Earley AI Podcast

Hosted by Seth Earley

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Jun 2026

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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."

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June 1, 2026Episode 9240 min

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 EnterpriseGuest: Ilya Levtov, Founder and CEO at Craft.co Host: Seth Earley, CEO at Earley Information Science Published on: June 1, 2026In 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 EarleyTune 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.LinksLinkedIn: https://www.linkedin.com/in/ilya-levtov/ \Website: https://www.craft.coThanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

May 26, 2026Episode 9031 min

Earley AI Podcast – Episode 90: Federated AI, Decision Intelligence, and the Data Architecture Reset

Why Centralization Is the Wrong Foundation for AI - and What Organizations Need to Build InsteadGuest: Todd Barr, CEO at Axonis.aiHost: Seth Earley, CEO at Earley Information SciencePublished on: May 13, 2026In this episode, Seth Earley speaks with Todd Barr, CEO of Axonis.ai, a company spun out of a government defense integrator that is bringing federated AI and decision intelligence to high-consequence enterprise workflows. They explore why the demo-to-production gap is one of the most costly misconceptions in enterprise AI today, why centralization was built for business intelligence and not for AI, and what it really means to send your AI to your data rather than the other way around. Todd shares a candid and direct perspective on decision artifacts, AI cost exposure, the risks of vendor lock-in, and why enterprises that give away how they make decisions may be giving away the most valuable thing they own.Key Takeaways:The demo-to-production gap is a form of malpractice - polished AI demos built on curated data create executive expectations that production reality cannot meet.Centralized data infrastructure was built for business intelligence, not AI - it is optimized for reporting, not reasoning or prediction.The premise of agentic AI is decentralization - if agents have to wait for data to be synced and centralized before acting, the architecture is working against itself.Data resists centralization for three distinct reasons: technical constraints, regulatory and compliance requirements, and organizational politics.Decision artifacts - cryptographically sealed records of data used, model applied, and reasoning followed - turn AI-assisted decisions into auditable, improvable corporate assets.Enterprises now face a clear choice: pay in tokens, pay in vendor lock-in, or invest in owning their own AI infrastructure through open source models.How an organization makes decisions is its most proprietary asset - giving that context to a third-party AI platform may be the most consequential thing enterprises are doing right now without fully understanding it.Insightful Quotes:"The misconception is really the gap between prototype and reality, and that's where a lot of these things are falling down right now. Getting people excited about something they can't have is almost malpractice." - Todd Barr"Centralization is almost a fallacy in itself. Whenever you are using data you are changing it, enriching it, doing something with it. It is a fractal nature of data that defies the whole concept of centralization." - Seth Earley"If I'm an enterprise, what do I own in this day and age? I own how I make decisions. Which data I use to make those decisions. If we are just going to give that away, that is like giving our brain away." - Todd BarrTune in to discover why the most important AI infrastructure decision an enterprise can make right now is not which model to use - but whether they are building a foundation they actually own.LinksLinkedIn: / tbarr Website: https://axonis.aiThanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

May 26, 2026Episode 9126 min

Earley AI Podcast - Episode 91: Real-Time Voice Intelligence, Fraud Detection, and AI Guardrails with Mike Pappas

