Winners' Circle

Harshit Kohli is helping enterprises understand where AI, cloud, streaming analytics, and cybersecurity are heading next. As a Senior Technical Account Manager at Amazon Web Services and a GenAI streaming specialist, Harshit works with customers on cloud adoption, artificial intelligence adoption, and real-time data use cases. He is also pursuing a doctorate in artificial intelligence and continues to serve as one of BIG’s All-Star Judges.In this episode, Russ and Harshit discuss what he is seeing across AI, cybersecurity, MCP, and agentic systems after speaking at industry events including RBLN and the MCP Dev Summit. Harshit explains why companies are moving quickly toward AI, but must also keep security, governance, authorization, and authentication at the center of every deployment.The conversation dives into model context protocol, or MCP, and how it is evolving beyond simple request and response workflows. Harshit shares how he demonstrated a streaming MCP architecture using Amazon MSK, WebSockets, IAM authorization, and Amazon Bedrock to push real-time context into AI agents. The goal is to help organizations detect anomalies, understand root causes, and act faster when minutes or seconds can carry major business risk.Russ and Harshit also explore the risks around compromised MCP servers, agent hijacking, shadow AI, least privilege access, sandbox execution, human in the loop validation, and the growing need for governance around AI agents that can now plan, act, execute code, and manage infrastructure.Along the way, Harshit shares why enterprises should not chase AI for its own sake, why many projects may fail without clear use cases and controls, and why the best companies are not replacing people with AI, but investing in their teams so people can do more with it.Topics Covered:[00:00] Welcome and intro, Harshit Kohli and BIG’s All-Star Judges[00:43] Harshit’s role at AWS and work in GenAI streaming[01:55] His doctorate in artificial intelligence and industry speaking[02:19] Key themes from RBLN around AI, cybersecurity, and governance[03:56] AI-driven phishing volume and security industry response[04:33] Why governance and security must be built into AI products[05:44] MCP Dev Summit and the evolution of model context protocol[06:23] Moving MCP beyond basic request and response[07:00] Harshit’s streaming MCP demo with Amazon MSK, WebSockets, IAM, and Bedrock[08:26] Why real-time anomaly detection matters in financial systems[08:53] Root cause analysis and remediation through AI agents[10:29] Observability, telemetry, and cost anomaly detection[12:16] MCP design flaws, exposed servers, and enterprise risk[12:52] Why AI agents now execute code and manage workflows[14:41] What a compromised MCP server means[15:05] Sandbox execution and least privilege access[17:23] Consent, verification, and human in the loop controls[18:40] The rise of enterprise AI agents[19:21] Why agent autonomy creates governance challenges[20:38] Deploying agents versus deploying applications[22:38] Agent hijacking and prompt injection risks[23:00] Poisoned documents, malicious tools, and compromised MCP servers[25:19] Patch windows, enterprise readiness, and AI cyber risk[26:30] Shadow AI inside organizations[26:55] Employees using unapproved AI tools at work[28:57] Data leakage, compliance violations, and hallucination risk[30:46] Why some AI projects may be canceled[31:07] Starting with real use cases instead of forcing AI[32:44] Whether AI has become infrastructure yet[33:39] What the best companies are quietly getting right[34:46] Final thoughts on investing in people while adopting AI

Sasibhushan Rao Chanthati, Senior Software Engineer at Hirekeyz and a two time BIG All Star Judge for Information Technology, joins the Winners' Circle to talk about agentic AI, the FinOps boom, small versus large language models, and his own original research into detecting IT burnout with AI.Guest info: Sasibhushan Rao Chanthati, Senior Software Engineer, Hirekeyz.Chapters[00:00] Welcome and congratulations on being a two time BIG All Star Judge[01:02] Sasi's role at Hirekeyz and his previous work at T. Rowe Price[02:55] What drew Sasi to judging IT nominations in the first place[04:41] Task specific agents versus chatbots, the real difference[10:13] Small language models versus large frontier models[13:25] The hybrid model trend engineers are actually using[14:58] Why cheaper AI products often cut corners on security[18:23] Customer support automation and where it is heading[22:08] The biggest shifts Sasi has seen in the last two years[25:12] Why FinOps is becoming mission critical[27:00] The origin story behind Sasi's AI burnout detection research[29:04] The moment that convinced Sasi this was worth building[32:27] Is AI making burnout better or worse[37:39] Closing thoughts and congratulationsKey TakeawaysAfter two years judging IT nominations for BIG, Globee, and Stevie, Sasi has learned that the strongest submissions describe a specific mechanism, what was actually built and how it gets used, rather than leaning on company size or mission statements alone.AI cost management went from a niche concern to a near universal practice in just two years, with FinOps Foundation research showing adoption jump from 31 percent of organizations in 2024 to 98 percent in 2026, as AI spend joins cloud spend as something every technology leader has to actively manage.Sasi's own research into AI driven IT burnout detection, built using vector embeddings and workplace communication analysis, is one of the few systematic attempts to measure a problem the industry has talked about informally for years but rarely tried to quantify.Resources MentionedHirekeyzSasibhushan Rao Chanthati on LinkedInSasi's research on Google ScholarSasi's research on ResearchGateSasi's ORCIDSasi is a two time BIG All Star Judge for Information Technology. See his full judge profile.🔗 Subscribe to the Winners' Circle podcast.

Amy Worley is helping business leaders rethink privacy, cybersecurity, AI governance, and data protection as one connected challenge. As a Managing Director at BRG, Amy brings a rare mix of experience as a former trial lawyer, in-house privacy leader, consultant, expert witness, and author of The Confidence Advantage. BRG recently won an AI Excellence Award, recognizing work at the intersection of AI, privacy, cybersecurity, and governance.In this episode, Russ and Amy explore why AI has made it harder for companies to treat privacy, security, and governance as separate functions. Amy explains how businesses often have legal teams talking about GDPR or HIPAA, cybersecurity teams talking about threat actors and attack surfaces, and AI governance teams working in still another language. Her Confidence by Design framework brings those worlds together through a shared set of principles, common language, and unified risk metrics.Amy also shares how her career shaped her perspective. She began in law, moved through data privacy and breach response as the internet and privacy statutes evolved, then went in-house to build a GDPR program for a multinational pharmaceutical company. That experience taught her the gap between giving advice and actually building programs that work inside a business.The conversation also covers what happens during data incidents, why communication and decision authority often break before technical response does, and how Amy uses a “pre-mortem” process to help companies identify what could derail a governance program before it starts.Russ and Amy also discuss AI deployment, data debt, enterprise LLMs, accountability, board responsibility, chief trust officers, and why digital trust should not be treated as a cost center. Amy’s message is clear: in a digital first, AI powered world, evidence based trust can become a real business advantage.Topics Covered:[00:00] Welcome and intro, Amy Worley, BRG, and the AI Excellence Award[00:22] What BRG does as a multinational expert services firm[00:53] Amy’s background as a lawyer and privacy professional[01:11] What a former trial lawyer sees in data breach response[02:00] Moving from legal advice to building real privacy programs[03:13] The Confidence Advantage and Confidence by Design[03:33] Why privacy, cybersecurity, and AI governance need to be unified[04:00] Building an 11 principle framework across three disciplines[05:05] What breaks when privacy, security, and AI teams are siloed[06:00] Creating a common language for executives and risk[07:05] Whether one team should own the unified governance vision[07:59] What day one looks like in a data incident or program build[08:14] Communication rules and decision authority during incidents[09:00] Using a pre-mortem to identify why a program might fail[10:07] Common roadblocks: executive understanding and team bandwidth[10:45] Defining what winning looks like before the work begins[12:08] What courtroom experience teaches about documenting governance[13:28] How AI responsibility has shifted from planning to cleanup[14:15] Why companies now need diagnostics for AI bottlenecks[15:00] Building agile governance and risk tiers for AI adoption[16:03] Confidence by Design in thirty seconds[16:40] Maximizing the value of business data[17:32] Data debt, enterprise LLMs, and old information resurfacing[19:49] How to identify who is truly accountable for risk[20:09] Why AI governance is becoming a board level issue[21:20] The case for a chief trust officer[22:18] What leaders should do differently tomorrow[22:31] Digital trust as a competitive advantage[23:22] Final thoughts on AI, privacy, cybersecurity, and the future of trust

Matt Spiegel is helping law firms rethink growth, client relationships, and the business side of practicing law. As CEO and Co-Founder of Lawmatics, Matt leads a legal tech company built to help law firms manage leads, automate marketing, track performance, improve intake, and strengthen the client journey from first contact through future referrals. Lawmatics recently won a Best Places to Work award, recognizing the culture Matt and his team have built as the company continues to grow.In this episode, Russ and Matt discuss how Lawmatics became much more than a CRM for lawyers. Matt describes it as a growth platform for law firms, supporting lead management, marketing automation, reporting, analytics, and the relationship moments that happen before and after a client matter.Matt also shares the story that led him into legal tech. As a criminal defense attorney, he received a bar complaint from a client that came down to one issue: communication. That experience helped him see how common slow follow-up and poor client communication were in the legal industry, which led him to launch MyCase and later return to the market with Lawmatics.The conversation also explores Lawmatics’ culture and why in-person collaboration matters to Matt. The company works in office Monday through Wednesday, which Matt believes creates the spontaneous conversations, customer insights, and team connections that are hard to replicate remotely.Russ and Matt also discuss the changing legal market, the rise of competitive legal marketing, Lawmatics’ expansion into personal injury, and how the company is building AI into a broader platform rather than relying on AI as a standalone feature. Matt also shares advice for founders building niche B2B SaaS companies and for lawyers who want to grow their firms like modern businesses.Topics Covered:[00:01] Welcome and intro, Matt Spiegel, Lawmatics, and the Best Places to Work award[00:31] What Lawmatics does beyond CRM[01:11] Matt’s background as a criminal defense lawyer[01:29] The client complaint that sparked his legal tech journey[02:00] Starting MyCase and building a practice management platform[02:38] Why Matt returned to legal tech with Lawmatics[03:45] Lawmatics’ culture and Best Places to Work recognition[04:02] Scaling culture as the company grows[05:00] Why Lawmatics is in office Monday through Wednesday[06:20] How in-person work sparks customer and product insights[07:29] Why random conversations can create great ideas[08:49] Building a company around smart, interesting people[09:44] Getting lawyers to adopt new technology[10:22] Why the market was not ready for Lawmatics earlier[11:00] How legal marketing changed law firm operations[12:10] Why consumer-focused law firms are more competitive[12:42] Personal injury, MSOs, ABSs, and new legal market dynamics[14:01] Lawmatics’ UI overhaul and AI-assisted features[14:26] How customer problems guide product development[15:19] Expanding deeper into personal injury workflows[16:00] Building an AI suite on top of a broader platform[17:01] Growth opportunities across practice areas and firm types[18:12] Legal marketing, pay-per-click, and client acquisition[19:06] Matt’s advice for founders building niche B2B SaaS products[20:00] Why AI alone is not enough to build a durable company[20:14] Matt’s advice for lawyers growing a law firm[21:23] Final thoughts on Lawmatics, culture, and legal tech growth

Chris Shyrock is helping Atlantic.Net strengthen customer support in one of the most demanding environments in technology: cloud infrastructure and data centers. As Director of Support Services, Chris leads the team responsible for supporting customers who rely on Atlantic.Net for cloud services, colocation, dedicated hosting, managed services, and infrastructure they cannot afford to have fail.In this episode, Russ and Chris explore how Atlantic.Net transformed its support organization while also completing a major data center migration with no customer impacts attributed to the move. Chris explains how the company improved queue ownership, daily governance, shift handoffs, escalation paths, and customer communication while maintaining 24/7/365 service for clients in high-stakes industries like healthcare.They also discuss what it takes to turn technically strong support staff into customer-ready communicators. Chris shares how he reviewed calls and emails, re-onboarded the team, trained people to own the call, and encouraged support professionals to de-escalate concerns, explain issues clearly, and meet customers at their level.Along the way, Chris discusses Trustpilot reviews, customer feedback, team empowerment, regulated customer environments, AI cloud infrastructure, NVIDIA GPU hosting, continuous improvement, and why support is often the trust layer between a technology provider and the customers who depend on it.Topics Covered:[00:01] Welcome and intro, Chris Shyrock, Atlantic.Net, and the Excellence in Customer Service Award win[00:45] Atlantic.Net’s long history in cloud and infrastructure services[01:23] What Atlantic.Net provides for cloud, colocation, dedicated services, and managed infrastructure[02:58] Why Atlantic.Net focused on customer service transformation[04:00] Teaching technical support teams to communicate with customers more clearly[05:21] Aligning staffing, mindset, and shift coverage[06:30] Queue ownership and daily governance in support operations[07:26] Managing customer change, technology change, and internal change at the same time[08:45] Training the team to own the call and de-escalate urgent customer issues[09:25] Why practice matters in customer support training[11:00] Getting support teams comfortable with doing the harder thing for customers[12:10] Why leaders need to be reachable during high-stakes incidents[13:40] Transforming the support team during a major data center migration[14:46] How the support organization contributed to a no-impact migration[16:10] How major operational events can elevate a team[16:44] The hardest habit to change in a long-running support organization[17:44] Using Trustpilot reviews as evidence of support quality[19:34] Why moving review scores is hard and meaningful[20:00] Turning customer problems into trust-building opportunities[22:36] Upskilling support teams for new infrastructure and AI workloads[23:32] Advice for technical support leaders with inconsistent customer experience[27:22] Final thoughts on Atlantic.Net’s transformation and continuous improvement

Kim Canning is helping Kaplan expand access to career readiness, test prep, licensure prep, credentialing, and professional development resources through a new model built for universities and students. As VP of Strategic Partnerships at Kaplan, Kim works with colleges and universities to bring Kaplan’s All Access License to learners at scale. Kaplan recently won a BIG Awards for Business honor for the program.In this episode, Russ and Kim explore how Kaplan has evolved over more than 80 years from test prep in Stanley Kaplan’s Brooklyn brownstone to a modern education company supporting students, universities, and career pathways across the country.They dive into Kaplan’s All Access model, which gives universities an enterprise license so students can access a broad catalog of Kaplan resources without each student having to pay individually. Kim explains how the program helps remove financial barriers while also introducing students to resources they may not have known existed, from MCAT and LSAT prep to critical thinking, data literacy, career readiness, credentialing, and licensure support.The conversation also covers how universities are using All Access in different ways, including first-year programs, career preparation, graduate school pathways, healthcare licensure, and support for historically black colleges and universities. Kim shares examples from Hampton University, NYU, Illinois public institutions, and other partners using the program to support retention, persistence, graduation, and student confidence.Along the way, Kim discusses education equity, soft skills, COVID-era learning gaps, critical thinking, healthcare pipelines, first-generation college students, government partnerships, and why universities may need to think differently about the resources students need to succeed.Topics Covered:[00:22] Welcome and intro, Kim Canning, Kaplan, and the BIG Awards for Business win[00:49] Kim’s 28-year journey with Kaplan[02:12] Kaplan’s 80-plus year history and Stanley Kaplan’s founding mission[03:25] What the Kaplan All Access License is[03:44] Moving from department-level purchases to university-wide access[06:07] How universities use All Access across different student needs[07:23] Cost, awareness, and access barriers for students[08:00] Why students may not know which resources can open doors[09:26] Helping students reduce anxiety and understand expectations[09:55] How All Access can support engagement, retention, and loyalty[11:37] Flipping the traditional student-pays model[12:40] Kaplan’s footprint with historically black colleges and universities[14:00] Why All Access can create immediate student impact[15:02] Supporting freshmen and sophomores with foundational skills[15:42] Hampton University and critical thinking as a student success priority[17:40] Why soft skills matter more in an AI-driven world[18:49] Building critical thinking resources beyond test prep[21:30] Supporting science courses and the medical school pipeline[22:43] The Illinois All Access initiative and statewide education equity[24:00] Supporting 17 Illinois institutions and saving students millions[24:43] How All Access can help first-generation and underrepresented students[25:27] Student and advisor feedback from the Illinois rollout[26:47] Coordinating across government, institutions, and education leaders[27:25] Why mission alignment helped move the Illinois initiative forward[30:06] Why All Access is one piece of a larger higher education puzzle[31:00] Personalizing student experiences and supporting future pathways[31:57] Why All Access complements universities rather than replacing them[33:24] Final thoughts on improving the student experience and expanding access

