Johnny Ayers, CEO of Socure

Turning News Into Intelligence, with Jeff Lurie, Perigon Chief Business Officer

Episode Overview In this episode of AI Leadership Lab,Ryan sits down with Jeff Lurie, Chief Business Officer of Perigon, to explore how AI is transforming the way organizations consume and act on news and real-time information. Jeff unpacks the fundamental problem of information overload explains how AI is needed to go beyond basic keyword monitoring to deliver structured, actionable intelligence. For anyone who has wrestled with clunky, expensive media monitoring tools or wasted hours sifting through irrelevant alerts, this conversation offers ideas on how to stay informed with AI.  Key Takeaways AI Monitoring vs. On-Demand Search: Two Different Tools ChatGPT and similar tools are great for on-demand, in-the-moment queries but they are not built for proactive monitoring. Perigon is designed to continuously watch hundreds of thousands of global sources without requiring users to ask for detailed updates. Actionable Intelligence, Not Just Data There is a wide gap between raw data and useful decisions — closing it will take better context, categorization, and entity recognition. Conversational AI Keeps Humans in Charge Some companies are going further than just keeping a "human in the loop." Perigon's agent is designed to ask follow-up clarifying questions rather than make assumptions, ensuring users stay in control of their monitoring setup. Hyper-Specificity Across a Multi-Persona Market Perigon's customer base across comms, financial institutions, law firms, and marketers share the common thread of needing to make precise decisions based on published data, whether the use case is brand tracking, competitive intelligence, or lead generation. The Future Is Agentic and Predictive Jeff outlines Perigon's roadmap vision: delivering intelligence directly into the tools people already use Slack, Google Sheets, HubSpot without requiring a login to yet another dashboard. One trend he spots is that platforms in this space will proactively suggest monitoring signals based on a user's past behavior, much like how Amazon anticipates consumer preferences. Chapter Timestamps [00:00] The problem Perigon is solving: taking monitoring to actionable intelligence [01:32] The spectrum of existing tools: too much vs. too little [03:00] Information overload as a centuries-old problem [03:41] Design flaws in legacy tools and the birth of the business [05:02] How natural language prompting changes the user experience [06:18] The AI analyst model follow-up questions and human control [07:00] Why ChatGPT alone is not a substitute for proactive monitoring [08:22] How results are delivered: tables, briefings, real-time alerts [09:44] Who uses Perigon and why the multi-persona market is both exciting and challenging  [11:12] Global sourcing and language translation capabilities [11:39] What's next: agentic delivery and predictive signal suggestions About the Guest Jeff Lurie is the Chief Business Officer of Perigon. With a background spanning sales, strategy, and business development in the data and media technology space, Jeff focuses on helping organizations move from passive information consumption to proactive, decision-ready intelligence. At Perigon, Jeff works closely with customers across communications, finance, legal, and marketing to ensure the platform delivers precision at scale. His approach to product evangelism centers on conversation encouraging users to simply describe their needs in plain language and working with AI to handle the refinement. Connect with Jeff & Perigon Perigon Website: https://www.goperigon.com [https://www.goperigon.com] LinkedIn (Jeff Lurie):https://www.linkedin.com/in/jeffry-lurie/ [https://www.linkedin.com/in/jeffry-lurie/]

Johnny Ayers, CEO of Socure

Episode Overview In this episode recorded at the World Economic Forum in Davos, Ryan sits down with Johnny Ayers, CEO of Socure, to discuss the evolving challenges of identity verification, fraud prevention, and trust in an age of AI-powered deepfakes and digital doppelgangers. Key quotes "The ability to discern between a human and a video feed, two years ago, you could see it with a naked eye. You can't see it anymore." "It's surprising that more folks haven't talked about how and when and where you can trust a counterparty on the internet." "A lot of AI is just creating average, right? It's creating stories that already exist. It's creating facts that, you know, humanity already knows. And that there's not a lot of creative breakthroughs." Themes discussed: Ayers shares insights on: * The trade-offs between moving fast and ensuring accuracy when building mission-critical systems * Why the internet's trust problem may become the defining conversation of 2026 and 2027. * The ease with which anyone can create digital twins and voice clones * The surprising lack of conversation at Davos about trust on the internet * Age verification requirements * The difference between high-stakes algorithms (KYC, AML) and experimental AI applications * Extensive testing frameworks for facial biometrics and accessibility * Testing for racial, age, and geographic biases before production deployment * When companies should leverage external partners vs. building internally * The value of unique data assets that see consumer behavior at scale * Combining institutional insights with third-party expertise for optimal decisions * Setting up agents correctly with degrees of freedom and autonomy to become "maestros of our agentic orchestra" * Human traffic to machine traffic ratios shifting from 1:2 to 1:90 * AI's tendency to create "average" content based on existing information * The continued importance of creative breakthroughs and novel patents * Areas where human ingenuity remains irreplaceable * Fraud proliferation across banking, lending, insurance, and government * Workforce fraud: fake employees, resumes, and interviews More about Socure: https://www.socure.com/https://www.mckinsey.com/industries/financial-services/our-insights/a-question-of-identity-talking-with-socures-johnny-ayers [https://www.mckinsey.com/industries/financial-services/our-insights/a-question-of-identity-talking-with-socures-johnny-ayers] More about Ayers: https://www.linkedin.com/in/johnnyayers/ [https://www.linkedin.com/in/johnnyayers/]