Why Voice Is Not a Solved Problem - and What Real-Time Audio Intelligence Changes for Enterprise AIGuest: Mike Pappas, CEO at ModulateHost: Seth Earley, CEO at Earley Information ScienceIn this episode, Seth Earley speaks with Mike Pappas, CEO of Modulate, whose work began in gaming - one of the most demanding environments for real-time voice intelligence - and has since expanded to enterprise applications including fraud detection, customer abuse prevention, AI agent guardrails, and sales coaching. They explore why transcription is not the same as understanding, what gets lost when audio is reduced to text, and why voice is the most powerful tool fraudsters have. Mike shares candid and specific insights on deepfake detection, the fine line between safety and surveillance, and what organizations need to put in place before deploying voice AI at scale.Key Takeaways:Transcribing voice and understanding voice are not the same thing - intonation, emotion, cadence, and timbre carry information that transcripts cannot capture.Voice AI demos are typically built for pristine environments; the real challenge is building systems that hold up under noise, jargon, and emotional complexity in production.Real-time intervention changes behavior more effectively than after-the-fact review - feedback delivered in the moment produces measurable reductions in repeat offenses.Voice is the most powerful tool for manipulation because it bypasses rational judgment by triggering emotional responses - and AI is now making voice fraud scalable.AI voice agents cannot introspect - they cannot tell when a call is going wrong, which is why a separate supervisory layer is essential for any enterprise voice deployment.The line between safety systems and surveillance systems is real; collecting and storing only what is necessary for the specific risk being addressed is both a privacy and a trust requirement.Before deploying any voice AI, organizations need to define their KPIs clearly - if the system is driving customer satisfaction down, the deployment is failing regardless of what else it is doing.Insightful Quotes:"When you hear a voice, you hear the intonation, you hear the emotion, you hear pregnant pauses - there is so much information being carried in that audio that gets lost when you pull down to a transcript. And whenever we talk to someone who professionally works in a contact center, they are always saying, we know these transcripts are losing tons of good value." - Mike Pappas"If I am actively harassing you and the platform is able to come in and put a stop to it live in the conversation, that feedback actually systematically changes behavior. Getting an email 30 minutes later saying we noticed you did something wrong - that just infuriates people, it does not lead to change." - Mike Pappas"There is a fine line between safety systems and surveillance systems. How do you design voice AI that improves safety and trust but does not cross that boundary that makes users and employees uncomfortable?" - Seth EarleyTune in to discover why real-time voice intelligence is one of the most consequential and least understood frontiers in enterprise AI - and what organizations need to get right before they deploy.LinksLinkedIn: https://www.linkedin.com/in/mike-pappas-9a30a858/Website: https://www.modulate.ai Thanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

May 6, 2026Episode 8934 min

Earley AI Podcast Episode 89: Memory, Power, and the Hidden Constraints of AI Infrastructure

Guest: Steven Woo, Fellow and Distinguished Inventor at RambusHost: Seth Earley, CEO at Earley Information SciencePublished on: May 5, 2026In this episode, Seth Earley speaks with Steven Woo, Fellow and Distinguished Inventor at Rambus, where he has spent over 30 years at the frontier of memory technology. They explore why memory - not compute - is the binding constraint on AI performance today, how moving data between chips consumes more than half of all power in a high-end AI processor, and what the rise of agentic AI means for infrastructure planning. Steven shares a rare long-view perspective on the innovation curve for memory technology, the supply-demand dynamics driving prices higher, and the questions enterprise leaders should be asking before signing their next infrastructure contract.Key Takeaways:Memory, not compute, is the primary bottleneck limiting AI performance - and the gap between processor speed and memory speed is widening, not closing.Over 50 percent of the power consumed by high-end AI processors is spent simply moving data on and off the chip, not performing computation.Stacking memory components closer together can reduce energy costs dramatically but introduces new challenges around heat dissipation and power delivery.Training and inference have very different memory profiles - understanding both is essential for organizations architecting AI infrastructure at scale.Agentic AI compounds the memory challenge significantly, because one user can spin up multiple agents that each spawn further agents, multiplying context and capacity demands.Memory prices have risen sharply due to supply-demand imbalance - organizations are now signing long-term supply agreements to lock in capacity, just as they do for power.The most important question enterprise leaders can ask their infrastructure providers is how much experience and demonstrated reliability they have - downtime during model training can be catastrophic.Insightful Quotes:"Memory has become a big bottleneck. In many cases, in AI, your speed at which you can actually process information and create new large language models is really gated by the speed and availability of memory." - Steven Woo"More than 50 percent of the power is spent in circuits just trying to move data on and off the processor. It's pretty astounding to think that as companies plan how much power they need, a lot of it is really related to simply moving data back and forth." - Steven Woo"People think of compute in terms of gigawatts. But it turns out it's really the movement of that data - and nobody talks about that. It's the silhouette behind the curtain that's actually constraining everything else." - Seth EarleyTune in to discover why the future of AI depends as much on memory engineering as it does on model development - and what enterprise leaders need to understand about the infrastructure constraints shaping every AI investment they make.LinksLinkedIn: https://www.linkedin.com/in/stevencwoo/Website: https://www.rambus.comThanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

