How Dwelly Is Rebuilding The Rental Market With AI

What does it actually take to rethink the endpoint in a world shaped by AI, Zero Trust, and the growing convergence of IT and operational technology? Recording live from IGEL Now and Next in Miami, I sat down with Matthias Haas to unpack what he describes as a genuine transformation moment for enterprise computing. This wasn't a conversation about incremental change. It was about challenging long-held assumptions around devices, security models, and how work is delivered in modern organizations. Matthias shared how the idea of the "adaptive secure desktop" is moving beyond traditional thinking around VDI and desktop delivery. Instead of treating endpoints as static devices, the focus is shifting toward dynamic, context-aware environments that respond to who the user is, where they are, and what they need access to in that moment. It raises an important question for any organization. Are we still designing for devices, or for outcomes? We also explored the growing complexity that comes with flexibility. With multiple ways to deliver applications across SaaS, DaaS, browsers, and local environments, there's a real risk of recreating the same fragmented systems companies are trying to move away from. Matthias offered insight into how orchestration, policy enforcement, and centralized management can help bring order to that complexity without adding friction for users. Another key theme was the shift from static security models to continuous, contextual decision-making. As organizations move toward Zero Trust, the ability to evaluate risk in real time becomes essential. But that raises a delicate balance. How do you strengthen security without slowing people down? And how do you ensure that the user experience doesn't become the casualty of tighter controls? The conversation also touched on the challenges of bringing IT and OT environments together. While the opportunity to unify these worlds is significant, the realities are far more complex. Different risk tolerances, legacy systems, and operational priorities all come into play. Matthias offered a candid perspective on what it will take to make that convergence work in practice, not just in theory. So as enterprises continue to rethink their infrastructure in an AI-driven world, one question keeps coming up. Are we simply layering new technology onto old models, or are we ready to fundamentally change how the endpoint fits into the bigger picture? What do you think, are organizations truly ready to embrace adaptive, context-driven computing, or are we still holding on to outdated ways of working?

How do you rebuild an entire industry that most people accept as slow, fragmented, and frustrating? In this episode, I sit down with Dan Lifshits, co-founder of Dwelly, to explore how AI is being used to rethink the rental market from the inside out. What struck me most in this conversation is how Dwelly isn't approaching property management as a software layer you simply bolt on. Instead, they are acquiring rental agencies and rebuilding the operating model itself, embedding AI into every workflow, from tenant communication to maintenance coordination and rent collection. It is a very different mindset, and one that challenges how many businesses think about digital transformation. Dan brings a fascinating perspective shaped by his time competing in high-growth environments at companies like Uber and Gett. We talk about what those years taught him about scaling complex, operational businesses and how those lessons now apply to one of the largest and least digitized sectors in the economy. There is a clear parallel between ride-hailing and rentals, both are fragmented, both rely on two-sided marketplaces, and both have historically depended on manual processes that struggle to scale. As Dan explains, "long-term residential rentals ticks very similar boxes" to ride-hailing, which makes it ripe for reinvention. We also spend time unpacking what an AI-powered rollup actually means in practice. This is where the conversation becomes particularly interesting for founders and business leaders. Rather than selling software into traditional businesses and hoping for adoption, Dwelly takes control of both the operations and the technology. That allows them to redesign workflows, remove bottlenecks, and deliver a more consistent experience for landlords and tenants alike. The result is a model where a single operator can manage hundreds, even thousands, of properties with a level of service that would have been impossible just a few years ago. Of course, there are bigger implications here too. If this model works at scale, it raises questions about how many other service industries could be rebuilt in a similar way. It also highlights the growing role of venture-backed rollups, particularly with firms like General Catalyst backing this approach as a new investment category. But it is not without challenges. Changing operational behavior, integrating acquisitions, and maintaining service quality while scaling fast are all complex problems that cannot be solved by technology alone. This episode left me thinking about where the real value in AI sits. Is it in the tools themselves, or in the willingness to rethink how a business actually operates? And if AI can transform something as established as property management, which industries are next in line for the same kind of reinvention? I would love to hear your thoughts. Are AI-powered rollups the future of service industries, or do they introduce a new set of risks we are only beginning to understand?

