In this bonus episode of the Agile Coaches’ Corner podcast, Christy Erbeck, the Chief People Officer at AgileThought, is serving as your guest host for today’s conversation with Dr. Jerry Smith.

In their conversation today, they discuss how to make AI work for your enterprise. Dr. Jerry Smith explains why understanding causality is critical for AI-driven business transformation and how data science and analytics can help enterprise clients transform and become the digital winners that they desire to be.

Key Takeaways

• How AgileThought aids enterprises in understanding AI-driven business transformation:
  • Come up with a working set of definitions for AI, machine learning, and data science
• How AgileThought helps their enterprise clients solve their problems:
  •  The question: “What is data?” should be asked (Dr. Jerry Smith’s answer: “Data is the debris of human activity; it’s because of us, not in spite of us”)
    • Note: Data is not just spontaneously created in your data systems; it’s created from an application which captures an interaction between a human being (you, your customers, or your admins/salespeople) and that system
    • Note: The data we see is because of human actions
  • When we look at our capabilities, we should be asking the fundamental question: “What data in our enterprise is causal to our business outcomes?”
    •  For example, ask: “What data that you have spent time collecting is directly causing your revenue to perform the way it does?”
  • The very first thing to ask is: “What is causal?”
    • Once you know the causal data, you can go back to the application and the human and say, “How do I change the human behavior so that the application picks up the new behavior and changes the data?” This result is causal-based data engineering for AI, and is the only way to change your organization
• AgileThought helps companies institutionalize data science, machine learning, and AI at the enterprise level by breaking down the process (as shown below), so that each and every process resides in infrastructure and a set of capabilities
  • There are three kinds of data: Your enterprise, your IT, and your opensource – the goal is to get this data into a single machine learning record
  • This single machine learning record is critical in showing all of the variables in columns and observations in rows – from there, you can do basic analytics, and then, data science
  • Data scientists make sense of the data and create models out of the data, so the data no longer has to be used
  • In the machine learning phase, data scientists try to predict what these models are trying to do and how they’re going to change under certain variables
    • Note: AI is about prescriptions; making decisions
    • Note: The biggest value is not in generating or reading reports; it is in making an appropriate decision based on these reports

About Dr. Jerry Smith: Dr. Jerry Smith is AgileThought’s Managing Director of Analytics and Data Science. As a practicing AI & Data Scientist, thought leader, innovator, speaker, author, and philanthropist, Dr. Jerry Smith is dedicated to advancing and transforming businesses through evolutionary computing, enterprise AI and data sciences, machine learning, and causality.

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