April 27, 2026Episode 8848 min

Earley AI Podcast - Episode 88: Digital Twins, Agentic AI, and the Future of Work with David Shim

From Meeting Intelligence to Personal AI: How Digital Twins Are Reshaping How We WorkGuest: David Shim, Co-Founder and CEO at Read AIHost: Seth Earley, CEO at Earley Information SciencePublished on: April 27, 2026In this episode, Seth Earley speaks with David Shim, Co-Founder and CEO of Read AI, the fastest-growing meeting intelligence platform globally with over 5 million monthly active users. They explore how AI is moving beyond summarization toward recommendation and autonomous action, what it really means to build a digital twin grounded in your actual work history, and why the organizations getting the most from AI are the ones that treat it like a trainable intern rather than an out-of-the-box solution. David shares candid insights on agentic guardrails, data privacy, workforce transformation, and why access to personal AI may one day be considered a basic human right.Key Takeaways:AI is moving from task execution to recommendation - the next frontier is AI that proactively surfaces what you should do next.A digital twin is only as good as its context; weighting recent activity more heavily produces responses that actually reflect how you think and work today.Treating AI like a trainable intern - feeding it your emails, files, meetings, and tools - is what separates high-value users from disappointed ones.Native permissions are the cleanest foundation for digital twin privacy; building new rules for every edge case creates the vulnerabilities you are trying to avoid.Agentic guardrails should be built in from the start, not bolted on - autonomy without oversight erodes trust and adoption faster than it builds them.The tension between organizational IP and individual work style is real; your tone, voice, and preferences belong to you, even when the content belongs to the company.AI is a great leveler - emerging markets and individuals with access to these tools are already competing on equal footing with developed market counterparts.Insightful Quotes:"It's not plug and play today. You have to give it more context - your emails, your files, your CRM, your meetings. When you have all that data, now your intern is learning as you go, and it's pulling from your experience as the mentor." - David Shim"Your digital twin knows I hate meetings after three hours straight. After three hours, my engagement goes down, my sentiment goes down - so it puts in a buffer. That's the first part. Then it starts asking: what happens when people ask you a question?" - David Shim"You can't take the AI's version of the world as a representation of your version of the world. What's more valuable is your secret sauce, your knowledge, your expertise - you have to give it examples of your work, give it your perspective, not just take the LLM's." - Seth EarleyTune in to discover how digital twins and agentic AI are transforming the way individuals and organizations work - and what it takes to get real value from the technology before it gets ahead of you.LinksLinkedIn: https://www.linkedin.com/in/davidshim/Website: https://read.aiThanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

April 20, 2026Episode 8743 min

Earley AI Podcast – Episode 87: AI-Enabled Enterprise Data Migration with Dominik Wittenbeck

