Earley AI Podcast

In this episode of the Earley AI Podcast, host Seth Earley welcomes Charlie Betz, Principal Analyst at Forrester Research. With an extensive background in digital operating models, enterprise architecture, and the future of work, Charlie brings a systems thinking approach to how digital initiatives are planned, governed, and scaled. As a leading expert covering a $250 billion segment of the global IT market—including vendors like ServiceNow, Atlassian, and Dynatrace—Charlie provides invaluable perspective for technology and business leaders facing the complexities of AI enablement and digital operations in large organizations.Together, Seth and Charlie dive deep past buzzwords to uncover practical, actionable insights about harnessing AI, operationalizing feedback loops, and navigating legacy technical debt. Charlie shares his real-world experiences wrangling with generative AI tools—including building systems with Anthropic's Claude as a "junior developer"—and distills lessons for executives on aligning business needs with technological advancements.Key Takeaways:How AI, particularly generative models like Claude, has moved from simple code autocompletion to accelerating the development of full-fledged applications—and the challenges and opportunities this creates for non-developers and professionals alike.The architecture of the $250 billion IT control plane market, including IT Service Management (ITSM), AIOps, and the massive influence these domains have on enterprise performance and boardroom-level decision-making.Why the ultimate business value of AI lies in accelerating feedback loops and continuous learning, not just automation or chatbot deployments.Lessons from continuous improvement (lean, Deming cycles, etc.) and why previous attempts struggled at scale—plus how modern AI may finally make the learning organization a reality.The importance of architectural governance, data stewardship, and feedback loop closure in successful AI integration—plus concrete calls to action for executives and enterprise architects.A nuanced discussion of legacy systems and technical debt: why simply layering new technology on top of old can lead to "technical bankruptcy," and practical strategies for managing (and paying down) technical debt before it becomes existential.Cutting through the hype around AI agents and swarms: separating realistic enterprise use cases from risk-laden hype, and the current limitations and essential guardrails needed for safe, effective agentic operations.Insightful Quote from the Episode:"If you held my feet to the fire and you told me, 'Charlie, there’s only one point,' I would say look for the feedback loop... What AI is enabling is essentially a faster feedback loop than we've ever had before in industry. And this is where the old becomes new."– Charlie BetzTune in for an unvarnished, deeply practical conversation on making AI real in complex enterprise environments—packed with tangible guidance no matter where you are on your digital transformation journey.Links:LinkedIn: https://www.linkedin.com/in/charlestbetz/Website: https://www.forrester.comThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

This episode features a fascinating conversation with Sid Sheth, CEO and Co-Founder of d-Matrix. With a deep background in building advanced systems for high-performance workloads, Sid and his team are at the forefront of AI compute innovation—specifically focused on making AI inference more efficient, cost-effective, and scalable for enterprise use. Host Seth Earley dives into Sid’s journey, the architectural shifts in AI infrastructure, and what it means for organizations seeking to maximize their AI investments.Key Takeaways:The Evolution of AI Infrastructure: Sid breaks down how the traditional tech stack is being rebuilt to support the unique demands of AI, particularly shifting from general-purpose CPUs to specialized accelerators for inference.Training vs. Inference: Using a human analogy, Sid explains the fundamental difference between model training (learning) and inference (applying knowledge), emphasizing why most enterprise value comes from efficient inference.Purpose-built Accelerators: d-Matrix’s approach to creating inference-only accelerators means dramatically reducing overhead, latency, energy consumption, and cost compared to traditional GPU solutions.Scalability & Efficiency: Learn how in-memory compute, chiplets, and innovative memory architectures enable d-Matrix to deliver up to 10x lower latency, and significant gains in energy and dollar efficiency for AI applications.Market Trends: Sid reveals how, although today’s focus is largely on training compute, the next five to ten years will see inference dominate as organizations seek ROI from deployed AI.Enterprise Strategy Advice: Sid urges tech leaders not to be conservative, but to embrace a heterogeneous and flexible infrastructure strategy to future-proof their AI investments.Real-World Use Cases: Hear about d-Matrix’s work enabling low-latency agentic/reasoning models, which are critical for real-time and interactive AI workloads.Insightful Quote from Sid Sheth:“Now is not the time to be conservative and get comfortable with choice. In the world of inference there isn’t going to be one size fits all... The world of the future is heterogeneous, where you’re going to have a compute fleet that is augmented with different types of compute to serve different needs.”Tune in to discover how to rethink your AI infrastructure strategy and stay ahead in the rapidly evolving world of enterprise AI!Thanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of the Earley AI Podcast, host Seth Earley sits down with Charles Migos, a veteran toolmaker whose career has spanned animation, post-production, visual effects, and large-scale media systems. Charles is recognized for his innovative approach to making emerging technologies—particularly generative AI—intuitive and accessible for creatives at every level, not just technical experts. Drawing on decades of experience, Charles shares what it means to design tools that empower storytellers and production teams, accelerate creativity, and address industry-specific needs.Key Takeaways:The media and entertainment industry has been under stress due to macroeconomic changes and the disruptive impact of generative AI.Creative jobs and processes are being transformed as AI tools dramatically speed up and democratize production workflows, bridging the gap between concept and execution.Traditional creative roles are shifting: Directors, producers, and even clients can now participate directly in visual storytelling without years of technical training.Instead of relying solely on prompt-based AI systems, innovation lies in giving creatives precise, fine-grained control for rapid exploration and content iteration.Asset repurposability, modularity, and the ability to build on prior creative work are becoming crucial for agencies and studios to scale, reduce costs, and increase creative output.Protecting intellectual property and ensuring ethical AI use—particularly around deepfakes and content control—is fundamental as generative tools become more powerful.The foundation of successful creative AI lies in deeply understanding and honoring existing workflows, enabling faster collaboration, clearer communication, and greater creative trust.Insightful Quote from Charles Migos:"Your ability to create faster and better than you ever have before, your ability to collaborate with your team and your stakeholders in ways you never have before, and the ability to communicate around what you create as that team effort is what matters most."Tune in to explore how AI is reshaping the creative landscape and to gain actionable insight on building truly human-centered design into emerging tech.Thanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