Stephen Garcia is helping BreachRX bring structure, governance, and accountability to one of the most chaotic moments any organization can face: a cyber incident. As Chief Information Security Officer at BreachRX, Stephen brings years of experience from the customer side of cybersecurity, including work across financial services, technology, gaming, and enterprise environments. BreachRX recently won a Fortress Cybersecurity Award for its cyber incident response management platform.In this episode, Russ and Stephen explore why most organizations have tools to detect and contain incidents, but often lack a governance layer for everything that happens once a technical event becomes an enterprise crisis. Stephen explains how BreachRX helps coordinate legal exposure, regulatory deadlines, communications obligations, executive accountability, and audit trails in real time.They dive into the new category of cyber incident response management, or CIRM, and why binders, conference calls, and manual call trees are no longer enough. Stephen shares what the first hours of a serious incident can look like from the inside, why chaos often emerges even when plans exist, and how organizations can prepare more effectively before the crisis begins.The conversation also covers RexAI, BreachRX’s generative AI engine built specifically for incident response, as well as Mobile Command, out-of-band communications, regulatory readiness, tabletop exercises, executive liability, and the company’s CIRM warranty.Along the way, Stephen discusses resilience, trust, accountability, legal timing, simultaneous incidents, enterprise risk, AI agents, board communication, and why the organizations that survive the next wave will be the ones that can compress the time between knowing and doing.Topics Covered:[00:01] Welcome and intro, Stephen Garcia and BreachRX’s Fortress Cybersecurity Award win[01:03] What BreachRX does and why cyber incidents need a governance layer[01:30] Moving from technical containment to enterprise crisis management[01:57] Why Stephen switched from the customer side to BreachRX[02:17] The importance of managing incident chaos[03:54] What the first four hours of a serious incident can look like[04:14] Why preparation, logging, and lessons learned matter[07:05] Why response plans often fall apart under pressure[07:59] Handling multiple simultaneous incident inputs[09:29] BreachRX as a coordination layer and incident response fabric[09:46] Out-of-band communications during ransomware and major disruptions[10:30] Pulling in the right teams, including legal, at the right time[11:00] Why slowing down can help organizations speed up[12:52] Building governance structures before a crisis begins[14:19] What convinced Stephen that the BreachRX platform worked[15:20] Regulations, legal workflows, and global response requirements[16:42] Using tabletop exercise budgets to bring BreachRX into an organization[17:49] Why gaps and leaks can kill incident response[20:34] Why BreachRX’s warranty turns software into a trust decision[21:04] RexAI and purpose-built generative AI for incident response[21:35] How RexAI guides responders in high-pressure environments[22:18] Mobile Command and managing incidents from anywhere[24:03] Compressing the time between knowing and doing[24:22] How AI changes the incident response landscape[25:18] Expanding the definition of an incident beyond major breaches[25:40] Why IT, security, and business risk are increasingly connected[27:20] Security as trust management[28:00] What CEOs and boards should understand before the next breach[28:58] Final thoughts on BreachRX, response coordination, and cyber resilience

Avi Kedmi is helping SysAid rethink what customer service can look like in the age of AI. As CEO and Founder of SysAid, Avi leads a global IT service management company serving mid-sized clients in more than 100 countries. SysAid recently won an Excellence in Customer Service Award for its transformation of customer care using its own AI-driven tools, systems, and processes.In this episode, Russ and Avi explore how SysAid improved CSAT from 68 to 93, reduced detractors by 94 percent, cut escalations, reduced P1 incidents, shortened call times, and increased frontline resolution rates. Avi credits Asaf Goldstein and the care and support team for leading much of the transformation.They dive into what it means to become an agentic organization. Avi explains why companies need more than chatbots to make AI work. They need a strong data foundation, broad API access across systems, governance, monitoring, guardrails, and smart people in the middle who can identify and deploy high-value agentic use cases.The conversation also covers how SysAid uses AI to detect patterns, summarize issues, route tickets, identify unhappy customers, alert customer success teams, and help support teams act faster. Avi shares why the company is focused on using AI for work that is “unhuman,” while making human interactions more personal, compassionate, and meaningful when they matter most.Along the way, Avi discusses AI adoption, change management, employee training, customer empathy, proactive support, the future of IT service management, and why AI should help people do more, not make them feel replaced.Topics Covered:[00:01] Welcome and intro, Avi Kedmi and SysAid’s Excellence in Customer Service Award win[00:35] What SysAid does in IT service management[01:21] AI, uncertainty, and the biggest priorities for SysAid’s leadership team[02:00] How AI is redefining what can be built and how fast[02:45] Product AI, customer adoption, and building trust around AI[03:20] What it means to transform into an agentic organization[03:41] Helping employees adopt AI while feeling valued[04:55] SysAid’s customer service transformation results[06:07] Giving credit to Asaf Goldstein and the support leadership team[06:45] The two foundations of an agentic organization[07:10] Why the data lake strategy has changed in the AI era[07:50] Why systems need broad API and MCP access[09:46] How SysAid prioritized which AI use cases to tackle first[10:08] Using correlation to understand what drives detractors and poor CSAT[11:26] Why some support tasks are “unhuman” and ideal for AI[12:33] Why SysAid chose agentic AI instead of only building chatbots[13:08] The difference between chasing chatbots and controlling the AI beast[13:45] AI certainty thresholds and fast human failover[14:42] Connecting support, customer success, R&D, leadership, and product[15:02] Breaking silos with AI across the company[16:15] How support teams adapted to new AI tools and workflows[17:39] Why fast human response matters when AI falls short[18:26] Making AI-supported service feel more personal and compassionate[19:02] Adding agent names, photos, and human identity into customer interactions[20:41] How SysAid’s culture changed through the AI transformation[20:52] The pace, pressure, and opportunity of becoming an AI-first organization[22:15] What Avi learned about change management[23:49] The future of predictive and preventative support[24:11] How AI could resolve bugs and tickets end to end[26:18] How customer service may look three years from now[27:00] Why there may be fewer tickets in the future[29:37] Final thoughts on SysAid’s transformation and award recognition

Dr. Tina Srivastava is helping Badge rethink authentication for a world where humans, machines, and AI agents all need to prove they are who they say they are. As Co-Founder of Badge Inc., Dr. Tina is working to eliminate stored secrets from authentication and create a more secure root of trust for identity. Badge recently won a Fortress Cybersecurity Award for its work building a trust layer for the AI era.In this episode, Russ and Dr. Tina explore why traditional authentication has become so frustrating and vulnerable. Passwords, passkeys, push notifications, knowledge-based questions, stored biometric data, and static credentials all create risks, especially as AI makes phishing and impersonation attacks more convincing.They dive into Badge’s secretless authentication model and how the company allows users to derive a cryptographic key on the fly without storing private data in a central database. Dr. Tina explains how her background in national security and the Office of Personnel Management breach, which exposed millions of fingerprints, shaped the belief that people should not have to give up private data just to prove who they are.The conversation also covers fuzzy extraction, biometric privacy, zero standing privileges, workload authentication, and the growing need to authenticate AI agents safely. Dr. Tina explains how Badge can help ensure that agents only receive the permissions they need, only when they need them, and only when the verified human authorizes the task.Along the way, Dr. Tina discusses phishing-resistant authentication, agentic AI, identity without secrets, OEM partnerships, Badge as an embedded trust layer, and why authentication must become more seamless, private, and secure if people and companies are going to fully trust AI-powered systems.Topics Covered:[00:00] Welcome and intro, Dr. Tina Srivastava, Badge Inc., and the Fortress Cybersecurity Award win[00:25] Why authentication has become more complicated and more important[00:50] How Badge turns authentication on its head[01:31] The founding insight behind Badge and identity without secrets[01:48] The OPM breach and why stored biometric data creates lasting risk[02:43] Zero knowledge authentication and proving identity without stored secrets[03:24] Deriving keys on the fly and making the user the root of trust[04:28] What the user experience looks like without passwords[04:42] Supporting face, fingerprint, context, device characteristics, and PIN factors[05:20] Multi-factor authentication for workloads and removing static API credentials[05:50] Why agentic AI creates new permission and access challenges[06:37] What biometric fuzzy extraction means for privacy[07:33] Using fuzzy inputs to derive precise cryptographic keys[08:00] How agentic AI changes authentication and attack surfaces[08:31] Why AI makes phishing attacks more personalized and harder to detect[09:30] Using Badge to verify human approval before agents take sensitive actions[10:50] Zero standing privileges for AI agents[11:20] Giving agents temporary permissions only when authorized[12:13] Governance layers for employees and AI systems[13:08] Badge as an embedded “Intel inside” authentication layer[13:49] Working with existing identity providers and enterprise systems[14:12] Badge’s partner ecosystem and complementary integrations[15:31] Growth through OEM and embedded trust relationships[16:17] Authenticating humans, machines, and AI agents[16:55] Why AI agents should be treated like new employees that need guardrails[17:37] What happens if trust erodes in AI systems[18:20] Making secure access seamless across devices, systems, and environments[19:28] Final thoughts on authentication, quantum complexity, and the future of trust

Theresa Bui is helping SymphonyAI bring operational AI into some of the world’s largest enterprises. As CMO of SymphonyAI, Theresa works across a company serving customers in retail, financial services, manufacturing, enterprise IT, and media. SymphonyAI recently won three AI Excellence Awards for products built to help companies deploy AI at scale.In this episode, Russ and Theresa explore what makes SymphonyAI a vertical AI company and why that matters for enterprise adoption. Theresa explains how the company enters industries with prebuilt agents, ontologies, and models that already understand industry-specific workflows, allowing customers to move from implementation to use cases much faster than traditional horizontal AI platforms.They dive into Eureka, SymphonyAI’s foundational platform for enterprise AI. Theresa walks through its three core layers: shared industry context through domain knowledge graphs, adaptive orchestration that applies the right AI tool to the right workflow, and governance from day one so every decision point can be logged, reviewed, and trusted.The conversation also covers how Eureka works in real-world environments, from industrial manufacturing and predictive asset management to financial services investigations. Theresa shares examples of agentic AI detecting a worn part, checking inventory, creating a work order, and scheduling maintenance within minutes, as well as helping banks reduce investigation time while keeping humans in the loop.Along the way, Theresa discusses AI sovereignty, data ownership, human oversight, regulatory trust, scaling beyond pilots, and why the hardest part of enterprise AI is often not proving it works once, but making it work across hundreds of plants, workflows, and business units.Topics Covered:[00:01] Welcome and intro, Theresa Bui, SymphonyAI, and the AI Excellence Awards wins[01:23] What SymphonyAI does as a pure play AI company[02:00] Why SymphonyAI defines itself as a vertical AI company[02:30] Horizontal AI versus vertical AI in enterprise deployments[03:00] Prebuilt agents, ontologies, and models for industry-specific use cases[03:46] How Eureka supports enterprise AI deployment[04:20] Eureka’s three foundations: context, orchestration, and governance[04:40] Domain knowledge graphs and shared industry context[05:25] Adaptive orchestration and using the right AI tool for each workflow[06:00] Governance, audit trails, and trust in regulated industries[07:20] How Eureka powers industrial manufacturing use cases[08:00] Predictive asset management, process optimization, and frontline worker workflows[08:25] Agentic AI example: worn part detection, inventory checks, work orders, and maintenance scheduling[10:08] Perceive, reason, act and giving AI a structured path to solve problems[10:48] Human in the loop controls and tolerance thresholds[11:20] SymphonyAI Risk Intelligence for financial services investigations[12:00] How banks can adjust oversight as AI earns trust[13:14] How AI learns from human overrides and decision nuance[14:01] What AI sovereignty means in enterprise environments[15:00] Data ownership, private tenants, hosted environments, and competitive advantage[16:35] Why sovereign AI can become a company’s proprietary IP[18:05] What enterprise leaders misunderstand about AI implementation[18:36] Why scaling AI is harder than running a successful pilot[19:20] The challenge of deploying across many plants, lines, and use cases[20:00] Why data normalization and shared ontologies matter for scale[20:48] Comparing AI deployment to outsourcing processes and documenting workflows[21:30] Final thoughts on SymphonyAI’s growth, awards, and enterprise impact

Paul Mander is helping businesses reduce one of the fastest-growing cybersecurity risks: exposed personal data. As Chief Commercial Officer for Optery for Business, Paul works with enterprises to remove employee data from data broker sites and reduce the information available to threat actors. Optery recently won a Fortress Cybersecurity Award for its work helping organizations protect people, executives, and teams from AI-enabled social engineering attacks.In this episode, Russ and Paul explore what data brokers are, how they collect and sell personal information, and why exposed data has become a major enterprise security issue. Paul explains how personal details like phone numbers, home addresses, family connections, organizational charts, and email addresses can be used to create more convincing attacks.They dive into how Optery scans data broker sites, finds exposed profiles, submits opt-out and deletion requests, and verifies removals with screenshots, links, and proof inside the platform. Paul also explains why this is not a one-time cleanup, since data brokers constantly rebuild profiles, rebrand, resurface data, and create new exposures.The conversation also covers how AI has changed the threat landscape by making social engineering faster, cheaper, more personalized, and easier to scale. Paul shares why IT, finance, HR, and executives are common targets, why BYOD policies create additional risk, and why CISOs are increasingly treating personal data removal as part of the cybersecurity stack.Along the way, Paul discusses data broker directories, human plus machine workflows, enterprise attack surfaces, employee privacy, consumer control, open web data, and why removing exposed personal data is becoming a proactive defense against the next generation of cyberattacks.Topics Covered:[00:01] Welcome and intro, Paul Mander, Optery for Business, and the Fortress Cybersecurity Award win[00:41] What Optery does and how personal data removal works[01:15] Why data brokers create risk for enterprises[02:11] How exposed data enables social engineering attacks[03:12] Why IT, finance, HR, and executives are major targets[04:20] Selling personal data removal as an enterprise security solution[04:51] How CISOs are starting to treat exposed employee data as attack surface[05:40] Why phone numbers and personal devices can create breach risk[06:16] BYOD, personal phone numbers, and compromised devices[07:12] Why this attack surface has been overlooked[08:00] How AI has made social engineering easier to launch and scale[09:30] Moving from reactive employee training to proactive data reduction[10:21] Why data removal is never fully finished[10:45] How data brokers rebuild profiles and relist information[11:39] Data broker rebrands, shell companies, and whack a mole removals[12:16] How Optery proves what it found and what it removed[13:31] Why executive exposure can make the risk feel real[14:06] What Paul has seen in exposed personal data[15:27] Why AI search changes how people should think about exposure[16:06] Why new data appears constantly[16:22] Why Optery uses humans plus machines for removals[17:02] Why professionals may want visibility but still need control[19:24] Why Optery open sourced its data broker directory[20:31] Where the personal data removal industry is headed[21:01] Why data removal may become part of the cybersecurity stack[22:00] Consumer control, privacy laws, and where personal data goes next[22:43] Why personal data removal is more like an ongoing insurance policy[23:19] Why exposed data requires proactive monitoring[23:57] Final thoughts on social engineering, personal data, and cybersecurity risk

Katy Irving and Rory Mitchell are helping HRW, Healthcare Research Worldwide, explore a new way to understand one of the most important moments in healthcare: the doctor patient consultation. HRW recently won an AI Excellence Award for its work using interactive AI avatars to simulate consultation conversations in pharmaceutical market research.In this episode, Russ, Katy, and Rory explore why real doctor patient conversations are so difficult to study. Katy explains how those moments inside the consultation room are often the “holy grail” for pharma and medtech companies because they reveal how doctors ask questions, how patients describe symptoms, and how treatment decisions begin to take shape.They dive into HRW’s AI avatar project, which uses an avatar to play one side of the consultation while a real doctor or patient interacts with it. The goal is not to replace clinicians, but to create a more realistic research simulation that can uncover the actual language, questions, concerns, and behaviors that emerge in healthcare conversations.The conversation also covers the technical and ethical challenges of building avatars that behave like doctors or patients. Rory shares how the team had to carefully limit the doctor avatar to avoid medical advice, while also giving patient avatars enough personality, emotion, and realism to feel believable without becoming unpredictable.Along the way, Katy and Rory discuss avatar bugs, emotional realism, patient trust, the uncanny valley, self-funded innovation, conference reactions, cross-functional teamwork, training applications, and how AI avatars could help researchers, clients, and healthcare teams better understand the conversations that shape patient care.Topics Covered:[00:01] Welcome and intro, Katy Irving, Rory Mitchell, HRW, and the AI Excellence Award win[00:43] What Healthcare Research Worldwide does in pharma and medtech research[01:00] Why doctor patient consultations are so valuable to understand[01:30] Why real consultations are difficult to simulate in research[02:00] Using interactive AI avatars to play one side of a consultation[02:39] Why old research workarounds often fell short[03:06] Compliance, ethics, and brand-specific consultation simulations[04:22] Building doctor and patient avatars with different guardrails[04:46] Why the doctor avatar had to avoid medical recommendations[05:30] Creating patient avatars with personality, history, and emotion[06:30] Testing, refining, and “parenting” the avatars into better behavior[08:45] Why consultation insights matter for pharma strategy[10:03] What HRW underestimated when building the avatar experience[10:27] Platform bugs, delayed answers, and strange conversation paths[13:23] How HRW defined success in a self-funded proof of concept[14:30] The first successful doctor and avatar interaction[15:32] How patients reacted to avatar doctors[16:00] Testing different tones, including neutral, empathetic, and friend-like avatars[17:07] Why HRW shared failures and funny moments with the industry[19:25] The line between realistic enough and misleadingly real[20:33] Why HRW is not trying to replace doctors or nurses with avatars[21:55] How the cross-functional team shaped the project[23:36] Combining technology, behavioral science, and patient research perspectives[24:33] Future uses for AI avatars beyond consultation simulation[24:44] Using avatars as interactive research deliverables for clients[25:30] Training applications for medical and professional conversations[26:26] Using doctor avatars to understand product language and peer conversations[26:57] Final thoughts on AI avatars and the hidden world of healthcare research

