Learn what's possible and what's working with artificial intelligence in business today.Each week, Emerj founder Daniel Faggella interviews top AI and machine learning-focused executives and researchers in industries like Financial Services, Pharma, Retail, Defense, and more.Discover trends, learn about what's working now, and learn how to adapt and thrive in an era of AI disruption. Be sure to subscribe to "AI in Business."
Today we kick off the first in a series of discussions about AI in Europe with Martin Musiol, Sr. Data Scientist at IBM. Martin is a member of IBM's Technical Expert Counsel, and in this episode he speaks about what makes the European ecosystem different, providing what may be a peak ahead for other nations following Europe's leadership regarding regulation and data-sharing. Learn more with Emerj's Beginning with AI Guide: emerj.com/beg1
In this first episode in our 12-part Saturday "AI Futures" series, UC Berkley Professor and renown AI expert Stuart Russell shares his perspectives on the near and long-term considerations of AI governance. https://emerj.com/ai-podcast-interviews/stuart-russell-ai-governance/ Visit the blog post for this article, and subscribe to the podcast on your favorite podcast platform. From June 27th to September 12th, we'll be covering a new "AI Futures" episode about AI governance every Saturday. This is an experimental series designed to extend current AI trends to long-term AI consequences. We hope you'll enjoy.
Today we interview Babak Hodjat, VP of Evolutionary AI at Cognizant, one of world's largest IT integration & service firms. Babak has 20+ years of AI experience, starting his first AI startup in 1998 in the SF Bay area, and later founding Sentient Technologies which raised $174 million to innovate with AI in trading. In this episode, Babak says "AI should be disruptive.", and leaders should "go big". Want to learn more about how to make the business case for AI? Check out Emerj Plus: emerj.com/p1
Today's guest is Jim Barnebee, VP of AI and Infrastructure at GetSwift, a public company that has raised $143 million to bring AI to the world of Delivery - with a smart platform enabling driver management, dispatch tasks, and tracking in real-time. Jim highlights the 2 areas where he thinks logistics will be impacted the most, breaks down hurdles to successful AI deployment, and discusses multiple promising use cases. Emerj has assessed over 80 AI vendors in the logistics and supply chain space. Learn more about Emerj's AI Opportunity Landscape here: emerj.com/aiol
Today we interview Scott Nowson, AI Lead at PwC Middle East. Scott has a Ph.D. in Informatics, with a focus in NLP. And in today's episode, he shares his valuable perspective as well as actionable insights regarding 3 key phases for making a winning business case for AI i.e. getting sign-off, getting a valuable pilot started, and getting deployment rolling in the right direction. Also check out Emerj's guide: Practical Steps to AI Deployment at emerj.com/beg1
In this episode, we interview Yaron Lavie, VP of Products at Earnix. Yaron breaks shares powerful insights about how AI can be leveraged for customizing financial service products, as well as for other use cases in insurance and banking. For quick and valuable insights, download Emerj's AI in Insurance Cheat Sheet: emerj.com/ins1
What's next for AI in logistics? Listen as we break down prescient trends with Tim Gagnon, VP of Analytics & Data Science at CH Robinson, a $16+ Billion logistics & supply chain company based in the USA. Want to implement AI but you're not sure where to start? Discover 3 Key Insights for Non-Technical Professionals to Deploy AI: emerj.com/beg1
Today our guest is Nayeem Islam, CEO of Blue Hexagon, a bay-area cybersecurity firm that has raised $37 million+ for innovation in expansion. This episode focuses on AI applications for cybersecurity in Retail. For a quick breakdown, check out Emerj's AI in Retail Cheat Sheet: emerj.com/ret1
Discover how to nurture alignment that closes AI deals, as we interview Tim Estes, Founder & CEO of Digital Reasoning, an American AI company that raised over $130 million to bring cognitive computing services to intelligence agencies, financial institutions, and healthcare organizations. Whether selling or buying, technology leaders need to ask the right questions before purchasing or implementing AI solutions. In this episode, Tim offers his valuable perspective on how to match AI solutions with real business problems, how to estimate and quickly calibrate ROI, and how to effectively communicate with decision-makers. Discover our full range of high-ROI use cases for AI with Emerj Plus: emerj.com/p1
This week we interview Gary Swart, Partner at VC firm Polaris Partners, former CEO of oDesk (now UpWork, NASDAQ;UPWK), the world’s largest online service marketplace. Gary’s portfolio includes investments in innovative medtech and pharmaceutical companies. And in this episode, he shares his valuable perspective on how the pandemic may transform the opportunity landscape, at the intersection between AI and Healthcare technology. Discover our full range of high-ROI use cases for AI with Emerj Plus: emerj.com/p1
Discover how to leverage the learning capabilities of AI for strategic advantage, as we interview H.P. Bunaes, former Chief Data Officer at Suntrust, now Director of Banking for Data Robot, a Boston-based startup which has raised over $400 million to innovate enterprise AI solutions. H.P. shares his valuable perspective on how strategic leaders can outmatch competition by enhancing the speed at which the organization can learn, with AI-driven technology. What will separate winners from losers in the new economy? Discover 3 keys with Emerj’s Guide here: emerj.com/k3
Discover how AI is transforming procurement as we interview Edmund Zagorin, founder of a bay-area, venture-backed startup called BidOps. Edmund shares his valuable perspective about what the future of AI in procurement looks like and how it ties into the broader landscape of logistics and supply chains. Learn more about how to make the business case for AI in the enterprise: emerj.com/p1
This week we interview Stephan Pawlowski, VP of Advanced Computing Solutions at Micron, a $20 billion computing company based in America's Midwest. Stephan breaks down the necessary components for AI readiness, sharing pragmatic insights about the AI hype cycle and its relationship to real value for businesses. Download our PDF guide "3 Ways to Discover AI Trends" at: emerj.com/t3
Discover how AI may shift the workflows and approaches to trading as we interview Ash Fontana, managing director at Zetta Venture Partners, a Silicon Valley-based venture capital firm focused on AI-first companies with B2B business models. Ash shares his valuable perspective on the present and future of AI in trading. Access Emerj's AI in Financial Services Vendor Landscape Brief here: emerj.com/vl1
This week we interview Gary Swart, Partner at VC firm Polaris Partners, former CEO of oDesk (now UpWork, NASDAQ;UPWK), the world’s largest online service marketplace. In this episode, he offers a pragmatic perspective about opportunities and challenges for AI adoption in the current economic climate. Discover our full range of high-ROI use cases for AI with Emerj Plus: emerj.com/p1
Logistics is behind the time when it comes to data maturity, and it's nearly impossible to get predictive value from disjointed data. This week, Priya Rajagopalan or FourKites shares critical lessons for finding value in logistics data - as well as AI use-cases already making their way into the supply chain. Learn more about Emerj Plus (emerj.com/p1), and discover our complete AI White Paper Library - including our latest PDF brief: "AI and the Future of the Supply Chain - 3 Critical Trends".
When it comes to AI, the EU is known more for its regulation than its innovation, but it might not have to be that way. This week, Cedric O, French Secretary of State of the Digital Economy, shares his thoughts about balancing the preservation of values and rights with the prosperity and opportunity of new technology. If you sell AI-related produces or services, and want to find opportunities to sell into the US government, learn more about our "Public Sector AI Opportunity Report" at emerj.com/gov1. This interview took place in Paris at OECD headquarters. Thank you to the OECD for arranging this interview and providing a recording room.
Every six months, the landscape of AI vendors and enterprise use-cases is shifting. This week, Gary Hagmueller of Clara Analytics shares his perspective on the kinds of tasks and processes most rip for AI transformation in insurance - with examples and explanations that any businessperson can understand. Stay ahead of insurance AI innovation with our PDF white paper "AI in Insurance Executive Cheat Sheet" - download your copy: emerj.com/ins1
Ryan Smithright is a Research Fellow at Emerj Artificial Intelligence Research, and the lead author in our latest "US Public Sector AI Opportunity Report" (learn more at emerj.com/gov1). Ryan spent months investigating and documenting the various AI initiatives, budgets, and strategies for different branches of the US government, and in this interview he breaks down some surprising trends and data snapshots from the complete report. Anyone with an interest in selling AI to the US government should get a lot out of this unique episode.
