From The HP Garage To AI PCs: How HP Is Rethinking Work Technology

How is AI reshaping our relationship with work, and what does that mean for the tools we rely on every day? In this episode of Tech Talks Daily, I'm joined by Cory McElroy, Vice President of Commercial Product Management at HP. Our conversation begins with a reflection on one of the most famous garages in technology history. The original HP garage in Palo Alto is often described as the birthplace of Silicon Valley, and standing there recently reminded me how far the industry has come since those early days. But as Cory explains, we may be entering another turning point. The nature of work has shifted rapidly in just a few years. Hybrid work is now the norm for millions of people, and expectations around workplace technology have changed with it. Employees no longer see technology as a basic productivity tool. They expect it to adapt to them, reduce friction, and help them focus on meaningful work. Cory shares insights from HP's Work Relationship Index, which highlights a striking reality. Only around 20 percent of employees say they have a healthy relationship with work. That number sounds concerning at first, but it also points to an opportunity. When organizations provide the right tools and experiences, employees become more productive, more creative, and more likely to stay. A big theme throughout our conversation is the growing role of AI directly on devices. Running AI locally on PCs changes how people interact with technology. Tasks that once took hours, such as analyzing documents or extracting insights from data, can now happen almost instantly. In some internal deployments at HP, employees reported saving up to four hours each week. We also talk about the hardware innovations that are emerging in response to this shift. Cory explains how new devices like the HP EliteBook X and the EliteBoard reflect a rethink of the PC itself. The EliteBoard, for example, integrates a full PC inside a keyboard, allowing users to connect to any display and instantly access desktop-level performance. It is a design that reflects the flexibility people now expect from modern workspaces. Looking ahead, Cory believes the next few years will bring even bigger change. Devices will increasingly understand context, connect seamlessly with other tools, and respond to natural language requests. Instead of jumping between multiple applications to complete a task, users may simply ask their device to assemble information and produce the outcome they need. So as AI becomes embedded into the devices we use every day and work continues to evolve, what would a truly frictionless workday look like for you, and how will your relationship with technology change as a result?

*]:pointer-events-auto scroll-mt-(--header-height)" dir="auto" tabindex="-1" data-turn-id= "ea43d6d1-18ff-490f-b57a-5cbce94de2fb" data-testid= "conversation-turn-7" data-scroll-anchor="false" data-turn="user"> *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id= "request-WEB:d64929a5-9e90-46a8-96ce-fa26f8ed8adc-3" data-testid= "conversation-turn-8" data-scroll-anchor="true" data-turn= "assistant"> How do you secure a modern business when identities no longer belong only to employees, but also to partners, machines, applications, and increasingly AI agents? In this episode of Tech Talks Daily, I sat down with Paul Zolfaghari, President of Saviynt, to unpack why identity security has moved from a background IT function to one of the defining challenges facing modern enterprises. Over the past decade, the identity problem has expanded far beyond the traditional office worker logging into internal systems. Today's organizations must manage access across a vast digital ecosystem that includes contractors, suppliers, customers, APIs, machines, and now autonomous AI agents. Paul explains how this shift has fundamentally changed the way security leaders think about identity governance. The challenge is no longer limited to preventing unauthorized access from outside attackers. Instead, companies must manage the complex question of who, or what, should have access to specific data, systems, and processes at any given moment. When thousands of employees, partners, and automated systems interact across multiple cloud platforms, the complexity grows rapidly. We also explore how the rise of non-human identities is reshaping the security landscape. Machines, software services, and AI agents now operate alongside human employees inside enterprise environments. In many cases, these digital identities are already beginning to outnumber people. As AI agents gain the ability to gather information, adapt to context, and take actions autonomously, organizations must rethink how access permissions are granted, monitored, and governed. Another theme that emerged during our conversation is the idea that identity security is not only about protection. While it clearly sits within the cybersecurity domain, Paul argues that identity governance also acts as a business