The AI in Business Podcast

Whether we're talking about customer service, marketing, or building developer teams, what we try to do on our AI in Industry podcast is bring to bear lessons that are transferable. There are few more transferrable ideas than what makes a company ready to adopt AI. When it comes to the willingness and the ability to integrate AI into a company strategy and to fruitfully adopt the technology to really see an ROI, what do the companies that do so successfully have in common? What do the companies that are not ready or too fearful to do it have in common? There are probably few companies in the AI vendor space that are aiming to sell AI more ardently into the enterprise than Salesforce, and there are few people that know more about how that process is going than Allison Witherspoon, Senior Director of Product Marketing for Salesforce Einstein, which is their artificial intelligence layer on top of the Salesforce product. We speak to Witherspoon about the telltale signs of a company that understands the use cases of AI in their industry and that have a good chance of driving value with AI. We also talk about the common qualities of companies that might not ready for I adoption. Read our full interview article on Sunday at Emerj.com

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 Alejandro 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 what products might be popular. There are two primary takeaways from this episode. The first is the broad capability of monitoring the competition with artificial intelligence, something that can be applied across industries, not just in retail. The second is that EDITED is generating information from what is freely available on the web, and so it would seem their software doesn't require businesses to integrate it into inventory management systems in order to train the algorithm behind it. I'm not necessarily lauding the company; I haven't used their product nor read all of their case studies. That said, it's worth noting simply because its approach is fundamentally different than most AI vendors. Read the full interview article on emerj.com

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. What's the difference? That's the question that we decided to ask today of Per Nyberg, Vice President of Market Development, Artificial Intelligence at Cray. Cray is known for the Cray-1 supercomputer, built back in 1975. Cray continues to work on hardware and has an entire division now dedicated to artificial intelligence hardware. This week on AI in Industry, we speak to Nyberg about which kinds of business problems require an upgrade in hardware and which don't.

We speak this week with Aneesh Reddy, cofounder and CEO of Capillary Technologies. Capillary is a rather large firm based in Singapore. Aneesh is in Bangalore himself. The firm focuses on machine vision applications in the retail environment. How do we instrument a physical retail space so that, with cameras, we can pick up on the same kind of metrics that eCommerce stores can? Retail stores, as Reddy talks about in this episode, have to focus on the data that they get from the checkout counter, such as what kind of purchases were made, and potentially some kind of data about how many times the front door was opened or closed. That doesn't really lay out that much detail about who came in, what percent of them converted, and what the average cart value was for different people. A lot of that is completely greyed out when looking at the numbers that are accessible to brick and mortar retailers. But some of that is changing. Reddy talks about what's possible now with machine vision in retail, and what it opens up in terms of possibility spaces for understanding customers better in a physical environment. More importantly, Aneesh paints a bit of a future vision of where he believes retail is going to be when not just computer vision is included, but when audio and other kinds of sensor information are included.

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. Whether a business leader is hiring dozens and dozens of people or whether they 're just interested in understanding how AI can engage with individuals on more of a one-to-one basis, this should be a fruitful episode. In addition, the fundamentals of what we discuss in this episode, in terms of taking in data from profiles and responding and engaging with applicants, could be applied to all sorts of cases, such as customer service and marketing. Read the full interview article here: https://www.techemergence.com/how-to-use-ai-to-hire-and-recruit-talent

What makes a chatbot 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. This week we speak with Madhu Mathihalli, CTO and co-founder of Passage AI. We speak specifically about what kinds of tasks conversational interfaces are best at, what kinds of word tracks, what kind of questions and answer are they suited for and which are a bit beyond their grasp right now. In addition, we speak about what it takes to train these machines. In other words, how do we define the particular word tracks that we want to be able to automate and determine which of them might be lower hanging fruit for applying a chatbot or which of them might not? Read or listen to the full podcast here: https://www.techemergence.com/how-to-get-a-chatbot-to-do-what-one-wants-in-business/

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. Mishra talks with us about how the executive team should be able to imagine the future of specific work roles that might integrate AI technology or envision how those roles will shift in the short-term. In other words, how will AI affect workflows?

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.

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 pointss numbering in the billions that can be used to train machine learning algorithms. B2B companies operate under a different model: "propensity to buy," as it's called. A typical B2B company might at most make a couple hundred sales per year, and many B2B companies make only dozens. In other words, every sale matters. In this episode of the AI in Industry podcast, we interview Kiran Rama, Director of Data Sciences Center of Excellence at VMWare, about purchasing external data and to leveraging internal data. Rama also talks about using data to determine how likely certain leads are to turn into high-value customers. In addition, he discusses with us the "propensity to buy." We hope that this interview can help business leaders determine if and how AI can help their organizations identify which leads could yield the highest ROI and which customers are the most primed for reselling.

For business leaders who are thinking about integrating AI into their company or who are just in the very beginning of that journey, this may be a useful episode of the podcast. Many times, people think that finding the right talent is the biggest challenge when it comes to integrating AI into the enterprise. Much of our own research and conversations with machine learning vendors and the consultants trying to sell AI into the enterprise actually think there's another, bigger problem: combing the expertise of subject matter experts and that of data scientists to leverage information for future initiatives in business. This week, we interview Grant Wernick, CEO of Insight Engines in San Francisco. We speak with Grant about the initial challenges of organizing data and setting up a data infrastructure a business can use to leverage AI. We also talk about using data in leveraging normal workflows so that non-technical personnel can use it to drive better product innovation to help the company.