In the ever-evolving technology landscape, data analytics and data strategy continues to play a larger role in economics and business models. Director of the Center for Applied Artificial Intelligence at the University of St. Thomas, Dr. Manjeet Rege, co-hosts the “All Things Data” podcast with adjunct professor and Innovation Fellow Dan Yarmoluk. The podcast provides insight into the significance of data science as it relates to business models, business economics, and delivery systems. Through informative conversation with leading data scientists, business model experts, technologists, and futurists, Rege and Yarmoluk discuss how to utilize, harness, and deploy data science, data-driven strategies, and enable digital transformations.
Rege and Yarmoluk spoke with Michael Peter about AI strategy overall and how to bring value to your enterprise based on that strategy. Michael is a principal architect for Google and the author of Your Enterprise AI Strategy Just Backfired. Michael provides his perspective on why many enterprise AI strategies might be missing the mark by focusing solely on tools rather than leveraging strategic outcomes or the business. Here are some highlights from their conversation.
Q. How would you advise companies to start formulating an AI strategy?
A. Companies should be working with a coach, putting together an AI training journey or an AI campaign (a series of strategies depending on their level of digital maturity). Companies should define their target state, and with the help of a coach, define what they want to do and where they want to go. Their strategy or set of strategies should be based on their current state, the stage they’re at in their journey (e.g., education, learning, experimentation, etc.) and their target state.
Q. What are your most excited about with generative AI?
A. The interesting thing with generative AI, and AI in general, is the massive change that it affords. What’s also exciting is how fast all the solutions, all the architectures, are advancing. It’s the fastest spread of white papers and research I’ve seen published in the AI space in quite a while. You have multiple streams of different models being released, trained in different ways, using different data sets. We’re in the early days, but even without patterns and best practices established, we’re seeing massive capabilities being realized.
Listen to their conversation here:
