AI Podcast Interviews Articles and Reports

Our podcast interviews feature the best and brightest executives and researchers in artificial intelligence today, and each episode highlights current and near-term AI use-cases of value for business leaders. Explore our full list of AI podcast episodes below:

AI for Inventory Optimization in Retail

AI for Inventory Optimization in Retail

Episode Summary: This week we talk to Alejandro Giacometti, the data science lead at a company called EDITED, based in London. The company claims to help retailers with inventory optimization, and we speak with Giacometti about how artificial intelligence can be used to search the web for the product clusters and individual products of major retailers to help inform other retailers on which products might be popular.

What Sectors and Applications Will Require New Artificial Intelligence Hardware?

What Sectors and Applications Will Require New Artificial Intelligence Hardware?

Episode Summary: Some businesses are going to require a sea change in the way that their computation works and the kinds of computing power that they're leveraging to do what they need to do with artificial intelligence. Others might not need an upgrade in hardware in the near term to do what they want to do with AI.

Setting Up Retail Stores for Machine Learning – Cameras, Microphones, and More

Setting Up Retail Stores for Machine Learning – Cameras, Microphones, and More

Episode Summary: We speak this week with Aneesh Reddy, cofounder and CEO of Capillary Technologies, which focuses on machine vision applications in the retail environment.

How to Use AI to Hire and Recruit Talent

How to Use AI to Hire and Recruit Talent

Episode Summary: In this episode of AI In Industry, we interview Nick Possley, the CTO of a company called AllyO, based in the San Francisco Bay area. We speak with Nick about where artificial intelligence and machine learning are playing a role in recruiting today and how picking the right candidates from a pool is in some way being informed by artificial intelligence.

How to Get a Chatbot to do What You Want it To

How to Get a Chatbot to do What You Want it To

Episode Summary: What makes chatbots or a conversational interface actually work? What kind of work does one need to do to get a chatbot to do what one wants it to do? These are pivotal questions and questions that for most business leaders are still somewhat mysterious, but that's exactly what we're aiming to answer on this episode of the AI in Industry Podcast.

Balancing Machines and Human Employees When Adopting AI in the Enterprise

Balancing Machines and Human Employees When Adopting AI in the Enterprise

Episode Summary: In this episode of the AI in Industry podcast, we interview Rajat Mishra, VP of Customer Experience at Cisco, about the best practices for adopting AI in the enterprise and how business leaders should think about the man-machine balance at their companies.

How IT Services Firms Can Adapt to Artifical Intelligence

How IT Services Firms Can Adapt to Artificial Intelligence

Episode Summary: In this episode of the AI in Industry podcast, we interview Nikhil Malhotra, Creator and Head of Maker's Lab at Tech Mahindra, about how artificial intelligence changed the nature of IT services and business services in general. Malhotra talks about what businesses should consider to make themselves relevant for the future. In addition, he discusses the philosophy shift that has to happen for people to be appreciative of the process of problem-solving, and to see profit and growth from AI. We hope business leaders in the IT services industry will take from this interview the low-hanging fruit applications in the IT services industry.

Predicting Sales Propensity with Artificial Intelligence - Opportunities and Challenges

Predicting Sales Propensity with Artificial Intelligence – Opportunities and Challenges

Episode Summary: Prominent technology companies like Google and Amazon lead the way in the B2C world, having access to streams of searches, clicks, and online purchases. They have access to large volumes of consumer data points numbering in the billions that can be used to train machine learning algorithms.