Oli Ostertag is helping restaurant and convenience store operators use AI to improve performance without losing the human side of hospitality. At PAR Technology, Oli works on enterprise technology for restaurants and C-stores, supporting operations, loyalty, payments, engagement, and point of sale systems across more than 150,000 sites. PAR recently won a BIG Innovation Award for its work bringing AI into restaurant operations.In this episode, Russ and Oli explore why restaurant technology is becoming more connected, more data driven, and more important in a market where margins are tight and value wars are really price wars. Oli explains how AI can help operators play both offense and defense, increasing revenue while improving labor, inventory, pricing, and operational efficiency.They dive into Coach AI, PAR’s AI product designed to help operators understand store performance, spot waste, and make better decisions in real time. Oli shares why the performance gap between the best and worst stores can be massive, and how better data context can help more locations operate like the strongest ones.The conversation also covers why AI must be built into restaurant workflows instead of bolted on afterward. Oli discusses context equity, data integrity, hallucination risk, enterprise rollout challenges, and why AI should enhance people rather than replace the hospitality experience.Along the way, Oli discusses restaurant loyalty, franchise operations, pricing agents, fraud agents, kiosks, international adoption, operator training, and why the best restaurant technology should stay out of the way so food and people remain at the center.Topics Covered:[00:01] Welcome and intro, Oli Ostertag and PAR Technology’s BIG Innovation Award win[00:35] What PAR Technology does for restaurants and convenience stores[01:36] Why restaurant systems are often disconnected[02:22] Context equity, data integration, and enterprise restaurant complexity[03:58] Operator products, engagement products, and the PAR technology stack[04:20] How Coach AI helps operators understand performance in real time[05:00] The gap between top performing and underperforming stores[05:45] Moving from ask and answer AI to self-driving store optimization[06:42] Automated offers and AI-driven marketing campaigns[07:22] Closing the gap between technology rollout and real outcomes[08:21] AI fatigue and why outcomes matter more than AI hype[09:44] Data integrity and the importance of clean, connected systems[10:19] Playing offense and defense in restaurant operations[11:39] Value wars, price wars, and inventory-driven promotions[12:19] Using AI to optimize inventory, staffing, and profitability[13:54] Why PAR built AI into the operator engine instead of bolting it on[14:22] Built-in AI, context equity, and learning from workflow data[15:26] What PAR learned from enterprise restaurant customers[15:47] Avoiding hallucinations in high-stakes restaurant operations[17:31] Moving from manager insights to operator agents[18:39] Where Coach AI and PAR’s agent strategy go next[19:20] Pricing agents, fraud agents, and future restaurant AI use cases[20:20] Why AI should make people more effective, not replace hospitality[23:12] How younger consumers engage with restaurant apps and loyalty[23:38] How AI adoption differs across global restaurant markets[25:19] What the first week with Coach AI needs to prove[26:06] Training, services, and natural language usability for operators[27:04] Product lessons for AI builders in other industries[28:30] Why usage rates matter after the enterprise contract is signed[29:18] Final thoughts on better operations, better food, and smarter restaurant technology

Rakesh Ravuri is helping enterprises modernize legacy systems with AI while preserving the context, governance, and explainability that complex organizations require. As CTO of Publicis Sapient, he leads technology for a digital transformation company helping clients evolve through each major technology shift, from the internet and e-commerce to mobile, cloud, and now AI. Publicis Sapient’s Slingshot platform recently won an AI Excellence Award for its work accelerating software development and legacy modernization.In this episode, Russ and Rakesh explore how Slingshot began as an internal AI tool after the rise of ChatGPT, then evolved into a platform for AI-assisted engineering, modernization, and enterprise transformation. Rakesh explains why Publicis Sapient first built a secure internal chat tool to protect client data, then extended it with APIs, developer plugins, and eventually a modernization workflow.They dive into the legacy technical debt problem, especially large COBOL systems that still power critical business functions in finance, healthcare, telecom, and other enterprise environments. Rakesh explains how Slingshot breaks large codebases into intelligent chunks, extracts business rules, creates specifications, generates new code, and supports modernization without relying on armies of retired COBOL experts.The conversation also covers why context is the key to useful enterprise AI. Rakesh explains Publicis Sapient’s enterprise context graph, which connects strategy, product, engineering, experience, data, code, tests, prompts, and decisions so AI can understand not just what to build, but why it matters.Along the way, Rakesh discusses AI governance, provenance, explainable code, human-in-the-loop review, deterministic testing, regulated environments, reusable enterprise prompts, agentic workflows, and why the future of AI transformation depends on capturing both enterprise knowledge and enterprise behavior.Topics Covered:[00:01] Welcome and intro, Rakesh Ravuri and Publicis Sapient’s AI Excellence Award win[00:38] Publicis Sapient’s background in digital transformation[01:43] AI as the latest transformation trigger[02:33] How Slingshot began as an internal AI tool[02:53] Building a secure internal ChatGPT-style platform[04:10] Creating APIs and early developer plugins[05:13] The legacy technical debt problem[05:52] Using AI to understand millions of lines of COBOL code[06:45] Intelligent chunking and context layers for large codebases[07:55] Moving from code to specification to new code[09:10] Whyhot’s first-principles approach outperformed brute-force code conversion[10:24] Why COBOL modernization has waited decades[13:19] What an enterprise context graph is and why it matters[15:30] Local context versus enterprise context[17:25] Why developers need the business context behind a product decision[18:14] Slingshot as a GPS for modernization[20:00] Explainability, maintainability, and code provenance[21:56] Governance for regulated industries[22:11] Measuring how much code was generated by AI[23:24] Explainable code over working code[24:08] Using context to investigate hallucinations and errors[25:43] Making expert knowledge repeatable[27:15] Building trust through proof-of-concept work[29:10] Guardrails, test cases, and deterministic evaluation[30:53] First conversations CTOs should have about legacy modernization[32:04] How Slingshot differs from coding tools like Copilot and Cursor[35:43] How AI changes teamwork across the software lifecycle[36:11] Shared prompt libraries and enterprise standards[39:56] Capturing enterprise behavior, not just enterprise data[43:59] Final thoughts on AI-driven transformation and modernization

Abhay Jajoo is helping life sciences companies use AI to find better answers faster. As CEO and Founder of CustomerInsights.AI, Abhay works with pharma, biotech, and life sciences teams to identify patients, understand physicians, improve commercial decision making, and make sales and marketing operations more efficient. CustomerInsights.AI recently won an AI Excellence Award for its work bringing hyper-vertical AI into life sciences commercial analytics.In this episode, Russ and Abhay explore why pharma commercial operations have been slow to move away from people-heavy consulting models, and why AI is now creating a better path for answering business questions at scale.They dive into CI Parthenon, CustomerInsights.AI’s foundational platform for integrating, transforming, modeling, and visualizing commercial data. Abhay explains how the platform reduces data movement, speeds up time to insight, and helps teams move from weeks of analysis to near real-time decision support.The conversation also covers CI Athena, the company’s agentic AI platform built specifically for life sciences commercial analytics. Abhay shares how Athena uses intelligent agents, workflows, and conversational interfaces to help users ask business questions, get grounded answers, and explore insights without needing to navigate siloed systems.Along the way, Abhay discusses pharma data complexity, patient and physician targeting, market access, contracting strategy, compliance, hallucination control, token management, enterprise AI adoption, and why the future of consulting may shift from people-heavy delivery to outcome-based technology models.Topics Covered:[00:01] Welcome and intro, Abhay Jajoo and CustomerInsights.AI’s AI Excellence Award win[00:29] What CustomerInsights.AI does for pharma and life sciences companies[01:03] Why Abhay saw a broken model in life sciences consulting[03:40] Better, faster, and more cost-effective commercial analytics[04:30] The silo problem across sales, marketing, and market access teams[05:23] Reducing time to insight from weeks to hours[07:17] Why life sciences AI is harder than generic GenAI[09:46] Business rules, therapeutic nuance, and explainable insights[11:25] What CI means and how Parthenon and Athena got their names[13:09] What enterprise clients need to see before trusting AI[15:39] How Athena uses agents, workflows, and conversational interfaces[17:56] What CustomerInsights.AI learned from Parthenon deployments[20:21] Security, compliance, guardrails, and token management[21:55] Deploying Athena inside the customer’s own environment[24:36] Infrastructure agnostic architecture and where CustomerInsights.AI’s IP sits[28:22] How pharma CIOs are responding to AI mandates[30:43] Why buying hyper-vertical AI can accelerate enterprise deployment[32:04] How AI may reshape life sciences consulting[35:56] Why CustomerInsights.AI is staying focused on life sciences commercial analytics[38:38] Final thoughts on AI, commercial analytics, and patient impact

Coley Norman is helping Liviniti prove that healthcare customer experience can still be personal, transparent, and human. As a leader at Liviniti, a transparent pharmacy benefit manager, Coley has helped shape a service culture built around a simple belief: service is something people receive, but an experience is something they remember. Coley was named Executive of the Year in the Excellence in Customer Service Awards for his work leading that transformation.In this episode, Russ and Coley explore what a pharmacy benefit manager does, why PBMs sit inside one of the most complex parts of healthcare, and how Liviniti’s transparent, pass-through model is designed to make prescription benefit management easier to understand.They dive into Coley’s philosophy of client experience and why Liviniti has chosen to keep real people at the center of service. Coley explains why members and clients do not reach an AI bot or phone tree when they call Liviniti. They reach a person who can listen, help, and move quickly when medication access and benefit questions matter most.The conversation also covers how Coley rebuilt and strengthened the client experience organization with data, accountability, mentorship, and direct client communication. He shares how Liviniti uses voice of the customer surveys, client-specific goals, retention benchmarks, dashboards, and business reviews to make experience measurable without losing the human connection.Along the way, Coley discusses servant leadership, team-first culture, mentorship, client retention, AI as a complement to human work, and why old school relationship building may become a major differentiator in a more automated world.Topics Covered:[00:01] Welcome and intro, Coley Norman and Liviniti’s Customer Service Excellence Award win[00:53] What a PBM is and how Liviniti approaches pharmacy benefit management[01:40] Liviniti’s transparent, pass-through model[03:11] Why service is received, but experience is remembered[04:00] How Liviniti defines the experience business[05:46] Choosing a more human service model in an automated industry[06:33] Where AI fits, and where Liviniti keeps real people involved[08:30] Why benefit conversations require urgency and human care[09:01] Building a client experience team around servant leadership[10:30] Using data, KPIs, and retention benchmarks to guide service[12:30] The Know Your Numbers campaign and client-specific goals[13:02] Voice of the customer surveys and closing the feedback loop[15:05] Business reviews, dashboards, and consultative client relationships[16:17] Moving from passive channels to real conversations[16:52] Helping teams get comfortable with being uncomfortable[18:25] Creating entrepreneurial thinking inside a service organization[18:58] Why progress should not be blocked by titles or red tape[21:08] Improving satisfaction while growing the team[21:58] Mentorship and developing tomorrow’s leaders[23:30] Daily standups, priorities, barriers, and team accountability[24:58] Where automation helps and where it can become a false economy[25:31] Using AI for reporting, seasonality, and better client insight[28:03] The one customer experience principle leaders should take away[28:30] Why taking care of the team comes before taking care of clients[29:52] Final thoughts on leadership, service, and the Liviniti team

Khadim Batti is helping companies get more value from the software they already use. As Co-Founder of Whatfix, Khadim has spent more than a decade building digital adoption technology that sits on top of enterprise applications and helps employees and customers use software the right way, at the right moment. Whatfix recently won an Excellence in Customer Service Award for the way it uses its own platform, AI, and customer feedback to improve service, adoption, and outcomes.In this episode, Russ and Khadim explore why digital transformation often fails to deliver its promised ROI. Khadim explains how companies spend millions on ERP, CRM, CLM, and other platforms, only to see adoption lag because users do not receive the guidance, context, or support they need inside the workflow.They dive into Whatfix’s idea of “userization,” which means making software adapt to each user instead of forcing every user to adapt to the software. Khadim shares how AI is accelerating this vision by making nudges, training, guidance, and support more personalized to the user, the task, the role, and the moment.The conversation also covers how Whatfix uses its own tools internally, including digital adoption, simulations, AI agents, analytics, and customer service workflows. Khadim explains how customer support roles are evolving, why Whatfix has seen strong CSAT and NPS performance, and how AI can help teams reimagine work instead of simply automating old processes.Along the way, Khadim discusses software adoption, service as part of SaaS, AI transformation, enterprise training, customer advisory boards, product roadmap discipline, and why the future of digital adoption may move from showing users what to do to getting work done on their behalf.Topics Covered:[00:01] Welcome and intro, Khadim Batti and Whatfix’s customer service award win[00:42] How Whatfix started and why digital adoption became the core problem[02:16] Why enterprise software rollouts often fall short after training[03:03] How Whatfix pivoted from its original platform to digital adoption[04:00] Insurance, claims, medical supplies, and real-world adoption use cases[05:43] What “userization” means and why software should adapt to users[07:46] Why context matters inside enterprise software workflows[08:23] Personalized nudges for sales, compliance, and role-specific work[09:47] What fails when companies lack digital adoption technology[10:13] Ticket reduction, win rate improvement, and compliance gains[11:11] Why enterprise software is still hard to use[12:00] How AI may increase the need for adoption support[13:30] Using Whatfix inside Whatfix[14:07] CSAT, NPS, simulations, Mirror AI, and internal adoption tools[15:30] Authoring agents, analytics agents, and guidance agents[16:39] How Whatfix improves its own people, not just its own software[17:05] Reimagining customer support roles with AI[18:30] What happened when Whatfix rolled out new AI tools internally[20:16] How customer feedback shapes the Whatfix roadmap[21:00] Balancing customer requests with market direction and innovation[22:00] User groups, design partners, and customer advisory boards[23:37] Where digital adoption platforms may go over the next five years[24:00] Moving from guidance to getting work done for users[25:00] Advice for SaaS founders building in the AI era[26:32] The customer service principle Khadim would pass on to others[26:47] Why SaaS companies should not forget the service side of software[27:36] Final thoughts on software adoption in the AI age

Saima Khan is helping bring AI into one of the most overlooked but critical parts of the healthcare experience: patient meals. As SVP of Healthcare Digital at Compass Digital, the technology and innovation arm of Compass Group North America, Saima works on technology that supports healthcare food service operations, patient satisfaction, and safer workflows inside hospitals. Compass Digital recently won an AI Excellence Award for its Nutrition AI solution.In this episode, Russ and Saima explore why food in a hospital is much more than a meal. Saima explains how patient trays are tied to safety, recovery, satisfaction, dietary restrictions, allergens, medication timing, clinical workflows, and the overall patient experience.They dive into Nutrition AI, a computer vision system that scans patient meal trays before they leave the kitchen. The system checks whether the food on the tray matches the patient’s order and dietary requirements, then flags issues for staff before the meal is sent to the room.The conversation also covers why AI is being used to support staff, not replace them. Saima shares how human verification remains part of the workflow, why Compass Digital ran side by side pilots to prove value, and how the technology has helped improve accuracy while reducing the time from ticket print to meal delivery.Along the way, Saima discusses food as medicine, patient satisfaction, tray line workflows, kitchen staff adoption, malnutrition monitoring, thermal imaging, frontline innovation, and why the best AI implementations often come from listening closely to the people using the technology every day.Topics Covered:[00:00] Welcome and intro, Saima Khan and Compass Digital’s AI Excellence Award win[00:35] Compass Digital’s role as the technology arm of Compass Group North America[01:00] How Nutrition AI uses computer vision in healthcare food service[02:05] Saima’s path from clinical technology to healthcare food service innovation[03:37] Why food is medicine in a hospital environment[04:16] Patient safety, allergens, dietary restrictions, and tray accuracy[05:30] How meal errors can affect nurses, kitchen staff, patients, and workflows[07:00] Why tray accuracy was hard to solve before AI[07:20] Combining patient dining software, human checks, and AI assistance[08:30] Reducing time from ticket print to cart delivery[09:31] Why human staff still verify and correct flagged trays[10:34] Running side by side pilots to prove ROI and accuracy[11:53] Early reactions from staff and what showed the system was working[13:33] Adoption challenges inside hospital kitchens[13:59] Working with champions, operators, and frontline teams[15:21] The design principle behind Compass Digital’s healthcare platform[15:51] Why patient satisfaction is the North Star[17:32] Expanding Nutrition AI beyond tray accuracy[17:53] Using AI to monitor malnutrition and meal consumption[19:29] Closing the loop from meal creation to meal consumption[20:14] Operating at scale across millions of patient meals[21:41] Augmented intelligence and the role of AI in healthcare workflows[22:05] Using AI to surface recommendations instead of replacing humans[23:44] Lessons for logistics, manufacturing, hospitality, and other industries[24:01] Iterating on hardware, workflow, thermal imaging, and new use cases[25:31] Final thoughts on AI, patient specific meals, and healthcare innovation