enabler. When the right people and systems can access the right information at the right time, organizations operate more efficiently and collaborate more effectively across complex supply chains and partner ecosystems. We also discussed findings from Saviynt's CISO AI Risk Report, which highlights a growing concern among security leaders. AI adoption is accelerating rapidly, often moving faster than the governance frameworks designed to manage it. This creates a challenge for organizations trying to adopt AI responsibly while maintaining visibility and control over how these technologies interact with enterprise systems. With more than 600 enterprise customers and a recent $700 million growth investment backing its expansion, Saviynt is operating in a market that many investors now view as one of the defining layers of modern digital infrastructure. Identity, in many ways, is becoming the control plane for how businesses operate in an AI driven world. Looking ahead, Paul believes organizations must begin preparing for a future where digital identities dramatically outnumber human employees. That shift will require new approaches to governance, visibility, and control. So as AI adoption accelerates and businesses continue expanding across cloud platforms and digital ecosystems, one question becomes impossible to ignore. Is identity security ready to serve as the foundation for how organizations operate in the next decade? Useful Links Connect with Paul Zolfaghari Check out the Saviynt Website Follow on Facebook, LinkedIn, and X

How prepared are organizations for a world where today's encrypted communications could be quietly stored and cracked years from now? In this episode of Tech Talks Daily, I sat down with Nate Jenniges, Senior Vice President and General Manager at BlackBerry, to talk about why the conversation around quantum computing is moving from academic curiosity to operational reality.  For many leaders, quantum threats still feel distant, something for researchers and cryptographers to worry about. But as Nate explained, governments and adversaries are already capturing encrypted data today with the expectation that it can be decrypted later when quantum capabilities mature. This idea of "harvest now, decrypt later" attacks completely changes the timeline for security planning. If sensitive information needs to remain confidential for five, ten, or even twenty years, the exposure may already have started. That means the challenge is no longer theoretical. It is becoming a strategic issue that boards, CISOs, and government leaders must begin addressing right now. One of the most interesting parts of our conversation focused on something many people rarely think about. Metadata. While encryption protects the content of a message or phone call, the surrounding patterns often reveal just as much. Who spoke to whom, how often, from where, and at what time can tell a surprisingly detailed story. With modern analytics and AI tools, these patterns can expose command structures, business relationships, or crisis response activity even if the message itself remains encrypted. Nate explained why this is becoming a frontline issue in the emerging post-quantum era. As organizations integrate AI into communication platforms, new forms of metadata are emerging from model interactions, system queries, and inference activities. That means protecting communications requires a broader view than simply upgrading encryption algorithms. We also explored how governments and highly regulated sectors are preparing for this shift. BlackBerry today operates in a very different space than many people remember, focusing on identity-verified, mission-critical communications used by governments and institutions that cannot afford uncertainty. These systems are designed to operate during the moments that matter most, whether that involves cyber incident response, national security coordination, or emergency response to climate-related events. Another theme that stood out was the leadership challenge behind quantum readiness. Nate believes organizations should avoid treating quantum as a separate security initiative. Instead, it should be integrated into the technology refresh cycles that companies already manage, including hardware updates, software upgrades, and certificate renewals. The organizations that begin asking the right questions today will avoid scrambling later when regulatory expectations tighten and deadlines arrive. By the end of our conversation, one message became very clear. The first real defense in the post-quantum era may not come from stronger encryption alone. It may come from understanding and controlling the communication patterns and metadata that surround every digital interaction. As quantum computing research accelerates and governments begin setting deadlines for post-quantum security readiness, the question becomes increasingly hard to ignore. Are organizations truly prepared for the communications challenges that the next decade may bring?

