Earley AI Podcast

Seth Earley
  • 4.3 (7)
  • Technology
  • Updated Monthly

In this podcast hosts Seth Earley invites a broad array of thought leaders and practitioners to talk about what's possible in artificial intelligence as well as what is practical in the space as we move toward a world where AI is embedded in all aspects of our personal and professional lives. They explore what's emerging in technology, data science, and enterprise applications for artificial intelligence and machine learning and how to get from early-stage AI projects to fully mature applications. Seth is founder & CEO of Earley Information Science and the award-winning author of "The AI Powered Enterprise." 

  1. 1d ago

    Earley AI Podcast - Episode 99 Data Governance, Business Context, and Why AI Makes the Old Problems Worse with Zoher Karu

    Why the Same Data Problems That Existed Before AI Still Exist - They Just Get Expressed Faster, With More Confidence Guest: Zoher Karu, Founder and President at ZiZi Advisors Host: Seth Earley, CEO at Earley Information Science Published on: September 14, 2026 In this episode, Seth Earley speaks with Zoher Karu, Founder and President of ZiZi Advisors, who has spent his career building enterprise data and analytics programs at Sears Holdings, eBay, Citibank, and Blue Shield of California - and building personalization systems before personalization was something a large language model could attempt. They explore why data governance has become the most important discipline in the AI era, why giving an LLM clean data is still not enough if it does not understand your business, why the differentiating factor between organizations will not be the model but the context, and what executives most consistently get wrong when they point powerful new tools at the same old data problems. Key Takeaways: Data governance has become sexy again not because AI demands new governance, but because the cost of skipping the old kind now shows up faster, with more confidence behind the wrong answer.Pointing a more powerful AI engine at ungoverned data does not produce better answers - it produces bad decisions faster, with AI's characteristic knack for sounding right even when it is wrong.Multiple definitions of the same metric across the same organization - different versions of active customer, different versions of sales - are not AI problems, they are governance problems that AI amplifies.Cleaning data is necessary but not sufficient - the model also needs to understand the context of your business, the rules, the exceptions, and the institutional knowledge that lives in people's heads.The AI models themselves are moving toward commoditization; the differentiating factor will be how well organizations have captured and made available their own business context and knowledge.Start with productivity improvements to demonstrate early value, but the real value of AI is business process change - asking not just how to automate the notes after a phone call, but why you are taking phone calls at all.Governance is not internal bureaucracy - it is the brakes in the car. The reason you can go fast around a curve is that you know you have brakes. Controls let you operate at the limit rather than inching along out of fear.Insightful Quotes: "Just because you point powerful AI tools at your data doesn't mean it can figure out exactly what's what. There might be four columns called sales. How does it know which one you actually meant? And the classic problems - data silos, multiple sources of truth, ambiguity about how things connect together - they always existed, and they still exist." - Zoher Karu "You can give an LLM all the data you want, and it can be pristine, but if you don't tell it the context around the way to use that data, that's going to be the next wave of problems to solve. The way you run your business is also your asset - and that is typically captured loosely in documents, Slack messages, emails, or not captured anywhere at all." - Zoher Karu "The organizations that treat AI like magic are the ones that are getting burned. The same old problems - the data silos, the multiple sources of truth, the missing business context - do not disappear. They just get expressed faster, with more confidence." - Seth Earley Tune in to discover why the discipline that seemed least exciting in the AI era turns out to be the most consequential - and what it takes to build an AI foundation that actually reflects how your organization runs. Links LinkedIn: https://www.linkedin.com/in/zzkaru/ Ways to Tune In: Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home dLogos: https://dlogos.xyz/podcasts/earley-ai-podcast-271271ce Apple Podcast: https://podcasts.apple.com/podcast/id1586654770 Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/ Stitcher: https://www.stitcher.com/show/earley-ai-podcast Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast Buzzsprout: https://earleyai.buzzsprout.com/ Thanks to our sponsors: VKTREarley Information ScienceAI Powered Enterprise Book