Why Knowledge, Not Technology, Is the Foundation of Successful AI-Driven Data MigrationGuest: Dominik Wittenbeck, Group CTO at SNP GroupHost: Seth Earley, CEO at Earley Information SciencePublished on: April 20, 2026In this episode, Seth Earley speaks with Dominik Wittenbeck, Group CTO at SNP Group, a 1,600-person global software and solutions firm with 30 years of SAP-centric data migration expertise. They explore why AI is only as good as the institutional knowledge behind it, how agentic AI is transforming high-stakes enterprise migrations, and why organizations must treat data migration as a strategic opportunity rather than a cost-reduction exercise. Dominik shares hard-won insights on semantic architecture, governance, and what executives consistently get wrong when applying AI to critical enterprise processes.Key Takeaways:AI is not a silver bullet for data migration - it requires deep, domain-specific knowledge to produce deterministic, auditable results.Enterprise data migration is a team sport requiring cross-functional specialists; AI accelerates the work but cannot replace that expertise.The real opportunity in migration is not just moving data - it is cleaning it up and optimizing processes while the organization is already changing.Agentic AI is transforming the full migration lifecycle, from pre-sales solutioning and blueprint generation to rule creation and automated testing.Governance established once without ongoing enforcement decays quickly - organizations must build continuous oversight into critical processes from the start.Value mapping, not just structural mapping, is the dominant challenge in SAP migrations, and AI can significantly accelerate semantic alignment work.Executives should focus AI investments on problems that truly matter, not easy wins - meaningful impact comes from finding where differentiation really counts.Insightful Quotes:"In order to run complicated systems which have a critical impact on your business, they need enough grounding. You actually need to feed the knowledge into the agentic system that you're building on top of, in order to make sure that you get deterministic results in the end." - Dominik Wittenbeck"Rather than re-architecting the whole thing, try to identify what the critical processes really are, that if they are not exercised correctly, really hurt your business. Find where the value lies - or if you can't find that, find where your risk lies." - Dominik Wittenbeck"Sometimes cheap is quite costly, and sometimes slowing down speeds things up. If you're moving stuff from one system to another and you say, we'll clean it up later - that's never going to happen. It's like moving from one house to another with an attic full of boxes and junk." - Seth EarleyTune in to discover why successful AI-driven enterprise migration depends less on technology and more on institutional knowledge, governance, and treating transformation as a strategic opportunity.LinksLinkedIn: https://www.linkedin.com/in/dominik-wittenbeck-61a64669/Website: https://www.snpgroup.comThanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

April 17, 2026Episode 8648 min

Earley AI Podcast – Ep. 86: Open Source, Observability, and AI-Driven Engineering with Tom Wilkie

How Grafana Labs Built a Competitive Edge Through Openness, Agentic AI, and Engineering CultureGuest: Tom Wilkie, VP of Product at Grafana LabsHost: Seth Earley, CEO at Earley Information SciencePublished on: April 17, 2026In this episode, Seth Earley speaks with Tom Wilkie, VP of Product at Grafana Labs, a leading observability platform serving 25 million users across 50 global regions. They explore how Grafana's open source "big tent" philosophy creates unexpected competitive advantages in the AI era, why agentic AI is transforming how engineers respond to production incidents, and how the build-versus-buy debate is shifting with AI-assisted development. Tom shares candid insights on engineering culture, remote-first work, and why junior engineers may be more valuable than ever.Key Takeaways:Grafana Labs' open source strategy gave AI foundation models deep familiarity with their software, creating a powerful and unexpected competitive advantage.Agentic AI is transforming observability by automating root cause analysis of production incidents, reducing engineering response time significantly.Adaptive telemetry technology automatically identifies unused data, enabling organizations to cut observability costs dramatically without sacrificing coverage.The build-versus-buy debate is shifting, but the real hidden cost is long-term maintenance - not the initial development effort.Emergent engineering standards outperform top-down mandates; leaders consistently overestimate how much centralized consolidation is actually needed.Remote-first engineering works when companies deliberately engineer collaboration rather than relying on spontaneous hallway interactions that rarely happen anyway.AI-powered LLMs may solve the remote junior engineer onboarding problem by providing a low-ego, always-available resource for learning and guidance.Insightful Quotes:"By having 25 million users worldwide, they're out there blogging, publishing examples, tweeting, publishing videos - generating so much content on the open web about how to use Grafana. These foundation models are trained on that data. They know how to use our software better than proprietary competition." - Tom Wilkie"The cost of consolidation is often underestimated. And it's often dangerous to the culture, because as soon as you start telling engineers that have poured their heart and soul into this project to drop it - that's devastating to people." - Tom Wilkie"Openness - whether it's open source, open standards, open culture - is not just a philosophy. It really is a competitive strategy. It lowers switching costs, builds trust, and in the area of AI, it turns out to be the best way to make sure your models know how to use your technology." - Seth EarleyTune in to discover how Grafana Labs turned open source philosophy into a winning AI-era strategy - and what engineering leaders can learn about culture, observability, and building for the long term.LinksLinkedIn:https://www.linkedin.com/in/tomwilkie/Website: https://grafana.comThanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