Deloitte Global AI Leader Nitin Mittal

Episode Summary: In this compelling conversation, Nitin Mittal shares insights from his unique position as the AI strategy leader across Deloitte's global operations. From scaling AI implementations across Fortune 500 companies to navigating the rapid evolution from predictive AI to generative AI, Nitin discusses the practical realities of enterprise AI adoption. He explores the critical importance of trust frameworks, the emerging role of agentic AI, and why he believes we're entering a transformative period where AI will fundamentally reshape how organizations operate and compete. Key quotes "Trust is not just a nice-to-have in AI — it's the foundation. Without it, even the most sophisticated AI system will fail to deliver value." "We've moved from asking 'Can AI do this?' to 'How quickly can we scale AI to do this across our entire organization?'" "The organizations that will win with AI aren't necessarily those with the best technology, but those with the best change management and cultural readiness." "Agentic AI represents a fundamental shift—we're moving from AI as a tool to AI as a colleague." Top themes 1. Trust is foundational - Organizations must establish robust trust frameworks before scaling AI 2. Culture drives adoption - Technology alone isn't enough; successful AI transformation requires cultural change 3. Generative AI is transformative - The shift from predictive to generative AI represents a step-change in enterprise capabilities 4. Agentic AI is emerging - Autonomous AI agents will be the next major wave of innovation 5. Change management matters - The human side of AI transformation often determines success or failure About the guest * Career path from engineering to leading global AI strategy at Deloitte * Transition from traditional consulting to AI-focused leadership * Nitin works with Fortune 500 companies to navigate the complexities of enterprise AI adoption. His focus on trustworthy AI and practical implementation has made him a sought-after voice on the future of AI in business. Nitin Mittal on LinkedIn - linkedin.com/in/nitinmittal0101 [https://www.linkedin.com/in/nitinmittal0101/] Deloitte AI Institute - deloitte.com [https://www.deloitte.com/southeast-asia/en/what-we-do/capabilities/agentic-ai.html?id=my:2ps:3gl:4agenticai:5:6con:20260112] #AI #ArtificialIntelligence #GenerativeAI #AgenticAI #EnterpriseAI #DigitalTransformation #Leadership #Deloitte #TrustworthyAI

Uniphore CEO Umesh Sachdev - Moving from AI Pilots to Business Outcomes

In this episode of AI Leadership Lab, host Ryan Heath interviews Umesh Sachdev, CEO of Uniphore, live from the World Economic Forum in Davos. As the leader of a company serving over 2,000 customers globally, Umesh shares critical insights about the shift from AI experimentation to real business impact in 2026. The conversation explores how C-suite leaders are moving beyond the novelty of GPUs and LLMs to focus on outcome-as-a-service models, the importance of cost optimization across different AI use cases, and why the pace of decision-making has become the defining factor separating AI leaders from laggards.Key TakeawaysThe Era of AI Pilots is Over: Outcomes Matter NowIn 2026, the conversation has shifted from which GPU or LLM to use to what business transformation AI delivers. Companies that have figured out how to use AI as a growth enabler are starting to break away from the pack. One Size Does Not Fit All in AIDifferent use cases require different AI architectures. A real-time call center assistant needs sub-second response times with high-capacity GPUs, while CFO automation tasks can tolerate three-minute responses using smaller models on lower-capacity hardware. The key is matching infrastructure costs to the specific outcome required, rather than applying a uniform approach across all AI initiatives.AI Agents Must Work Within Existing WorkflowsThe thinking has evolved: companies need consistency for tasks repeated thousands of times daily. The 2026 breakthrough is making AI agents work reliably within current business structures rather than forcing organizational redesign.Open, Sovereign Architecture is Non-NegotiableClients demand flexibility to avoid vendor lock-in and the ability to adapt as new innovations emerge. More critically, especially outside the US, geopolitical developments are driving demand for sovereign AI architectures that ensure access cannot be cut off by any single government action. Speed of Decision-Making Defines AI LeadershipThe traditional playbook of research, analysis, and committee-based decisions is being discarded. CEOs across Fortune 500 companies recognize that moving at the speed of AI is essential to satisfy investors and Wall Street. The gap between companies that can execute with agility and those that cannot is widening dramatically.Chapter Timestamps[00:00] The Davos Reality Check: AI ROI in 2026[01:16] From Pilots to Business Transformation[01:34] Outcome-as-a-Service Business Model[02:03] Matching AI Architecture to Use Cases[03:00] Workflow and Organizational Design[04:25] Uniphore’s Product Roadmap and Platform Strategy[06:21] From Novelty to Business Basics[07:00] Leadership in the AI Revolution[08:30] Bringing the Workforce Along[09:37] Humans, Agents, and Sustainable Jobs[11:43] Near-Term Job Displacement vs Long-Term OpportunitiesAbout the GuestUmesh Sachdev is the CEO of Uniphore, a global AI platform company serving over 2,000 enterprise customers. Under his leadership, Uniphore has developed the Business AI Cloud, an open and sovereign platform that delivers enterprise-grade AI solutions with a focus on business outcomes rather than technical specifications. The platform runs multiple types of compute and LLMs, offering clients the flexibility to choose their technology components while maintaining security, scalability, and sovereignty.Connect with Umesh & UniphoreUniphore Website: https://www.uniphore.comAbout AI Leadership Lab AI Leadership Lab interviews C-suite leaders about their AI journeys, covering what's working, where they're getting stuck, how they pivot, and the lessons they take from the losses.Host: Ryan HeathWebsite: RyanHeathConsulting.comResources MentionedUniphore Business AI Cloud - Open and sovereign AI platform that encompasses multiple types of compute and LLMs, delivering enterprise-grade security and scalability - https://www.uniphore.com

