3453: How Wolters Kluwer Is Building Trustworthy AI for Regulated Industries

What happens when an AI strategy meets the real-world complexity of healthcare, law, and finance? That’s the challenge at the heart of my conversation with Mark Sherwood, CIO of Wolters Kluwer, a global leader in professional information services. With over three decades in technology leadership across Microsoft, Symantec, and Nuance, Mark brings a rare combination of enterprise depth and hands-on pragmatism to the AI discussion. Mark explains why cloud-native architecture and data governance are the twin foundations of trustworthy AI. He shares how Wolters Kluwer is embedding AI across highly regulated industries—from helping doctors access life-saving insights through natural language queries to giving tax and legal professionals faster, more accurate guidance on complex regulations. Behind the innovation lies a disciplined approach: governing data, managing risk, and building confidence in AI systems that must meet the highest standards of accuracy and compliance. We also explore how to build high-trust, low-friction partnerships between IT and business teams to prevent shadow IT while accelerating digital transformation. Mark offers candid insights into the rise of AI agents, the emerging risks of quantum security, and why he believes that high-quality data is the most valuable currency in digital transformation. His philosophy is simple: speed means nothing without trust, and trust starts with clean, well-governed data. From cloud transformation to the future of AI regulation, this episode offers a grounded look at how global enterprises can scale responsibly in an era where innovation often outruns policy. So as AI becomes inseparable from how professionals think and work, how do we balance speed with stewardship? And are we truly ready for the ethical, technical, and quantum frontiers ahead? Share your thoughts after the episode.

What does it take to build AI that enterprises can actually trust? That’s the question I explored with Nirankush “Kush” Panchbhai, Senior Vice President of Platform Fundamentals at ServiceNow, in a conversation about AI governance, human-centered design, and how the company’s AI Control Tower is reshaping enterprise adoption. Kush describes the AI Control Tower as an “air traffic controller” for AI agents, a central command center that provides visibility, accountability, and governance across every part of an organization’s AI ecosystem. It embeds compliance, legal, and risk workflows directly into the development process, replacing endless approval cycles with automated guardrails that accelerate innovation rather than slow it down. The result is a system where humans remain firmly in control, supported by transparent, explainable AI that acts as a teammate rather than a tool. We also discuss how ServiceNow is helping enterprises move beyond the “POC palooza” of pilot projects that never scale. By treating AI agents as members of a digital workforce—with performance tracking, retraining, and measurable ROI—companies can finally connect investment to real outcomes. Governance, in this context, isn’t a constraint; it’s a catalyst for confidence and adoption. At its core, ServiceNow’s philosophy is about taking the work out of work, not the human out of work. From password resets to process automation, AI is freeing employees to focus on creative, high-value problem-solving while building trust through transparency and accountability. As organizations begin managing both human and digital workforces, one question lingers: can AI governance truly become the accelerator that turns trust into enterprise-scale transformation? And what does it take to ensure AI always serves people, not the other way around? Share your thoughts after the episode.

What if the biggest sustainability challenge in tech isn’t hardware or cloud emissions, but the invisible mountain of unused data sitting in storage? That’s the question driving my conversation with Piero Gallucci, Vice President and General Manager for NetApp UK and Ireland, as we discuss how single-use data is quietly shaping the environmental and financial footprint of enterprise IT. Piero explains that 38 percent of stored data is never used again, yet it continues to consume energy and resources indefinitely. He describes how this digital hoarding—often driven by regulatory caution and the overvaluation of data—has become one of the most overlooked contributors to emissions in modern infrastructure. With the rise of AI accelerating data growth by an estimated 50 percent, the challenge is no longer simply about capacity but responsibility. Through examples such as Aston Martin Formula One and the NFL, Piero outlines how NetApp is helping organizations identify unused data, automate lifecycle policies, and design intelligent, energy-efficient infrastructure that supports both innovation and sustainability. We also explore the tension between AI adoption and environmental impact. As enterprises rush to train new models, Piero argues that smarter data governance, not bigger datasets, is the key to sustainable AI. He highlights the importance of educating teams on the true cost of data—both financial and environmental—and why leaders must build intentional strategies that align performance with purpose. NetApp’s vision is clear: make data management as sustainable as it is powerful. But as AI reshapes how we store and use information, can the tech industry finally balance digital growth with environmental stewardship? And what would your company look like if every byte of data had to justify its existence? Share your thoughts after the episode.