Kenny Thompson is helping BASYS prove that great customer experience can still be a competitive advantage, even in a highly commoditized payments industry. BASYS works across healthcare, banking, SaaS, distribution, manufacturing, construction materials, media, and other payment-heavy industries, while keeping a strong focus on in-house support and real human service. BASYS was recently recognized for its customer experience work and its ability to blend AI efficiency with human connection.In this episode, Russ and Kenny explore why payments are the lifeblood of so many businesses, especially for SaaS companies, banks, healthcare providers, and small business operators. Kenny explains how BASYS supports customers through complex payment workflows while helping software partners create a more seamless experience for their own users.They dive into how BASYS uses AI, chatbots, and internal support tools without losing the human touch. Kenny shares why the company still answers calls with live people, how its support teams are structured, and why its Kansas City-based model remains central to the company’s identity.The conversation also covers healthcare payment complexity, fragmented systems, customer support standards, partner integrations, Net Promoter Score, company culture, and why BASYS has chosen steady growth and long-term trust over shortcuts.Along the way, Kenny discusses community banks, SaaS partnerships, support escalation, employee hiring, customer retention, and why great service still starts with people, even when AI is helping behind the scenes.Topics Covered:[00:01] Welcome and intro, Kenny Thompson and BASYS[00:54] BASYS’ role in payments across healthcare and other industries[01:20] Why healthcare payment experiences can be clunky and frustrating[02:36] The fragmented nature of hospitals, vendors, and payment systems[03:15] BASYS’ work across payments, distribution, manufacturing, construction, and SaaS[04:19] Blending AI efficiency with live human support[05:03] Why BASYS still answers phone calls with a real person[06:00] Building an in-house support team instead of outsourcing service[07:10] How BASYS integrates payments into software platforms[08:20] Reducing the swivel chair problem in payments workflows[09:32] Why payments are mission critical for SMBs and SaaS users[10:10] Supporting small business owners who rely on payments as their revenue channel[11:27] Why many industries follow each other when technology works[12:17] Why BASYS chose Kansas City-based support over offshore service models[12:50] Tracking Net Promoter Score as a core business metric[14:00] Hiring for customer service quality and cultural fit[15:42] What happens during a typical BASYS support call[16:06] Using AI and internal chatbots to support customer service agents[17:32] Escalation from tier one to tier two support[18:06] How strong onboarding and support reduce customer problems[18:40] Why more processors do not invest this heavily in service[19:33] Maintaining support quality while growing integrations and verticals[20:30] Protecting company culture during growth[22:24] Nonnegotiables for building a service-led company[23:59] How BASYS helps SaaS partners grow revenue and prepare for exits[25:13] Why customer service can differentiate SaaS and payment platforms[26:45] Why Kenny believes human trust still matters in business[27:03] What the payments industry could learn about customer service[29:42] Final thoughts on blending humans, AI, and long-term customer care

Paul Danter is helping enterprise teams make global content faster, smarter, and more brand accurate. At Welocalize, Paul works on Opal, an AI powered platform designed to improve how companies translate, adapt, validate, and manage multilingual content at scale. Welocalize recently won an AI Excellence Award for its work helping global brands operationalize AI across language workflows.In this episode, Russ and Paul explore how enterprise localization has changed over the last 20 years, from traditional human translation to neural machine translation to AI powered post editing and quality estimation. Paul explains why translation is no longer just about converting words from one language to another. It is about preserving brand voice, tone, terminology, intent, and business impact across global markets.They dive into Opal and how it helps companies process millions of words while using AI to improve translation quality, route content through the right workflow, and determine when human review is still needed. Paul shares why different content types carry different levels of risk, and why a support article, product launch campaign, and brand tagline should not all be treated the same way.The conversation also covers how AI is creating more content than ever before, why enterprises need governance around multilingual workflows, and how continuous feedback from human linguists can help models improve over time.Along the way, Paul discusses content risk, quality scoring, brand sensitive workflows, reinforcement learning, AI governance, long tail languages, global support content, and why the future of multilingual AI may include living systems that monitor performance and automatically improve content across markets.Topics Covered:[00:01] Welcome and intro, Paul Danter and Welocalize’s AI Excellence Award win[00:53] Welocalize’s background in global language services[01:55] Why enterprise teams need language services to unlock global markets[02:20] How Opal operationalizes AI for translation and brand voice[03:10] Human translation, machine translation, and AI post editing[04:31] Training AI models to sound like a specific brand[05:21] How generative AI changed language quality and automation[07:47] Matching the right workflow to the right content type[09:14] Moving from academic quality scoring to content risk[10:42] Auditing enterprise content and connecting translation workflows[12:42] The role of AI in support content and customer experience[13:20] Why AI governance matters as content volume explodes[14:57] What companies underestimate about multilingual content operations[16:59] How Welocalize measures whether Opal is working[17:25] Faster turnaround times and reduced human editing effort[18:40] What early Opal deployments revealed[20:58] Building trust with enterprise content teams[21:29] Quality testing, certified languages, and human validation[23:40] Why quality estimation matters before human review[25:01] Continuous editing, feedback loops, and model improvement[25:58] Lessons other industries can learn from language AI[28:13] What multilingual AI could look like in five years[29:20] Improving source content before translation begins[30:00] Using performance data to improve localized marketing content[31:25] Advice for founders building AI in brand sensitive workflows[33:05] Language as part of closing the global digital divide[33:40] Final thoughts on Opal, AI, and the Welocalize team

Hila Segal and Roni Kandel are helping enterprises rethink how employees learn, practice, and apply new skills inside the tools they use every day. At WalkMe, they are building Learning Arc, an AI native learning solution designed to close the gap between training content and real work. WalkMe recently won an award for its work helping organizations deliver learning at the moment employees need it most.In this episode, Russ, Hila, and Roni explore why traditional corporate training often fails to translate into execution. Hila explains how WalkMe’s digital adoption platform has spent years helping employees navigate software workflows, and why customers began asking for a deeper learning experience that could build lasting proficiency, not just guide clicks.They dive into Learning Arc and how it combines AI powered content creation with in app learning delivery. Roni shares how the product helps organizations train employees before they enter sensitive systems, then resurfaces the same learning content when they are actually doing the work.The conversation also covers why learning and software adoption have historically been disconnected, how AI can help L&D teams create and refresh content faster, and why human oversight remains essential. Hila and Roni explain how Learning Arc gives authors visibility into what AI created, what source material was used, and how learning content can be refined to fit business needs.Along the way, they discuss sales training, ERP rollouts, AI literacy, multimodal learning, personalization, learner choice, enterprise software development, and why the future of workplace learning will be more contextual, flexible, and embedded directly into the flow of work.Topics Covered:[00:01] Welcome and intro, Hila Segal, Roni Kandel, WalkMe, and Learning Arc[00:45] WalkMe’s role in digital adoption and enterprise software usage[02:00] Why customers needed deeper skills and lasting proficiency[02:30] What WalkMe Learning Arc is designed to solve[03:36] Moving from traditional training to learning in the flow of work[04:26] Knowing versus doing in sales methodology and workflows[06:12] Why Learning Arc became a standalone product[06:39] Training before access to sensitive systems and processes[07:32] Why the learning and execution gap has lasted so long[08:20] Connecting L&D outcomes to actual work performance[09:25] Why the pace of workplace change is outgrowing traditional training[10:20] Using AI to create and refresh learning content faster[11:48] Early customer use cases and major transformation programs[12:50] Using Learning Arc to support ERP rollouts and onboarding[13:54] Building trust with L&D teams[14:14] How AI can turn months of content work into minutes[15:30] What learning in the flow of work means inside Learning Arc[16:34] Using screen context to surface relevant learning content[17:00] AI literacy training and responsible AI tool access[18:15] Reimagining the learning portal experience[19:50] Giving learners flexible formats like audio, video, and text[21:00] Why different employees need different learning experiences[21:44] Why human oversight remains central to AI generated learning[23:21] How authors control, review, and refine AI created content[24:30] Preventing hallucinations and grounding AI in approved source material[26:19] Balancing personalization and scalability[28:53] What Learning Arc signals about the future of enterprise software[30:45] How AI may change product, development, and software team roles[31:30] Training employees on tools that change constantly[33:14] Why change is central to WalkMe’s mission[35:56] Final thoughts on Learning Arc and the future of workplace training

Robert Klamser is helping make one of the most complex corners of the legal system easier to navigate. As the leader of Stretto’s AI focused innovation efforts, Robert works on technology that supports professionals, creditors, vendors, employees, courts, and other stakeholders involved in bankruptcy cases. Stretto recently won an AI Excellence Award for Conductor, its AI powered platform built to make Chapter 11 information more accessible, understandable, and useful.In this episode, Russ and Robert explore why bankruptcy is such a document heavy, high stakes, and often confusing process. Robert explains how large Chapter 11 cases can involve thousands of pages of filings, multiple jurisdictions, local rules, federal bankruptcy code, and millions of affected stakeholders who may have never encountered bankruptcy before.They dive into Stretto Conductor and how it helps users ask plain language questions about complex bankruptcy documents. Robert shares how the platform is designed to understand filings in the context of the specific case, the related docket history, the bankruptcy code, and the local rules that may shape the answer.The conversation also covers why general purpose AI is not enough for this kind of work. Robert explains the importance of domain specific AI, grounded answers, citations, legal precision, multilingual access, and making sure the platform provides information without crossing into legal advice.Along the way, Robert discusses creditor communications, call center operations, hallucination concerns, attorney trust, AI adoption in the legal industry, and why the future of legal technology will likely depend on purpose built systems that do one thing extremely well.Topics Covered:[00:01] Welcome and intro, Robert Klamser and Stretto’s AI Excellence Award win[00:17] Stretto’s role in bankruptcy support services and technology[02:11] The communication challenges inside large Chapter 11 cases[03:00] How Conductor helps vendors, creditors, and lawyers understand filings[04:34] What is broken about how information flows in bankruptcy cases[06:31] Why basic case information can be hard for stakeholders to access[07:00] How bankruptcy case websites changed access to documents[09:18] Why documents still need context from the bankruptcy code and local rules[10:54] What Stretto Conductor does differently[11:12] Teaching AI the rules, nuance, and structure of bankruptcy[13:31] Why legal AI must be grounded, intelligent, and precise[15:37] Why purpose built AI matters in bankruptcy law[17:02] Why one document rarely tells the full story in a Chapter 11 case[19:20] Naive retrieval, missing context, and reasoning errors in general AI tools[21:01] Building trust with skeptical legal professionals[21:21] Why every answer must be cited and grounded in the right source[22:38] How users are learning to trust Conductor[23:00] Why Conductor answers questions instead of drafting legal documents[24:21] How large bankruptcies create massive temporary operating structures[24:55] Supporting bankrupt entities while the business keeps operating[27:08] Making newly filed documents understandable within minutes[28:00] Multilingual access for stakeholders in global bankruptcy cases[28:54] How Conductor can reduce pressure on large call centers[31:45] Why lawyers have traditionally been slow to adopt new technology[32:27] How courts, caution, and hallucination concerns affect AI adoption[35:53] Where legal AI may be heading over the next five years[37:00] The need for clearer standards around AI use in legal work[38:25] Why clients may soon expect attorneys to use AI efficiently[39:58] Final thoughts on making bankruptcy easier to understand

Jim Napolitano is helping brands, engineers, product teams, and enterprise leaders experience product development in a more immersive way. As North American Services Director at Dassault Systèmes 3DEXCITE, Jim works with teams using virtual twins, spatial computing, and the 3DLive application to bring complex product data into interactive 3D environments. Dassault Systèmes recently won an innovation award for 3DLive and its work with Apple Vision Pro.In this episode, Russ and Jim explore how virtual twins are changing the way companies design, simulate, manufacture, market, and improve products. Jim explains how a virtual twin becomes a data fueled version of a product, allowing teams to test, review, and understand products before they exist physically.They dive into 3DLive and how immersive technology helps engineers, executives, designers, marketers, and end users collaborate inside product data. Jim shares examples from Formula 1, automotive design, aviation, consumer packaging, manufacturing training, and even medical applications like a living heart.The conversation also covers why live data matters, how spatial computing can make product review more intuitive, and how partnerships with Apple and Nvidia are helping Dassault Systèmes bring virtual twin experiences to life in new ways.Along the way, Jim discusses immersive collaboration, faster decision making, physical prototyping, simulation, AI, training scenarios, consumer research, and why virtual twins may soon become central to how companies build and improve nearly everything.Topics Covered:[00:01] Welcome and intro, Jim Napolitano and Dassault Systèmes’ 3DLive award win[00:39] What Dassault Systèmes does and the role of virtual twins[01:25] Connecting agency leadership, brand work, and immersive technology[02:41] What a virtual twin is and how it differs from an agent[04:02] Spatial computing, sense computing, and intuitive product interaction[05:14] Using Formula 1 to show simulation data in immersive 3D[06:30] Exploring vehicle components inside a virtual twin[07:32] How 3DLive supports collaboration across different locations[09:00] Making immersive tools useful for non technical users[10:00] Using 3DLive for consumer packaging research[11:07] Replacing expensive physical iteration with immersive product review[11:50] Aviation use cases and reconfiguring passenger cabin spaces[13:20] What surprised Dassault Systèmes during real customer deployments[14:00] Collaboration with Apple on Apple Vision Pro experiences[15:26] Knowing when a virtual twin is good enough for decision making[16:13] Using existing product data instead of creating one off experiences[17:10] Training technicians with virtual equipment and troubleshooting scenarios[18:22] Why live data matters in product development[20:05] Dassault Systèmes’ partnerships with Apple and Nvidia[21:25] Common principles across automotive, aerospace, and medical use cases[22:00] The living heart example and virtual twin applications in medicine[24:28] How immersive tools and AI can shorten product development cycles[25:17] How AI may support engineering, science, and strategic decision making[26:27] Final thoughts on the future of 3DLive and virtual twin technology

Alexey Sheremetyev is helping homeowners, designers, real estate professionals, builders, and contractors reimagine how physical spaces become digital. As Co-Founder and CPO of Planner 5D, Alexey helped build a platform that allows users to scan rooms with a phone camera and turn them into editable 3D home plans. Planner 5D recently won an AI Excellence Award for its Home Scanner technology.In this episode, Russ and Alexey explore how Planner 5D grew from a personal renovation problem into a platform used by more than 100 million people around the world. Alexey shares how his background in web design and user experience shaped the product, and why the goal was always to make home planning simple enough for consumers but powerful enough for professionals.They dive into Home Scanner, Planner 5D’s AI powered feature that uses computer vision to turn real rooms into editable digital twins. Alexey explains how the technology can recognize room layouts, measurements, furniture, colors, textures, flooring, and objects without requiring expensive hardware or specialized training.The conversation also covers why editable 3D plans matter more than static renderings, how AI helps handle messy real world spaces, and why Planner 5D’s years of user generated floor plans and designs have become one of its most valuable assets.Along the way, Alexey discusses renovation planning, real estate workflows, professional design collaboration, smart home possibilities, home maintenance, property history, and his vision for Planner 5D becoming a persistent digital memory for the home itself.Topics Covered:[00:01] Welcome and intro, Alexey Sheremetyev and Planner 5D’s AI Excellence Award win[00:29] What Planner 5D does for homeowners and professionals[00:37] Creating digital twins and editable 3D home plans[01:27] Turning room design into a consumer grade 3D experience[01:56] How Alexey’s own apartment renovation inspired Planner 5D[03:53] Why traditional room planning can be frustrating and inaccurate[04:18] How a design background shaped the Home Scanner experience[05:00] Using AI to automate manual measurements and room recreation[05:59] Why Home Scanner is technically harder than it looks[06:17] Planner 5D’s advantage after 15 years and more than 100 million users[07:35] Solving spatial reconstruction through software instead of costly hardware[07:56] Using computer vision instead of relying only on LiDAR[10:12] What Planner 5D learned from real users scanning real spaces[11:00] How real estate professionals use digital twins in their workflows[12:14] Building for both consumers and professional users[13:20] How builders and contractors use Planner 5D as a presale tool[14:27] Why editable 3D plans create a different user experience than static images[15:50] How Planner 5D checks scan accuracy for renovation planning[16:18] AI validation, human review, user feedback, and correction tools[18:51] Handling furniture, bad lighting, unusual room shapes, and messy spaces[19:30] Training AI with a large base of floor plans, designs, and user data[22:00] Using Planner 5D as a filing cabinet and memory system for the home[24:10] Why spatial reconstruction may go beyond home design[25:57] Planner 5D as the next stage of the smart home[27:46] Why the digital twin could travel with the house, not the owner[28:00] Smart home integrations, appliances, and connected home systems[29:30] Helping homeowners understand and maintain older properties[30:00] Using AI to recommend repairs and improvements that may increase home value[32:00] Why user expectations for instant answers and context are changing[33:59] Final thoughts on the future of Planner 5D and generative AI

Anton Dam is helping enterprise risk, audit, and compliance teams rethink how AI can support highly regulated work without removing human judgment from the process. As SVP of Product and AI at Optro, formerly AuditBoard, Anton works with global enterprises using AI to make risk management more adaptable, strategic, and aligned with business goals. Optro recently won an AI Excellence Award for its work bringing AI into audit, risk, and compliance workflows.In this episode, Russ and Anton explore how internal audit and GRC teams are using AI to move beyond manual coordination, document review, and repetitive evidence analysis. Anton explains why audit teams often spend the majority of their time on labor intensive review work, and how AI can help shift that effort toward higher value risk management.They dive into Optro’s approach to assistive AI, copilot experiences, and agentic workflows. Anton shares why quality thresholds change depending on the use case, why human review remains a nonnegotiable, and why nothing should enter a system of record without a person in control.The conversation also covers AI governance, shadow AI, regulatory change, customer trust, and why large, highly regulated companies may be moving faster on AI adoption than many people expect. Anton explains why Optro does not train on customer data, how the company thinks about AI transparency, and why governance will become as central to business operations as cybersecurity.Along the way, Anton discusses human creativity, AI alignment, enterprise trust, AI security reviews, audit team adoption, the future of web apps, and why internal audit may become a more strategic advisory function as AI takes on more repetitive work.Topics Covered:[00:01] Welcome and intro, Anton Dam and Optro’s AI Excellence Award win[00:27] Optro’s mission in audit, risk, and compliance[01:09] Why GRC is changing in a more regulated AI environment[01:28] Anton’s path from LinkedIn and Workday to Optro[04:41] Why Anton believes in betting on human creativity[05:00] What AI can automate, and what remains deeply human[08:16] A typical day for internal audit teams before AI[09:00] Manual coordination, evidence gathering, and document review[10:00] How AI can reduce time spent on repetitive audit tasks[12:00] What good enough AI means in high stakes risk and compliance work[12:26] Assistive AI, copilot workflows, and agentic AI[14:48] Why human review remains required before records are updated[15:29] Staying current with changing regulations and standards[16:24] Tracking data sets, model tuning, and development decisions[17:44] Building trust with enterprise customers[18:12] Why quality and workflow fit drive AI adoption[20:03] What surprised Anton about AI adoption in large enterprises[22:57] AI security reviews and integrating into enterprise AI ecosystems[23:20] Why the traditional web app may change dramatically[24:24] Measuring AI impact in risk management[26:23] Optro’s nonnegotiables for deploying AI[26:35] Why Optro does not train on customer data[27:21] Using AI governance to help organizations govern AI[28:30] Why AI governance may follow the same path as the CISO function[31:17] Shadow AI and lack of visibility inside organizations[31:50] The risks of employees using unapproved AI tools[32:30] Why companies must enable AI safely instead of simply blocking it[33:40] What internal audit could look like in five years[34:20] Moving from risk mitigation to risk management[35:12] Final thoughts on internal audit as a strategic advisory function

Siobhan Hanna is helping AI companies protect one of the most important parts of model development: the human data layer. As a leader at WeLo Data, she works with foundational LLM builders and enterprise technology companies to provide high quality multilingual human data across languages, cultures, and markets. WeLo Data’s NEMO framework recently won an AI Excellence Award for helping detect fraud, misrepresentation, and data integrity risks in AI training pipelines.In this episode, Russ and Siobhan explore why high quality human data is essential to building better AI models, and why that data is increasingly vulnerable to fraud. Siobhan explains how contractor based, globally distributed AI data workflows can create opportunities for identity fraud, coordinated manipulation, account sharing, and other risks that can degrade model performance.They dive into NEMO, WeLo Data’s fraud mitigation and misrepresentation detection framework. Siobhan shares how the system uses continuous monitoring, behavioral analytics, rules based logic, AI driven detection, and organizational psychology to identify suspicious activity across the contributor life cycle.The conversation also covers why AI data integrity should be treated as part of the broader data quality and governance conversation. Siobhan explains why point in time checks are not enough, how WeLo Data borrowed ideas from financial services and KYC models, and why continuous monitoring is critical when training data is so strategically valuable.Along the way, Siobhan discusses multilingual AI, cultural context, data provenance, contributor verification, regulatory trends, and why protecting the human layer of AI development may soon move from best practice to formal requirement.Topics Covered:[00:01] Welcome and intro, Siobhan Hanna and WeLo Data’s AI Excellence Award win[00:28] WeLo Data’s role as a multilingual AI human data provider[01:05] Why AI training data quality matters[01:24] How fraud can enter human data workflows[02:29] Why fraud mitigation in AI data has been underserved[02:36] The speed of AI development and the blind spot around human data integrity[04:28] How fraudulent or misrepresented data can affect model performance[04:57] Why data integrity issues can be hard to trace after model degradation[06:08] Why fraud is difficult to detect in global AI data pipelines[07:02] Which AI systems are most exposed to training data integrity risks[08:10] Identity validation and why AI data fraud differs from traditional fraud[08:35] Borrowing KYC and transaction monitoring ideas from financial services[10:27] How WeLo Data validates that NEMO is catching the right activity[11:24] Behavioral variables, rules based detection, and AI driven monitoring[13:04] The role of organizational psychology in fraud detection[13:53] Stopping threats before they reach the model[14:28] What surprised WeLo Data about the AI fraud landscape[15:30] Why multilingual and cultural context make fraud detection harder[17:02] Why continuous monitoring beats one time screening[18:04] What translated from financial services and what had to be reinvented[19:20] AI regulation, data integrity, and governance requirements[19:48] Why contributor verification may become a formal AI requirement[20:50] Why data provenance should be part of responsible AI infrastructure[21:23] Questions AI companies should ask about who produced their data[22:43] Which parts of AI infrastructure are most vulnerable[23:04] Advice for AI founders, operators, and leaders[23:53] Final thoughts on fraud, trust, and protecting AI training data

Latane Conant is helping companies rethink customer service as a relationship builder, not just a cost center. As CMO of Parloa, she is working at the intersection of AI agents, voice, customer experience, and enterprise support, helping companies replace outdated IVR systems with conversational AI that can make every customer interaction feel as easy as talking to a friend.In this episode, Russ and Latane explore why the customer service side of the buyer journey has become one of the biggest missed opportunities in business. Latane explains how companies spend heavily to get customers to engage, but often fail them when they actually need help.They dive into Parloa’s AI voice agent platform and how it helps enterprises deliver secure, low latency, natural language conversations across languages, dialects, and customer scenarios. Latane explains why voice is the hardest modality to get right, why reliability matters more than flashy demos, and why regulated industries need AI that can handle authentication, tool calling, context, and secure interactions at scale.The conversation also covers Parloa’s AI mystery shopping study of the Fortune 2000, which found major gaps in customer support access, chat resolution, IVR experiences, and agent readiness. Latane shares why she believes companies need to prepare for an agent to agent future where customers may soon expect their personal AI agents to interact directly with enterprise systems.Along the way, Latane discusses customer journey leaks, the limits of “check the box AI,” the importance of use case selection, enterprise deployment timelines, simulation testing, agent drift, and why customer service should become a driver of loyalty, revenue, and lifetime customer value.Topics Covered:[00:01] Welcome and intro, Latane Conant and Parloa’s award wins[00:21] Why Latane moved from 6sense to Parloa[00:32] The customer journey leak inside customer service[02:09] Why CMOs should care about support and service experiences[02:44] Parloa’s mission to make customer interactions feel like talking to a friend[04:14] How Parloa differs from basic LLM-based call tools[04:43] Replacing outdated IVR systems with conversational AI[06:20] Why traditional IVR experiences lose context and frustrate customers[07:03] Parloa’s AI mystery shopping study of the Fortune 2000[08:08] Why many companies hide or limit customer support access[08:33] Chatbot resolution rates and poor human handoff performance[09:02] Why only 1% of companies are ready for agent to agent interactions[09:58] The coming wave of personal AI agents contacting enterprises[10:38] AI agents as relationship builders, not just transaction handlers[10:49] Travel, payments, insurance, and roadside assistance use cases[13:11] Solving context loss across customer service interactions[13:41] Building a broader customer context fabric[15:16] Deploying AI agents at enterprise scale[15:38] Parloa’s foundation in real-time translation and voice technology[17:48] Why AI can accelerate customer service deployments[18:17] Fast enterprise deployment through use case prioritization[19:50] Prebuilt integrations and reusable AI skills[20:35] Why AI agents need training before going live[22:48] Reliability, authentication, tool calling, and production latency[24:43] Transactional versus high stakes customer service interactions[26:37] How customer comfort with AI will evolve over time[27:26] Common mistakes executives make when deploying service AI[28:25] Why companies should rethink the front door of customer service[29:12] Customer service as an opportunity to build loyalty[30:09] Final thoughts on personal agents and the future of customer experience

Rich Hull is helping restaurants modernize operations with AI-powered robotics that solve real labor, safety, and profitability challenges. As CEO of Miso Robotics, he leads the company behind Flippy Fry Station, an AI-enabled robotic fry station designed to help quick serve restaurants, stadiums, and food service operators increase throughput, improve consistency, reduce injuries, and redeploy workers into more valuable customer-facing roles.In this episode, Russ and Rich explore how Miso Robotics evolved from an early restaurant robotics startup into a platform company focused on modern food service operations. Rich explains why the first generations of Flippy were essential learning tools, and how the third generation became smaller, faster, easier to install, and more reliable for commercial kitchens.They dive into the labor crisis facing restaurants, including rising wages, high turnover, staffing shortages, and the difficulty of filling physically demanding roles like the fry station. Rich explains why Flippy is not about replacing people, but about automating unsafe and repetitive work so employees can focus on guest experience, upselling, quality, and higher-value tasks.The conversation also covers Miso’s broader platform vision, including Zippy, an employee revenue engine designed to incentivize frontline workers to sell more and help operators improve profitability. Rich shares how Miso is using data, third-party validation, Nvidia technology, predictive automation, and restaurant operations software to build a connected platform for the future of food service.Along the way, Rich discusses reliability, ROI, employee adoption, restaurant margins, Sweetgreen’s automation success, White Castle deployments, stadium use cases, and what founders need to understand about building category-defining robotics companies.Topics Covered:[00:01] Welcome and intro, Rich Hull and Miso Robotics’ AI Excellence Award win[01:10] How Miso gathers restaurant robotics and AI data[01:38] Moving from quick serve restaurants into stadiums and food service[02:18] Rich’s arrival at Miso and the company’s next phase[03:00] Building the third generation of Flippy[04:54] What Rich changed after joining Miso[05:45] Why labor shortages are forcing restaurant modernization[06:50] The lack of innovation inside restaurant kitchens[07:19] How rising labor costs and thin margins pressure restaurants[08:32] Why operators want technology that drives revenue and profit[09:10] Introducing Zippy, Miso’s employee revenue engine[10:20] Flippy’s original burger-flipping concept[11:35] Employee burns, injury risk, and unsafe kitchen work[12:47] How Flippy improves speed, quality, and throughput[13:44] Why restaurant robotics must move from novelty to ROI[14:42] Why Flippy has to work at enterprise scale[16:06] Measuring ROI and proving value in real time[17:05] Sweetgreen’s automation example and restaurant margin impact[19:45] Solving restaurant problems today, not in the distant future[20:23] Redeploying workers into more valuable roles[21:31] How Flippy changes kitchen workflows[23:26] Employee reactions to Flippy and why adoption improves quickly[26:24] Expanding the labor pool through safer automation[28:13] Third-party validation and proving Flippy’s ROI[30:04] Miso’s strategic partnership with Nvidia[31:37] Using Nvidia technology for vision, AI, digital twins, and decision-making[35:06] Miso’s acquisition of Zignal and the Zippy product vision[37:25] Bringing restaurant data into one operations layer[39:23] How Zippy helps employees drive more sales[41:01] Lessons for robotics founders[43:28] Final thoughts on Flippy, restaurant adoption, and the future of food service

Nataly Youssef is helping employers and employees recover healthcare dollars they are already entitled to, but often never receive. As CEO and Founder of Reclaim Health, she uses AI and healthcare claims data to detect billing errors, missed reimbursements, unused benefit opportunities, and plan inefficiencies that leave money behind. Reclaim Health recently won its second AI Excellence Award for turning healthcare insights into real recovered value.In this episode, Russ and Nataly explore why healthcare affordability has become such a major burden for both employers and employees. Nataly explains how rising deductibles, out of pocket expenses, medical bills, and employer healthcare costs are putting pressure on households and company budgets.They dive into how Reclaim analyzes claims data, enrollment data, eligibility data, and benefit information to identify opportunities most employers and employees would otherwise miss. Nataly shares how Reclaim reviews claims across covered lives to find duplicate charges, copay issues, billing errors, missed reimbursements, and underused voluntary benefits.The conversation also covers why insight alone is not enough. Nataly explains how Reclaim moves beyond reporting by filing claims, substantiating documentation, monitoring reimbursement progress, and helping get dollars back into employee wallets and employer budgets.Along the way, Nataly discusses voluntary benefits, claims audits, healthcare data security, consultant partnerships, ERISA fiduciary concerns, employee trust, and why AI should do work for people instead of creating more work.Topics Covered:[00:01] Welcome and intro, Nataly Youssef and Reclaim Health’s second AI Excellence Award win[00:31] Reclaim Health’s mission to recover healthcare dollars for employers and employees[01:55] The rising burden of premiums, deductibles, and out of pocket expenses[04:37] How Reclaim finds dollars already owed to employers and employees[07:45] Day one reporting and how Reclaim analyzes claims, enrollment, and eligibility data[08:30] Using AI to review claims and covered lives[09:10] Billing errors, duplicate charges, copay issues, and benefit opportunities[11:51] How siloed benefits systems create missed reimbursement opportunities[13:25] How Reclaim files, substantiates, and monitors benefit claims on behalf of members[15:33] How common billing errors appear in healthcare claims data[18:11] How AI and automation can influence billing and upcoding patterns[19:41] Healthcare as one of the largest employer P&L costs[21:23] Why Reclaim uses the member advocacy channel to resolve billing issues[22:37] How Reclaim helps employers model benefit plan changes[23:03] Reclaim as a financial concierge for employees[26:38] Building AI pipelines in a regulated and fragmented healthcare environment[28:10] Why employee trust and privacy are central to Reclaim’s mission[30:06] Why Reclaim is designed to connect the benefits ecosystem, not replace it[37:30] Why employers need clearer visibility into voluntary benefit payouts[41:07] ERISA fiduciary concerns and the responsibility to plan participants[43:11] AI beyond insights and why action matters[43:34] Why AI should reduce work for people, not create more work

Kartal Goksel is helping advertisers move beyond identity-based targeting and toward a more privacy-first, context-aware future. As Chief Technology Officer at Seedtag, he leads technology for a company focused on contextual advertising, using AI to understand what content means, how it feels, and what intent it signals without relying on personal identity or invasive tracking.In this episode, Russ and Kartal explore how digital advertising is changing as third-party cookies, privacy expectations, AI tools, and consumer behavior reshape the industry. Kartal explains why contextual advertising can reduce the cognitive friction consumers feel when ads follow them around the internet, and why aligning ads with the content someone is consuming can create a better experience for users, publishers, and brands.They dive into Seedtag’s AI engine, Liz, and how it analyzes content across text, images, video, metadata, language, emotion, interest, and intent. Kartal explains how neuro-contextual advertising goes beyond keywords to understand the full meaning of content and match campaigns to the right moment.The conversation also covers why privacy-first architecture changes how advertising systems are built. Instead of focusing on who the user is, Seedtag focuses on the content itself and the interaction between that content and the person consuming it.Along the way, Kartal discusses the engineering challenges behind real-time ad decisioning, the scale of programmatic advertising, agentic workflows, campaign planning, publisher trust, consumer privacy, and why the future of media buying may involve AI agents working together to plan, activate, and optimize campaigns faster.Topics Covered:[00:01] Welcome and intro, Kartal Goksel and Seedtag’s AI Excellence Award win[00:38] Seedtag’s background in contextual and in-image advertising[01:51] How AI is changing both the consumer and publisher sides of advertising[02:55] Contextual advertising versus identity-based advertising[04:38] Why retargeted ads create cognitive friction[05:33] How contextual advertising helps publishers keep users engaged[06:55] Introducing Liz, Seedtag’s AI engine[07:24] Detecting interest, emotion, and intent from content[08:51] Third-party cookies and the shift toward privacy-first advertising[10:08] Why younger users are more privacy conscious[11:19] Why current content can be more valuable than old behavioral history[11:50] How marketers can evaluate contextual advertising performance[13:59] Building privacy-first advertising architecture[15:23] Why context is more than keyword matching[15:48] Moving from keywords to embeddings and full content understanding[18:33] Working with neuroscience to validate neuro-contextual advertising[20:08] Analyzing text, images, video, metadata, and language at scale[20:48] Engineering challenges in real-time ad decisioning[22:46] Scaling models, latency, caching, and cloud costs[23:43] Why milliseconds matter in digital advertising[25:40] Liz Agent and moving from planning to activation[26:01] Agentic workflows for building custom audiences[27:37] Airline campaign results and product recognition lift[30:25] Can contextual advertising rebuild consumer trust?[32:04] Neuro-contextual advertising as a campaign planning layer[33:31] Reducing human dependency in digital advertising workflows[34:47] How context can reduce targeting mistakes and wasted spend[36:24] What brand marketers and media buyers should discuss with agencies[37:50] Final thoughts on stopping irrelevant ads from following users around the internet

Alex Urist is helping solve one of the biggest barriers to EV adoption: charging infrastructure that can scale without waiting years for grid upgrades. As co-founder of XCharge North America, he is leading the development of EV charging and energy storage solutions designed for the North American market. XCharge’s GridLink platform recently won a BIG Innovation Award for its battery-integrated DC fast charging technology.In this episode, Russ and Alex explore why EV charging is harder to deploy than many people expect. Alex explains how grid limitations, transformer delays, permitting, financing, and utility capacity can slow down new charging sites, especially when high-speed DC fast chargers require power that many locations do not already have.They dive into GridLink, XCharge’s battery-integrated, bi-directional DC fast charger. Alex explains how the system stores energy in an onboard battery, boosts output to vehicles, accepts solar power directly, and can even support buildings or the grid when needed. This approach can help reduce dependence on immediate utility upgrades while making more sites viable for fast charging.The conversation also covers the economics of EV charging, including utilization, demand charges, equipment costs, operational costs, and the importance of choosing the right real estate. Alex shares why fleets, hospitality sites, dealerships, travel centers, and commercial properties all represent major opportunities for battery-backed charging infrastructure.Along the way, Alex discusses solar integration, bidirectional energy, energy storage, fleet depot charging, the future of grid resiliency, and why EV charging stations may become part of a broader distributed energy network.Topics Covered:[00:00] Welcome and intro, Alex Urist and XCharge’s BIG Innovation Award win[00:40] XCharge’s background in EV charging and energy storage[01:55] Why charging availability affects EV adoption[02:31] Why grid limitations slow EV infrastructure deployment[04:19] What 480 three-phase power means in practical terms[05:20] Matching charging locations to real-world activity[07:31] EV charging speed and the smartphone charging comparison[08:19] Perceived charging availability and consumer confidence[10:08] Grid competition from AI data centers and other energy users[10:51] Where deployments break down: financing, permitting, and power availability[12:27] How battery-integrated charging works[12:48] AC, DC, and how fast chargers transfer power to vehicles[15:12] How integrated batteries can speed deployment timelines[16:03] Why financing is critical to EV infrastructure growth[18:13] Demand charges and how they affect charging economics[18:59] Modeling ROI for charging networks[20:36] Using solar and batteries to address grid capacity issues[21:27] GridLink’s ability to support buildings and return power to the grid[23:45] Benefits for property owners and commercial sites[24:07] What a 60 kilowatt solar array looks like in practice[25:05] Why direct solar integration improves charging efficiency[26:08] Efficiency loss from energy conversion[26:34] Fleet depot use cases and load balancing[28:56] Why utilities are becoming interested in battery-backed chargers[29:07] Charging stations as part of local energy infrastructure[31:42] How EV charging stations become part of the broader energy grid[33:44] What the next five years could look like for EV charging[35:35] Why real estate matters most in EV charging investments[36:15] Why gas stations are not always easy charging sites[37:37] Convenience stores, hotels, rentals, and dealerships as charging opportunities[39:03] Finding power availability and working with utilities

Madan Moudgal is helping healthcare organizations use AI to improve operations without removing the human judgment that sensitive clinical decisions require. As Chief Digital Officer at Sagility, he leads technology transformation for a healthcare operations company serving U.S. payers and providers. Sagility’s Nurse Assist solution recently won an AI Excellence Award for helping clinical teams review prior authorization cases faster and more accurately.In this episode, Russ and Madan explore why healthcare is one of the hardest industries to modernize with AI. Madan explains how legacy systems, strict regulation, data privacy requirements, and complex workflows make healthcare transformation different from other industries.They dive into prior authorization, one of healthcare’s most difficult and controversial processes. Madan explains why the process exists, how it helps address waste, fraud, and abuse, and why the challenge is balancing cost control with patient access to appropriate care.The conversation also covers why Sagility uses the term augmented intelligence instead of full automation. Madan explains that AI can summarize documents, extract relevant clinical details, compare information against guidelines, and provide recommendations, but nurses and clinical experts still need to make the final decision.Along the way, Madan discusses domain-specific AI models, clinical language models, guardrails, PHI protection, data curation, AI governance, change management, and why successful healthcare AI requires careful testing, incremental rollout, and trust-building over time.Topics Covered:[00:00] Welcome and intro, Madan Moudgal and Sagility’s AI Excellence Award win[00:32] Sagility’s background as a healthcare operations company[01:21] Why healthcare and payment systems are so complex[01:43] The challenge of adopting AI in a regulated healthcare environment[02:37] Lessons from implementing technology change in healthcare[02:47] Working around large legacy healthcare systems[03:45] Why prior authorization is such a difficult healthcare problem[03:57] Balancing waste reduction, cost control, and patient access to care[05:27] Why Sagility uses augmented intelligence instead of automation[05:40] Keeping humans in the loop for clinical decision-making[06:46] Where AI can help and where humans must remain accountable[09:14] Extracting and summarizing clinical data from case documents[10:27] Why Sagility focuses on domain-specific AI models[11:03] Building trust through clinical language models[12:01] Why accuracy is essential in healthcare AI[13:18] Guardrails for compliance, PHI, and regulatory requirements[14:37] Reducing review time and what that means for patients[15:35] Reviewing medical records, clinical guidelines, and recommendations[16:34] How Nurse Assist supports nurse reviewers[17:43] Early benefits from speed, efficiency, and lower costs[18:38] Integrating AI with legacy healthcare systems[18:56] Why data curation matters before AI can work effectively[20:24] AI governance and aligning with client policies[20:47] Change management in enterprise healthcare workflows[21:50] Balancing innovation and risk management in healthcare[22:42] Why healthcare AI rollouts cannot be rushed[24:44] Whether healthcare will ever become fully automated[25:06] Why healthcare is more likely to remain augmented than fully automated[27:44] Other healthcare areas ready for AI transformation[28:22] Automating simpler member and patient interactions[29:27] Virtual agents and consumer expectations in healthcare[30:50] Claims accuracy and payment integrity opportunities[32:04] What healthcare may look like in the next 30 years

Asaf Goldstein is helping IT support teams use AI to create faster, more proactive, and more human customer experiences. As Senior Director of Global Customer Care at SysAid, he leads a global support organization using AI to identify trends, resolve issues faster, deflect routine tickets, and give agents better context before they ever speak with a customer.In this episode, Russ and Asaf explore how AI is changing customer care from a reactive cost center into a proactive driver of customer loyalty. Asaf explains why the future of support is not just automation, but the right balance between AI, human empathy, and expert problem solving.They dive into how SysAid uses AI across its support organization, including AI copilots, internal AI agents, sentiment analysis, ticket deflection, quality scoring, and proactive issue detection. Asaf shares examples of how AI helped his team identify critical issues, assemble engineering and DevOps teams quickly, and resolve customer problems before they escalated.The conversation also covers the risks of over-automation. Asaf explains why AI should solve simple questions, summarize context, and guide agents, but also know when to hand a customer to a human. He shares how SysAid reduced unhappy customer survey responses from around 40 per quarter to 5 by combining AI-enabled insights with personal follow-up from team leads.Along the way, Asaf discusses the rise of AI managers, the skills support agents need to stay relevant, why guardrails matter, and how companies can create customer service experiences that feel faster, smarter, and more personal.Topics Covered:[00:01] Welcome and intro, Asaf Goldstein and SysAid’s customer service award win[00:28] SysAid’s background in IT service management software[01:10] What is changing in AI-powered customer support[01:26] Moving support from reactive to proactive[02:33] Managing global customer care in an AI-driven environment[02:39] How AI helps identify trends before customers report issues[03:20] Using AI and Discord to detect critical customer issues[04:22] Why a great support experience can increase customer loyalty[04:57] Creating a “wow experience” in support[05:41] Where pressure to automate support comes from[05:57] How SysAid uses AI to resolve routine tickets[07:30] Why complex support still needs expert human agents[07:50] The risk of over-automating customer interactions[08:55] Defining the human layer in modern support organizations[09:26] Why personal touch still matters in technical support[10:18] When humans are still absolutely critical[10:30] Reducing unhappy customers through personal follow-up[13:00] Empathy as a core part of customer service[13:36] Where AI works well and where it falls short[15:38] Deciding what gets automated and what stays human[17:14] How AI changes the role of support agents[19:04] Skills that matter most for the future of customer care[20:38] The rise of AI managers inside support teams[21:13] How AI helps agents personalize customer interactions[22:27] How customer expectations will change as AI becomes common[23:27] Common mistakes when rolling out AI in support[23:47] Why AI answers need validation, formatting, and guardrails[25:23] Lessons from the shift from phone support to chat support[26:10] Metrics AI support managers should track[27:52] Principles for adding AI without losing the human experience[28:15] Guardrails, monitoring, and continuous improvement[29:43] Final thoughts on creating wow moments with AI and people

Tim Ray is helping apartment communities verify renters faster, more fairly, and with greater trust. As co-founder and CEO of Verifast, he leads an AI-powered verification platform built for the U.S. multifamily market, where property managers need to confirm identity, income, assets, and fraud risk at scale without relying only on traditional credit scores.In this episode, Russ and Tim explore why renter verification is becoming more complex as more people earn income through gig work, self-employment, side hustles, benefits, investments, and nontraditional financial paths. Tim explains why credit scores only show historical payment behavior, not a renter’s real-time ability to pay.They dive into how Verifast uses open banking, direct-source data, AI, biometrics, phone carrier checks, email history, document analysis, and behavioral signals to help determine whether someone is who they say they are, makes what they say they make, and has what they say they have.The conversation also covers the rise of sophisticated rental fraud, including fake IDs, synthetic identities, credit profile manipulation, fake documents, and organized fraud playbooks shared across social media. Tim explains why legacy systems often miss these risks and why property teams need purpose-built tools rather than asking leasing agents to act like private investigators.Along the way, Tim discusses explainability, renter trust, Trustpilot reviews, human-in-the-loop review, AI-assisted workflows, gig worker verification, and Verifast’s vision for portable renter trust that could help applicants avoid paying multiple application fees when they do not qualify for a specific property.Topics Covered:[00:00] Welcome and intro, Tim Ray and Verifast’s AI Excellence Award win[00:35] Tim’s founder background and Verifast’s focus on multifamily housing[01:00] Powering trust at scale for large apartment communities[02:11] Why nontraditional renters need better verification options[02:39] How Tim joined Verifast as an investor and late-stage co-founder[03:54] Scaling quickly with a lean team and limited capital[05:08] Why credit scores do not tell the full renter story[05:31] Propensity to pay versus ability to pay[07:13] Why some financially stable renters are invisible to traditional screening[07:54] Modern rental fraud and how fraudsters exploit apartment screening[09:03] CPNs, synthetic identities, and social media fraud playbooks[09:46] Why legacy property management systems miss these risks[11:36] Why deposits and debits both matter in income verification[11:46] Detecting cash cycling and fake income patterns[13:34] Verifying renters with assets or investment income instead of W-2 income[14:17] Biometrics, fake IDs, phone carrier checks, and email history[15:51] Layering documents, bank data, criminal records, and identity signals[17:13] Building explainability and transparency into renter verification[17:42] Using Trustpilot reviews as a quantitative trust signal[19:50] Why human-in-the-loop review matters for housing decisions[20:18] Why every renter matters when application fees and housing are on the line[21:58] How years of real-world data strengthen Verifast’s models[23:34] How Verifast changes the property manager workflow[25:41] Assessing gig workers, contractors, and side-hustle income[26:16] Grouping income sources and showing the math behind approvals[28:08] How Verifast speeds up verification compared with manual review[29:34] Portable renter trust and reusing verified applicant data[31:07] Helping renters find properties where they actually qualify[31:29] Tim’s advice on earning trust as an entrepreneur[32:42] Final thoughts on reputation, accountability, and trust at scale

Shannon Copeland is helping companies find hidden savings, reduce procurement complexity, and make better decisions across supply chain and spend management. As CEO of SIB, he leads a company with 35 years of experience helping clients negotiate with vendors, improve competitiveness, and manage procurement across dozens of spend categories. Now, with SpendBrain, SIB is bringing AI into the procure to pay process in a way that prioritizes privacy, accuracy, and human expertise.In this episode, Russ and Shannon explore how procurement has changed from traditional cost reduction work into a more intelligent, data driven discipline. Shannon explains why simply asking vendors for discounts is not enough, and why true procurement expertise requires understanding vendor language, pricing models, service bundles, operational needs, and industry context.They dive into SpendBrain, SIB’s AI platform that creates a private semantic ontology for each client. Shannon explains why the platform is not built around a traditional LLM model, how each client gets its own private brain, and why SIB does not harvest or learn from client pricing data.The conversation also covers how SpendBrain works with messy, federated data across contracts, invoices, systems, and departments. Shannon shares how the platform can identify errors, surface savings opportunities, support forecasting, and help clients move from reactive analysis to proactive cost intelligence.Along the way, Shannon discusses handcrafted AI, human in the loop learning, why procurement experts remain essential, and how CFOs and supply chain leaders can start uncovering cost leakage by following their own intuition about where problems may be hiding.Topics Covered:[00:02] Welcome and intro, Shannon Copeland and SIB’s AI Excellence Award win[00:35] SIB’s 35 year history in procurement and cost reduction[01:00] What procure to pay means in practical terms[02:10] Why procurement often feels like buying tires without knowing the best deal[02:42] Why vendor negotiations require category expertise[04:23] Avoiding disruption while improving vendor relationships[05:30] How procurement work helps clients clarify needs and reduce overspending[07:44] Introducing SpendBrain and SIB’s AI transformation[08:02] Why traditional benchmarking databases are no longer enough[10:22] Building private semantic ontologies for each client[12:37] Why regulated industries need private, accurate AI systems[14:54] Working with messy data across contracts, invoices, and systems[15:37] Pulling data from existing systems without replacing them[16:21] Handcrafted AI and the role of humans in the loop[17:31] What CFOs see when SpendBrain analyzes spend data[18:05] Moving from periodic audits to real time spend intelligence[20:48] Detecting invoice errors and billing issues[21:31] Why roughly 40% of invoices contain some type of error[23:03] Data ownership, explainability, and traceability[23:32] Why clients own both their raw data and their private brain[25:52] Surfacing hidden savings in overlooked spend categories[26:19] Finding major savings in waste and recycling spend[28:28] How SpendBrain can deliver strong ROI for clients[29:39] Deciding what AI handles versus what humans validate[30:00] Reducing mundane work so teams can focus on strategy[33:33] What cost intelligence may look like five years from now[35:52] AI as an accelerator for human expertise[37:25] First steps for CFOs who suspect cost leakage[39:11] Why some organizations still struggle to get answers from AI tools[40:06] Final thoughts on pragmatic, low cost, accurate AI for procurement

Marcel Stellaard, Stephán Kemp, and Barton Harris are helping rethink vehicle security by shifting the focus from tracking stolen cars to preventing theft before a vehicle ever moves. As leaders with Titan Secure, they are bringing an anti-theft platform built in South Africa to global markets, including the United States, where vehicle theft, insurance costs, and recovery challenges continue to create major problems for consumers, dealers, fleets, insurers, and law enforcement.In this episode, Russ speaks with the Titan Secure team about why traditional GPS tracking is not enough. Marcel explains how the company was built around a simple idea: if you have to go looking for a stolen vehicle, the battle is already lost. Instead, Titan Secure uses a multi-layered approach that integrates with vehicle systems to immobilize key functions and help owners find their vehicle where they parked it.Stephán shares how the product began as a mechanical engine lock before evolving into an electronic and software-enabled hardware platform. He also discusses Titan Secure’s proactive approach to theft prevention and the company’s reported 100% success rate in preventing vehicle losses.The conversation also explores how organized vehicle theft has become a global enterprise, with stolen vehicles and parts moving through sophisticated networks. Barton explains the U.S. market opportunity, the challenge of changing consumer expectations, and why dealers, insurers, and law enforcement are looking for anti-theft solutions instead of recovery tools.Along the way, the team discusses OEM collaboration, EV security, insurance implications, bait car programs, telematics, tamper alerts, and how Titan Secure stays ahead of fast-changing theft tactics through continuous product improvement.Topics Covered:[00:01] Welcome and intro, Titan Secure’s BIG Innovation Award win[00:44] Titan Secure’s origins in South Africa and the vehicle theft problem[02:14] How the product began with a mechanical engine lock[03:29] Moving from hardware lock to software-enabled vehicle security[03:49] Why prevention matters more than tracking after theft[05:42] How traditional tracking tools often serve insurers more than consumers[06:30] Why vehicle damage usually starts once the wheels move[07:19] Organized vehicle theft, parts trade, and global syndicates[07:54] Working with OEMs without becoming just another tracking device[09:19] Why theft methods change quickly and require constant innovation[10:02] Titan Secure’s reported 100% success rate[10:52] Go-to-market strategy in South Africa and global expansion[12:43] Entering the U.S. market and changing consumer expectations[14:16] Why dealers need stronger value propositions than traditional add-ons[15:44] The bait car program in New Mexico and law enforcement impact[17:12] The real consumer cost of vehicle theft and insurance claims[19:14] Why recovery is stressful for consumers and difficult for law enforcement[20:37] How auto theft can fuel broader community crime[21:36] Vehicle security for EVs, motorcycles, trucks, and heavy equipment[23:50] Insurance premium implications and theft risk reduction[25:23] Why Titan Secure’s installation approach differs from GPS trackers[27:57] Staying ahead of key spoofing, jammers, and changing theft tactics[28:18] Continuous improvement and new product development[30:27] Australia, global theft trends, and expanding market demand[31:16] Future product focus, telematics, modular security, and tamper alerts[32:54] Final thoughts on consumer education and global adoption