How are businesses supposed to grow when technology is moving faster than regulation, customer expectations keep shifting, and AI is changing the rules in real time? In this episode, I sat down with Derya Matras, Vice President of EMEA at Meta, to talk about what growth really looks like for businesses operating in Europe, the Middle East, and Africa right now. This was a fascinating conversation because it went far beyond the usual talking points around AI and advertising. Derya brought a broader view of the pressure many businesses are under today, from macroeconomic uncertainty and political complexity to changing consumer behavior, tighter margins, and the need to adapt to a world where AI is now part of everyday decision making. What really stood out to me was her point that this moment is about far more than adopting new tools. It is about culture, leadership, and having the discipline to know what you are actually trying to achieve. Derya spoke about the importance of having a clear North Star goal, getting the foundations right, and making sure businesses are not simply adding AI into broken systems or unclear strategies. Because as she put it, AI can make everything more powerful, but it can also amplify mistakes. That is such an important point, especially at a time when so many companies are racing to show they are doing something with AI without always knowing what success should look like. We also explored how Meta sees its role in supporting growth across Europe's digital economy. Derya shared insights into how Meta's platforms are helping businesses of all sizes reach customers in ways they simply could not do on their own. For large companies, that may mean better measurement, faster optimization, and more personalized engagement. But for smaller businesses, the stakes can be even higher. She shared examples that brought those numbers to life, including entrepreneurs using Instagram and WhatsApp to reach global markets, support their families, and create jobs in ways that would have been out of reach just a few years ago. Another part of the conversation I found especially interesting was the tension between innovation and regulation in Europe. Derya was honest about how complicated and fragmented the environment has become, and how that complexity can slow progress or delay the rollout of new products. At the same time, she made a strong case that Europe still has a real opportunity ahead if it can find the right balance. That balance matters not only for big tech companies, but for startups, small businesses, creators, and the wider economy that increasingly depends on digital tools to compete and grow. We also talked about creativity, measurement, AI assistants, wearables, and even how these technologies are beginning to shape life at home as much as at work. It all made for a conversation that felt very current, but also deeply practical. So as AI becomes woven into advertising, business operations, and everyday life, are organizations truly building the foundations they need to benefit from it, or are they still chasing the next shiny thing? And what do you think Europe needs to get right to make sure innovation and opportunity can keep moving forward?

What happens when AI ambition starts moving faster than the infrastructure built to support it? In this episode, I spoke with Lee Caswell, SVP of Product and Solutions at Nutanix, about the latest Enterprise Cloud Index and what it tells us about where enterprise IT really is right now. There is no shortage of AI headlines, product launches, and promises about what comes next, but this conversation gets behind the noise and into the operational reality that many business and technology leaders are now facing. As Lee explained, AI is not arriving in isolation. It is pulling containers, data strategy, hardware decisions, governance, and application modernization along with it. One of the biggest themes in our conversation was the growing link between AI workloads and container adoption. Lee made the point that applications still sit at the top of the org chart, and infrastructure exists to serve them. As more AI-enabled applications are built by developers who favor containers and Kubernetes-based environments, enterprises are being pushed to rethink how they support those new workloads. We talked about why containers are becoming such an important part of modern application strategy, how they help organizations handle distributed AI use cases, and why many businesses are trying to balance speed and flexibility without giving up the resilience and control they have spent years building into their infrastructure. We also spent time on the less glamorous side of AI adoption, but arguably the part that matters most. Shadow AI, data sovereignty, unpredictable token costs, and infrastructure readiness are all becoming board-level issues. Lee shared why so many organizations are realizing that AI cannot simply be layered onto existing systems without deeper changes underneath. New hardware, new software, new governance models, and a more consistent approach across edge, on-prem, private cloud, and public cloud environments are all part of the picture now. What I enjoyed most about this conversation was that it never framed AI as magic. It framed it as work. Real work that demands better architecture, sharper oversight, and faster decision-making from IT teams that are already under pressure. So if your organization is racing to adopt AI, are you also building the foundation needed to support it responsibly, and where do you think the biggest risk sits right now? Share your thoughts with me.