  2. 1d ago

    Earley AI Podcast - Episode 98 Agentic AI in Finance, the Trusted Advisor Advantage, and the Pricing Model Reckoning with Nikita Komarov

    What It Takes to Build AI That Is Accurate Enough, Traceable Enough, and Trustworthy Enough for High-Stakes Financial Work Guest: Nikita Komarov, CEO and Founder at Dobs.AI Host: Seth Earley, CEO at Earley Information Science Published on: September 9, 2026 In this episode, Seth Earley speaks with Nikita Komarov, CEO and Founder of Dobs.AI, who spent seven years at McKinsey advising Fortune 1000 executives before founding a company that is rebuilding financial due diligence, internal audit, and vendor overpayment recovery from the ground up as agentic AI systems. They explore why financial professionals are the most resistant to AI adoption and why that resistance is rational, how orchestrating teams of AI agents with financial controls built in produces outputs that are deterministic enough for audit, why the difference between an efficiency tool and a production-ready AI system is enormous, and how the trusted advisor status accountants have built over decades becomes a platform for entirely new services in the AI era. Key Takeaways: Financial professionals are among the most resistant to AI adoption for a rational reason - LLMs are non-deterministic by nature, and accounting requires numbers that are 100% accurate and traceable.Building production-grade financial AI requires three levers working together: orchestrating teams of agents with defined roles, building financial controls and guardrails into the pipeline, and solving for data extraction accuracy before any analysis begins.The difference between an efficiency tool like Claude or ChatGPT and a production-ready AI system is not the model - it is the architecture, the controls, and the product thinking required to get from unstructured input to a final output a human can take to a client.DOBS AI compresses financial due diligence from a six-week engagement to 72 hours for the management meeting - cutting the cycle from week and a half to three days on that critical milestone alone.Accounting firms have more trust with clients than management consultants or lawyers, and that trust combined with recurring access creates a platform for expanding into advisory services that AI now makes possible.The pricing model reckoning is real - time and materials no longer makes sense when AI does the work in hours, and firms need to shift to value-based pricing anchored to the outcome delivered, not the hours spent.The long-term trajectory is positive, but the mid-term transition is the risk - AI is compressing decades of technological change into five to ten years, and organizations and individuals who are not adapting will be left behind.Insightful Quotes: "Large language models, they predict the next word. That's why these systems are non-deterministic. You can't say what the output will be next. That's the problem in financial services - you need 100% accuracy, but you don't know what the system is going to tell you." - Nikita Komarov "That's exactly the difference between an efficiency tool and a production-ready solution. When people say we use AI, they most likely mean Copilot or ChatGPT - and that's 5 to 10% of what's actually possible." - Nikita Komarov "You can't automate what you don't understand. The first thing you have to do is say, what is the expected output and the outcome, and then how do I verify that I actually get there?" - Seth Earley Tune in to discover why financial AI is one of the most demanding and highest-stakes applications in the enterprise - and what it actually takes to build systems that are accurate and auditable enough to trust. Links LinkedIn: https://www.linkedin.com/in/nikita-komarov/ Website: https://dobs.ai Ways to Tune In: Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home  Apple Podcast: https://podcasts.apple.com/podcast/id1586654770  Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/  Stitcher: https://www.stitcher.com/show/earley-ai-podcast Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast  Buzzsprout: https://earleyai.buzzsprout.com/ Thanks to our sponsors: VKTREarley Information ScienceAI Powered Enterprise Book

  3. Aug 14

    Earley AI Podcast - Episode 97: Biological Computing, Brain-Derived Algorithms, and the Future of AI Efficiency with Alex Ksendzovsky

