Stay ConnectedThe AI in Business Podcast is for non-technical business leaders who need to find AI opportunities, align AI capabilities with strategy, and deliver ROI.
Each week, Emerj research staff and journalists interview top AI executives from Fortune 2000 firms and unicorn startups – uncovering trends, use-cases, and best practices for practical AI adoption.
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Copyright: © Daniel Faggella, 2014
This is the final episode in the month-long series on getting started with AI. In this episode, Emerj CEO Daniel Faggella breaks down the key insights from all four of this month's interviews, distilling them into core best-practices for getting started with artificial intelligence in business. In addition, Daniel discusses insights from our newest report:
Getting Started with AI: Proven Best-Practices for AI Adoption
This week we interview Jan Kautz, Vice President of Learning and Perception Research at NVIDIA. Kautz talks about what people underestimate when they start an AI initiative. In addition, he emphasizes the critical value of data storage.
Kautz dives into the importance of getting started with an AI project when you already have a barometer of success. Essentially, he talks about why it's important to select a first AI project in an area where you already have a way of measuring success.
Learn more about AI adoption in our full report, Getting Started With AI: Proven Best Practices for AI Adoption.
This week, we speak with Jan Neumann, Senior Director of Applied AI Research at Comcast. Comcast is an enormous company; it has lots of data, lots of application areas for AI, and a lot of opportunity for confusion about AI. As such, Neumann speaks with us about scaling AI expertise in the enterprise.
Neumann talks about a very strong distinction between software and AI and how to think through problems to determine whether or not it's a software problem or an AI problem.
He also talks about scaling the problem-solving abilities of business experts in the organization. Lastly, Neumann talks about his ideas for how to determine a first AI initiative.
This week we speak with David Carmona, General Manager of AI at Microsoft. Carmona discusses how redefining a business process is a very different kind of AI adoption project than working on something that is horizontal.
He discusses how to attack both of these scenarios, which to handle first, and why.
In addition, Carmona talks about proprietary data and things that are close to your own IP. How do you take advantage of the real strategic data value within your own organization? How should you be thinking about that differently? Carmona poses three different questions to determine where those valuable opportunities are for you.
It's the first episode of the new style of AI in Industry, in which we spend a month at a time on a specific theme. This month is AI adoption.
This week we speak with Vlad Sejnoha at Glasswing Ventures, an AI-focused VC firm. Sejnoha spent many years as the CTO at Nuance Communications. He talks to us about the table stakes AI insights the C-suite have to know and the dangers of relying entirely on consulting firms and vendor companies for these insights.
In addition, Sejnoha discusses the need for a "BS-o-meter" for when someone is making a claim about AI to determine if it's real or hype.
Lastly, Sejnoha discusses how he would go about choosing a first AI project.
This episode of the AI in industry podcast is all about where the rubber meets the road for AI in Insurance. We interview Jerry Overton, Head of AI and a Fellow at DXC Technology. He speaks to us about his experience implementing AI in insurance, about where there's real traction with AI in insurance, and where there's only hype. In particular, Overton discusses how anomaly detection technology is a natural fit for AI in the insurance sector.
This is the last episode of its kind on AI in Industry. Starting next Tuesday, we'll be kicking off a new format for the show. Each month, we'll focus on a specific theme, and in August, we're focusing on AI adoption in the enterprise. We hope you'll join us.
When we polled our audience about what they were interested in, the most selected response was "business intelligence." As a follow-up, we asked them what business intelligence meant to them, and their responses boiled down to anything about understanding the data businesses are already collecting.
That kind of broad definition gets to the heart of the confusion surrounding the differences between business intelligence and artificial intelligence. The line is starting to get blurry.
Our guest this week is Elif Tutuk, Senior Director at Qlik. Tutuk talks about how business intelligence is evolving and how we might define it now that a lot of BI is becoming AI. Tutuk discusses where AI is making its way into business intelligence and what that might enable for businesses.
Read our comprehensive definition of machine learning for business leaders here: https://bit.ly/2Ya2NxK
This week, we interview Jay Budzik, CTO at ZestFinance, about where AI applies to the world of auto-lending. We speak with Budzik about how underwriting and credit scoring is evolving as a result of advances in machine learning.
In addition, we talk about how companies might solve the "black box" of machine learning in finance, particularly how ZestFinance is focusing on transparent models. The financial sector has to contend with complex regulations that prevent certain information from being leveraged in credit models. It can be near impossible to determine how machine learning comes to the conclusions it does, but ZestFinance claims their software in part solves this problem.
Some say that the competitive dynamics between the US and China in terms of AI are overblown, but there's a lot of truth to them. The US has access to more of the base research, but China can orchestrate various organizations (corporations, government bodies) and secure government funding.
That said, very few people talk about K-12 education and what countries are doing to prepare their future workforce for AI. David Touretzky talks to us about just that. He is a research professor in the Computer Science Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University. He's heading up an initiative for K-12 education, and he discusses what countries should be doing to secure their positions and technological leadership in the 21st century.
While AI is certainly finding its footing in finance, we still find most of our subscribers are in a phase where they're trying to catch up in terms of data and data infrastructure and figure out where there's real traction with AI in finance: in banking, investing, or insurance.
In this episode, we explore AI use-cases in a number of these areas of the financial industry. We interview Carlos Pazos and Anwar Ghauche at Spark Cognition about how to maximize a smaller data science team at a financial institution, how AI and alternative data is being used for quantamental investing, and how AI is automating some financing and underwriting processes.
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