How far can we trust research that is generated without asking a single human being? In this episode, I sat down with Jordan Harper from Qualtrics to unpack one of the most talked-about developments at the Qualtrics X4 Summit, synthetic research. It is a topic that sparks curiosity, excitement, and a fair amount of skepticism in equal measure. And honestly, that tension is exactly why this conversation matters. Jordan brings a rare mix of scientific thinking and real-world technology experience, which makes him well placed to cut through the hype. We explored what synthetic panels actually are, and just as importantly, what they are not. While many assume this is simply about asking a large language model for answers, the reality is far more nuanced. The approach Jordan and his team are building is grounded in how humans respond to surveys, trained on vast datasets to reflect the inconsistencies, biases, and unpredictability that make human insight valuable in the first place. What stood out throughout our conversation was the idea that synthetic research should be seen as additive rather than a replacement. It offers speed, flexibility, and the ability to test ideas quickly, but it does not replace the depth and lived experience that only real people can provide. In fact, some of the most interesting insights come from comparing synthetic responses with human ones, revealing patterns, biases, and even blind spots in traditional research methods. We also got into the practical side of things. From controlling for issues like survey fatigue and social desirability bias, to experimenting with question design in ways that would be difficult with human respondents, synthetic research opens up new ways of working. At the same time, it raises important questions about validation, trust, and where to draw the line when decisions carry real-world consequences. For me, this episode is about perspective. In a world where AI is accelerating everything, it can be tempting to look for shortcuts. But as Jordan explains, the real value comes from using these tools thoughtfully, alongside human insight rather than in place of it. So as this technology continues to evolve, how should researchers and business leaders strike that balance? And where could synthetic research help you ask better questions before you make your next big decision?

What does customer experience really mean when every company claims to put the customer first? In this episode, I sat down with Jeannie Walters, founder of Experience Investigators, to unpack why so many organizations talk about customer experience yet struggle to turn it into something that drives real business outcomes. With more than two decades of hands-on work across industries, Jeannie brings a perspective that cuts through the noise and focuses on what actually works inside complex organizations. Our conversation took place at the Qualtrics X4 Summit, where one theme kept resurfacing. While AI dominated headlines, there was a noticeable shift back toward strategy, discipline, and accountability.  Jeannie has been making that case for years. As she explained, customer experience cannot sit on the sidelines as a reporting function or a collection of metrics. It has to become a daily business discipline, one that shapes decisions across leadership, operations, and culture. We explored the thinking behind her new book, Experience Is Everything, and the patterns she has seen repeated across organizations. Leaders invest in tools, gather feedback, and build dashboards, yet still struggle to connect those efforts to outcomes like retention, revenue, and long-term trust. Jeannie argues that the missing piece is often clarity.  What does customer-centric actually mean for your organization? What are you trying to achieve, and how will you measure success in a way that matters to the business? Without those answers, even the best technology will fall short. There were also some honest reflections on AI. While it is accelerating everything, it also raises the stakes. Customers are becoming more aware of how their data is used, and trust is becoming harder to earn and easier to lose. That creates both an opportunity and a risk. Organizations that treat customer experience as a strategic priority can use AI to strengthen relationships, while those that treat it as a cost center may simply scale poor experiences faster. What stood out most in this conversation was the shift from theory to action. From redefining teams that were stuck reporting on metrics to empowering them to lead business change, Jeannie shared practical examples of how mindset, strategy, and execution come together. It is a reminder that customer experience is not owned by one team. It is something that either shows up in every interaction or not at all. So as AI continues to reshape how businesses operate, are we using it to deepen trust and deliver better experiences, or are we simply amplifying what already exists? And where does customer experience truly sit inside your organization today?

