explore the challenges in adoption of AI in the market and what’s the deeper causes behind that. We also address the approach business leaders need to follow, and the VC mindset they need to embrace, in order to avoid the challenges.  Here are some of his quotes: I was getting out of grad school at U-Pen, it became pretty obvious that a lot of these corollaries with neuroscience with cognitive science around how we learn, we're being experimented with and explored in machines. By the time I got out, I sort of realized that, should this trend continue, this is going to be some really wacky wild future we're headed into in 20 years, if AI continues to be able to do what it could do in vision. So, I decided I should get dedicated to this stuff and understand its impact for humanity in the long term. Everybody, I think, usually expects me to be the hype, man of AI, you know, talk about how awesome and transformative things are. And certainly in the long haul, I think that we're in for a pretty wild ride. I think that probably in the next 40 years, we'll see post human intelligence potentially. Right now, it's a lot of struggle to be honest with you. So, most AI, you know, quote, unquote, kind of AI innovation efforts within the enterprise, are really at a pilot level at a proof of concept level. And if we look at a space like banking or insurance, is it safe to say that most of these pOcT fail rather than win. There are very serious major challenges During this interview, you will learn three things: Why, in order to create sustainable competitive advantage, data-dominance is the foundation to strive for That succeeding in AI requires you to step out of the sand-box, and stress test your application with your feed in the mud, and overcome the real-world data challenges Why solving a big problem with your solution is not enough. Getting a deep understanding of the objectives and objections that move the needle in the hearts of the people that need to buy from you is what’s required as well. See acast.com/privacy for privacy and opt-out information. Learn more about your ad choices. Visit megaphone.fm/adchoices