Why are employees still drowning in administrative work despite years of digital transformation, new software platforms, and constant promises that technology will make work easier? In this episode of Tech Talks Daily, I explore that question with Jason Spry from Ricoh Europe. What begins as a discussion about a new Ricoh research report quickly turns into a much broader conversation about how modern workplaces actually operate day to day. The findings are striking. Employees across Europe are losing an average of 15 hours every week to routine administrative tasks. That is time spent searching for documents, reentering data across systems, preparing reports manually, and navigating layers of disconnected tools. For many organizations, this creates a strange contradiction. Leadership teams often believe that new platforms and software will simplify workflows, yet many employees feel the opposite. The tools designed to make work easier sometimes create additional layers of complexity. Jason shares his perspective from nearly three decades in document processing and outsourcing, explaining how years of digital initiatives have often resulted in systems stacked on top of one another rather than genuinely simplified workflows. The result is a fragmented experience where finding the latest version of a document or locating the right information for a meeting can consume far more time than it should. We also discuss the hidden risks behind these inefficiencies. When documents are scattered across systems or poorly managed, the consequences go beyond frustration. Ricoh's research shows that many organizations have experienced compliance breaches or near misses because important documents were missing, misfiled, or simply impossible to locate at the right moment. Jason explains why governance, visibility, and consistent document management are becoming increasingly important in a world where decisions rely on accurate information. Another theme that runs throughout this conversation is the idea of marginal gains. Small inefficiencies like searching for files, reentering data, or preparing documents for meetings might seem trivial in isolation. Yet when they happen hundreds of times across a workforce, they add up to a serious productivity drain. Jason compares it to the concept of improving performance by one percent at a time. Removing even a few of these micro frustrations can transform how people experience their workday. Naturally, we also talk about automation and AI. But Jason offers a refreshing perspective here as well. Rather than starting with the technology, he argues that organizations should begin by identifying the real pain points employees face. That often means speaking directly with the people doing the work and asking what frustrates them most. Once those challenges are clear, automation and intelligent document management tools can start delivering results quickly, sometimes within weeks rather than years. By the end of the conversation, it becomes clear that solving the admin overload problem does not always require massive transformation projects. Often the answer lies in simplifying processes, connecting systems more intelligently, and removing the small friction points that slow everyone down. So I am curious. How much time do you think your organization loses to administrative work each week, and what simple changes could give employees that time back?

How do you build trust in a business environment where security reviews, compliance demands, and vendor risk checks can slow everything down just when companies are trying to move faster? In this episode, I sit down with Adam Markowitz, CEO and co-founder of Drata, to talk about why trust has become one of the most important business conversations in tech. Adam brings a fascinating perspective to the table. Before building Drata, he worked on NASA's space shuttle program, and today he leads a company that has grown rapidly by helping organizations rethink compliance, governance, risk, and assurance through automation and AI. What stood out to me in this conversation was how clearly he framed the real issue. Compliance may have been where many companies started, but trust is the bigger story. In a world shaped by cloud services, third party vendors, and constant security scrutiny, old point in time audits and reactive processes are starting to look painfully outdated. We also talked about Drata's acquisition of SafeBase and what that says about the direction of the market. Adam explained how security and GRC teams have too often been treated as back office functions, expected to stay quiet and keep the company out of trouble. But he sees things very differently. He argues that these teams can actively help close deals, accelerate revenue, and remove friction from the buying process. That shift matters because trust now plays a direct role in business growth. If customers can quickly get answers to security questions and understand how a company manages risk, sales cycles move faster and security teams stop being bottlenecks at the final stage of a deal. Another part of the conversation that really stayed with me was Adam's view on AI. He sees it as both a tailwind and a test. AI is helping automate highly manual GRC workflows, improve continuous compliance monitoring, and support newer frameworks tied to AI risk itself. At the same time, he is realistic about the pressure this puts on businesses. AI may introduce fresh concerns, but it also shines a harsher light on issues that have been around for years, things like access creep, weak controls, and data integrity problems. That honesty gave this discussion a lot of weight because it moved beyond hype and focused on what companies actually need to do. We also touched on Drata's momentum as a business, from opening a new San Francisco headquarters to expanding globally and moving further into the enterprise market. But even there, Adam kept coming back to culture, discipline, and a deep understanding of the customer problem. For me, that was the thread running through the whole episode. Trust is not a side issue. It is part of how modern companies grow, compete, and prove they can be relied on. If your business still sees compliance as a checkbox exercise or a cost center, this conversation will give you plenty to think about. Where do you see the relationship between trust, security, and growth heading next, and what did this episode make you question about the way your own organization handles compliance? Share your thoughts with me.

