Earley AI Podcast

In this episode of the Earley AI Podcast, host Seth Earley sits down with Forrest Zeisler, co-founder and Chief Technology Officer at Jobber. With years of experience building technology for service professionals, Forrest Zeisler has played a pivotal role in empowering small businesses—from landscapers and plumbers to cleaners and contractors—to harness AI and automation for streamlined operations and growth.Discover how Forrest Zeisler and his team scaled Jobber from three customers to over 300,000, delivering more than $100 billion in services, and learn how their journey demonstrates the transformative impact AI can have on businesses of all sizes.Key Takeaways:Small businesses can benefit enormously from AI, especially for reducing administrative tasks and boosting productivity.Adopting new technology isn't just about features—it's about building trust and reliability for the end user.Jobber’s growth began with direct customer conversations, leading to a highly configurable platform supporting over 55 industry verticals.The journey from manual onboarding and white-glove service to sophisticated self-serve and AI-driven automations took years of iteration and customer feedback.Integrating AI isn’t just about chatbots or flashy features; the real impact comes from making technology disappear in the background, allowing users to focus on their craft.Reliable automation, rooted in real customer behavior and best practices, is key to driving widespread adoption of AI across industries.Building trust with AI systems should mirror how you onboard new employees: review, supervise, and gradually increase autonomy as reliability is proven.Orchestrating multiple AI models and agents allows platforms like Jobber to deliver context-aware, intelligent assistance that feels human and personalized.Insightful Quotes:"AI is beginning to simplify that work and reduce administrative overhead and reduce those efforts and help small companies provide more consistent and more efficient and more reliable results." - Seth Earley“Our goal is not to stick a lot of chatbots in front of our customers. It's to make Jobber just magically always seem like it knows what you need when you need it. We want to measure our success by how little we're sticking in front of our customers.”-  Forrest ZeislerTune in for a behind-the-scenes look at building scalable, reliable AI for small business—and the lessons you can apply whether you're an entrepreneur or driving digital transformation in a larger enterprise.LinksLinkedIn: https://www.linkedin.com/in/forrestzeisler/Website: https://www.getjobber.comThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

Join us for a compelling episode of the Earley AI Podcast as host Seth Earley sits down with George Swetlitz, CEO and Co-Founder of RightResponse AI. George brings decades of expertise in natural language technologies, enterprise AI adoption, and building advanced models to solve real business challenges—especially in the realm of customer engagement, feedback, and competitive analysis.Tune in as George shares how AI-powered systems are changing the way organizations capture, understand, and act on customer feedback to deliver more relevant, personalized, and valuable experiences. He discusses why sounding “human” isn’t enough, the importance of contextual relevance, and how to transform the review response process at scale for both efficiency and revenue growth.Key Takeaways:Relevance Over Sounding Human: The real power of AI in customer experience lies in delivering contextually relevant responses, not just in mimicking human conversation.Granular Sentiment Analysis: Advanced AI systems can break down reviews into meaningful phrases, better identify true intent and sentiment (even with sarcasm), and map feedback to business KPIs.Building Fact Repositories: Onboarding AI involves creating a dynamic library of facts drawn from reviews, responses, and website content, enabling responses that are tailored to specific, high-value customer concerns.Operational Impact at Scale: Large organizations can redeploy significant resources by automating repetitive review responses, freeing up staff to focus on complex, high-touch customer problems.Personalized Review Requests: AI can personalize review requests by incorporating context from customer interactions, dramatically improving conversion rates and generating more insightful customer feedback.Competitive Insights: AI-driven analysis of both your reviews and your competitors’ can highlight where you’re outperforming or falling short—especially at the hyperlocal level.Future of AI in CX: As AI models become more advanced, onboarding and implementation will become smoother, and the quality of customer engagement will only improve.Insightful Quote:“What you’re trying to do with AI is get the best of both worlds. You’re trying to be relevant to somebody in the space or in the place that they’re in… The best customer service rep would do that. And now, at scale, AI can help organizations truly meet customers where they are.” George SwetlitzListen now and discover how leveraging AI in customer feedback can transform both experience and outcomes!Links:LinkedIn: https://www.linkedin.com/in/george-swetlitz-7b43812/Website: https://www.rightresponseai.com Thanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