What does a great patient experience really look like when people are at their most vulnerable? In this episode, I sat down with Stanford Health Care's SVP and Chief Patient Experience and Operational Performance Officer, Alpa Vyas, to explore how one of the world's leading healthcare organizations is rethinking the human side of care. From the outside, healthcare is often seen as a system of processes, technology, and clinical outcomes. But as Alpa explains, every interaction sits within a deeply emotional moment in someone's life, where fear, uncertainty, and complexity collide. That reality shapes everything. Our conversation goes back to the early days of Stanford's transformation, where Alpa recognized a gap that many organizations still struggle with today. Improvement efforts were underway, systems were being optimized, yet the patient voice was largely absent. Inspired by design thinking principles from Stanford's own d.school, her team began with empathy as the foundation. That shift changed the direction of everything that followed, from how feedback was gathered to how decisions were made across the organization. We also explored the role of technology, and where it truly fits. There is often a temptation to lead with AI or automation, but Alpa brings the focus back to culture, behavior, and trust. Technology, including platforms like Qualtrics, became powerful once the right questions were being asked and the right mindset was in place. Moving from delayed paper surveys to real-time feedback transformed not only how quickly issues could be addressed, but how patients felt heard. One story stood out where a patient received a follow-up call before even leaving the parking lot, a simple moment that redefined their perception of care. We also touched on "Operation Blue Sky," an initiative that looks beyond traditional surveys to capture insight from call recordings, messages, and other unstructured data sources. It opens the door to a future where healthcare providers can anticipate problems before they happen and intervene at the right moment. That raises important questions around pace, trust, and readiness, especially in an industry that has good reason to move carefully. This episode is ultimately a conversation about balance. Between innovation and responsibility, between efficiency and empathy, and between data and human connection. So how do we ensure that as healthcare becomes more advanced, it also becomes more human? And what lessons from this journey could apply far beyond healthcare?

What happens when customer experience stops being a soft metric and starts becoming a direct driver of revenue, retention, and real-time action? In this episode, I sat down with Jeff Gelfuso, SVP and Chief Product and Experience Officer at Qualtrics, during X4 Summit in Seattle to talk about how AI is changing the way businesses understand and improve customer relationships. Jeff shared how his role sits at the point where product, experience, and business outcomes meet, helping customers use Qualtrics in ways that are both practical and measurable. One of the biggest themes in our conversation was the shift from simply listening to customers to actually doing something in the moment. For years, many companies have relied on surveys, dashboards, and reports that told them what had already gone wrong. Jeff explained how that model is changing fast. With AI, organizations can now understand signals as they happen and trigger action before a poor experience turns into churn, frustration, or lost revenue. We talked about examples from brands like Marriott and TruGreen, and this is where the conversation became especially interesting. In TruGreen's case, AI-powered analysis helped reveal that service quality, not price, was the real reason customers were leaving. That kind of insight changed the conversation from guesswork to financial impact. When one point of retention can mean $10 million in annual revenue, experience suddenly becomes a boardroom issue, not just a customer service metric. Jeff also offered a refreshingly clear view on agentic AI. Instead of treating it as another layer of hype, he described it as a way to turn experience data into action, using context to help businesses close the loop faster and with greater precision. That means moving beyond smarter dashboards and toward systems that can surface priorities, recommend next steps, and help teams act without getting buried in complexity. Another standout part of the discussion was how Qualtrics is helping customers move beyond pilot purgatory. Jeff was candid that meaningful AI progress still takes work, focus, and the discipline to solve the right problems first. The companies seeing real value are not trying to do everything at once. They are identifying specific use cases, tying them to real business outcomes, and building from there. What I enjoyed most about this conversation was how clearly Jeff connected technology to human experience. Yes, there was plenty of discussion around AI, automation, and context, but at the heart of it all was something much simpler. Better experiences build stronger relationships, and stronger relationships drive loyalty, trust, and growth. So if your business is still treating experience as a nice-to-have instead of a measurable driver of performance, what might you be missing right in front of you? I would love to hear your thoughts after listening.