*]:pointer-events-auto scroll-mt-(--header-height)" dir="auto" tabindex="-1" data-turn-id= "effc95df-294b-4192-9cc6-00e1eb5e3a7e" data-testid= "conversation-turn-1" data-scroll-anchor="false" data-turn="user"> *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id= "request-WEB:25e8c325-0da6-411e-9e0d-dec02898c751-0" data-testid= "conversation-turn-2" data-scroll-anchor="true" data-turn= "assistant"> What happens when the most frustrating part of customer service, waiting on hold, repeating yourself, and fighting your way through endless phone menus, finally starts to disappear? In this episode, I sit down with Neil Hammerton, CEO and co-founder of Natterbox, to talk about how AI is reshaping customer experience in ways that feel practical rather than theatrical. We begin with a conversation about the gap between what customers have tolerated for years and what they expect now. Whether it is a bank that still puts you through layers of outdated IVR menus or a service team that answers straight away and solves the issue, those experiences stay with us. Neil makes the case that voice is far from dead. In fact, he believes voice is becoming one of the most exciting places to apply AI, especially when businesses want faster, more human interactions at scale. What I found especially interesting was Neil's view that AI should be treated like a new employee. That means training matters. Tone matters. Context matters. If businesses want AI assistants and agents to succeed, they have to teach them how the organization works, how conversations should sound, and when a human needs to step in. We talk about the difference between using AI for simple triage and using it to complete tasks end to end, from handling password resets to helping callers outside office hours or during spikes in demand. Neil also shares why the smartest path is rarely a giant leap. It is usually a series of smaller, lower-risk steps that build confidence and real results over time. We also get into one of the biggest concerns hanging over every AI conversation right now, whether these tools are replacing people or helping them do better work. Neil's answer is refreshingly balanced. In many cases, AI is taking care of the repetitive jobs that frustrate staff and slow down service, while freeing human agents to handle the conversations where empathy, judgment, and experience still matter most. That shift can improve customer experience while also making work more rewarding for the people on the front line. There is also a strong message here for business leaders who are still stuck in pilot mode, testing AI without ever quite moving forward. Neil explains why smart pilots need clear goals, good training data, and realistic expectations. He also shares how Natterbox is using AI internally, including producing board packs in a fraction of the time, while still keeping people involved to check, challenge, and refine the output. This episode is a thoughtful look at where customer experience is heading next, and why the future probably belongs to businesses that know when to let AI lead, when to keep humans in the loop, and how to blend both into something customers actually value. What are your thoughts on the balance between AI efficiency and human connection in customer service, and where do you think businesses are still getting it wrong?

How do you turn trillions of user interactions into meaningful decisions without drowning in data? In this episode of Tech Talks Daily, I sit down with Todd Olson, co-founder and CEO of Pendo, to talk about the future of product-led organizations and why AI is reshaping how software companies grow, build, and compete. Pendo tracks trillions of product usage events to help organizations understand how customers actually interact with their software. That level of data sounds powerful, but it also raises a challenge many teams face today. How do you turn massive data sets into clear signals that teams can act on without falling into analysis paralysis? Todd explains how Pendo approaches this problem by organizing product data around real user journeys, feature adoption, and areas where people drop off. Instead of leaving teams buried in dashboards, the goal is to surface insights that matter. Increasingly, AI is helping by acting as a kind of embedded analyst that highlights the patterns product teams should focus on. Our conversation also revisits the idea behind Todd's book, The Product-Led Organization. When it was published around the time of the pandemic, it argued that great products should do much of the heavy lifting traditionally done by sales or support teams. Looking back now, Todd believes the core idea remains intact. AI simply accelerates the model by allowing companies to experiment faster and scale product-driven experiences with far fewer people. But that shift is also creating tension in the software industry. We talk about the so-called reckoning in SaaS economics and the growing debate around whether AI will make traditional software companies obsolete. Todd offers a more measured perspective. While AI allows anyone to prototype software quickly, the companies that survive will still be the ones solving difficult problems, navigating compliance requirements, and building products that customers trust. Another theme we explore is geography and innovation. Pendo is headquartered in Raleigh, North Carolina, far from the usual coastal tech hubs. Todd shares how building outside Silicon Valley has shaped the company's culture, talent strategy, and mindset. There are advantages to being close to the center of the AI boom, but there is also value in building away from the echo chamber. We also spend time unpacking the rise of AI-assisted development and the trend many people call "vibe coding." Todd believes AI will dramatically reshape product teams, but he also pushes back against the idea that humans will disappear from the development process. Engineers will still need to review code, teach AI systems best practices, and ensure security and reliability. One of the most interesting moments in our conversation comes near the end when Todd shares a belief that originality will become one of the most valuable assets in the age of AI. As automated content and automated code become easier to generate, he believes people will increasingly value craft, taste, and original thinking. So in a world where AI can generate almost anything with a prompt, the real question becomes far more human. What problems are actually worth solving? If you care about the future of software, product strategy, and how AI is reshaping the economics of building companies, this is a conversation that offers plenty to think about. And after listening, I would love to hear your perspective. As AI becomes embedded in every product and workflow, do you believe originality and craft will become the true differentiators in the software industry?

