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.

One of most fun parts about doing our geolocation pieces at TechEmergence is that we are able to interview so many people within a given country or city. Recently we did a huge piece on AI in India. We got to interview folks from the government and the bigger existing businesses, as well as a handful of people at the unicorns in Bangalore. One of those companies is Fractal Analytics. Fractal Analytics works in a number of spaces. One of them, consumer packaged goods, is an area on which we haven’t done much coverage. Many of our readers are in the retail space, but CPG has some pretty curious AI use cases. This week, we interview Prashant Joshi, Head of AI and Machine Learning at Fractal Analytics, about the different applications of machine learning in the CPG sector: doing chemical tests or finding new buyer segments within existing groups of consumers to determine who is buying from a company and who is buying from competitors. Hopefully, for those in retail, this interview will not only highlight some of the interesting use cases of AI in the CPG world but also provide some ideas about winning market share from what some of the bigger CPG firms are doing with Fractal Analytics.

In this episode of the AI in Industry podcast, we interview Grant Ingersoll at Lucidworks, about enterprise search. Ingersoll talks about how companies have massive amounts of siloed data, making it difficult to find within enterprise systems. We hope businesses might take away from this interview what is required and what is involved in building search applications to make corporate data more accessible and structured. Ingersoll will also discuss how data strategies are going to evolve and how scientists and data experts might come together to build an enterprise search application.

We receive a lot of interest from business leaders in the domain of data enrichment, and we've executed on a few campaigns for these businesses. At the same time, our audience seems particularly interested in the collection of data to train a bespoke machine learning algorithm for business, asking questions related to how to get started on data collection and from where that data could come. This week on AI in Industry, we seek to answer those questions. We are joined by Daniela Braga, CEO and founder of DefinedCrowd, a data enrichment and crowdsourcing firm, who discusses with us how a business might determine what kind of data it might need for its AI initiative. We hope the insights garnered from this interview will help business leaders get a better idea of how they could go about starting an AI initiative and seeing it through from data collection or enhancement to solving its business problem.

There’s more to successful AI adoption than picking the right technology. Business leaders should be aware of the technical requirements of the initiative they’re undertaking, and few of those requirements are as important as data. For this episode, we spoke with Mark Brayan, CEO of Appen, a firm that offers crowdsourced training data for machine learning applications. We discuss how developing a sound data strategy is essential for using AI to solve business problems. Brayan also helped us detail how and when a business can make use of certain data collection and enrichment methods depending on their business goals.

Over the last year, we've covered a lot of marketing applications. Many people know of our deep marketing research we've done on the landscape of machine learning in marketing applications and which industries will be affected first. But marketing doesn't tell the whole story when it comes to B2B sales. At some point, we need to take these clicks and turn them into appointments, for example. In this episode of AI in Industry, we are joined by Vitaly Gordon, VP of Data Science and Engineering at Einstein, Salesforce’s customer relationship management application driven by artificial intelligence. We speak with Vitaly about where AI is serving a role in sales enablement today and how the CRM and sales tool ecosystem might be different in the near-term future; how will salespeople be able to leverage AI to make themselves more productive? Vitaly paints an interesting picture of where he sees the low hanging fruit and the unique challenges with sales data and B2B data that are quite different from the challenges those in the B2C world might deal with.