    Why Making AI More Biological May Be the Most Consequential Development in the History of Computing Guest: Alex Ksendzovsky, CEO and Co-Founder at The Biological Computing Company Host: Seth Earley, CEO at Earley Information Science Published on: August 14, 2026 In this episode, Seth Earley speaks with Alex Ksendzovsky, CEO and Co-Founder of The Biological Computing Company, a neurosurgeon and neuroscientist who spent nearly two decades studying how the brain processes information - including implanting electrodes into human brains to understand epilepsy and growing neurons in a dish to study them at the molecular level. They explore why the AI field diverged sharply from biology in the 1980s and what was left behind, how TBC grows real brain cells on electrode arrays to derive mathematical principles that improve AI algorithms, what a 13-20% improvement in video generation quality and a 4-5x efficiency gain means against an industry where 1-2% counts as significant, and where biological computing is headed in the next decade and beyond. This is one of the most technically ambitious and genuinely novel conversations the podcast has had. Key Takeaways: AI diverged sharply from biology in the 1980s when backpropagation was introduced - it produced highly performant systems but at the cost of massive energy inefficiency that the brain solved hundreds of millions of years ago.TBC grows hundreds of thousands of neurons on electrode arrays with 4,096 electrodes, encodes information as electrical patterns, and derives mathematical principles from how those neurons actually process and represent that information.The adapter products built from these biological principles plug into existing transformer architectures and produce 13-20% improvements in video quality metrics where a 1-2% improvement is considered publication-worthy.On efficiency, TBC's adapters currently produce a 4-5x improvement in frames per second - and when combined with existing optimization strategies, the two approaches are synergistic rather than conflicting.The catastrophic forgetting problem - AI's inability to learn continuously without losing what was previously learned - is one TBC is directly attacking by studying how biological synapses change during closed-loop learning and deriving new learning rules from that process.The brain is millions of times more efficient than silicon; even capturing a minuscule portion of that through biologically-derived principles has produced gains that suggest the ceiling for this approach is enormous.The ethical framework is clear: the cultured neurons used in TBC's experiments are fundamentally different from a brain - lacking the three-dimensional structure, scale, and emergent properties associated with sentience - and TBC actively works with bioethicists to maintain those guardrails.Insightful Quotes: "Moving forward past the 1980s into 2026, you have extremely performant AI systems, but they're being trained with brute force and they're extremely inefficient. At TBC, we think the reason for this is because they became extremely non-biological." - Alex Ksendzovsky "Just making it a tiny, tiny bit more biological reached these massive gains. It's a testament to the complexity of how the brain operates, and the more of these principles and primitives we can derive and apply, the more improvements we'll get in terms of performance and efficiency." - Alex Ksendzovsky "The gap is not a coincidence. It's a result of hundreds of millions of years of evolution solving the same problems that we are now trying to solve in silicon." - Seth Earley Tune in to discover why biological computing may be the most consequential and least-understood frontier in AI infrastructure today - and what it means for the energy crisis that is already shaping every data center investment being made. Links LinkedIn: https://www.linkedin.com/in/alexander-ksendzovsky-31732711/ Website: https://www.tbc.co Blog: https://www.tbc.co/blog   Thanks to our sponsors: VKTREarley Information ScienceAI Powered Enterprise Book

  4. Aug 4

    Earley AI Podcast - Episode 96: AI in Clinical Trials, the Vibe Coding Fallacy, and Bending Eroom's Law with Patrick Leung

