Matt Berseth, Co-Founder of NLP Logix, discusses transitioning to deep data involvement in AI products, adapting lasagna vs. pizza analogy. He emphasizes starting with simple assumptions, gathering feedback, and iterating AI development. The podcast explores the importance of integrating AI from the start, mindset shifts, and embracing iteration cycles in AI product development.
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Quick takeaways
Transitioning to deep AI involvement requires adoptable strategies.
Enhancing products with AI involves iterative learning and user-centric improvements.
Deep dives
Transition to Deep Involvement with Data & Data Science in AI Deployments
Enterprises are transitioning from light involvement with data and data science to a deeper engagement in their AI deployments. This shift involves essential lessons in adoption strategies. The analogy of lasagna versus pizza is revisited, emphasizing the need to integrate AI capabilities fundamentally into digital products to enhance user experiences.
Gradual Process of Levelling Up Data-Powered Products
Matt Burseth discusses the process of enhancing current digital products with AI-powered features. He highlights the gradual approach of leveraging data to improve user efficiency, convenience, and functionalities. Starting with surface-level data exploration, the iterative process involves learning from initial implementations to make strategic advancements.
Value of Starting with Initial Assumptions and Iterative Testing
Initial hypotheses and assumptions play a crucial role in AI projects. The importance of thorough data exploration, testing assumptions, and iterative model development is emphasized. Through frameworks like the '10 Q model,' experts acknowledge the significance of testing hypotheses, understanding data intricacies, and achieving early wins before deepening AI integration.
Today’s guest is Matt Berseth, Co-Founder and CIO of NLP Logix. NLP Logix is a fast-growing AI services firm based in Florida that serves both the public and private sectors. In this episode, we’re exploring the transition from light involvement in data and data science to much more deep participation, where there are a lot of transferable lessons in adoption ideas. We also discuss the lasagna vs. pizza analogy from an excellent past interview. Matt describes how we can take the data leveraged by that product and use it to help the users save time, give them more convenience, or open up more capabilities. He also articulates the gradual process of working at the surface level, learning lessons, and rebuilding some of the core components of a product. This episode is brought to you by NLP Logix. To learn more about Emerj Media and how we help AI services firms reach a global audience, be sure to visit emerj.com/ad1.
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