Today’s guest is Ash Fontana, Special Advisor at Zetta Venture Partners based in the Bay Area. After recently publishing his book, “The AI-First Company,” Ash joins us again on the show to discuss some of the core transferable lessons he’s learned. Our topic for this episode is advantage. Ash discusses a way of thinking about building momentum around data and gaining long-term advantage as a result. This episode is one of two episodes Ash recently recorded with us. If you enjoyed today’s episode, be sure to tune in to his second episode on The AI Consulting Podcast to gain insight on how services companies can turn into AI product companies. 

Today’s guest is Dr. Charles Martin, Founder of Calculation Consulting. Charles has worked on real enterprise deployments with companies like eBay, BlackRock, and exciting startups like Aardvark, which Google acquired. Today’s episode is part of our ongoing Monday series highlighting one key factor for one specific successful enterprise deployment. In today’s episode, Charles discusses a project with Walmart focusing on the use-case of product recommendations in different languages and geo-regions. The main success factor he speaks about is the champion who brings the project through to success. He specifically describes the qualities and behaviors of a good champion. Stay tuned until the end of this episode to learn more about our resources to help AI champions in the enterprise.

Today is episode five out of five of this week’s special series on Achieving ROI with Early AI Projects. We’ve heard from leaders from Intel, Oracle, and other big names, and today, we’re wrapping this series up with someone from the startup world. Our guest is Nikunj Mehta, Founder and CEO of Falkonry. Falkonry is an AI services business focused on heavy industry and manufacturing based in the Bay Area. In this episode, Nikunj describes a portfolio approach to find several related AI projects and capabilities that are likely to turn into a win. He also discusses why an enterprise’s most important objectives are not always the best fit for early AI projects. Stay tuned until the end of this episode to learn about the AI ROI reports that we’re making available for a limited period.

Today’s episode is part four out of five in our Achieving ROI with Early AI Projects series. This week, we have published one episode per day, starting with the Head of the AI Centre of Excellence for Intel. Today, we’re bringing it down to more of a consultancy and vendor perspective from someone who has worked with some of the largest enterprises in the world. Our guest is Dr. Charles Martin, a Silicon Valley AI Consultant with hands-on machine learning experience with organizations like Ebay, BlackRock, and more. In this episode, Charles discusses the concept of data quality mismatch, which can serve as a useful diagnostic tool for estimating what kinds of tasks it will be best suited for. He also speaks about picking projects where you have the data assets to achieve ROI. Be sure to visit emerj.com/p1 to access Emerj’s frameworks for AI readiness, ROI, and strategy.

Today is the third episode of this five-part series being published every day this week on achieving ROI with early AI projects. We’ve heard from the Head of the AI Center of Excellence at Intel and the Head of Insure AI at Munich Re, and today, we speak with the VP of Engineering for Oracle’s Digital Assistant, Raefer Gabriel. Raefer was a previous guest on the show, and he’s also worked on Amazon, including their Alexa platform, and had a startup before then. In this episode, we speak from his perspective on how we can achieve that early AI ROI. He also details how you can diagnose the data assets required to meet a specific goal to determine if your data would be a good fit and start with more modest projects on the crawl, walk, run scale. Be sure to tune in to the rest of the series to gain valuable insights and advice from enterprise leaders on achieving ROI with early AI projects.

Head of Insure AI at Munich Re, Michael Berger, discusses quantifying and managing AI risk. Topics include evaluating risks in AI projects, minimizing uncertainties, incorporating uncertainty information in AI predictions, and adopting new AI methods for increased confidence in applications.

Today is the start of a five-day series covering achieving ROI from early AI projects. In this series, we interview diverse experts from companies large and small about what it takes to pick AI projects with the highest chance of ROI. Our first guest is Gopalan Oppiliappan, Head of the AI Centre of Excellence at Intel Corporation. In this episode, Gopalan discusses how to structure project selection and AI project management in a purpose-built to find ROI. As we launch this series this week, we have also made the “AI ROI in Action” report free for download at emerj.com/r5. Previously only available to Emerj Plus members, you can get it for free now to learn more about predicting the costs and returns of AI projects and how to communicate these findings to leadership in a way that sets the right expectations.

Today’s guest is Emmanuel Walckenaer, CEO of YSEOP. YSEOP is a natural language generation AI vendor serving life sciences and drug development. In today’s episode, Emmanuel speaks about speeding up the drug development process through the lens of the life sciences industry being in extreme flux and what happens when a new normal is achieved and how companies have to scramble to keep up with it. Emmanuel also talks about the expansion of AI projects and capabilities in the enterprise. Today’s episode is brought to you by YSEOP. If you’re an AI vendor looking to reach a global enterprise audience, you can learn more about how we work with vendors from sponsored podcasts, articles, co-branded research, lead generation, and more at emerj.com/ad1.

In the third episode of our AI Success Factors series, we’re discussing a public sector application of AI. This Monday series is designed to deliver concise, industry-agnostic insights to start your week with a specific use-case and the most crucial success factor that brought it to life. Today, our guest is Richard Benjamins, Chief AI and Data Strategist at Telefónica. As one of the world’s largest telecommunications companies, Telefónica is a business that makes over $50B in annual revenue and has more than 100,000 employees worldwide. In this episode, Richard speaks to us about a specific project designed to reduce COVID cases by using data related to the movement of people. He also discusses the project’s results detailing how it was used in the real world and what factors led to its success. If you enjoyed this episode, be sure to tune in for the rest of the AI Success Factors series on The AI in Business Podcast at podcast.emerj.com.

Today’s guest is Daniele Cordioli, Head of Solutions Consulting for EMEA at Expert.ai. In this episode, Daniele discusses what contract analytics means and the concrete use-cases we can get from it. There are two big transferable lessons that Daniele highlights in this interview. The first lesson is determining the low-hanging fruit cases where AI can save money and reduce risk when dealing with your volume of contracts. Second, the proper method to establish where the business value lies in your own business. This episode is sponsored by expert.ai. If you’re interested in reaching Emerj’s global AI-focused executive audience through podcasts, emails, sponsored content, and more, you can contact us at emerj.com/ad1.