Chatbots are a hot topic, but it's just the tip of the iceberg in terms of the broader evolution of the customer experience. This week we're joined by Suhas Uliyar, VP of Digital Assistant AI & Integration at Oracle. Suhas breaks down key areas of user experience change, and how AI is helping (or could help) to usher in that transformation. If you're new to chatbots and NLP applications, download our "Unlocking the Business Value of NLP" PDF guide: www.emerj.com/nlp1
Emerj CEO Daniel Faggella recently spoke at the OECD headquarters in Paris for the creation of their AI Policy Observatory. At the event, we spoke with this week's guest: Lynne Parker, Deputy Chief Technology Officer at the White House Office of Science and Technology Policy. Lynne talks to us about what the US is doing with AI currently at a national level and what the country's priorities are when it comes to AI. This interview is made all the more relevant by the fact that the private sector is freezing up due to the coronavirus. Meanwhile, the public sector is still spending money on AI and innovation. If you want to learn what the opportunities are for vendors and service providers that want to work with the US government, get our US Public Sector AI Opportunity Report at emerj.com/gov1.
Small companies and enterprises alike are drastically changing their AI strategies in response to the coronavirus. This week, we talk about what it looks like to develop an AI strategy the right way during these disruptive times. We interview Joel Minnick, Head of Marketing for AI at Amazon Web Services . Joel breaks down his take on how AI can be used to competitive advantage and why an AI strategy is the foundation of that advantage. If your company is just getting started with AI strategy, be sure to download our Beginning With AI guide at emerj.com/beg1.
In this episode, we focus on digital twins and how AI will impact the future of manufacturing. Our guest is Laurent Laporte, CEO of Braincube, a company focused on AI In manufacturing. This episode is sponsored by Braincube. For more information on sponsorship opportunities, visit emerj.com/advertise.
Although the insurance industry doesn't get as much press about its use of AI as banking, the AI applications large insurance carriers are developing are no less transformative to core processes. This week, we speak with Tom Harrington, the head of the insurance wing at Pegasystems. Tom talks about how automation and AI are making their way into different insurance processes today and how they will change the way employees at large insurance carriers work. If you're interested in discovering best practices for AI adoption and deployment, consider joining our Emerj Plus membership at emerj.com/plus.
Supply chains are frozen, teams are working remote, and sales cycles are unpredictable. This week, we interview Paul Noble, CEO of Verusen, an AI-enabled logistics company. Paul discusses the impact of AI on supply chain and logistics and the new AI capabilities that will be all the more relevant in a post-coronavirus world. Paul also reviews some of the key themes from our AI Business Continuity Action Plan, which comes complimentary with Emerj Plus, including critical ideas about how business leaders can make their business disaster proof. Learn more about the report and Emerj Plus at emerj.com/ap1.
This week, we speak with Liga Semane, Policy Advisor at the European Banking Federation, about how tech priorities for business leaders in financial services are shifting right now because of the coronavirus crisis. Liga shares some of her best ideas about business continuity and risk, as well as some of her experience witnessing the reaction from global European banks and their priority shifts in AI projects and initiatives. This is one of dozens of interviews we've put together for our latest report, the AI Business Continuity Action Plan. It will eventually be offered for sale, but we are currently giving it away free with a subscription to Emerj Plus. Learn more and get the report at emerj.com/ap1.
In this episode, we speak to Saurabh Suri, CIO and managing partner at CerraCap Ventures, about building AI products for the enterprise. Saurabh discusses what he's seen work and not work across his portfolio of AI investments when it comes to their interface with the enterprise. We talk about the challenges that come with not only building a product but actually deploying it in the enterprise. If you want to learn more about how we discover which you can do so at emerj.com/aiol
When it comes to applying AI in the enterprise, it's useful to get the perspectives of companies that have bought AI products before. But it's also useful to get the perspective of companies that have sold AI. They have an idea of what it's like when a company buys AI for the wrong reasons and when a company buys AI for the right reasons. This week we speak with Don Vadakan, Head of Sales at Fractal Analytics. He gives his advice for vendors and service providers that are selling AI in the enterprise, including how to attract attention from potential buyers the right way. His advice is also applicable to enterprise leaders thinking about working with vendors. If you're selling AI into the enterprise, be sure to download our B2B AI Lead Generation Guide at emerj.com/b2b1
This week, we interview Marsal Gavalda, Head of Machine Learning at Square. Marsal gives his take on how to leverage AI for competitive advantage. Specifically, he talks about the elements of culture and teams. If you want to to build a company culture in which teams can leverage AI in a nimble fashion and wield it in different areas of the business, Marsal's advice will be useful. If you're looking to get started with AI at your company, download our Beginning With AI Guide at emerj.com/beg1.