What if the next big leap in business AI isn’t generative at all, but predictive? That’s the question at the heart of my conversation with Zohar Bronfman, CEO and co-founder of Pecan AI, a company helping business teams forecast outcomes with precision and turn historical data into future insights. Zohar explains why he believes predictive AI will deliver far greater enterprise value than the generative models dominating headlines. He points to research showing that most generative AI projects fail to produce ROI, while predictive systems built on a company’s own data can directly improve revenue, reduce churn, and guide smarter decisions. With Pecan’s no-code platform, marketing and operations teams can now create predictive models without needing data scientists—bridging the gap between technical expertise and business execution. Through stories like Little Spoon’s, a direct-to-consumer baby food brand that used Pecan AI to identify and retain at-risk customers, Zohar illustrates how predictive analytics turns data into real business impact. He also shares common mistakes companies make when implementing AI—starting with unclear objectives and misaligned resources—and why success depends on defining the problem before choosing the tool. Looking ahead, Zohar envisions predictive AI as the backbone of every organization, shifting business intelligence from reactive analysis to proactive action. As companies move beyond dashboards and toward dynamic decision-making, predictive insights may soon become as fundamental as spreadsheets. So, if your company could anticipate every challenge before it happened, how different would your strategy look? And are business leaders finally ready to treat predictive AI as core infrastructure rather than a passing trend? Share your thoughts after the episode.

What happens when artificial intelligence meets the everyday heroes of local government? That’s the question driving my conversation with Justin Dennis, co-founder and COO of Urban SDK, a geospatial AI company helping more than 250 North American cities make faster, safer, and smarter decisions. Justin shares how a Smart Cities Challenge from the U.S. Department of Transportation inspired him to co-found Urban SDK in 2018, and why he believes the future of public safety depends on replacing manual data collection with real-time intelligence. From traffic fatalities to hurricane recovery, he explains how the company’s HALO platform gives local leaders and emergency responders the insights they need to act before crises escalate. In a single platform, they can identify dangerous road zones, predict high-risk intersections, coordinate clean-up operations, and rebuild infrastructure based on data rather than guesswork. We also explore how AI is quietly reshaping government operations, from disaster management to traffic enforcement. Justin discusses the challenges of introducing cutting-edge technology into systems that still rely on spreadsheets and siloed workflows. Yet his optimism is clear. He believes governments are beginning to embrace AI not as a buzzword but as a practical tool to save time, resources, and lives. As one Florida community recently reported a 40 percent drop in traffic fatalities, the impact is already measurable. Urban SDK’s story is about technology meeting public service with purpose. So as we enter another year of rapid AI progress, how can data-driven insights continue to empower local leaders to protect citizens and improve quality of life? And what could your city achieve if every decision were powered by real-time intelligence? Share your thoughts after the episode.

Agentic AI is only interesting when it leaves the lab and takes responsibility for real outcomes. In this episode, I reconnect with Raj Koneru, Founder and CEO of Kore.ai, to talk about what that shift looks like inside large enterprises. Raj has been building conversational systems long before chatbots became dinner table conversation, and he is clear about where the action is now. Understanding intent is table stakes. The next frontier is planning, reasoning, and executing tasks through agents that can work across departments, respect policies, and prove their value in minutes, not months. We walk through how Kore.ai frames the stack. There is a layer below the line, where chips, data centers, clouds, and model providers keep advancing at speed. Above the line is where business value shows up. That is where companies design, deploy, and manage agents for customer service, employee productivity, and process automation. Raj describes Kore.ai as the operating system for that upper layer. The goal is simple to say and hard to deliver. Let teams build agents without writing code, plug into whichever models and clouds they prefer, and keep control through governance, security, and measurement. Interoperability runs through this conversation. Kore.ai partners with Microsoft, AWS, and G42’s Inception so customers can pick the environment that suits them. Vendor lock-in is a real anxiety for leaders. Raj’s answer is to be model agnostic, data agnostic, and cloud agnostic, while conforming to open standards. That way, enterprises spend their energy on the agents themselves rather than on maintaining a platform. It is a pragmatic view that reflects what I keep hearing on conference floors. Scale brings its own lessons. At Kore.ai’s volume, latency, accuracy, and security are non-negotiable, and governance is a daily practice rather than a slide in a deck. Raj talks candidly about no-code democratization, where a business user can assemble an agent for a focused task, then graduate to more complex workflows when ready. We also touch on the rise of on-device models in smartphones and where the boundary sits between quick local tasks and heavier actions that still rely on hosted models. If you care about agentic AI that does real work, this one is worth your time. Raj shares a thoughtful take on collaboration, standards, and why leaders should read widely, question everything, and start building with what is available today. Links are in the show notes.

In this episode, Mike Baker, Vice President and Global CISO at DXC Technology, says the cyber industry has been focusing on the wrong side of AI. He believes too many companies use it only to block threats instead of studying how criminals use it to scale phishing, bypass defenses, and deploy adaptive malware. Attackers are learning faster than ever, and security teams must catch up. Mike argues that defenders need to think differently and use AI as both protection and opportunity. He shares how DXC is already doing this. The company has brought autonomous AI agents into its security operations through a partnership with 7AI. These agents process alerts that used to require hours of human effort. The result is faster detection, less burnout, and more time for analysts to investigate real threats. By cutting manual work by more than eighty percent, DXC has shown how AI can make cybersecurity teams stronger, not smaller. Zero Trust remains a core part of DXC’s strategy. Mike calls it a journey that never ends. It needs cultural change, constant learning, and leadership that keeps security invisible to end users. AI now plays a role here too, improving identity checks and spotting access issues in real time. Yet, he reminds us, AI still needs people in the loop for oversight and judgment. We also talk about supply chain risks. Too many companies still treat risk assessments as one-time tasks. Mike pushes for continuous monitoring and close collaboration with suppliers. He closes the conversation on a hopeful note. AI will not replace people in cybersecurity, he says. It will make their work more meaningful and more effective if used with care and common sense.