Dan Stickel is helping families navigate one of life’s most stressful and unavoidable responsibilities: settling an estate after someone dies. As CEO of EstateExec, he is leading an online software platform that gives executors step-by-step guidance, accounting tools, jurisdiction-specific tasks, and AI-supported help to make the estate settlement process more organized, understandable, and affordable.In this episode, Russ and Dan explore why estate settlement is so confusing for most people, especially when they are already dealing with grief. Dan explains how even highly capable people can feel overwhelmed by probate, legal requirements, asset tracking, creditor notices, tax filings, accounting reports, and family communication.They dive into how EstateExec helps executors understand what to do, when to do it, and how to keep everything organized. Dan shares how the platform customizes tasks based on state, jurisdiction, estate details, real property, wills, probate needs, and other variables that can dramatically change the process.The conversation also covers how EstateExec uses AI in practical ways, including its AI assistant Lenore, will analysis, heir and bequest extraction, task guidance, transaction categorization, and support for estate accounting. Dan explains why AI is helpful as a backstop, but why human-generated source content, legal accuracy, and transparent links back to underlying guidance remain essential.Along the way, Dan discusses common executor mistakes, the high cost of traditional probate support, the differences between U.S. and Canadian estate rules, and why clear communication can help prevent family conflict during a long and emotional process.Topics Covered:[00:00] Welcome and intro, Dan Stickel and EstateExec[00:29] What EstateExec does for estate executors[01:08] Why estate settlement has remained difficult for centuries[01:37] Dan’s personal experience settling his parents’ estates[02:18] How AI opened new possibilities for EstateExec[03:16] Using AI to unlock estate settlement guidance and documentation[05:42] What estate settlement looks like without software[06:03] Why the average estate can take around a year and a half[07:01] Key executor responsibilities after someone dies[08:05] Why hiring a probate attorney does not remove all the work[09:54] The four main ways people settle estates today[12:08] Common mistakes executors make during grief and confusion[14:41] How EstateExec moves beyond answering questions to managing the process[14:50] Lenore, EstateExec’s AI assistant[15:20] Customized task lists based on state, estate details, and probate needs[16:40] Using AI to analyze wills and extract heirs, executors, bequests, and assets[17:56] AI support for estate transactions and accounting categories[19:23] Managing different estate laws across states and provinces[21:33] Reducing hallucination risk by grounding AI in human-generated content[22:48] Expanding EstateExec into Canada[23:00] U.S. step up in cost basis versus Canadian deemed disposition[25:02] Typical time and cost involved in settling an estate[27:40] Why executors should not lose their own lives inside the process[29:19] Keeping heirs informed and reducing family conflict[29:39] How EstateExec can reduce legal fees and professional costs[32:41] User feedback and EstateExec’s Trustpilot rating[33:59] Helping non-lawyers and non-accountants understand the process[35:34] What surprised Dan about how people use the platform[37:05] Final thoughts on simplifying estate settlement for families

Bill Verhelle is helping small businesses access equipment financing faster, easier, and with less friction. As CEO of QuickFi, he is leading a company that won a 2026 BIG Innovation Award for its embedded lending platform, which allows small business borrowers to self-serve equipment leases and loans directly from a mobile device.In this episode, Russ and Bill explore how QuickFi is changing the traditional equipment finance model by placing financing directly at the point of sale. Bill explains why small businesses have lost direct access to many traditional banking relationships, how intermediaries add cost and complexity, and why embedded financing can create a better experience for borrowers, equipment sellers, manufacturers, and banks.They dive into how the QuickFi platform lets a business owner apply for financing, select loan or lease terms, sign documents, and complete the transaction from a mobile device. Bill shares how the system supports the full customer lifecycle, from initial credit application through servicing, buyouts, repeat purchases, insurance tracking, and long-term manufacturer relationships.The conversation also covers how QuickFi is using AI agents in practical, operational ways, including automating insurance certificate tracking, reading and drafting emails, contacting insurance companies, and reducing manual workflows across the lending process.Along the way, Bill discusses why banks have struggled to serve small businesses efficiently, how OEMs can use embedded financing to improve sales and customer loyalty, and why scalable digital platforms may help bring lower-cost capital back to the small business market.Topics Covered:[00:01] Welcome and intro, Bill Verhelle and QuickFi’s BIG Innovation Award win[00:39] QuickFi’s background and shift from traditional finance to a digital platform[01:30] Why small businesses struggle to access capital[01:53] How a small business borrower uses QuickFi to finance equipment[03:52] What embedded lending means in practical terms[04:03] Bringing financing directly to the point of sale[05:27] Why reducing friction matters for small business purchases[06:34] Servicing, buyouts, repeat purchases, and customer lifecycle support[07:44] How customers and manufacturers are responding to the platform[09:31] How recognition helped create market awareness[10:04] Using AI across the lending lifecycle[11:05] AI agents for onboarding, servicing, collections, and insurance workflows[12:46] Practical AI adoption versus surface-level AI tools[13:51] Why small and medium-sized businesses remain underserved by banks[14:50] How cost keeps banks from serving smaller loans efficiently[16:44] Lessons from Stripe and missed technology opportunities in banking[18:52] How OEMs rethink sales when financing is embedded[21:03] Expanding into new countries, languages, regulations, and currencies[23:01] Why end-to-end customer lifecycle control creates flexibility[23:29] Why bank adoption may be the next major growth milestone[24:32] Final thoughts on embedded finance and small business access to capital

Aaron Pribil and Todd Haley are helping legal teams, corporations, and government clients bring AI into one of the most sensitive business environments: e-discovery. As leaders at HaystackID, they are working at the intersection of legal data, governance, security, and artificial intelligence to help organizations manage massive volumes of information without sacrificing defensibility, accuracy, or control.In this episode, Russ, Aaron, and Todd explore how AI is changing the way legal teams review, classify, and understand complex data. Todd explains why AI in legal environments cannot be treated like a general purpose chatbot, and how closed, secure systems help reduce risk when working with court documents, financial records, health records, and other sensitive information.They dive into HaystackID’s CoreFlex platform, described as a command and control system that gives clients a front door into services, data, matters, users, and AI powered workflows. Aaron shares why the intake process matters so much, how HaystackID guides clients before data ever reaches an AI model, and why expert consultation is essential for better results.The conversation also covers validation, hallucination concerns, human in the loop workflows, and the role of HaystackID’s Legal Data Intelligence team. Todd and Aaron explain why governance and quality must come before speed, especially in regulated industries where errors can create legal, financial, and reputational risk.Along the way, they discuss the ROI of AI in e-discovery, including time savings, cost reduction, better case strategy, and improved risk management. They also offer practical advice for organizations building AI initiatives, including why companies should focus on a small number of high impact use cases before scaling more broadly.Topics Covered:[00:01] Welcome and intro, Aaron Pribil and Todd Haley of HaystackID[00:32] HaystackID’s background as a data services and products company[01:26] Why legal and government clients worry about AI validation[02:58] Handling sensitive data in e-discovery environments[03:42] Why closed AI systems differ from open AI tools[05:57] What CoreFlex means as a command and control system[07:12] Why the intake process improves AI quality[09:09] Why governance and quality come before speed[11:11] The role of HaystackID’s Legal Data Intelligence team[12:28] How consultative AI workflows save time and money[13:16] Addressing the black box problem in AI[14:51] Reducing legal review timelines with AI[16:34] Measuring ROI through time, cost, and risk reduction[17:54] Why legal teams are adopting AI faster than expected[20:23] Whether companies are approaching AI the right way[21:39] The shift toward structured, purpose built AI systems[23:42] How CoreFlex supports defensibility and compliance[24:45] Lessons for organizations building AI platforms[26:52] What is next for HaystackID and AI driven legal workflows

Simon Hodgkins is helping global brands solve one of the fastest growing business challenges of the AI era: how to create content at scale that still feels local, trusted, and accurate. As Chief Marketing Officer of Vistatec, he is leading a company that has spent nearly 30 years helping the world’s largest organizations translate, localize, and optimize content across languages, cultures, and markets.In this episode, Russ and Simon explore how the explosion of AI generated content has created both massive opportunity and new complexity for enterprises trying to grow globally. Simon explains why faster content creation alone is not enough, and how poor localization, inconsistent outputs, and weak governance can quickly damage trust, compliance, and brand reputation.They dive into how Vistatec combines AI with human expertise to help companies manage written content, video, audio, and multilingual customer experiences at enterprise scale. Simon shares how tools like AI dubbing, output verification, and workflow orchestration are helping brands expand into new markets faster while maintaining quality and control.The conversation also covers why many AI models still struggle outside major languages, the importance of in market linguistic experts, and how companies can avoid common mistakes when scaling customer facing content internationally.Along the way, Simon offers practical advice for leaders navigating global expansion, why collaboration with trusted AI integrators matters, and what the future of content transformation looks like as businesses move beyond translation into truly localized growth.Topics Covered:[00:01] Welcome and intro, Simon Hodgkins and Vistatec’s AI award win[00:33] Vistatec’s 30 year evolution from translation to global content solutions[02:01] The global content explosion in the AI era[03:15] Why AI adds both speed and new risk for brands[04:09] How translation used to be slow, expensive, and uncertain[05:08] Why trust, governance, and verification now matter more than ever[07:38] How Vistatec AI differs from traditional translation vendors[08:20] AI dubbing and multilingual voice content at scale[09:02] Verifying AI outputs for tone, style, and precision[10:12] Using AI to orchestrate humans and machines together[11:44] Moving from translation to content transformation[13:56] Protecting brand voice across dozens of languages[16:34] Managing compliance in regulated industries at scale[19:49] Why Vistatec sees AI as a growth enabler[20:33] How AI changes workflows for enterprise customers[23:27] Unlocking new markets through localization[27:20] Common mistakes companies make with AI content[30:27] Advice for leaders scaling global content strategies[32:28] Why language remains one of business’s biggest barriers

Lukas Haffer is helping solve one of the biggest problems facing small businesses: access to affordable capital. As founder and CEO of Casca, he is modernizing the outdated lending systems used by banks so small business owners can get loans faster, easier, and at dramatically lower costs.In this episode, Russ and Lukas explore why millions of small businesses still face a broken lending process. Traditional banks offer the best rates, but applications can take months and require endless paperwork. Meanwhile, predatory online lenders promise fast cash while charging crippling interest rates that can exceed 100 percent APR.They dive into how Casca helps banks compete by replacing manual workflows with AI powered software that streamlines everything from online applications to underwriting preparation. Lukas explains how their platform reads financial documents, organizes messy data, and helps bankers make faster, more informed decisions while keeping humans fully in the loop.Along the way, he shares the personal story that inspired the company, growing up in Germany during the financial crisis and seeing firsthand how financial systems impact families and communities. He also breaks down why community banks are uniquely positioned to help local businesses thrive if they have the right technology.The conversation also covers the future of AI in financial services, how smaller banks can modernize customer experience, and why empowering entrepreneurs may be one of the most important economic opportunities in America today.Topics Covered:[00:01] Welcome and intro, Lukas Haffer and Casca’s innovation award win[00:43] Casca’s mission to help banks lend faster to small businesses[02:00] Why traditional lending is too slow for many entrepreneurs[03:01] The dangers of predatory online loans and triple digit APRs[04:32] Why community banks need modern lending technology[05:28] The economic importance of small businesses in America[07:22] Lukas’ personal story and the origins of Casca[10:33] Early traction with community bank customers[11:40] Using AI to improve loan application completion rates[13:10] Raising conversions from under 10 percent to over 80 percent[15:40] Why underwriting is the true bottleneck in lending[16:31] Reading tax returns and financials with AI[18:52] Keeping humans in the loop for explainable decisions[20:36] Reducing loan timelines from months to weeks[22:31] Partnerships with top SBA lenders in the country[24:16] How quickly banks can implement modern software[25:36] The future of AI powered lending workflows[28:16] Where smaller banks most need to improve customer experience[29:14] Why modernizing legacy institutions mattersAbout The Winners Circle:The Winners Circle is hosted by Russ Fordyce, CEO and founder of Business Intelligence Group. Each episode features leaders and innovators recognized through BIG’s annual award programs across AI, innovation, sustainability, cybersecurity, and more. Subscribe wherever you get your podcasts. Nominate your company or team for a BIG award: https://www.bintelligence.com/

Dr Robert Roop is leading innovation in one of the most overlooked but essential industries in the world. As Chief Technology Officer at Axalta, he and his team are redefining what coatings can do, from improving durability and performance to enabling the next generation of electric vehicles, data centers, and sustainable manufacturing.In this episode, Russ and Robert explore how coatings go far beyond paint, playing a critical role in protecting surfaces, extending product life, and improving efficiency across industries. Robert explains how Axalta combines chemistry, material science, and customer insight to develop high performance solutions that meet evolving demands without compromising on sustainability.They dive into the company’s “no compromise” approach to sustainability, where performance and environmental impact improve together, not at the expense of one another. Robert shares how innovations like waterborne coatings, lower energy curing, and reduced material usage are helping customers cut emissions, lower costs, and improve outcomes simultaneously.The conversation also explores the growing role of coatings in electrification and AI driven infrastructure. From improving battery safety and thermal management in EVs to enabling more efficient data centers, coatings are becoming a critical layer in modern technology systems. Robert also breaks down how AI is accelerating innovation internally, helping teams move faster, solve problems globally, and scale solutions across markets.Along the way, he shares how Axalta maintains a culture of cross functional innovation, how the company evaluates long term bets around megatrends like electrification, and what it takes to consistently deliver real world, commercialized innovation at scale.Topics Covered:[00:01] Welcome and intro, Robert Roop and Axalta’s innovation award wins[00:31] Overview of Axalta and the coatings industry[01:49] Why coatings are more complex than paint[03:06] Customer driven innovation and solving real world problems[04:33] Managing diverse markets from automotive to industrial[06:07] What “no compromise” sustainability means in practice[07:45] Balancing performance, cost, and environmental impact[09:30] Energy efficiency and emissions reduction through coatings[10:44] Advances in material science and polymer chemistry[12:07] Real world examples of sustainability driving performance[13:18] The role of coatings in electric vehicles[15:28] Data centers, AI infrastructure, and thermal management[16:47] Safety considerations in high performance systems[18:16] Coatings in renewable energy and power systems[19:40] Building a culture of cross functional innovation[21:39] Delivering real world, commercialized innovation[22:10] Balancing short term and long term R&D investments[23:23] How AI is accelerating product development[25:00] The impact of robotics and automation on coatings[25:21] Identifying megatrends and preparing for the future

Lori Tompos is on a mission to transform how leaders show up, communicate, and bring out the best in their teams. As founder of Avail Consulting and a Horizon Award winner, she draws on experience from West Point, Desert Storm, and the corporate world to challenge one of the most ingrained habits in business: managing people instead of leading them.In this episode, Russ and Lori explore why so many organizations still struggle with ineffective leadership despite decades of books and training. Lori explains how most professionals are never truly taught how to lead, and how that gap leads to micromanagement, low trust, and high turnover.They dive into Lori’s core philosophy, lead people, manage projects, and what that actually looks like in practice. From building trust and psychological safety to asking better questions and tailoring leadership to individuals, Lori shares a fundamentally different approach rooted in empathy, accountability, and real human connection.Along the way, she reflects on her experience as one of the first women in combat during Desert Storm, how preparation and confidence shaped her leadership style, and what today’s leaders can learn from high stakes environments where trust and clarity are non negotiable. Lori also breaks down why people leave managers, not jobs, how AI is reshaping the workplace, and why high human skills like empathy, critical thinking, and communication matter more than ever.Topics Covered:[00:01] Welcome and intro, Lori Tompos and Horizon Award recognition[00:46] Lori’s background from West Point to corporate leadership[02:15] Why leadership training is still missing in most organizations[02:55] The universal truth: no one likes to be micromanaged[03:32] “Lead people, manage projects” explained[05:52] Leadership lessons from Desert Storm and real world stakes[09:01] Building confidence through preparation and expertise[10:40] Transitioning into corporate and leadership gaps[12:50] Lessons for navigating uncertainty and AI disruption[14:51] Why companies default to managing instead of leading[15:40] Psychological safety and why people leave managers[18:08] The mindset shift required for real leadership[20:45] Recognizing and investing in people[23:42] Growth mindset and learning from failure[24:25] Leadership in a post COVID and AI driven world[25:59] Crucial conversations and giving effective feedback[27:26] Why leaders think they are approachable when they are not[29:37] Reading culture and leadership signals in organizations[31:50] Progress and perspective on women in leadership[33:17] What younger generations understand about leadership[34:39] Advice for building leadership skills in the future

