The AI in Business Podcast

We continue our theme this month on buying and procuring AI in the enterprise with this week's guest: Shane Zabel, Head of AI at Raytheon. Shane has seen plenty of internal AI applications and heard plenty of AI vendor pitches. As such, he has some simple and succinct advice for picking an AI vendor that he shares with us on this episode of AI in Industry. If you haven't already, download our free PDF guide: 5 Keys to Selecting the Right AI Vendor at emerj.com/buy1

It's our first AI in Industry episode of the decade. January's theme is on buying and procuring AI in the enterprise. Many of our readers at Emerj want to know which vendors and use-cases are legitimate and which are riding the hype. When it comes to picking an AI vendor, that knowledge is critical. Our guest this week is Pranay Agrawal, CEO of Fractal Analytics. Pranay shares some of his advice on the technical and cultural considerations for finding the right vendor partner. If you're interested in learning more about working with an AI vendor, we've created a quick PDF guide called 5 Keys to Selecting the Right AI Vendor, which you can download at emerj.com/buy1.

We've touched on a lot of different kinds of expertise in this month-long series on using AI for competitive advantage. We end things by speaking with someone from the startup world: Adam Oliner, Head of Machine Learning at Slack. He speaks with us about building a data moat around your business when it comes to building AI solutions in-house. He puts the overall theme for leveraging AI for competitive advantage very succinctly. If you're just getting started with AI in your business, be sure to download our succinct guide to adopting AI, Beginning With AI, at emerj.com/beg1.

This week, we speak with Ylan Kazi, VP of Data Science and Machine Learning at UnitedHealth Group. Ylan speaks with us about his take on how AI can be leveraged for competitive advantage, including how to build a "flywheel" of data and build on critical capabilities for adopting AI in the enterprise. If you're at an existing business and looking to get started with AI, be sure to download our free report, Beginning With AI, at emerj.com/beg1.

This week, we speak with Abigail Hing Wen, Co-Chair of the Fairness, Transparency, and Accountability Expert Group, Machine Learning Transparency. We discuss which AI capabilities actually have the most traction in terms of the science. Most importantly, Abigail talks about how business leaders can wield these promising capabilities in their industries. If you're interested in getting started with AI capabilities, be sure to download ur free Beginning with AI report at emerj.com/beg1.

This week on AI in Industry we continue our theme of focusing on "The Competitive Advantage of AI." Babak Hodjat shares his insights about critical decision-points within a business, and how to leverage them to find high-ROI AI opportunities. Download our free PDF report titled "3 Ways to Discovery AI Trends in Any Sector": emerj.com/t3

This month's theme is on using AI for a competitive advantage. We speak first with Monika Wilczak, Managing Director of AI at EY. Monika speaks to us about how large companies can start to get an edge over the competition by leveraging AI, emphasizing how companies can get started with gaining that advantage. If you're looking for areas of AI opportunity, be sure to download our "3 Ways to Discover AI Trends in Any Sector" report by going to emerj.com/t3.

It's the final week of our month-long series on planning your corporate AI strategy. This week we speak with Shane Zabel, Head of AI at Raytheon. Shane talks to us about the phases of building an AI strategy. What are the steps? He discusses the importance of finding an AI pioneer at a company who can build some initial ideas of what AI use-cases could be viable at the company. If you're in the process of analyzing AI use-cases for your company or clients, we created a guide for this exact topic. Learn more about it at emerj.com/t3.

This week, we speak with Scott Nowson, AI Lead at PWC Middle East, about the critical capabilities nontechnical business people need to understand to be able to advance their career and apply AI in their industry even if their company hasn't started with AI yet. Scott has an uncanny ability to convey business lessons on AI, and he's one of the few people who got our full Getting Started with AI report before today's formal launch. The report is finally open, and in it, listeners can find the must-know knowledge that will allow you to take your AI interest and turn it into real career opportunity without learning any code. Listeners can learn more about it at emerj.com/a1

In this episode, we speak with Adam Bonnifield, VP of AI at Airbus, one of the youngest executives at the firm. He talks about how to think about starting a corporate AI strategy, which for him entails beginning with the data assets. Adam thinks through how to take account of those assets and what kinds of people need to be part of the conversation to unlock the most fruitful AI applications in an established company.

This is a special bonus episode of AI in Industry about advancing your career in the era of AI, specifically for non-technical professionals. If you don't want to learn to code but still make yourself tremendously valuable in the era of AI, this episode is for you. We put together a report on this topic that will be coming out this month, all about getting started with AI in business for nontechnical professionals. Interested listeners can go to emerj.com/c1 to learn more. This week, we have Germán Sanchis-Trilles on the podcast. He's one of our technical advisors, well-schooled in natural language processing, and extremely experienced in applying AI in business. In this episode, he reviews some of the key themes from our upcoming report, including critical ideas about how nontechnical professionals can involve themselves in AI while at work.