This episode of the Earley AI Podcast features Betsy Mello, a seasoned retail executive whose career includes leadership roles at Dorel Home, Levi’s, Old Navy, Sears, and several retail startups. With deep expertise in merchandising, inventory management, and eCommerce strategy, Betsy brings a pragmatic perspective shaped by years of leading digital transformation and managing major marketplace relationships with Amazon, Bed Bath & Beyond, and others.Host Seth Earley talks with Betsy about how retail leaders can navigate the ongoing shift from traditional operations to AI-driven business models. Their discussion explores how structured data, process discipline, and organizational alignment form the foundation of successful digital and AI initiatives—and why the fundamentals still matter, even in the age of automation.Key Takeaways:How the move from brick-and-mortar to digital commerce has transformed consumer expectations and the pace of retail innovation.Why marketplaces are data supply chains—and how brands must adapt content, taxonomy, and product positioning across diverse channels.The importance of clean data, standardized terminology, and clear use cases before adopting AI solutions.Strategies for breaking down silos, aligning KPIs, and ensuring cross-functional collaboration around data and insights.Leading through change: the value of transparency, experimentation, and learning from failure during AI-driven transformation.What leaders often overlook when preparing for AI—and how to make foundational data work visible and measurable.The building blocks for sustainable AI success: information architecture, governance, and accountable data ownership.Show Quotes:"You need to have everything standard. You need to have clean data and very clear workflows and accountabilities... The key is having the team set in place and very clear defined processes and roles and responsibilities. It’s incredibly critical to make sure your foundation is correct. You need to be always starting at the basics." – Betsy Mello"Supply chain is an information supply chain. And every time you have a new way of distributing your product, you have to think about, how do I distribute the data with that product?" - Seth EarleyTune in to hear how retail and eCommerce leaders can turn complexity into clarity—and build the cultural and data foundations that make AI work.LinksLinkedIn: https://www.linkedin.com/in/betsymello/Website: https://www.dorelhome.comThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

Bharat Guruprakash, Chief Product Officer at Algolia, joins host Seth Earley on this episode of the Earley AI Podcast. With years of experience helping organizations leverage AI to connect people with information, Bharat brings deep insight into the evolving world of AI-powered search, retrieval, and agentic technologies. At Algolia, a global leader in AI-driven search and retrieval, he helps shape what’s next in unifying data, building intelligent systems, and designing platforms that understand real-time context.Key Takeaways:Misconceptions about Search and AI: Many organizations think large language models (LLMs) can handle all search needs, but true effective AI solutions require robust retrieval systems beneath the surface.The Role of RAG and Memory: Retrieval Augmented Generation (RAG) remains important, but the future is moving toward agentic architectures that require "memory" and stateful interactions, not just stateless search.Clean, Structured Data is Crucial: The importance of having clean, accessible, and normalized data stores as the backbone for any successful AI and search initiative.Experimentation and Innovation: Enterprises struggle with a culture of experimentation and face challenges in safely running and scaling AI experiments. Autonomous experimentation, where AI can test and optimize different approaches, is emerging as a solution.The Rise of Agentic Technologies: The distinction between generative AI (focused on content creation) and agentic AI (focused on task automation and execution), and how agents will soon drive more dynamic, event-driven workflows.Guardrails and Risk: Implementing proper protocols (like MCP and Google’s A2A SDKs) and guardrails is essential to ensure agents act safely and within business parameters.Privacy and the Future: As agents learn more about users than users know about themselves, privacy, transparency, and identity become critical concerns. The speed of change is challenging, but small, iterative steps help organizations evolve responsibly.Insightful Quote from the Show:"It's very risky to say, let's just boldly go forth into the unknown, right? I think you have to have experiments, right? You have to control them, experiments, but you have to Be careful about that and have a mechanism for managing that and for controlling it and for monitoring the results." Seth Earley"It's okay to start small. Find the small places where you can improve...and keep multiplying them. Over time, when you look back after a year or two, you'll have a very different company from when you started." – Bharat GuruprakashTune in for a thoughtful deep dive into the challenges, opportunities, and responsible strategies for embracing AI, search, and agentic technologies in your organization.Links:LinkedIn: https://www.linkedin.com/in/bharatguruprakash/Website: https://www.algolia.comThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of the Earley AI Podcast, host Seth Earley welcomes Eric Rehl, Vice President of Digital Customer Experience in North America at Schneider Electric. With over 25 years of expertise in digital strategy and customer experience, Eric has guided global organizations through complex digital transformations, always keeping business outcomes and customer needs at the core. Drawing on his deep industry knowledge, Eric shares how large enterprises can move beyond buzzwords like “digital transformation” and “AI,” instead choosing a pragmatic, data-driven approach to drive real business value.Join Seth and Eric as they discuss the evolving role of digital capabilities in business strategy, the foundational importance of high-quality data, the unique challenges faced by B2B organizations, and how AI can power truly personalized customer experiences—from the ground up.Key Takeaways:Digital transformation should be rooted in business outcomes, not technology hype; focus on the “so what” for your customer and organization.Strong, clean, accessible data is critical for scaling digital experiences and enabling AI-driven personalization—without it, even the best tools will fail.B2B companies often lag in digital maturity due to legacy data architectures and complex customer relationships, but can catch up by investing strategically in foundational capabilities.A robust digital journey relies on operationalizing and continually improving product and customer data, rather than one-off fixes.Maturity in B2B digital experiences evolves from simply “doing no harm,” to enabling ease of business, and ultimately leveraging digital platforms for growth and commercial impact.AI’s promise lies in moving from segmented personalization to real-time, dynamic customer engagement powered by integrated data and knowledge.Preparing for AI-driven customer discovery means syndicating high-quality, semantically-structured content across channels—both on and off your own domain.The next frontier is operationalizing knowledge (not just product or customer data) to fuel AI tools for differentiation and problem-solving.Continuous experimentation and responsible opportunism allow organizations to discover new outcomes and business value.Insightful Quotes:"I think as you start building maturity, you're learning how to orchestrate those pieces. You're getting more of that harmonization of organizing principles across those disparate departments, across knowledge and content and customer experience and product information. And so that becomes kind of the holistic journey that you're thinking about." - Seth Earley“We always start with the outcome. Like, why are we talking about capabilities here? Why are we talking about AI? What are we actually going to do with it to get to what the business outcome we’re trying to drive or the experience outcome we’re trying to drive?” - Eric RehlDon’t miss this in-depth conversation packed with practical advice and forward-looking insights for anyone leading or navigating digital transformation initiatives in the AI era.LinksLinkedIn: https://www.linkedin.com/in/ericrehl/Website: https://www.se.com/us/en/Thanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