Have you ever contacted customer support with a simple request, only to find yourself trapped in a loop of scripted chatbot responses that never actually solve the problem? It's an experience many of us know all too well.  AI has made customer service more conversational over the last few years, yet there is still a gap between answering a question and actually resolving an issue. That gap is exactly where today's conversation begins. In this episode of Tech Talks Daily, I spoke with Mike Szilagyi, SVP and General Manager of Product Management at Genesys Cloud, about a new chapter in AI-powered customer experience. Genesys has announced what it describes as the industry's first agentic virtual agent built on Large Action Models, or LAMs. While Large Language Models have dominated the conversation around AI for the past few years, they have largely focused on generating responses, retrieving knowledge, or answering questions. What they have struggled with is execution. Mike explained how Large Action Models take the next step. Rather than simply telling a customer how to solve a problem, these systems can plan and execute the steps needed to complete a task. Imagine contacting an airline after a sudden flight cancellation.  Instead of navigating multiple menus or repeating information to a human agent, an agentic virtual assistant could understand your situation, check alternative flights, apply airline policies, and complete the rebooking process across several systems. In other words, the AI moves from conversation to action. We also explored how Genesys approached the design of this technology with enterprise governance in mind. From explainable decision paths and audit logs to guardrails that ensure every automated action can be traced and understood, the goal is to make autonomous AI trustworthy inside complex organizations. Mike also shared insights into Genesys' partnership with Scaled Cognition and how integrating specialized models helps deliver reliable execution in real-world customer service environments. Perhaps most interesting was our discussion about the human role in this evolving contact center landscape. As automation begins to handle routine and multi-step workflows, human agents are free to focus on situations that require empathy, judgment, and expertise. That shift raises interesting questions about how organizations design customer experiences in the years ahead. So how will customers respond when virtual agents move beyond answering questions and begin resolving problems on their behalf? And once one brand delivers that experience, will it quickly become the expectation? Useful Links Connect with Mike Szilagyi Learn more about Genesys Genesys Agentic Virtual Agent Powered by LAMs for Enterprise CX Follow on LinkedIn

*]:pointer-events-auto scroll-mt-(--header-height)" dir="auto" tabindex="-1" data-turn-id= "616a78a9-936c-48a2-92f7-e1bbd7029cf6" data-testid= "conversation-turn-1" data-scroll-anchor="false" data-turn="user"> *]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id= "request-WEB:6d125332-007b-4d80-9352-ebefa0828121-0" data-testid= "conversation-turn-2" data-scroll-anchor="true" data-turn= "assistant"> How do global companies make confident decisions when supply chains are constantly disrupted by tariffs, geopolitical tension, shifting consumer demand, and unpredictable global events? In this episode of Tech Talks Daily, I sat down with Dr. Ashwin Rao, EVP of AI and R&D at o9 Solutions, to talk about how artificial intelligence is changing the way organizations plan, forecast, and respond to uncertainty. Ashwin brings a fascinating mix of experience to the conversation. After earning a PhD in mathematics and computer science, he spent fifteen years on Wall Street working on derivatives trading strategies at Goldman Sachs and Morgan Stanley before moving into the world of enterprise technology. Today, he operates at the meeting point between business and academia as both a senior AI leader and an adjunct professor at Stanford University. Our conversation begins with Ashwin's unusual career path and how those early experiences in finance shaped the way he thinks about risk, decision making, and real world AI deployment. The journey from theoretical mathematics to trading floors and eventually into Silicon Valley offers an interesting lens on how analytical thinking can travel across industries and still remain highly relevant. We then move into the work happening at o9 Solutions, where AI is helping organizations make smarter decisions across supply chain planning, demand forecasting, and inventory management. In a world that Ashwin describes using the acronym VUCA, volatility, uncertainty, complexity, and ambiguity, businesses are under pressure to react faster and make better informed decisions. He explains how enterprise AI platforms can connect fragmented data across departments and create a more complete view of the business. One example