This week, we speak with Carlos Escapa, Global AI and ML Practice Leader for Amazon Web Services. Carlos speaks with us this week about starting an AI strategy with a more practical approach. Instead of thinking about how to radically reshape a key part of your business with AI or use AI for AI's sake, Carlos talks about instead thinking about where AI fits in with what your business is already doing. He provides some thought experiments to run through for thinking through this and how to get started with AI.

Last month, we focused on advancing your career in the age of AI, and this month we have a new theme: building your corporate AI strategy. At Emerj, much of our work in the public and private sector is in building an AI strategy and giving organizations data on where the ROI is in the AI world. This week, we speak with Ian Wilson, former Head of AI at HSBC and a research advisor for our banking work. He has rare experience applying AI strategically at one of the largest banks in the world, and I think he is just the person to start off this month's theme. Ian talks about beginning to plan your AI strategy.

This is our final episode in our series on advancing your career in the era of AI this month. We had more Linkedin messages on this theme than any we've done before, and it got me excited to think about what we could do with this kind of series in the future. In this episode, we distill the insights from this month's series with insights from our broad catalog of interviews with AI-minded executives throughout the many years doing this podcast. We also cover three AI-related career roles that do not involve coding.

We continue our theme on advancing your career in the era of AI. This week, we speak with another AI lead from a gigantic IT services firm: Cognizant. Bret Greenstein is Head of AI at Cognizant, and he talks about what folks who think about AI in terms of strategic direction, project management, etc., have in common. Brett also discusses how non-technical folks can think about AI in order to take on leadership roles in AI projects, including having a firm understanding of what is possible with AI in their industry.

This week, we speak with Sriram Ramanathan, CTO at Genpact, about what the important non-technical roles exist for making AI work in the enterprise. Everything from project management to quality control and beyond, Sriram lists out areas where nontechnical experts play a critical role in bringing AI to life.

If you're listening to this podcast, you at least have an interest in leveraging AI in the enterprise. But how do you take that interest and use it to move up in your company and advance your career? In this week's episode, we speak with Muriël Serrurier Schepper, who worked with AI at Rabobank and Shell managing advanced analytics projects. She now has her own AI consulting firm. Muriël speaks with us about her experience using her prior skillset to enter the world of AI, take the reigns of exciting AI projects, and open up more career opportunities for herself.

In October, we're focusing on how non-technical employees can still gain an edge in the era of AI even if they've never learned any code. I can't think of a better guest off the bat than our quest this week: Wijay Wijayakumaran, Chief Architect of Machine Learning and AI at IBM Australia. Wijay emphasizes how much stock he places in the critical importance of subject-matter experts and business leaders with domain knowledge. He also runs through possible career opportunities that non-technical employees can look for in the era of AI and questions they can ask to get more involved with AI projects at their organization.

This is the final episode of our series on the ROI of AI. This week is the monthly analyst call, in which Emerj CEO Daniel Faggella breaks down some of the key themes from this month's interviews. In particular, Daniel puts a large emphasis on connecting the dots between near-term and long-term ROI. A lot of these themes and core questions are discussed and answered for clients of our AI Product Development Roadmap services.

This week, we spoke with David Carmona, the GM of Artificial Intelligence at Microsoft, about his approach to AI ROI with the enterprise clients of Microsoft. The biggest takeaway from this episode comes right at the beginning. David talks about how to think about artificial intelligence ROI in the long-term and the near-term. That is to say, how are we going to see a relatively near-term return with AI that might be able to improve our condition while keeping in mind the longer-term disruption in our industry?

It's clear that there's a revolution in how artificial intelligence is done with neural networks as opposed to the old school systems of the '80s and the '90s. It's clear that hardware is beginning to evolve, and it's also quite clear that the way that we power these hardware systems is going to have to change. GPUs and AI hardware are tremendously power-intensive, and this week we speak with Robert Gendron of Vicor Corporation, a company focused on powering AI systems. Vicor is in partnership with Kisaco Research, which is putting on the 2019 AI Hardware Summit September 17 and 18 in Mountain View, California. Robert speaks about why the way that they are powered needs to be different than traditional manufacturing equipment. He also discusses how the powering of these systems need to work if businesses want to reduce energy costs and be as efficient as they can when it comes to AI.