This week on the newly rebranded AI in Business Podcast, we speak with Rashida Hodge, VP, Insurance Industry at IBM, about buying and selling AI in the enterprise. If you are someone at a big company looking to buy AI, you're going to learn a lot about what to do and what not to do in this episode. If you're looking to sell AI to the enterprise, you will learn how to avoid wasting time and money without gaining traction in the enterptise. If you are selling AI, be sure to download our B2B AI Lead Generation Guide, a 7-page PDF put together for vendors at emerj.com/b2b1.
This week, we dive into how AI is evolving personalization as we now know it. Brian Walker is Chief Strategy Officer at Bloomreach. He speaks with us about how Personalization works today in retail and how it will work tomorrow. He also discusses how AI can add additional context to the user's experience and how it can allow companies to make stronger calls to action. If you're interested in more AI use-cases in retail, be sure to download our AI in Retail Executive Cheat Sheet at emerj.com/ret1.
In this special bonus episode of the podcast, Emerj spoke with Dileep George, co-founder of AI company Vicarious, which has raised over $100 million in venture funding, for Kisaco Research's AI Hardware Summit in Europe, which takes place March 10 - 11 in Munich, Germany. We spoke with Dileep about the unique requirements and considerations for adopting AI in robotics use-cases, as well as where AI-enabled robotics will play a role in business in the new decade.
This week, we explore a novel use-case of AI in marketing: copywriting. Can AI write a better email subject line or social media post than a human being? Sometimes, the answer is yes. We interview Parry Malm, CEO of Phrasee, an AI company that got its start optimizing email subject lines with AI. Parry speaks with us about this particular AI capability and how it will impact the future of marketing. If you have an interest in discovering more about the AI use-cases in your industry, watch the 2-minute video about our AI Opportunity Landscape Service at emerj.com/aiol.
On this special bonus episode of the podcast, we spoke with Victoria Rege, Director of Alliances & Strategic Partnerships and Graphcore, for Kisaco Research's AI Hardware Summit in Europe, which takes place March 10 - 11 in Munich, Germany. We discuss the current challenges enterprises are facing in adopting and using AI hardware. We then project where AI hardware may find a home in business in the new decade.
Personalization is an area that is going to move fastest in retail and will spin out into other industries from there. This week, we interview the CTO of a company that does exactly that: Tyler Foster of Evolv.ai. Typer gives his perspective on what personalization can do today and where AI will make its way into the future of retail. Download our AI in Retail Executive Cheat Sheet, a short collection of AI use-cases in retail and a rundown of AI-related terms in retail at emerj.com/ret1.
This week, we're continuing our month-long focus on AI use-cases in retail and eCommerce. Our guest this week is Mahmoud Arram, CTO and Founder of Bluecore. Mahmoud's focus is on the marketing and advertising side of retail. He provides an interesting perspective on how AI could evolve marketing processes in terms of getting the right message in front of the right person at the right time. Be sure to download our AI in Retail Executive Cheat Sheet, where we lay out the basic use-cases of AI in retail in eCommerce and define key AI-related terms in the industry at emerj.com/ret1.
This month on AI in Industry, we focus on AI in retail and eCommerce. Our first guest is Guru Hariharan, CEO of CommerceIQ. Guru shares his thoughts on the current impact of AI on retail and eCommerce today, including in sales, marketing, and supply chain. We've recently put together our AI in Retail Executive Cheat Sheet, an overview of prominent AI use-cases in retail and eCommerce. Also included is a glossary of key terms businesspeople in retail need to know to navigate the AI conversation in their industry. Download it at emerj.com/ret1.