This episode of the Earley AI Podcast features Rudy Abitbol, a recognized expert in B2B commerce and AI. With years of hands-on experience helping global enterprises with digital transformation—especially around product information management, large-scale AI adoption, and making cutting-edge technology truly practical—Rudy brings a pragmatic view to how AI is revolutionizing B2B e-commerce. He’s passionate about making AI accessible and effective for tackling real-world business challenges.Key Takeaways:AI-Driven Transformation: AI is dramatically streamlining the content generation and management process in B2B commerce, especially when dealing with vast, complex product catalogs.Platform Flexibility Matters: Tools like Shopify are lowering barriers for B2B companies by offering user-friendly interfaces and robust ecosystems that allow for rapid configurations—often with no coding required.Vibe Coding & Rapid Prototyping: “Vibe coding” empowers product owners to quickly move from conversations and requirements to working wireframes and functional specs, tightening feedback loops and boosting innovation.Balancing Efficiency & Differentiation: While AI tools help standardize processes, true competitive advantage comes from infusing proprietary business knowledge into digital solutions.Data Readiness & Quality: AI can enrich incomplete or messy data, but organizations must still focus on good data structure and master data management to fully capitalize on AI and automation.AI Adoption Hurdles: The most common blockers are cultural—resistance to change and undertraining. Hands-on learning, POCs, and fostering a culture of curiosity are essential.Empowering Teams: True AI readiness isn’t just about tools but about giving teams the time, training, and encouragement to experiment—and even risk failure—as they integrate AI into daily workflows.Avoiding Over-Reliance on AI: While AI enhances productivity, it can’t replace human judgment. Insightful, context-driven use of AI yields the biggest returns.Building Core Competency: Don’t outsource the heart of your digital transformation; instead, build internal expertise and maturity to stay ahead in a rapidly evolving landscape.Insightful Quote from Rudy Abitbol:"All the insight within the phrasing, within the way that it’s done...still needs to come from you. You still need to have someone that is a product owner with a great vision, because that's the sole person that is able to infuse [the business]."Tune in to learn how AI is fundamentally reshaping B2B commerce and how leaders can stay ahead of the digital curve. LinksLinkedIn: https://www.linkedin.com/in/rudyabitbol/Website: https://www.trustinsights.aiThanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