This week, we have a bonus episode. We spoke with Jonathan Ross, CEO and Founder of Groq, an AI hardware company, about software-defined computing. Groq is in partnership with the AI Hardware Summit happening in Mountain View, California on September 17 and 18. Software-defined computing is a way of thinking about how computing can be optimized for machine learning functions. Ross talks about some of the pros and cons of GPUs and where software-defined computing might make its way into future machine learning applications.

This week, we speak with Dr. Charles Martin of Calculation Consulting. He's a bit of a mentor of mine when it comes to AI knowledge. Charles speaks to us about the pitfalls in getting to ROI, particularly the cultural elements within enterprises that make it so hard to get a return from AI projects. Charles and I tend to go off in a variety of directions when we talk—he's an animated guy—so be prepared for that. But I think this is an awfully fun episode of the podcast. For more on the fundamentals of getting started with AI in business, learn more about our newest report: Getting Started with AI: Proven Best Practices of Adoption.

We have a bonus episode this week. We spoke to Moe Tanabian, General Manager of Intelligent Devices at Microsoft, who is speaking at the AI Hardware Summit in Mountain View, California on September 17 and 18. Tanabian discusses how to think about and reframe business problems to make them more accessible for AI, as well as AI at the edge, which involves doing AI processing on individual devices rather than in the cloud. The edge could open up new potential for business problems to be solved with AI. Tanabian also provides representative use cases of intelligent devices.

This month, we focus on the ROI of AI, and our guest this week is Sankar Narayanan, Chief Practice Officer at Fractal Analytics, a global AI & Analytics firm headquartered in New York City. In this episode, Narayanan discusses how to measure the ROI of AI in ways that aren't just financial return. In addition, he provides examples from his hands-on experience implementing AI to provide business leaders with ways of thinking about success when it comes to AI projects. For more on measuring the ROI of AI, learn about our newest report Getting Started With AI: Proven Best Practices of AI Adoption.

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.

Building an AI strategy - there's hardly anything more vague and open-ended than that. Business leaders have probably gotten the idea that they should develop one, but where should they start? That's what we talk about this week with Charles Martin, PhD. Martin talks about how to go about starting an AI strategy, what to avoid, and the challenges and struggles of applying AI at existing businesses. Also, Martin discusses what business leaders should ignore and what business leaders should tune into and prioritize for an effective AI strategy that will propel them toward success in the coming years.

One of the best conversations I ever had on the topic of AI business strategy on the podcast was with the guest I've brought back this week: Madhu Shekar, Head of Digital Innovation for Amazon Internet Services in Bangalore. I wanted to do a deeper session with Madhu, who has seen a lot of companies go from no AI to beginning with AI, about where to start with AI adoption. How do companies build the expertise and experience with AI that lets them scale it to their organization? He also talks about how to prepare realistically for AI, including data requirements, integration times, and more.

As it turns out, often times terms like predictive analytics and data science are used incorrectly. By the end of this podcast, you'll have greater clarity on five potentially vague AI and data science terms that are sometimes overused in conversations about AI in the enterprise. This week, I introduce you to German Sanches, who focused his PhD on NLP and has done a lot of AI work in business. He also helps us with our research projects. This episode is all about addressing use-cases in reference to five terms that a lot of folks get wrong.

This week, we interview Arnab Kumar, Founding Manager, Frontier Technologies for the NITI Aayog, the wing of the Indian government focused on rolling out AI into areas like healthcare and agriculture. In this episode, we talk about critical factors for applying AI at the national level, such as where to begin applying AI and what the low-hanging fruit is for gaining traction, leverage, and data assets that are going to transfer elsewhere. We also talk about how governments, much like enterprises, need a future vision for critical capabilities they're going to enable with AI. Finally, Kumar discusses what he thinks are the most transferable lessons for the enterprise from his experience building out a national AI strategy.

Erin Knealy is the portfolio manager of the cybersecurity division of the Us Department of Homeland Security. She is the interface between the US government and the startup and tech ecosystems. We speak with her about transferable lessons from the AI use-cases in the public sector into the private sector. How does an existing organization pick the right first AI project? How should look through a lens of opportunity when it comes to AI? In this episode, we discuss how these lessons learned in the public sector can apply to the private sector.

It's curious to see how much more there is of sensor tech and internet of things than there was 18 months ago. This week, we speak with Cormac Driver, PhD and Head of Product Engineering at Temboo, an IoT vendor. We talk about how to spot AI and IoT opportunity where sensors and equipment in the physical world can actually deliver ROI and drive value for an enterprise. In addition, Cormac discusses how to get the most out of an IoT project and what's involved in terms of data and infrastructure. Finally, I ask Cormac in what sector IoT will become ubiquitous first.