What does it really mean to lead in AI when the headlines are loud, the claims are endless, and the real signals are often buried under hype? In this episode, I sit down with Ed White from Clarivate to make sense of one of the most important questions in technology right now, who is actually leading the AI innovation race, and what does the data really tell us?  Ed leads the Clarivate Centre for IP and Innovation Research, where his team analyzes enormous volumes of intellectual property and innovation data to understand where technology is heading, who is building it, and which ideas are likely to shape the future. That matters because AI is no longer a side story inside tech. It is becoming an economic issue, a business issue, and increasingly a geopolitical one too. Our conversation centers on fresh Clarivate research showing that AI patent filings passed 1.1 million overall by 2025, with growth accelerating at a pace that is hard to ignore. Ed helps unpack what that actually means in practical terms.   I found this especially interesting because the report does not simply point to the familiar names everyone already talks about. It also highlights academic institutions, automotive companies, and businesses working behind the scenes with far less noise. What I enjoyed most about this discussion is that Ed brings a rare mix of technical depth and real clarity. He does not just throw out huge numbers and leave them hanging there. He explains what they mean for investors, enterprise leaders, governments, and anyone trying to understand where this market is heading next.  We also get into one of the biggest tensions in AI today, the balance between speed and assurance. That part really stayed with me. In a market obsessed with moving fast, Ed makes a strong case that trust, explainability, and usability may end up shaping who actually wins. This is a conversation about much more than patents. It is about power, strategy, timing, and how innovation spreads across borders, industries, and institutions. If you want to cut through the noise and hear a more data-led view of the AI race, this episode will give you plenty to think about. As always, I would love to hear what stood out to you most after listening, so please share your thoughts with me. When you look at the AI race today, do you think the real leaders are the companies making the most noise, or the ones quietly building for the long term?

What does it really take to move AI from impressive demos into the hands of the people who keep the world running every day? In this episode of Tech Talks Daily, I sat down with Kriti Sharma, CEO of IFS Nexus Black, to explore a side of AI that rarely gets the spotlight. While much of the conversation around artificial intelligence focuses on chatbots and copilots, Kriti is working in environments where failure is not an option. Manufacturing plants, energy grids, airlines, and field service operations all depend on precision, experience, and consistency. What struck me early in our conversation was how she reframes the entire AI debate. The challenge is not building the technology, it is building trust in it. Kriti's journey into AI began long before it became a boardroom priority. From building her first robot as a teenager to advising global organizations and policymakers, she has always focused on solving real problems rather than chasing trends. That perspective carries through into her work today, where she spends time on factory floors wearing safety gear alongside engineers and technicians.  It is a hands-on approach that reveals something many leaders miss. People do not adopt AI because it is advanced. They adopt it when it solves a problem they recognize in their day-to-day work. One of the most interesting themes we explored was the widening gap between what AI can do and how quickly organizations are ready to use it. Kriti described how that gap plays out on the ground, especially among deskless workers who make up the majority of the global workforce. In these environments, the conversation is far less about replacing jobs and far more about preserving knowledge, improving consistency, and helping people perform at their best. When a veteran worker with decades of experience walks out the door, that expertise often leaves with them. AI, when designed well, can help capture and share that knowledge across an entire workforce. We also discussed how IFS Nexus Black is tackling what many describe as "pilot purgatory," where companies experiment with AI but struggle to deploy it at scale. Kriti shared how building solutions alongside customers, rather than handing over generic tools, leads to faster adoption and measurable results. Real-world examples brought this to life, including how industrial AI is helping organizations move from reactive firefighting to proactive decision-making, reducing downtime and improving operational performance in ways that directly impact the bottom line. As our conversation moved toward the future, Kriti offered a clear message for leaders. The best way to prepare for AI is to start using it. Not as a novelty, but as a daily tool that can amplify how work gets done. The organizations that encourage experimentation and share those learnings across teams are the ones most likely to see real impact. So as AI continues to evolve at pace, the question is no longer whether the technology is ready. It is whether organizations and their people are ready to meet it halfway, and what happens if they are not?