    Why Applying AI to Drug Development Is One of the Most Technically Demanding Problems in the Industry - and What Is Finally Making It Solvable Guest: Patrick Leung, Chief Technology Officer at Faro Health Host: Seth Earley, CEO at Earley Information Science Published on: August 4, 2026 In this episode, Seth Earley speaks with Patrick Leung, Chief Technology Officer at Faro Health, who spent over a decade at Google including working on Google Duplex before bringing that technical depth to one of the most regulated and high-stakes domains in medicine. They explore why generative AI is in the trough of disillusionment in pharma, what the vibe coding fallacy costs organizations that believe they can build clinical software by prompting, how classical machine learning models and modern LLMs are working together to forecast trial outcomes, and why every day of clinical trial delay can cost up to half a million dollars in lost revenue. Patrick shares candid and specific insights on prompt injection as the new SQL injection, why human experts cannot be removed from clinical AI workflows, and what bending Eroom's Law would mean for patients worldwide. Key Takeaways: Generative AI is in the trough of disillusionment in pharma - the initial hype that AI could automate entire clinical processes has collided with the real complexity of the domain and the limits of the technology.Vibe coding hits an event horizon of complexity - demo apps are achievable by prompting, but real enterprise software requires proper engineering, security design, testing discipline, and architectural decision-making that AI cannot replace.Prompt injection is the new SQL injection - any tool that uses AI to process user input is now vulnerable to a class of attacks that did not exist before, and most organizations are not yet protecting against them.Classical machine learning models and modern LLMs are more powerful together than either is alone - survivor curve models from insurance analytics proved directly transferable to clinical trial forecasting with strong results.Every day of clinical trial delay can cost up to half a million dollars in lost revenue - and a typical amendment forcing a trial redesign and resubmission runs three to six months.Generic general-purpose models cannot replace domain-specific knowledge engineering in clinical contexts - the claim that they can is an easy sales pitch that does not survive contact with the actual complexity of the problem.The goal is not to automate clinical professionals out of existence but to remove the rote and repetitive work so they can focus on the judgment calls that only they can make.Insightful Quotes: "There's no escaping the fact that you need to test software. There's no escaping the fact that you need to have specs that are really well thought out. As you add more features to a codebase, it gets more complex and unwieldy and difficult to maintain. You can't vibe code your way out of those key design decisions." - Patrick Leung "I found myself applying models I'd learned about in a completely different domain. Survivor curve models we used for predicting insurance policy claims worked pretty well when applied to clinical trials. Transferability is really a thing." - Patrick Leung "Eroom's Law is not sustainable. Any exponential increase in cost is not sustainable by definition. So we want to bend Eroom's Law - and hopefully reverse it. Why not?" - Patrick Leung Tune in to discover why AI in clinical drug development is one of the hardest and most consequential problems in the field - and what is finally making it tractable. Links LinkedIn: https://www.linkedin.com/in/puiwah/ Website: https://www.farohealth.com Thanks to our sponsors: VKTREarley Information ScienceAI Powered Enterprise Book

  5. Jul 30

    Earley AI Podcast - Episode 95: Contract Intelligence, Context Engineering, and Building AI That Scales with Deepak Bapat