Join Seth Earley and guest Abhi Yadav, founder of iCustomer AI, as they dive into the future of customer data management. Abhi, a pioneer in the Customer Data Platform realm, discusses the shift from traditional data methods to innovative multigraph frameworks. The duo unpacks the importance of a unified semantic layer over mere data repositories and explores the complexities of zero-party, first-party, and third-party data in marketing. They also address the crucial challenge of integrating AI with decision intelligence for organizations navigating today's landscape.

In this episode of the Earley AI Podcast, host Seth Earley sits down with Yang Li, a leading figure in AI and software innovation. Yang is the Chief Operating Officer of Cosine, an advanced AI development firm, with deep experience driving startups, scaling organizations, and pioneering advancements in engineering and software development. Yang’s work focuses on leveraging AI to empower the next generation of developers, especially in navigating the increasingly complex landscape of modern and legacy codebases.Yang and Seth dive into how AI is reshaping the role of software engineers, the evolving challenges of handling massive backlogs and legacy systems, and what creativity and efficiency really look like in an age of AI-powered software development.Key Takeaways:AI’s Impact on Software Engineering: AI is shifting the developer’s role from hands-on coding to more creative, iterative, and strategic work.Tackling Legacy Code: Cosine is pioneering new ways for AI to handle outdated and complex codebases (like COBOL and Fortran) that most engineers—and AI models—struggle with.Augmenting, Not Replacing, Engineers: AI tools like Cosine’s Genie reduce ramp-up time for engineers, help address daunting backlogs, and act as creative partners rather than outright replacements.The Challenge of Benchmarks: Yang explains why public coding benchmarks can be misleading when bringing products to real-world enterprise environments, especially with diverse codebases.The Emergence of ‘Vibe Coding’: Idea-to-prototype time is shrinking, allowing non-technical team members to quickly bring their ideas to life using AI assistants.Risks & Limits: Over-reliance on AI, standardization versus differentiation, and the need for new evaluation criteria in engineering organizations.Future Skills: The importance of risk-taking, adaptability, and prompt engineering as software development evolves, plus insights into how organizations are rethinking career ladders and promotions in an AI-powered world.Insightful Quote from Yang Li:"Previously you had to use words and language to describe your idea, you can now show people your idea... The time between you having thought of an idea to actually be able to show people that idea has now reduced almost to zero because of vibe coding."Tune in to discover what’s next for software engineering in the age of AI, and how to stay ahead in this rapidly changing landscape.Thanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of the Earley AI Podcast, host Seth Earley sits down with industry analyst and advisor Tony Baer, a seasoned expert in data, cloud, and analytics. With decades of experience guiding global tech leaders like AWS and Oracle, Tony brings a nuanced perspective on how knowledge engineering is evolving—and why context is the missing link in many enterprise AI initiatives.Together, Seth and Tony explore the shift from static data models to dynamic knowledge frameworks, the renewed importance of governance, and how graph databases and generative AI are reshaping enterprise intelligence. This is a conversation packed with hard-earned lessons and actionable insight for data, IT, and transformation leaders aiming to make AI work in the real world.Key Takeaways:Knowledge engineering today is about dynamic, adaptive structures—not static ontologies or rigid models.The role of the knowledge engineer is shifting: it’s less about technical mastery and more about bridging data, business, and domain expertise.Context is foundational. The five W’s—Who, What, When, Where, Why (and How)—unlock meaningful, actionable intelligence.Graph databases and AI are enabling real-time connections across data, turning static information into living knowledge.Generative AI delivers the most value when rooted in organizational context. RAG strategies demand clean data and strong information architecture.Successful AI initiatives are focused. Start with well-bounded, high-impact processes—avoid boiling the ocean. Core principles from previous data waves still apply. It’s about evolving governance, stewardship, and architecture for the AI era. Sustainable value comes from feedback loops, iteration, and alignment—not silver bullets. Tune in to discover how to make AI practical, actionable, and intelligent for your organization.Quote of the Show: "Just because something is old does not make it wrong. There are a lot of disciplines we've built up over the years—governance, data stewardship—that still matter. The principle was right. We just adapt it and use our learnings from each cycle to become more knowledgeable and proficient." Tony BaerLinksLinkedIn: https://www.linkedin.com/in/dbinsight/Website: https://www.dbinsight.ioThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of the Earley AI Podcast, host Seth Earley