Adapting To Rising Costs And Constant Threats

What does it really take to move AI from endless experimentation into something that creates real business value? In this episode, I sat down with Tom Alexander, Head of Innovation and Transformation at CrossCountry Consulting, to talk about why so many organizations still struggle to turn AI ambition into meaningful outcomes. Tom works closely with executive and CFO teams that are either unsure where to begin or frustrated that early AI efforts have not delivered what they hoped for. We talked about why this is rarely just a technology issue. In many cases, the real blockers are ownership, change management, weak alignment across the business, and a failure to connect AI initiatives to the problems that matter most. One of the big themes in our conversation was the need to treat AI as an enterprise-wide program rather than a collection of isolated tools. Tom shared how leaders can focus on business processes first, identify where automation can genuinely improve performance, and avoid getting distracted by hype. We also unpacked the growing accountability challenge around AI, including who should own it, how stakeholders can align, and why strong foundations in data, governance, and training matter so much. This episode is packed with practical takeaways for anyone trying to make sense of AI adoption inside a business. If you are trying to figure out where to start, how to scale, or how to avoid another stalled initiative, there is a lot in here for you. After listening, I would love to hear your thoughts. How is your organization approaching AI, and where do you think most businesses are still getting it wrong? Useful Links CrossCountry website Connect with Tom Alexander on LinkedIn Field Notes podcast

Is the endpoint still just a device, or has it quietly become one of the most important control points in modern enterprise security? Recording live from IGEL Now And Next in Miami, I sat down once again with Darren Fields for what has become an annual check-in on how fast the industry is really changing. And this time, the conversation feels very different. Over the last 12 months, the discussion has moved well beyond traditional endpoint management. From global supply chain pressure driven by AI demand to rising hardware costs and unpredictable refresh cycles, the assumptions that once shaped endpoint strategy are starting to fall apart. Darren shares how organizations are now being forced into difficult decisions, absorb rising costs, delay investment, or rethink the model entirely. We also explore how that shift is changing the conversation at the leadership level. What was once seen as a procurement decision is increasingly being reframed as a resilience strategy. Extending hardware life, reducing dependency on supply chains, and maintaining operational continuity are becoming just as important as performance and cost. Security, of course, sits at the center of it all. With the majority of breaches still originating at the endpoint, Darren highlights how organizations are starting to rethink where they focus their efforts. Rather than focusing solely on data centers and cloud environments, there is growing recognition that control, visibility, and enforcement must occur at the edge. The conversation also touches on the reality of modern cyber threats. From constant attack attempts to incidents that leave organizations offline for weeks, the challenge is no longer just restoring systems but restoring access. And that shift has major implications for how recovery and continuity are designed moving forward. We also look at the growing convergence of IT and OT, the role of contextual access, and the balancing act between stronger security and user experience. With organizations at very different stages of their journey, there is no single path forward, but there is a clear sense that change is already underway. So as the pace of technology, risk, and demand continues to accelerate, one question remains. Are organizations adapting fast enough, or are they still relying on models that no longer reflect the world they are operating in? What do you think, are we finally seeing a shift toward treating the endpoint as a strategic priority, or is there still a gap between awareness and action?

What does it really take to move from talking about Zero Trust… to actually making it work in the real world? Recording live from IGEL Now And Next in Miami, I caught up with John Walsh for what has now become something of a tradition, our third conversation together, and one that reflects just how much has changed in the last 12 months. When we last spoke, the focus was on securing the edge and rethinking security through a preventative lens. This time, the conversation has expanded from IT to OT, from devices to platforms, and from theory to real-world implementation across manufacturing floors, healthcare environments, and government organizations. John shared how IGEL is increasingly being adopted as a global standard across both IT and operational environments, bringing new challenges and new insights. From kiosks and signage on factory floors to shared workstations in hospitals, the need for persona-based and now context-aware access is becoming far more than a technical concept. It is shaping how organizations think about identity, risk, and control at scale. We also explored how the idea of the "adaptive secure desktop" is evolving beyond traditional VDI thinking. Instead of static devices, the focus is shifting toward environments that respond dynamically to the user, their role, their location, and the level of risk in that moment. It raises an important question. How do you deliver that level of control without introducing friction for the user? AI inevitably entered the conversation, but not in the way many might expect. Rather than focusing on features, John highlighted the acceleration of threat velocity. The time between vulnerability discovery and exploitation is shrinking rapidly, and with AI amplifying that speed, traditional detection and response models are struggling to keep up. The implication is clear. Security strategies need to shift toward prevention and control, not just reaction. We also touched on emerging challenges around agentic AI, non-human identities, and the need to apply Zero Trust principles beyond people to machines. As organizations begin to explore these new models, questions around identity, access, and guardrails are becoming more complex and more urgent. And throughout the conversation, one theme kept coming back and reducing complexity while increasing control. Whether it is through immutable operating systems, centralized policy enforcement, or contextual access, the goal is to simplify the environment while strengthening security outcomes. As organizations continue their journey toward modernization, one question remains: Are we still layering new technology onto old models, or are we ready to rethink how access, identity, and control are delivered from the ground up? What do you think, is Zero Trust finally becoming real at the endpoint, or is there still a gap between strategy and execution?

How long would it actually take your organization to recover every endpoint after a major cyber incident? Recording live from IGEL Now And Next in Miami, I sat down with James Millington to explore a question that most businesses think they've answered, but rarely have. Because when you move beyond theory and start mapping out the real process, the numbers tell a very different story. James shared examples from real organizations that tried to calculate recovery at scale. One estimated it would take over 5,000 person-hours to rebuild their estate. Another believed they could recover quickly, until they realized the scale of their environment made that assumption unrealistic. It raises a deeper question. Are we focusing too much on recovery and not enough on resilience?  The conversation quickly moved into what James calls the "endpoint recovery gap." While most organizations have invested heavily in data center resilience, failover environments, and backup strategies, far fewer have a clear plan for reconnecting users when endpoints are compromised. And without a working endpoint, even the most advanced infrastructure becomes inaccessible.  We also explored why so many organizations continue to rely on reimaging devices as a primary recovery strategy, despite the time, complexity, and operational disruption it creates. In many cases, it's not just slow. It's impractical at scale. And perhaps more concerning, some organizations still admit to having no defined plan at all. One of the most memorable moments in the conversation came through a simple analogy. For years, we've been carrying the weight of outdated endpoint strategies, even though the solution has been sitting in front of us. Just like it took thousands of years to put wheels on a suitcase, the shift toward simpler, more resilient models often requires a moment of realization before change actually happens. As application delivery continues to move toward SaaS, DaaS, and cloud environments, the role of the endpoint is also being redefined. Analysts are now calling for a move toward immutable, non-persistent endpoints that reduce attack surface and enable faster recovery. But as James points out, the real challenge is not awareness. It's an action. As organizations continue to invest in security, infrastructure, and AI, one question remains: Are we still planning for recovery from failure, or are we finally designing systems that avoid it in the first place? What do you think, are businesses ready to rethink endpoint strategy, or are we still carrying the baggage of the past?

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?