    Why Making Complex Revenue Simple at Scale Requires More Than Throwing Contracts Into a Chat Interface Guest: Deepak Bapat, Co-Founder and CTO at Tabs Host: Seth Earley, CEO at Earley Information Science Published on: July 30, 2026 In this episode, Seth Earley speaks with Deepak Bapat, Co-Founder and CTO at Tabs, a revenue and accounts receivable management platform built for B2B companies. They explore why dropping contracts into a general-purpose AI tool is not a strategy for enterprise scale, what generative AI unlocked that OCR and legacy machine learning could never solve, why context engineering beat fine-tuning for contract extraction, and why newer and larger models are not always better for specialized tasks. Deepak shares candid and specific insights on building atomic AI pipelines, the provability requirement that financial compliance demands, and what finance and data leaders consistently underestimate before deploying AI on their contracts. Key Takeaways: Dropping contracts into a chat interface is a reasonable experiment but not an enterprise strategy - doing things at scale requires specific tooling, specific expertise, and integration across systems.The SaaSpocalypse framing misses the point - the more interesting question is not whether chat replaces UI, but how platforms can understand intent and preempt the actions users would otherwise have to click through manually.Generative AI solved the contract problem by reasoning over ambiguous natural language at document level - something OCR and rules-based systems fundamentally could not do.Context engineering beat fine-tuning at Tabs because merchant preferences vary so significantly that fine-tuning per merchant became cost-prohibitive - a well-prompted generalized model proved faster and more elastic.Newer and larger models are not always better for specialized tasks - Deepak's eval sets show that models from six months ago outperform newer versions on certain contract extraction jobs, likely due to overfitting on coding.Provability is the non-negotiable requirement in financial AI - it is not enough to produce correct output, you must be able to prove the output is correct and traceable back to the source contract.Organizations that want to deploy AI on their contracts first need to standardize internally on what outcomes they actually want - two people on the same team asking the same question about the same contract should not produce two different answers.Insightful Quotes: "The misconception is that difficult problems can just be solved by throwing something into ChatGPT and having the answer come out the other side. In our case, the at-scale piece is everything. Those intelligence tools are still individualized tools - to do things at scale for an entire enterprise still takes specific tooling, specific thought, and specific expertise." - Deepak Bapat "What we're trying to do is move from a place of unstructured data to provable and correct structured data. That is what Tabs is built around - and that is what most of these other systems simply cannot handle." - Deepak Bapat "When you think about the legacy players that were more rigid SaaS tools with manual entry and brittle connectors - what was intractable about that model is exactly what generative AI made solvable. The ability to reason over the words in a document, understand what they meant, and understand what the output should be - that changed everything." - Seth Earley Tune in to discover what it actually takes to build AI that is accurate enough, auditable enough, and elastic enough to handle enterprise revenue data at scale - and what most organizations underestimate before they start. Links LinkedIn: https://www.linkedin.com/in/deepakbapat/ Website: https://www.tabs.inc Thanks to our sponsors: VKTREarley Information ScienceAI Powered Enterprise Book

  6. Jul 28

    Earley AI Podcast - Episode 94: Cybersecurity, AI Risk, and Why Security Is a Sales Motion with Taylor Hersom

    How the Threat Landscape Is Being Rewritten and What Organizations Need to Do Before It Gets Ahead of Them Guest: Taylor Hersom, Founder of Eden Data and Managing Director at Riveron Host: Seth Earley, CEO at Earley Information Science Published on: July 28, 2026 In this episode, Seth Earley speaks with Taylor Hersom, Founder of Eden Data and Managing Director at Riveron, a cybersecurity and compliance firm he built and grew before its acquisition in 2025. They explore why security is still treated as a cost center when it should be treated as a sales motion and competitive differentiator, how AI has exponentially expanded the attack surface, why most organizations have adopted AI with almost no security program around it, and how the subscription model Taylor pioneered is now reshaping how professional services firms price and deliver work. Taylor shares candid and specific insights on AI governance standards, the limits of automated threat detection, and why information architecture is the foundation security professionals are finding missing everywhere they go. Key Takeaways: Security is still treated as a cost center by most organizations when it should be viewed as a trust-building and sales motion that directly impacts revenue and brand reputation.Pre-revenue startups are now arriving with a million lines of AI-generated code - the attack surface has expanded exponentially and security programs have not kept pace.Most organizations have adopted AI across the enterprise without any AI-specific security program, controls around LLM access, or governance over what models are allowed to do.ISO 42001 and the NIST AI Risk Management Framework are the clearest starting points for organizations that want to de-risk their AI environment without reinventing the wheel.AI in security has shifted the human role from doing the work to supervising it - but final judgment on whether a threat is legitimate still requires a human and always will.Unorganized, incorrect, or inaccessible data creates systemic risk in AI environments - poor information architecture is what leads to the snowball effect of cascading security failures.The subscription model for security services - pricing for outcomes rather than hours - has proven durable across market conditions and is now becoming the industry expectation.Insightful Quotes: "We naturally leaned into AI from a technology standpoint, and there is almost no security around it to speak of. If you go ask the average company that's using AI across their enterprise, they probably don't have an AI-specific program where they have controls around their LLM and their processes and their access - and that is terrifying." - Taylor Hersom "Rather than go the FUD route - fear, uncertainty, and doubt - you can look at security as a way to build your brand and make it a part of your identity, and be proactive in how you use this when educating customers about how you protect their data." - Taylor Hersom "There's no AI without IA. Security requires information architecture - access controls, data organization, knowing what you have and where it lives. When you start losing control of your data, you start to create risks you don't even know about." - Seth Earley Tune in to discover why cybersecurity in the AI era is no longer just a technical problem - and what organizations need to put in place before the threat landscape gets ahead of them. Links LinkedIn: https://www.linkedin.com/in/taylorhersom/ Website: http://www.riveron.com  Website: https://www.edendata.com   Thanks to our sponsors: VKTREarley Information ScienceAI Powered Enterprise Book