There's an entire artificial intelligence ecosystem for enterprise search. Most of this is in a purely digital world. Most vendors help with a layer of AI-enabled search that understands terms or phrases and is able to return the results or answers to questions that someone types in. But the problem is compounded when it comes to searching the physical world. That is the topic of this week's episode of AI in Industry. Our guest is Anke Conzelmann, Director of Product Management at Iron Mountain. Iron Mountain is a four-billion-dollar physical and digital storage company based in the Boston area. They handle the records of some of the largest financial, health care, and retail brands around the world. Conzelmann speaks with us about the future potential of artificial intelligence for search within an enterprise, not just of digital files, but across formats.

The AI in Industry podcast is all about transferrable lessons. Today we speak with Andrew Byrnes, an investment director at Comet Labs in San Francisco about the competitive edge with AI. What does it look like when companies adopt AI in a way that gives them a competitive advantage? Byrnes breaks down the idea into two categories: automation and augmentation.

We did a lot of focus on healthcare for the World Bank, and we presented a lot of that research in South Africa. When I was there, I interviewed DataProft cofounder Frans Cronje about the intersection of AI and manufacturing. We talk about what's possible with AI in manufacturing today and just how instrumented and challenging it is to add a layer of AI insight into a manufacturing environment. This is much harder than a lot of other domains where data is maybe more accessible, and in some cases it's also higher risk.

This week we speak with founder and CEO of Aidoc, Elad Walach, about the challenges of adopting AI to become part of a workflow in healthcare. We speak to him about what it is that makes it so challenging to get these tools to become part of the process of treating patients.

This week, we speak with arguably one of the best-known folks in the domain of neural networks: Jurgen Schmidhuber. He's working on a lot of different applications now in heavy industry, self-driving cars, and other spaces. We talk to him about the future of manufacturing and more broadly, how machines and robots learn. Schmidhuber uses the analogy of a baby learning about the world around it. He has a lot of interesting perspectives on how the general progression of making machines more intelligent will affect other industries outside of where AI is arguably best known today: consumer tech and advertising. If you're in the manufacturing space, this will be an interesting interview to tune into. If you're just interested in what the next phase in AI might be like, I think Schmidhuber actually frames it pretty succinctly.

The AI In Industry podcast is often conducted over Skype, and this week's guest happens to be one of its early developers. Jaan Tallinn is recognized as sort of one of the technical leads behind Skype as a platform. I met Jaan while we were both doing round table sessions at the World Government Summit, and in this episode, I talk to Tallinn about a topic that we often don't get to cover on the podcast: the consequences of artificial general intelligence. Where's this going to take humanity in the next hundred years?

In this episode of the AI in Industry podcast, we speak with Marshall Choy, VP of Product at SambaNova, an AI hardware firm based in the Bay Area. SambaNova was founded by a number of Oracle and Sun Micro Systems alumni. We speak with Choy on two fundamental questions: How will business models fundamentally change with respect to new AI hardware capabilities? How can business leaders think about their AI hardware needs? SambaNova is one of many firms that's going to be advertising at the Kisaco Research AI Hardware Summit in Beijing June 4th and 5th.

Danny Lange heads up the AI efforts at Unity, one of the better-known firms in terms of simulations and computer graphics. They work in several different industries, but this week we speak mostly about automotive. This is a man that has been in the AI game since before it was cool, and now he is working on some cutting-edge projects with Unity. In this interview, we speak with Danny about where simulated environments are becoming valuable. We hear about simulations mostly in the context of video games, and of course, Unity does apply their technology in that domain, but what about a space like automotive, where navigating within an environment is important? Certainly we need to have physical cars on the road to drink in data from physical roads and physical environments, but is it possible to splinter some digital cars into digital environments that model the physics, that model the roads, that model the same number of pedestrian risks, and see how well they succeed in all these different environments with no real physical risk of damaging an actual vehicle or an actual person on the road? As it turns out, there's value there.

Have you ever been frustrated with how Alexa or Siri don't always understand your verbal requests? If so, then you already understand the problem that our guest this struggles with. He's Tom Livine, co-founder and CEO of Verbit.ai. Verbit is a company that focuses on AI for transcription. They use a combination of machine learning and human experts to transcribe audio in different accents, in different noise environments, with different diction, to give people more accurate results and hopefully help the process scale. In this episode, Levine explains five different factors that go into getting transcription right and getting AI to be able to aid in the process. In addition, Tom talks about some of the critical factors for where transcription will come into play in terms of bringing value into business.