Our guest this week is Dr. Charles Martin of Calculation Consulting, a bit of a mentor of mine when it comes to AI in the enterprise. He understands a lot of the ups and downs of what it takes to apply AI in business. He speaks with us this week about how to determine your vendor needs. Are you looking for a consultant? A specific vendor? Are you looking to partner with a vendor? Figuring this out right off the bat might be the easiest way to save money and time. If you're picking a vendor for your company or for your clients, be sure to download our free pdf guide on the topic: 5 Ways to Select the Right AI Vendor
Our guest this week is Gaurav Srivastava, CTO at Fareye, an AI company in the logistics space. Gaurav talks about which elements of the existing procurement procedures in the enterprise can stick around and which need to be different when it comes to shopping for AI and looking for new AI solutions. He talks about demos, pilots, social proof, and troubleshooting some of the potential issues that come up when looking for the right AI vendor. If you haven't already downloaded our PDF guide, 5 Ways to Select the Right AI vendor, you can do so at emerj.com/buy1
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 compute. Groq is in partnership with the AI Hardware Summit happening n Mountain View, California on September 17 and 18. Software defined compute is a way of thinking about how compute can be optimized for machine learning functions. Ross talks about some of the pros and cons of GPUs and where software defined computer 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 unicorn company in Bangalore. 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. IConzelmann 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.
I hope that by the end of this episode of the AI in Industry podcast, you'll not only be able to hire better data scientists who will be a fit for your business problems and build better data science teams, but also pick the AI applications and use cases that you should bring into your business versus those that you shouldn't. This episode, we interview Brooke Wenig, the machine learning practice lead at Databricks. Databricks was founded by the folks who created Apache Spark. Those of you who are technically savvy with AI will be familiar with Apache Spark as an open source language for artificial intelligence and distributed computing. Wenig works with a lot of companies with Databricks. Databricks is now close to 700 folks and helps implement AI applications into, oftentimes, large enterprise environments. Wenig speaks with us this week about what to look for in an actual data scientist and how to find data science folks with the right skills to be able to communicate to business people, not just to work with models. What should people be capable of; how should they be capable of thinking? Hopefully, some of you will have better interview questions by the end of this podcast. In addition, we ask Brooke about what the value of covering the cutting edge applications of AI is, looking at what's working in industry. How does that help us in our own business make better decisions? Read the full article on Emerj.com
If you want to understand the international competitive dynamics of artificial intelligence, particularly the US and China, starting with the United Nations is probably not a bad move. This week, I spoke with Irakli Beridze, the head of the Center for Artificial Intelligence and Robotics at the UN, particularly under the wing called UNICRI, the organization's crime and justice division. Irakli was kind enough to invite me to speak at a recent event in Shanghai held by the UN and by the Shanghai Institutes for International Studies on national security, and when we were there, we talked a good deal about China's unique AI-related strengths. I spoke with Irakli about the strengths of the ecosystem in China for artificial intelligence and how that stacks up against the US. In addition, I asked Irakli about what it's going to look like to encourage more and more multilateral action. In other words, how do we get countries to be on the same page so AI doesn't become an arms race?
Discover how so-called autoML, or automated machine learning, could bring AI to more businesses by allowing users to build AI models faster and cheaper. Read the full article, where we go into further detail, at Emerj.com. Search for "AutoML and How AI Could Become More Accessible to Businesses"
AI has numerous use cases in legal, from document search to compliance and contract abstraction. This week, we speak with Lars Mahler, Chief Science Officer for LegalSifter, about what's possible with AI for legal departments today and how AI applications for legal teams, such as natural language processing-based contract analysis, work. In addition, Mahler discusses how lawyers at companies and data scientists work together to train machine learning algorithms. He provides some insight into how a company has to make its way into the legal space and the challenges of training an NLP system and collecting data for it. Read more about AI in legal at Emerj.com
There's a lot of venture money pouring into artificial intelligence in healthcare. From pharma to hospitals and beyond, the potential applications in healthcare are promising. Late last year, we spoke for The World Bank about our proprietary AI in healthcare research, and speaking with governments, it's clear that there are hurdles that healthcare companies have to overcome to access data for training AI systems. Broadly, most of the folks that we speak with who are innovating in AI and healthcare are frustrated with how hard it is to streamline the data to make use of it for applications such as diagnosing illnesses. But why is that? That's a question that we asked our guest this week. Our guest this week is Zhigang Chen, and he speaks about why this problem exists and how it can be overcome. In addition, Chen talks about the AI ecosystem in China and how it differs from Silicon Valley.