In this episode of Earley AI Podcasts, host Seth Earley welcomes Mark Anderson, co-founder and CEO of Pattern Computer, for a fascinating exploration of what lies beyond the current AI mainstream. With a career grounded in technology, strategy, and scientific innovation—including receiving the Alexandra J. Nobel Award for his contributions to computing and medicine—Mark brings nearly a decade of experience in developing proprietary pattern recognition technologies that move far beyond traditional machine learning models.Together, Seth and Mark dive deep into the journey of Pattern Computer, unveiling its revolutionary Pattern Discovery Engine—a platform with the unique ability to make discoveries in data that have eluded conventional approaches. Mark explains how his passion for science and the shortcomings of the classic scientific method sparked the creation of new mathematical and architectural foundations in AI, leading to major breakthroughs not only in medicine but also across enterprise applications.Key Takeaways:Origins of Pattern Computer: The story behind the formation of Pattern Computer and its foundational mission to turn pattern discovery from an art into a science.A New Approach to AI: How the Pattern Discovery Engine goes beyond finding incremental improvements, enabling true discovery by flipping the traditional scientific method.Breakthroughs in Medicine: The real-world impact of Pattern Computer’s approach, including the discovery of gene patterns and the development of new drugs for triple negative breast cancer.Pattern Discovery vs. Large Language Models: The critical differences between pattern discovery engines and LLMs, and how these technologies can work together to combine human-friendly communication with genuine scientific discovery.Explainable AI and Ethics: Why true explainability, interpretability, and ethical data are at the heart of next-generation AI—and how Pattern Computer is leading the way with interpretable outputs and transparency.Enterprise & Science Applications: Use cases in aerospace, mining, healthcare, and more, where Pattern Computer’s approach has led to major discoveries in seconds—successes that eluded brute-force methods for years.Advice for Organizations: How businesses and innovators can access and test the Pattern Discovery Engine for their own complex data challenges.Insightful Quote from Mark Anderson:“Instead of having a hypothesis and then you run, you want to go again, it’s the opposite. You’re not allowed to have any hypothesis. You can’t bring your bias to the game. And instead of that, you have good data. You run the data and you generate the hypothesis. That’s the right way to solve problems.”LinksLinkedIn: https://www.linkedin.com/in/markandersonpredicts/Website: https://www.patterncomputer.comThanks 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 Christopher Penn, co-founder and Chief Data Strategist at Trust Insights. Widely known as an authority on analytics, data science, and AI, Chris brings a wealth of practical experience, thought leadership, and cutting-edge perspective to the conversation. With a proven track record as an author, keynote speaker, and trusted advisor in digital transformation, Chris delves deep into the realities of AI for today’s enterprises.Join Seth and Chris as they cut through the hype surrounding generative AI and focus on what truly matters for organizations: effective AI adoption, data strategy, and delivering measurable value.Key Takeaways:AI Content Detection is Overrated: Most people don’t care if content is AI-generated; what matters is whether it solves their problem or meets their needs.Effective AI Use Is About Differentiation: Real business value comes from leveraging your unique voice, expertise, and data—not simply automating generic processes.Enterprise AI Adoption Challenges: Organizational inertia, politics, and risk-averse cultures are frequently the biggest barriers to successful AI projects, not technology itself.Failure is Essential for Innovation: Enterprises with zero tolerance for failure will struggle with AI: experimentation and learning from failure are critical.AI’s Impact on Jobs: Entry-level roles are rapidly changing or disappearing with automation, especially for routine and repetitive tasks.Data Quality Still Reigns: The success of tools like Microsoft Copilot depends on having clean, well-organized foundational data—bad data in, bad results out.Agentic AI and Model Context Protocols: The future of AI involves building robust agentic infrastructure, understanding APIs for AI, and navigating security/privacy in enterprise integrations.Transform, Don’t Just Optimize: Leaders must ask if they’re truly transforming or just making old processes faster; meaningful change starts with people and process—not tech-first thinking.Insightful Quote from Christopher Penn:"In the enterprise, it is actually more important to navigate the people and the politics than it is the technology. The technology is easy. It is the humans that are the hard part."LinksLinkedIn: https://www.linkedin.com/in/cspenn/Website: https://www.trustinsights.aiMAICON 2025 Code: PENN200 Thanks to our sponsors: VKTR Earley Information Science AI Powered Enterprise Book

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