March 27, 2026Episode 8546 min

Earley AI Podcast - Episode 85: AI Security, Shadow IT, and the Governance Reset with Rob Lee

Why Security Teams Are Being Asked to Do Three New Jobs - and What to Do About ItGuest: Rob Lee, Chief AI Officer and Chief of Research at SANS InstituteHost: Seth Earley, CEO at Earley Information SciencePublished on: March 27, 2026In this episode, Seth Earley speaks with Rob Lee, Chief AI Officer and Chief of Research at SANS Institute, about why AI governance is broken in most organizations - and what it actually takes to fix it. They explore why security teams are being asked to simultaneously govern, adopt, and defend AI, why the default framework of no is driving shadow IT rather than preventing risk, and what a practical reset of AI governance actually looks like. Rob also shares why agents should be treated like workers rather than software, and why executives cannot afford to outsource their understanding of AI to anyone else.Key Takeaways:Security teams are now being asked to do three new jobs at once - evaluate AI tools for the organization, drive their own AI transformation, and manage governance and regulatory compliance.The default framework of no does not prevent AI use - it drives it underground, creating shadow IT that is far harder to monitor and control than sanctioned tools.Governance needs a stoplight model - green means experiment freely, yellow means involve security as a lifeguard, red means stop - with the default answer being yes unless there is a clear reason to say no.AI governance documents written before generative AI arrived are already outdated - most say nothing about agentic workflows, human-in-the-loop requirements, or connector permissions.Agents should be treated like workers, not software - they reason, improvise, and operate 24-7, which means they require the same zero-trust principles, oversight structures, and ethical guardrails as human employees.Executives cannot outsource their understanding of AI to security teams - AI literacy at the C-suite level is a competitive requirement, not an optional capability.Good governance is not about documenting every possible bad outcome - it is about establishing overarching goals and building a culture of trust with enough guardrails to prevent the truly stupid risks.Insightful Quotes:"The framework security teams are using is a framework of no. And that framework of no is causing people to use AI secretly, regardless of what the security team says." - Rob Lee"An agent in the future - and some organizations are already treating it this way - is a worker. Everything you ask about governing agents, replace that with a human who just got hired. The same rules apply." - Rob Lee"You can't automate what you don't understand - and with agents, the stakes are even higher. An agentic mistake isn't a wrong paragraph, it's a blocked critical system." - Seth EarleyTune in to discover how security and executive leaders can move from a governance posture of restriction to one that enables innovation, manages real risk, and keeps organizations competitive in the age of agentic AI.Links:LinkedIn: https://www.linkedin.com/in/leerob/Website: https://www.sans.orgSponsor: Vector - https://www.vktr.com/ Thanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