Saying that your company does artificial intelligence might still have a slightly cool ring to it if you're talking to one of your peers at a conference, but it doesn't mean very much to venture capitalists today, who've been battered with machine learning and artificial intelligence in every pitch deck they've seen for the last three or four years. I wondered, from a venture capitalist perspective, what makes an AI company's value proposition actually strong? What is it that makes an AI startup actually seem like a company that maybe could use AI to really win in the market? Not just to be another company that says they're going to do it or says they are doing it, but where can it actually provide enough of that competitive edge to make a VC want to pull the trigger? Getting a grasp of the answer to that question seems pretty critical. This week, we speak with Tim Chang, partner at Mayfield Fund in Menlo Park, California. Chang and I both spoke at the Trans Tech Conference, held every year in Silicon Valley, focused on wellness and health-related technologies. Chang talks about what it is about an AI company's pitch, product, and market that actually makes AI an enhancement to the business in a way that's compelling to someone who wants to invest potentially millions and millions of dollars.
If one wants to start a general search engine, they're going to have to compete with Google. If one wants to start a general eCommerce platform, they'll have to compete with Amazon. But the same dynamics play out on a smaller scale. There are going to be some established players, some big tech giant, be it IBM or someone else, who already has a product. When it comes to getting a new AI product out to market, how does one compete with the big guys? This week's guest is Mike Edelhart, who runs Social Starts and Joyance Partners, seed stage investment firms out in the Bay Area. Edelhart has invested in a number of companies, and in this episode, we get his perspective on not only the patterns among successful AI startups and where AI plays a role in their competitive strategy, but what a "land and expand" strategy looks like for a new product that already has larger and more established competitors.
A lot of AI in the press is CMOs or marketing people talking about what a company can do in a way that really is aspirational. They're speaking about what they can do, but in reality, the things that they're talking about, the capabilities won't be unlocked for maybe a year or more. These are just things on the technology road map, but people speak about them like they exist now. This week, we speak with Abinash Tripathy, founder and Chief Strategy Officer at Help Shift. They've raised upwards of $40,000,000 in the last six years to apply artificial intelligence to the future of customer service, and we speak about the hard challenges of chatbots and conversational interfaces, as well as how long it's going to be until those are actually robust. This in opposition to how people at large companies might put out a press release touting their own chatbots that simply aren't capable of doing what they say they can to any meaningful degree. We also talk about where AI can augment and make a difference in existing customer service workflows. Even if we can't have all-capable chatbots to handle banking or insurance or eCommerce questions from people, where can AI easily slide it's way in and actually make a difference today? In this episode, we draw a firm line on where the technology currently stands. Overall, though, this episode is about the challenges of actually innovating in AI. We talk about why it really is the big companies that do a lot of the actual cutting edge breakthroughs of AI and why others are going to have to license those their technologies from large firms like Google and Amazon. We also discuss why companies maybe need to have a realistic expectation about where they can apply AI, as well as why actually innovating and coming up with new AI capabilities on their own might just be wholly unreasonable given their data, their company culture, and their density of AI talent. Read the full interview article on emerj.com
This week we interview a leader at Facebook. Jason Sundram is the lead of World.ai at Facebook, which is one of their efforts to work with public data around roads and population and other projects of that kind. But Sundram is also highly involved in the Boston office here, where Facebook will soon have around 650 employees. Many of them focus on data science and artificial intelligence. Last time we talked about personalization in AI with Hussein Mehanna, who was Director of Engineering at Facebook at the time. This time, we'll talk about two topics that all established sectors need to be focusing on: How does one build ML and data science teams? How does one pick an AI project? For business leaders who are considering hiring data science talent or thinking about how to start with AI in terms of making a difference in their bottom line, this should be a useful episode.
One of the promises of artificial intelligence is aiding humans in making smarter decisions. Whether it's in pharma, retail, or eCommerce companies, the idea of being able to pool together streams of data and coax out the insights that would help make the best call for the organization to reach its goals is the promise of artificial intelligence. As it turns out that same dynamic is sort of happening in the public sector where AI is now being used to inform policy. This week we interview Professor Joan Peckham at the University of Rhode Island. Previously, she was Program Director at the National Science Foundation. PhD in computer science and she runs the Data Science Initiatives at URI. The University of Rhode Island is home to DataSpark, an organization that helps policymakers inform the decisions that they're going to make about the economy, the environment, the opioid crisis, a variety of social issues, based on deeper assessments of the data. The ability to find objective insights might help policymakers make better decisions about where they allocate budget and what decisions are made. Right now, policymakers are beginning to tune into artificial intelligence as a source of informing their decisions. The same dynamic will likely play out in the C-suite, particularly when the data is actually there. For more on AI in government, visit Emerj.com