  7. Jun 17

    Earley AI Podcast - Episode 93: AI Translation, Brand Voice, and Global Content with Olga Beregovaya

    Why the Gap Between an AI Translation Demo and Enterprise Production Is Wider Than Most Organizations Realize Guest: Olga Beregovaya, VP of AI at Smartling Host: Seth Earley, CEO at Earley Information Science Published on: June 17, 2026 In this episode, Seth Earley speaks with Olga Beregovaya, VP of AI at Smartling, who brings 25 years of experience across every major evolution in natural language processing - from rules-based systems through statistical models, neural translation, and now LLMs. They explore why plugging into a commercial model at token-level pricing is not a translation strategy, how brand voice fractures at 300,000 employees, why information architecture is just as essential for language pipelines as it is for retrieval, and what it actually takes to deliver consistent, on-brand, multilingual content at enterprise scale. Olga shares candid and specific insights on language complexity, the human-in-the-loop imperative, and why the organizations that are finally succeeding with AI have stopped treating it as art for art's sake. Key Takeaways: The price of a commercial model's tokens is not the cost of enterprise AI translation - data integrity, pipeline architecture, linguistic assets, and human review are the real cost drivers. Brand voice fractures the moment every employee can generate content autonomously - a Fortune 10 company discovered it had 300,000 voices overnight after deploying a co-pilot tool. Information architecture is equally essential for language pipelines as for retrieval - nested HTML tags, tokenization failures, and unstructured content break translation before the model ever sees the text. LLMs unlocked context that neural machine translation never had - resolving pronouns, disambiguating terminology, and working at document level instead of sentence by sentence. The assumption that AI translation works equally across all languages is one of the most dangerous misconceptions in the space - morphological complexity, writing systems, and training data representation vary enormously. Human review is not optional even in fully automated pipelines - it is how models learn, how ground truth is established, and how brand consistency is maintained over time. The organizations now succeeding with AI translation have moved from implement-and-fail to measured deployment - defining use cases, respecting prerequisites, and matching tooling to actual requirements. Insightful Quotes: "Yes, you can totally consume your million tokens at a super low price point, but what exactly are you buying for this money? Everybody can totally produce a translation or generate copy, but is it going to represent your brand? That's a different question." - Olga Beregovaya "He installed a co-pilot tool and said, it's great, except my company has 300,000 employees and now my company has 300,000 voices. That's not necessarily what I was prepared for in different countries." - Olga Beregovaya "If you want your models to evolve, and if you want your models to learn, you obviously need somewhere for these models to learn from - and this is where human review comes in. It is always twofold: guaranteeing the quality to your customers, and helping your models evolve." - Olga Beregovaya Tune in to discover why AI translation at enterprise scale requires far more than a model and an API key - and what the organizations getting it right have built that their competitors have not. Links LinkedIn: https://www.linkedin.com/in/olga-beregovaya-04b5/ Website: https://www.smartling.com Thanks to our sponsors: VKTREarley Information ScienceAI Powered Enterprise Book

  8. Jun 1

    Earley AI Podcast – Episode 92: Supply Chain Intelligence, Knowledge Graphs, and the Limits of the Easy Button with Ilya Levtov