he shares brings the concept down to earth. Even predicting how many bananas a grocery store should stock on any given day requires analyzing internal sales trends alongside external signals such as weather, social media trends, and economic conditions. Machine learning systems can now process those signals in real time and continuously update forecasts so businesses can respond quickly to changes. We also explore the rise of neuro- and symbolic AI, a concept Ashwin believes represents the next stage in enterprise decision-making. Rather than relying only on large language models, this approach blends the structured reasoning of symbolic systems with the pattern recognition of neural networks. The result, he suggests, feels less like a chatbot and more like having an expert coach embedded inside the decision-making process. Along the way, we also discuss why many organizations still struggle to embed AI successfully. Technology is only one piece of the puzzle. Ashwin believes the toughest obstacle is organizational change management, bringing teams together, connecting data across silos, and helping leaders guide their organizations through transformation. If you have ever wondered how AI moves beyond chatbots and into the systems that quietly power global supply chains, this conversation offers a thoughtful and practical perspective. So, how prepared is your organization to make decisions in a world defined by volatility and uncertainty, and could AI become the trusted partner that helps guide those choices? Useful Links Ashwin's blog Ashwin's LinkedIn o9 Solutions Website o9 LinkedIn  

What does it take to design a data center for a world where the technology inside it may change several times before the building even opens? In this episode of Tech Talks Daily, I sit down with Jackson Metcalf, Principal at Gensler, to talk about how AI is forcing a complete rethink of data center design. Jackson has spent nearly two decades working on critical facilities, and in our conversation he explains how the shift from traditional cloud workloads to dense AI environments is changing everything from building form and cooling strategy to long-term infrastructure planning. What struck me most in this conversation is the sheer mismatch in timescales. Data centers can take two and a half to three years to design and build, while chip and GPU roadmaps are evolving in cycles of months. Jackson explains why that means designing for a fixed end state no longer makes sense. Instead, the future may belong to facilities built with flexibility at their core, spaces that can be reconfigured, upgraded, and even conceptually rebuilt over time rather than treated as static assets. We also talk about what hyper-flexibility actually means in practice. This is not just a buzzword. It is about designing buildings with enough structural and engineering headroom to support very different cooling and power models over their lifespan. As AI workloads push cabinet densities to levels that would have sounded impossible only a few years ago, the need for plug-and-play mechanical and electrical infrastructure becomes far more than a design preference. It becomes essential. Another fascinating part of the conversation centers on sustainability. Jackson shares why durable, well-built structures can create long-term environmental value, even in an industry often criticized for its energy demands. We discuss embodied carbon, adaptive reuse, and why a high-quality building may have a much better second life than something built purely for short-term speed. That leads into a wider conversation about repositioning underused real estate, from former industrial facilities to vacant office buildings, as potential digital infrastructure. We also get into the growing energy challenge behind AI. With demand for power rising fast, and the US grid under increasing pressure, many operators are now weighing options such as on-site natural gas generation while waiting for cleaner long-term alternatives to mature. Jackson offers a thoughtful perspective on the tension between urgent infrastructure needs and environmental responsibility, as well as the uncertainty surrounding future energy roadmaps. Looking further ahead, I ask Jackson what will define a successful data center campus in the years to come. Will it be raw megawatts, adaptability, carbon intensity, location strategy, or something else entirely? His answer opens up a much bigger conversation about whether these buildings can become more connected to the communities around them, and what role they may play in a future where digital infrastructure is no longer hidden in the background, but central to how society functions. So if AI is pushing data center design to extremes, how do we build facilities that are ready for what comes next without becoming obsolete almost as soon as they open? And what does sustainable, adaptable digital infrastructure really look like in practice?