What happens when the future of teamwork collides with the power of AI? That’s the question at the heart of this episode as Tiffany from Atlassian joins me from Barcelona during Team 25, where Atlassian is showcasing how AI-powered collaboration is redefining how work gets done. We talk about how Atlassian’s mission to unleash the potential of every team is coming to life through its bold decisions, from sunsetting data center products to expanding its multi-cloud partnerships with Google. Tiffany offers a front-row view of how Atlassian’s evolving cloud platform is designed to help customers work smarter while enabling secure, scalable innovation across some of the world’s most complex enterprises. The conversation also uncovers the thinking behind the teamwork graph, Atlassian’s powerful data intelligence layer that connects billions of work objects to create truly personalized AI experiences. Tiffany shares how companies like Royal Caribbean and Mercedes-Benz are already seeing measurable performance gains and how AI is becoming a real teammate that unifies knowledge, connects tools, and drives better outcomes. We discuss what it means to build a “system of work,” why flexibility and context matter, and how Atlassian’s open approach allows teams to build custom systems tailored to their own culture and workflows. Beyond the technology, this is a story about continuous learning, adaptability, and human-centered progress. Tiffany’s reflections on learning from the toughest customers, embracing change, and reimagining the browser as an active workspace reveal how Atlassian is blending AI with empathy and purpose. As AI becomes inseparable from teamwork, what steps will you take to unleash what your team can do next? I’d love to hear your thoughts.

As AI tools race into every corner of software development, a simple question keeps coming back to me. Will AI replace human testers, or will it force us to rethink what great testing looks like in the first place. In today’s conversation, I talk with Santiago Komadina Geffroy, a Software Engineer at Jalasoft and an educator with Jala University, about what changes, what stays, and what teams should do next. Santiago shares how his day job and teaching intersect. He points to a gap he sees often. Engineers are experimenting with large language models without fully understanding how they work, which leads to overconfidence and avoidable rework. He argues for clearer interaction patterns between tools and people. Think less about magic prompts and more about protocols, context sharing, and agent to agent collaboration. That shift frees testers to do the thinking work that AI still struggles with, from exploratory testing and usability judgment to spotting the weird edge cases that only show up when real humans use real products. We also get into bias and ethics. AI is only as fair as the data it learns from, and that matters in healthcare, finance, and hiring where a mistake can carry life changing consequences. Santiago calls for stronger education around data quality, authorship, privacy, and environmental impact, not as a side note but as part of how engineers are trained. He believes governance helps teams move faster with fewer regrets when they take AI into production. Security sits in the mix too. Many AI tools need deep system access. If compromised, they can distort results or leak sensitive information. Santiago is candid about the limits of any single safeguard. He recommends a culture of shared responsibility where engineers understand when to call in security specialists and how to design workflows that keep humans in the loop for consequential decisions. We close with what Jalasoft has learned from building with AI inside a nearshore model in South America. More thinking time. Smaller, controllable scopes. Clear lines between routine automation and human judgment. The headline is simple. AI will change testing. Human testers will remain at the heart of quality.

Here’s the thing. Most of us still picture a hotel lobby with a counter, a queue, and someone typing furiously while we wait after a long flight. In this episode, I sit with Richard Valtr, founder of Mews, to ask whether that scene is quietly fading. Backed by Tiger Global, Goldman Sachs, and Battery Ventures, Mews recently raised 75 million dollars to scale an AI-powered platform that already processes more than 10 billion dollars in payments each year. Richard argues the real bottleneck in hospitality isn’t software. It’s mindset. If hotels rethought workflows around guests rather than systems, the front desk would feel less like a checkpoint and more like a welcome. Richard shares the origin story of building for hoteliers as well as guests, and why the property management system should function like a central nervous system. He explains how automation handles the repetitive pieces of check-in so staff can actually look people in the eye and start a conversation. That’s the promise of AI here. Not gimmicks, but orchestration across bookings, payments, inventory, and service so the boring parts disappear into the background and the human parts come forward. We also talk about underused tech. Richard uses a memorable comparison for many hotel platforms that have Ferrari-level capability but get driven like Volvos. The data is there. The intent to serve is there. What’s missing is the leadership confidence to rewire the stack, measure outcomes, and keep pushing. When that happens, hotels stop thinking only in terms of rooms and start monetizing the full journey. Daybeds, coworking passes, last-mile upgrades, spa time after back-to-back meetings. AI can surface the right offer at the right moment without turning the experience into a sales pitch. By the end, Richard paints a picture of hospitality where screens fade, transactions happen on the guest’s time, and every interaction feels more personal precisely because the admin has been taken out of the way. If you want a grounded view of how AI will change hotels without stripping away the reason we love staying in them, this conversation is a helpful place to start.