welcomes two insightful guests from Anders, a top 100 CPA firm: David Hartley and Dave Blatt. David Hartley is a seasoned CPA with a profound understanding of the synergy between finance and technology. He advocates for how AI can enhance traditional accounting roles rather than replace them. Dave Blatt brings a wealth of knowledge in AI automation and analytics, focusing on empowering mid-sized companies to harness AI for competing with larger players.Join us as we dive into the world of AI applications in the finance, accounting, and mid-market operations sectors. Our guests dispel common myths and fears surrounding AI, exploring how small and medium-sized enterprises can practically and effectively adopt AI technologies to drive transformation and growth.Key Takeaways:Demystifying AI: Understanding AI in the context of mid-market companies and addressing misconceptions around AI replacing human jobs.AI for Mid-Sized Enterprises:** How AI is accessible and beneficial for mid-sized businesses, allowing them to compete with larger organizations.Impact on Accounting: Enhancing traditional accounting roles through AI and freeing up time for more value-added activities.Implementation Strategies: Best practices for implementing AI in mid-sized companies, focusing on education, small projects, and quick wins.Real-World Applications: Case studies in industries like construction and manufacturing, where AI has improved efficiency and productivity.Communication and Trust: The importance of communication and building trust among team members to ensure successful AI adoption.Quote of the Show: "Start small and not make it so daunting... get some quick wins that will be a catalyst to doing more projects and bigger efforts." - Dave BlattLinks:LinkedIn: https://www.linkedin.com/in/davehartley/LinkedIn: https://www.linkedin.com/in/daveblatt/Website: https://anderscpa.comArticle: AI Adoption Is Not as Hard as You Think – Start Now or Fall BehindThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of the Earley AI Podcast, we welcome guest, Jack Lampka, an accomplished advisor and speaker with over 27 years of experience in corporate roles within the tech and pharma sectors. Now based in Munich, Germany, Jack specializes in enhancing data storytelling and cultivating a product mindset among technical employees. His extensive career journey includes living and working in countries like Poland and the United States.Key Takeaways from this Episode:The importance of a product mindset for technical teams when developing AI solutions.Understanding the misconceptions and realistic expectations for AI and generative AI in businesses.How to successfully sell AI solutions internally by focusing on business needs and creating a comprehensive product marketing plan.The role of data storytelling in bridging the gap between technical and non-technical users.Insights into the hype surrounding Agentic AI and its relevance to current business applications.Thanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this discussion, Adam Honig, founder of Spiro AI and a former leader of a major Salesforce consultancy, shakes up the CRM landscape with his AI-driven innovations. He shares why traditional CRM systems are outdated and reveals how Spiro automates data capture for sales teams. Adam dives into the unique challenges of manufacturing and distribution sectors while contemplating the job displacement that AI might bring. Plus, he offers a sneak peek at future advancements like autonomous AI agents that promise to elevate customer relationships.

In this episode, Seth Earley is joined by Brian Magerko, a professor of digital media at Georgia Institute of Technology and a pioneer in applied AI and computer-human interaction. Brian shares his journey through the academic realm and his fascinating experiences, including co-founding EarSketch, an educational platform that merges coding with music for nearly 2 million users. Together, Seth and Brian explore the bridging of technical language gaps, the role of AI in creativity, and unravel common misconceptions about generative AI. Listen in as they discuss the complexities of AI-driven creativity, the importance of fostering AI literacy in organizations, and the ethical considerations that come with the integration of cutting-edge technology. This episode is packed with insights that are sure to provoke thought and inspire innovation in any AI enthusiast or professional. Join us as we continue our journey into the evolving landscape of artificial intelligence.Key Takeaways:The evolution of AI from aspirational concepts to today’s realities in the marketplace.Insights into the bridging of technical language gaps and the role of AI in creativity.Common misconceptions and myths about generative AI and its responsible use.Importance of a holistic organizational approach to adopting AI technology, including involving diverse stakeholders.Discussion on the implications of biases in AI and the significance of responsible data handling.Quote from the show:"They are tools, not oracles, you know, they're things that are great in the hands of people that know how to use them." - Brian MagerkoLinks:LinkedIn: https://www.linkedin.com/in/magerko/Website: https://expressivemachinery.gatech.eduX: https://x.com/thatmagerkoThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book