    Why Supply Chain Visibility Is One of the Most Consequential and Underestimated Applications of AI in the Enterprise Guest: Ilya Levtov, Founder and CEO at Craft.co Host: Seth Earley, CEO at Earley Information Science Published on: June 1, 2026 In this episode, Seth Earley speaks with Ilya Levtov, Founder and CEO of Craft.co, a supplier intelligence platform that uses AI and knowledge graphs to give enterprises and government agencies visibility into their full supply networks. They explore why most organizations believe they have adequate supply chain visibility when they do not, why a simple risk score will always mislead, and how cross-correlating data streams surfaces risks that no human - and no generic LLM - would ever find alone. Ilya shares candid and specific insights on building knowledge graphs for mission-critical infrastructure, why only one percent of enterprise knowledge exists inside today's LLMs, and how the give-to-get model is turning supply chain intelligence into a shared strategic asset. Key Takeaways: Most enterprises believe their top-supplier relationships give them adequate visibility - but the middle and long tail of a supply network, which can run to 20,000 or 30,000 suppliers, remains almost entirely opaque.Supply chain is a misnomer - it is a complex, multi-dimensional network where companies are simultaneously suppliers, customers, and competitors to each other.A simple risk score is not meaningful and not actionable; supplier risk is deeply contextual and requires human judgment to weigh cost, probability, and consequence together.Cross-correlating data streams reveals hidden risks that no single source can surface - including correlations between employee morale and cybersecurity vulnerability that have proven highly predictive.Only approximately one percent of enterprise knowledge exists inside today's LLMs - which is exactly why a specialized knowledge graph grounded in proprietary data is essential before applying AI.AI has compressed analyst work on a supplier report from eight hours to under 30 minutes - but the decision of what to do with those findings still requires human judgment and always will.The give-to-get model and supplier passporting allow enterprises to share intelligence across a shared supply network without compromising their own competitive position.Insightful Quotes: "Only 1% of enterprise knowledge approximately exists inside the LLMs today. Companies don't want to give all of their data to the LLMs. Data providers don't want to give it for free either. That's why you need a specialized approach - leverage the power of the models on your own data set and on your knowledge graph." - Ilya Levtov "A financially vulnerable supplier becomes a target for adversarial capital - entities coming in from unfriendly nations looking to survive. You're connecting two different data sets, connecting entities, and getting to a very significant risk insight you need to act on before it becomes a problem for your enterprise." - Ilya Levtov "Organizations compete on their knowledge - knowledge of customers, knowledge of solutions, knowledge of supply chains, knowledge of routes to market. Those are competitive advantages. You do not want those inside an LLM. That is why doing this in a way that is internal and proprietary is so important." - Seth Earley Tune in to discover why supply chain visibility is one of the most important and most underestimated applications of AI in the enterprise today - and what it actually takes to build intelligence at the scale the problem demands. Links LinkedIn: https://www.linkedin.com/in/ilya-levtov/ \Website: https://www.craft.co Thanks to our sponsors: VKTREarley Information ScienceAI Powered Enterprise Book

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Ratings & Reviews

4.3
out of 5
7 Ratings

About

In this podcast hosts Seth Earley invites a broad array of thought leaders and practitioners to talk about what's possible in artificial intelligence as well as what is practical in the space as we move toward a world where AI is embedded in all aspects of our personal and professional lives. They explore what's emerging in technology, data science, and enterprise applications for artificial intelligence and machine learning and how to get from early-stage AI projects to fully mature applications. Seth is founder & CEO of Earley Information Science and the award-winning author of "The AI Powered Enterprise." 

Information

  • Creator
    Seth Earley
  • Years Active
    2021 - 2026
  • Episodes
    99
  • Rating
    Explicit
  • Copyright
    © 2026 Earley AI Podcast
  • Show Website
    Earley AI Podcast

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