The Future of Data with Philip Rathle, Neo4J CTO

In this episode of AI Leadership Lab, host Ryan Heath sits down with Philip Rathle, Chief Technology Officer at Neo4j, to explore how graph databases are revolutionizing AI infrastructure and enterprise knowledge systems. Philip reveals why understanding the relationships between data points is more powerful than having all the facts, and how companies like Google built trillion-dollar businesses on graph algorithms. From explaining knowledge graphs in plain language to discussing how graph-based retrieval can make AI more trustworthy and explainable, this conversation delivers actionable insights for leaders seeking to build more effective AI systems. Takeaways Relationships Matter More Than Facts Understanding connections between data points often reveals more than the data itself. Philip demonstrates this with a striking example: knowing how friends-of-friends-of-friends behave is a better predictor of someone's behavior than having comprehensive facts about that individual person. This principle applies across business contexts, from customer 360 systems to organizational analysis. The Real vs. Declared Org Chart Graph technology can reveal an organization's true power structure by analyzing email patterns, Slack messages, and information flows. Companies are using this to identify single points of failure—like one person receiving all questions on a critical topic—and to facilitate warm introductions by mapping who knows whom across company boundaries. Graph RAG Delivers Better Results with Less By combining knowledge graphs with language models, companies are achieving superior answers while using two-thirds less data in context windows. This "graph RAG" approach queries a knowledge graph first, then feeds only the most relevant results to the model, resulting in faster responses, lower costs, and reduced energy consumption. AI Systems Need Knowledge Layers, Not Just Language Models Language models alone have fatal flaws for enterprise use: they hallucinate, lack company-specific data, operate as black boxes, and can't discern what information is appropriate for which purpose. Successful AI implementations complement LLMs with knowledge graphs that provide exact, explainable results while maintaining the context and causality that business users understand. Explainability is the Path to Trust and Adoption Graph-based systems enable accountability by providing traceable answers. Timestamps [00:00] Introduction [01:12] Philip's journey from consulting to graph databases [04:00] Facebook and Google as graph pioneers [05:18] What is a knowledge graph? [07:44] The true org chart: mapping real power structures [09:30] Making AI more explainable and trustworthy [14:13] Build vs. buy considerations for graph technology [16:07] How graphs will reshape AI infrastructure [18:08] Graph RAG and the future of AI applications [20:00] Human impact: accountability and agency in AI About the Guest Philip Rathle is the Chief Technology Officer at Neo4j, a company that has been pioneering graph database technology and knowledge graphs for AI applications. Philip's career began in consulting, where he quickly became convinced that data serves as a mirror of business operations — the better your data, the better handle you have on your business. He built United Airlines' first passenger 360 system. Connect with Philip & Neo4j Neo4j Website: https://neo4j.com [https://neo4j.com] LinkedIn: Search for Philip Rathle, CTO at Neo4j Support the Show If you'd like to appear on the show or know someone who should be featured, visit RyanHeathConsulting.com. Please leave a five-star rating or review to help more leaders discover these insights.