March 20, 2026Episode 8427 min

Earley AI Podcast - Episode 84: AI in Legal Operations with Mike Anderson

Accuracy, Trust, and the Interface Revolution: How AI is Transforming Legal WorkflowsGuest: Mike Anderson, Chief Product Officer, FilevineHost: Seth Earley, CEO at Earley Information SciencePublished on: March 20, 2026In this episode, Seth Earley speaks with Mike Anderson, Chief Product Officer at Filevine, about what it takes to bring AI into one of the most demanding and high-stakes environments in the enterprise - legal operations. They explore why AI will not replace attorneys but will dramatically extend what legal professionals can accomplish, how real-time deposition analysis is transforming courtroom preparation, and why information architecture remains the critical foundation beneath every AI capability. Mike also shares why the interface - not the model - is the biggest unlock AI offers the legal industry.Key Takeaways:AI will not replace attorneys or paralegals - legal services are already severely undersupplied, and AI's role is to extend what existing professionals can accomplish.The billable hour model is evolving, but the bigger opportunity is eliminating non-billable administrative burden so attorneys can focus on higher-order legal thinking.Real-time deposition analysis - live transcription cross-referenced against case files - is one of the most powerful and practical AI applications in legal today.Boolean search cannot be replaced in legal because accountability for document populations requires transparent, auditable logic that external parties can evaluate.Effective AI in legal requires three information retrieval lenses: semantic search, Boolean search, and attribute-based filtering - all three are necessary.Information architecture - defining the is-ness and about-ness of legal objects like matters, contracts, depositions, and clients - remains the foundation for AI to work accurately.The interface is the single biggest unlock AI offers legal professionals - the ability to ask a question in natural language rather than navigate complex click paths changes everything.Insightful Quotes:"The demand for legal services already outpaces supply, and it has for some time. We should be talking about the productivity and extensibility of legal professionals - not obsolescence." - Mike Anderson"If only I had this analysis of the deposition during the deposition. That one customer comment kicked off an entire depositions platform for us." - Mike Anderson"You still need the is-ness and about-ness. The interface changes, but the underlying information architecture is still what makes AI work correctly." - Seth EarleyTune in to discover how legal teams are moving past AI skepticism and building the foundations that make AI accurate, trustworthy, and transformative in practice. LinksLinkedIn: https://www.linkedin.com/in/michael-anderson-374299163/Website: https://www.filevine.comThanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

March 9, 2026Episode 8340 min

Earley AI Podcast - Episode 83: AI, Governance, and the Execution Gap with Brian Stafford

From Vision to Value: How Leaders Can Close the Gap Between AI Ambition and Operational RealityGuest: Brian Stafford, CEO at DiligentHost: Seth Earley, CEO at Earley Information SciencePublished on: March 9, 2026In this episode, Seth Earley speaks with Brian Stafford, CEO of Diligent, a $700 million global software and AI company focused on governance, risk, and compliance. They explore why most organizations understand that AI is transformative but still struggle with the how of actually getting there, and what it takes to move beyond pilots into real operational change. Brian shares how Diligent is helping clients in compliance, audit, and risk functions do more with less through AI-wired software and agents, and why context, leadership, and process understanding are the real drivers of successful AI transformation.Key Takeaways:Most organizations have crossed from asking what AI can do to struggling with how to actually execute and drive measurable transformation.Calling initiatives pilots gives organizations an excuse to fail - framing AI as transformation from the start changes accountability and outcomes.AI maturity is less about sector or company size and more about the quality and commitment of executive leadership driving change.Compliance, risk, and audit functions face a structural mandate - increasing obligations with flat or shrinking budgets - making AI adoption a necessity, not a choice.Agents should be thought of like a smart new associate - trained gradually, checked in with frequently at first, then trusted to operate with more autonomy over time.Context is the key differentiator for AI solutions - partners who already understand your domain, regulatory environment, and workflows will deliver faster, better outcomes.AI-native employees who are intellectually curious and fluent in modern tools can deliver 5 to 10 times the output of peers who resist adopting new capabilities.Insightful Quotes:"I hate the term pilot. Pilot gives organizations the license to call something unsuccessful. You're not piloting a transformation - you're either driving it or you're not." - Brian Stafford"Most of our clients don't care if I ever said the word agent. They care about an outcome. The technology is just what helps deliver it." - Brian Stafford"You can't automate what you don't understand. And once you do understand it, agents change everything - but the process clarity has to come first." - Seth EarleyTune in to discover how forward-thinking leaders are closing the gap between AI ambition and real operational impact across governance, risk, and compliance functions.LinksLinkedIn: https://www.linkedin.com/in/brian-k-stafford/Website: https://www.diligent.comThanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book