Vikram Venugopal is transforming how life sciences companies communicate by turning one of the most persistent business challenges into a competitive advantage. As the leader behind Prezent, he is helping organizations move beyond cluttered, ineffective presentations to clear, audience driven storytelling powered by AI.In this episode, Russ and Vikram explore why business communication, especially presentations, is so consistently broken despite being central to decision making. Vikram explains how most professionals are never trained to translate complex ideas into compelling narratives, and how that gap slows decisions, creates confusion, and ultimately delays impact in high stakes industries like healthcare.They dive into how Prezent’s AI powered platform flips the traditional workflow by starting with audience context, building “fingerprints” for how individuals consume information, and generating tailored presentations that resonate with decision makers. Vikram also breaks down how their purpose built AI goes beyond generic outputs to handle highly technical content like clinical data, while still simplifying it into clear, actionable stories.Along the way, they discuss the balance between automation and creativity, the role of human expertise in shaping narratives, and how better communication directly accelerates decision making and business outcomes. Vikram also shares insights on scaling communication quality across large organizations, the rise of AI assisted storytelling, and what it takes for teams to consistently deliver high impact presentations.Topics Covered:[00:01] Welcome and intro, Vikram Venugopal and Prezent’s award win[00:38] What Prezent does and its focus on life sciences communication[01:24] Why business communication and presentations are fundamentally broken[03:48] The biggest mistakes people make when building presentations[05:26] How communication quality impacts decision making speed[07:46] Moving from slide hunting to AI powered presentation creation[08:22] Building audience “fingerprints” to tailor communication[10:31] Adapting presentations for different stakeholders and roles[11:44] Using enterprise knowledge to improve storytelling[13:35] Why generic AI fails and the need for purpose built models[14:50] Where AI helps most and where humans still matter[16:42] Balancing automation with creativity and emotional storytelling[17:30] Measuring impact beyond time savings and cost reduction[18:50] Standardizing communication while preserving individual style[20:16] Helping presenters deliver stories effectively, not just build slides[21:59] The future of AI assisted business communication[23:34] Raising expectations and leveling the playing field with AI[24:50] Advice for leaders improving team communication[26:44] The biggest opportunity companies still overlook[28:59] The roadmap toward an AI powered communication agency

Steve Rotter is helping global organizations turn language from a bottleneck into a competitive advantage by treating translation as core infrastructure, not a side task. As Chief Marketing Officer at DeepL, he focuses on specialized AI models that deliver near human translation quality at scale, supporting everything from contracts and healthcare information to e commerce catalogs and live conversations.In this episode, Russ and Steve dig into why general purpose AI models struggle with nuance and context, and why DeepL has doubled down on domain specific models trained on years of proprietary data across more than 100 languages. Steve explains how this specialization leads professional translators to prefer DeepL in blind tests and why that matters when mistranslations can damage brands, misinform patients, or distort news.They explore how DeepL fits into real workflows, from quick “two box” translations and drag and drop document conversion to API integrations that keep products like Notion and large e commerce sites up to date in many languages in near real time. Steve shares how this turns months of manual work into a continuous, automated process that opens new markets faster and makes “borderless” business more realistic than ever.Russ and Steve also discuss trust, accuracy, and human in the loop review. Steve outlines DeepL’s layered approach, where low risk content is fully automated, higher stakes material is routed for targeted checks, and short, high impact assets like ad copy still rely on specialists. They then turn to DeepL Voice, real time multilingual meetings, and why latency and comprehension are just as critical as raw accuracy when people are trying to follow a live conversation.Topics Covered:[00:01] Welcome, intro, and DeepL’s AI award recognition[00:30] Steve’s background and why he joined an AI language company[01:02] Why language is a fundamental marketing and business challenge[01:40] Specialized language models versus general purpose LLMs[02:26] Blind tests and when translation quality becomes mission critical[03:26] Capturing nuance and context across 100+ languages[12:23] How customers use DeepL: text, documents, and APIs[13:46] Examples from Notion and large e commerce brands[15:39] Time to market and simultaneous multilingual launches[21:59] Human in the loop tiers from low risk to premium content[27:16] Borderless business and DeepL Voice in real time communicationAbout The Winners Circle:The Winners Circle is hosted by Russ Fordyce, CEO and founder of Business Intelligence Group. Each episode features leaders and innovators recognized through BIG’s annual award programs across AI, innovation, sustainability, cybersecurity, and more. Subscribe wherever you get your podcasts. Nominate your company or team for a BIG award: https://www.bintelligence.com/

Nithin Mummaneni is rethinking how enterprises negotiate high stakes contracts by turning years of procurement consulting experience into an AI powered “negotiation intelligence” platform. As cofounder and CEO of Infinity Loop, he is helping large organizations finally answer a deceptively simple question at scale: did we get a good deal or not, and how do we do better next time.In this episode, Russ and Nithin walk through how procurement and contract negotiation work today, with teams manually reading contracts, benchmarking market rates, and preparing RFPs across hundreds of millions or even billions of dollars in third party spend. Nithin explains how Infinity Loop ingests contracts, amendments, and rate sheets, then uses machine learning and macro data to score each deal like a report card, highlight where companies are overpaying, and recommend specific negotiation strategies to improve both price and risk.They dig into why Infinity Loop stops short of letting bots negotiate million dollar plus deals and instead focuses on augmenting human negotiators. Nithin shares how automating the prep and analysis can free procurement managers to handle two to three times more deals, avoid expensive consulting engagements, and design better protections into contracts, such as index based pricing or caps that could have softened shocks like the tenfold spike in ocean freight costs during COVID.Russ and Nithin also explore the trust and change management required to introduce AI into a sensitive, bottom line critical domain. Nithin explains how Infinity Loop acts as a middleware intelligence layer integrated with tools like Coupa, SAP, Oracle, DocuSign, and Ironclad without touching redlining or existing workflows, and how transparent pilots and clear ROI help win over both security teams and procurement staff who may initially fear that automation threatens their roles.Along the way, Nithin shares lessons from building a lean, fast moving team in a space dominated by large incumbents, including why speed of iteration and deep domain expertise matter more than trying to automate everything. He outlines Infinity Loop’s roadmap to expand across more industries and categories, and why he believes negotiation intelligence is one of the most underexploited frontiers for AI driven margin improvement.Topics Covered:[00:01] Welcome, Infinity Loop intro, and AI Excellence Award recognition[01:15] Turning “did we get a good deal” into a data problem[02:03] How enterprise procurement and contract negotiations work today[02:51] Why prep and benchmarking consume most procurement time[04:14] Integrating with RFP, P2P, and legal systems as an intelligence layer[06:47] How Infinity Loop scores contracts and surfaces savings opportunities[09:04] Improving outcomes beyond price with better contract risk design[11:41] How AI changes the role and impact of procurement teams[12:10] Democratizing consulting grade expertise through software[13:01] Building trust: security, data protection, and enterprise diligence[15:03] Competing as a lean, fast moving team in an entrenched space[16:02] From concept to enterprise deployment and proving ROI[17:22] Why negotiation intelligence is a largely untapped AI frontier[20:07] Roadmap and expanding across more industries and categoriesAbout The Winners Circle:The Winners Circle is hosted by Russ Fordyce, CEO and founder of Business Intelligence Group. Each episode features leaders and innovators recognized through BIG’s annual award programs across AI, innovation, sustainability, cybersecurity, and more. Subscribe wherever you get your podcasts. Nominate your company or team for a BIG award: https://www.bintelligence.com/

Faruk Capan is rethinking how pharmaceutical companies bring products to market by rebuilding the entire commercialization model around AI. As Chief Innovation Officer at Eversana, he is leading the development of an AI powered platform that replaces slow, manual, and highly regulated workflows with a faster, more scalable system driven by intelligent agents and human expertise.In this episode, Russ and Faruk explore why traditional pharma marketing and commercialization can take months to execute and how Eversana’s AI agency model is cutting that timeline down dramatically. Faruk explains why simply adding AI tools to existing workflows is not enough, and why his team took a startup approach to rebuild processes from the ground up with an 80 percent AI and 20 percent human model.They also dive into how the platform orchestrates the entire lifecycle from strategy and content creation to compliance and activation across channels, while maintaining strict regulatory standards. Faruk shares how partnering with Google enables enterprise grade security and scalability, why explainability and trust are critical in healthcare, and how AI is unlocking true personalization in an industry that has historically struggled to deliver it.Along the way, he discusses the real business impact including faster speed to market, reduced costs, and fewer people required per project, as well as the organizational challenges of driving AI transformation at scale. Faruk also offers practical advice for leaders on where to start, why pilots often fail, and how to balance innovation with real business accountability.Topics Covered:[00:01] Welcome and intro, Faruk Capan and Eversana’s AI award win[00:44] The role of commercialization in pharma and why it is so complex[02:03] Compliance challenges and why traditional workflows take months[03:45] Why AI requires rethinking the business, not just adding tools[04:25] Taking a startup approach to rebuild workflows from scratch[05:56] The 80 percent AI and 20 percent human in the loop model[08:25] Building an end to end AI platform from strategy to execution[10:01] Orchestrating campaigns across channels with AI agents[11:57] Partnering with Google for security, scale, and compliance[14:25] Explainability and avoiding black box AI in healthcare[15:47] Measurable impact including speed, cost savings, and efficiency[17:14] Unlocking true personalization in pharma marketing[17:58] AI generated doctor avatars and patient acceptance[20:04] Balancing automation with human trust in healthcare[21:04] Driving organizational change and leadership buy in[23:26] Why hands on AI adoption matters more than theory[24:16] Prioritizing innovation with real business accountability[26:11] Advice for leaders on AI strategy and avoiding pilot traps[28:43] Scaling the platform and iterating at startup speedAbout The Winners Circle:The Winners Circle is hosted by Russ Fordyce, CEO and founder of Business Intelligence Group. Each episode features leaders and innovators recognized through BIG’s annual award programs across AI, innovation, sustainability, cybersecurity, and more. Subscribe wherever you get your podcasts.Nominate your company or team for a BIG award: https://www.bintelligence.com

Jim Straus is helping power the next wave of semiconductor innovation by improving one of the most critical and overlooked parts of chip manufacturing: the process. As Head of Sales and Service at ACM Research, Jim works with leading chipmakers around the world to increase yield, reduce waste, and unlock more production from every wafer through advanced wet cleaning and electroplating technologies.In this episode, Russ and Jim explore how ACM Research fits into the global semiconductor ecosystem, not by building chips themselves, but by designing the highly complex equipment and processes that make modern chip production possible. Jim explains how their technology integrates mechanical, chemical, and software systems to deliver measurable improvements in yield, and why that has been the foundation of their rapid growth.They also discuss the massive demand being driven by AI, the increasing complexity of chip design, and how innovations like panel level packaging could reshape the economics of semiconductor manufacturing. Jim breaks down how moving from traditional round wafers to larger square panels can dramatically reduce waste and costs while increasing output, and why this transition presents both a major opportunity and a significant industry challenge.Along the way, Jim shares insights on sustainability in semiconductor manufacturing, the importance of aligning roadmaps with customers, and why close collaboration across the ecosystem is essential to keeping up with rapid development cycles. He also highlights ACM’s expansion in the United States and what it will take to bring more semiconductor manufacturing and innovation back home.Topics Covered:[00:01] Welcome and intro, Jim Straus and ACM Research’s award win[00:36] What ACM Research does in the semiconductor manufacturing ecosystem[02:00] Global growth, operations in Asia, and expansion into the US and Europe[03:22] How wet clean technology impacts every step of chip production[04:24] AI driven demand and the global importance of semiconductor supply[05:43] Innovations in wafer level and panel level packaging[07:21] Why moving to square panels can reduce cost and increase efficiency[08:46] Sustainability efforts in reducing chemicals and waste[10:00] Ideal customers from large chipmakers to specialized innovators[11:00] Breakthroughs in electroplating and improving uniformity[12:17] How customers measure yield improvements from ACM technology[13:25] Accelerating product development cycles in the AI era[14:28] Investing in US manufacturing and R&D capabilities[16:16] The importance of co creation and aligning with customer roadmaps[17:24] Challenges ahead with panel level packaging adoption[18:51] Advanced cleaning techniques without damaging wafer structuresLinks and Resources:ACM Research: https://www.acmrcsh.comBusiness Intelligence Group: https://www.bintelligence.comBIG Awards for Business: https://www.bintelligence.com/awards/big-awards-for-businessAbout The Winners Circle:The Winners Circle is hosted by Russ Fordyce, CEO and founder of Business Intelligence Group. Each episode features leaders and innovators recognized through BIG’s annual award programs across AI, innovation, sustainability, cybersecurity, and more. Subscribe wherever you get your podcasts.Nominate your company or team for a BIG award: https://www.bintelligence.com

Amol Waishampayan, Chief Product Officer at Full Throttle AI, is helping mid market brands and agencies move beyond click based attribution and walled gardens to real, outcome driven advertising on the open internet. In this episode, Russ and Amol explore how Full Throttle AI combines cookieless, household level identity, omni channel activation, and AI powered measurement so marketers can see which campaigns actually sell more cars, services, and high consideration products instead of just driving cheaper clicks.They discuss why over investing in Google and Meta has become a race to the bottom, how signal loss and privacy changes exposed the limits of legacy ad tech, and how Full Throttle’s patented approach turns anonymous web behavior into first party household intent that marketers can segment and act on. Amol explains where agentic AI really shows up under the hood, how the platform uses models to interpret URLs, score propensity, and price bids, and why human strategy still matters in deciding audiences, offers, and guardrails. He also shares practical examples from automotive and home services, lessons from building a cookieless platform back in 2018, and the mistakes teams make when they chase AI enabled widgets instead of truly AI powered architecture.Topics Covered:[00:01] Welcome and intro, Amol Waishampayan and Full Throttle AI’s award win[00:35] Full Throttle AI’s mission to be the “easy button” for the open internet[01:30] Serving mid market brands and agencies that are underserved by legacy ad tech[02:00] Why click based attribution pushed spend into Google and Meta and broke media mix[03:30] Seeing the full journey from CTV, display, and mail to search, social, and in store sales[05:25] The real problems mid market marketers and agencies are trying to solve today[08:50] Building a cookieless, household based identity approach before third party cookies faded[11:30] Why household level measurement beats hashed emails for big ticket, multi decision purchases[16:20] Making “spray and pray” direct mail obsolete by focusing only on in market households[18:10] Using AI to interpret URLs, infer product interest, and score propensity in real time[22:30] Why AI should be the hygienist and marketers the dentist in always on campaigns[24:10] Automotive and service case studies driving more leases, ROs, and lifetime value[29:50] What surprised Amol about self service vs managed service usage on the platform[34:50] Common AI mistakes and why AI powered architecture plus human intelligence winsLinks and Resources:Full Throttle AI: https://www.fullthrottle.aiBusiness Intelligence Group: https://www.bintelligence.comBIG Awards for Business: https://www.bintelligence.com/awards/big-awards-for-businessAbout The Winners Circle:The Winners Circle is hosted by Russ Fordyce, CEO and founder of Business Intelligence Group. Each episode features leaders and innovators recognized through BIGs annual award programs across AI, innovation, sustainability, cybersecurity, and more. Subscribe wherever you get your podcasts.Nominate your company or team for a BIG award: https://www.bintelligence.com

Nick Reed, Chief Strategy Officer at Bizzdesign, helps large enterprises turn complex, siloed organizations into coherent, change ready systems that can actually deliver on their strategies. In this episode, Russ and Nick unpack Bizzdesign’s three way merger that tripled the size of the company, how their transformation suite bridges the strategy to execution gap, and why AI and agentic systems are forcing leaders to rethink architecture, governance, and speed.​They discuss how Bizzdesign’s platform connects scattered data into living enterprise models so teams can see impacts and dependencies, avoid transformation “oh no” moments, and tame AI sprawl before costs and risk explode. Nick explains the three pillars of transformation planning, design, and governance, how a new collaboration layer brings sticky note workshops and expert architecture tools together, and why most transformations fail at the very start through misalignment, slow mobilization, and hidden landmines. He also shares what it took culturally and technically to integrate three companies, why customer centricity guided every decision, and how enterprises can use AI experiments plus better model context to get from idea to value faster without losing control.​Topics Covered:[00:01] Welcome and intro, Nick Reed and Bizzdesign’s Big Awards for Business win​[00:25] Tripling the company by merging with two former competitors​[01:15] Discovering enterprise architecture as a way to manage complexity and silos​[03:00] How Bizzdesign’s software builds connected models of the enterprise​[05:20] Pandemic, regulation, and AI increasing pressure on transformation speed​[07:20] AI sprawl, shadow tools, and why portfolio visibility matters for value and risk​[10:45] Blending architecture, strategy, and governance in an AI and agentic world​[14:15] Eating their own cooking during a three way merger and culture integration​[16:30] Moving to a solutions based portfolio and improving global customer coverage​[18:20] The three pillars of transformation planning, design, and governance​[21:20] Connecting workshops and whiteboards to governed enterprise models​[22:40] Why most transformations fail at the start and how to avoid common traps​[27:50] Where AI and agents can 10x transformation speed and architect impact​[31:40] Examples from energy, automotive, and professional services transformations​[34:10] Customer centricity as the guiding principle for Bizzdesign’s own transformation​Links and Resources:Bizzdesign: https://www.bizzdesign.comBusiness Intelligence Group: https://www.bintelligence.comBIG Awards for Business: https://www.bintelligence.com/awards/big-awards-for-businessAbout The Winners Circle:The Winners Circle is hosted by Russ Fordyce, CEO and founder of Business Intelligence Group. Each episode features leaders and innovators recognized through BIGs annual award programs across AI, innovation, sustainability, cybersecurity, and more. Subscribe wherever you get your podcasts.Nominate your company or team for a BIG award: https://www.bintelligence.com