Natterbox And The Future Of Voice AI In Customer Experience

*]: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 Whether it is a bank that still puts you through layers of outdated IVR menus or a service team that answers straight away and solves the issue, those experiences stay with us.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?

How close are we to the moment when quantum computing moves from scientific curiosity to real-world infrastructure? In today's episode of Tech Talks Daily, I speak with Christian Weedbrook, Founder and CEO of Xanadu, a company pushing the boundaries of what quantum computers might soon achieve. Xanadu has taken an unconventional route in the race to build practical quantum systems. Instead of relying on electronic approaches used by many others in the field, the company builds quantum computers using photonics, effectively computing with particles of light. Christian explains why this matters and how working with photons could unlock advantages in energy efficiency, scalability, and networking as quantum machines grow into large data center–scale systems. The conversation also arrives at a fascinating moment for the company. Xanadu has announced plans to go public through a SPAC deal that values the company at around $3.1 billion. Christian shares what that milestone means, not only for Xanadu but for the broader quantum ecosystem. According to him, the excitement surrounding quantum computing is no longer limited to research labs. Governments, enterprise partners, and investors are increasingly paying attention as the technology edges closer to commercial relevance. One of the most engaging parts of our conversation is Christian's own journey into the world of quantum physics. Before earning a PhD in photonic quantum computing, he began as a film student who admits he once dreamed of becoming a filmmaker. That winding path eventually led him into physics and entrepreneurship, where he founded Xanadu in 2016 with a mission to make quantum computers useful and accessible to everyone. We also discuss PennyLane, the open-source quantum programming framework developed by Xanadu that has quietly become one of the most widely used tools in the quantum developer community. Now taught in universities across more than 30 countries, PennyLane plays an important role in building the next generation of quantum talent. Christian also shares a realistic timeline for where the industry stands today. Quantum computers already exist, but they remain smaller than what is needed for commercial breakthroughs. Xanadu's roadmap points toward large-scale quantum data centers by the end of the decade, systems capable of tackling problems in drug discovery, materials science, logistics, and finance that traditional computers struggle to simulate. For enterprise leaders listening today, the message is clear. The quantum future is closer than many people assume, and organizations that begin exploring use cases now will be far better prepared when these systems mature. So how should businesses prepare for a computing paradigm based on the mathematics of quantum physics rather than traditional software logic? And what lessons can founders learn from a journey that began with filmmaking ambitions and led to building one of the most ambitious quantum companies in the world? Let's find out together.

How can companies invest heavily in AI and still struggle to see meaningful returns? In this episode of Tech Talks Daily, I sit down with Thomas Scott, CEO of Wrike, to unpack a growing tension many organizations are facing right now.  Artificial intelligence adoption is accelerating rapidly across the workplace, yet the structures needed to support it are struggling to keep pace. Wrike's latest research into the "Age of Connected Intelligence" reveals that more than 80 percent of employees are already using AI at work. Yet fewer than half have received any formal training, guidance, or governance around how these tools should be used. That gap between enthusiasm and enablement is creating a new workplace phenomenon that many leaders are only just beginning to notice.  Shadow AI. When employees cannot find approved tools that solve their problems quickly, they often turn to unapproved applications or personal accounts instead. Wrike's data shows that 42 percent of workers admit they have already done this. For organizations handling sensitive data, intellectual property, or regulated information, that trend raises serious questions about security, compliance, and trust. Thomas explains why this pattern is not surprising. Whenever a new technology emerges, the builders and experimenters move first. They explore possibilities, test new tools, and discover productivity gains long before formal policies or training frameworks arrive. The challenge for leadership teams is learning how to harness that momentum without letting experimentation turn into fragmentation. We also explore one of the most overlooked barriers to AI return on investment. Integration. Many employees are now juggling multiple AI tools every week, yet those systems rarely communicate with one another or connect deeply into the core business platforms where real work happens. As a result, context gets lost, workflows become fragmented, and organizations end up running expensive pilots that never scale into meaningful transformation. Thomas introduces the idea of connected intelligence as a possible solution. Instead of deploying AI tools in isolation, companies need systems that understand context across projects, teams, and workflows. When AI can access structured data, shared history, and operational context, it becomes far more capable of supporting real decision making rather than simply generating isolated outputs. Our conversation also explores how leaders can move beyond scattered experimentation and start building structured AI adoption across their organizations.  Thomas argues that the most successful companies start with highly specific problems, empower small groups of motivated builders, and maintain strong executive involvement throughout the process. AI transformation is rarely driven by technology alone. It requires people, process, and leadership alignment working together. So if your organization has already deployed AI tools but still struggles to see real impact, perhaps the question is not whether you are using AI. The real question might be whether those tools are truly connected to the work your teams are trying to do every day.

How should businesses rethink infrastructure when applications, data, and users are increasingly spread across thousands of locations? In this episode of Tech Talks Daily, I sit down with Mark Cree, President and Chief Operating Officer at Scale Computing, to talk about why the future of enterprise infrastructure is moving closer to where data is actually created. This conversation was recorded following the 66th edition of The IT Press Tour, where some of the most interesting conversations in enterprise infrastructure centered on what happens when businesses move away from oversized, monolithic stacks and start focusing on practical, distributed solutions. From retail stores and airports to remote industrial sites, the edge is becoming a critical part of modern IT strategy. Mark shares how Scale Computing has spent years building an edge-first platform designed to run critical workloads reliably across everything from a single location to tens of thousands of distributed sites. Mark also reflects on his own journey through the technology industry, which includes founding companies acquired by Cisco and NetApp, working as a venture capitalist, and leading major storage initiatives at AWS. That experience gives him a unique perspective on how enterprise infrastructure has evolved, particularly as organizations reconsider the balance between centralized cloud environments and local processing closer to users and devices. During our conversation, we explore why edge computing is becoming increasingly important for AI workloads, especially when large volumes of data are generated outside traditional data centers. Mark explains how processing information locally can reduce costs, improve performance, and enable entirely new use cases, from monitoring customer behavior in retail environments to running intelligent systems in remote locations. We also talk about the ongoing reassessment happening across enterprise IT teams following major industry shifts, including changes in the virtualization market and growing concerns around vendor lock-in. Mark explains how Scale Computing is positioning itself as a flexible alternative by combining virtualization, containerization, networking, and security into a platform designed specifically for distributed environments. Looking ahead, Mark shares his perspective on where enterprise infrastructure is heading over the next five years. As smaller AI models become more capable and organizations seek greater control over their data and systems, the role of edge platforms may become even more important.  Instead of relying solely on massive centralized environments, companies may find new value in distributing intelligence closer to the places where real-world activity happens. So as organizations rethink how they deploy applications, manage data, and control infrastructure, is the next big shift in enterprise IT happening right at the edge? And how prepared is your organization for that change?

How can organizations use AI to transform hiring while still protecting the human element at the heart of work? In this episode of Tech Talks Daily, I sit down with Mahe Bayireddi, co-founder and CEO of Phenom, to explore how artificial intelligence is reshaping the way companies attract, hire, and develop talent.  Our conversation comes at an interesting moment for the company, following the announcement that Phenom has acquired Be Applied, an AI-driven cognitive assessment platform designed to validate candidate and employee capabilities at scale. The move follows an earlier acquisition of Included, an AI-native people analytics platform focused on delivering deeper workforce insights and faster decision making. Mahe shares how Phenom's long-term mission to help a billion people find the right job is evolving as AI becomes embedded throughout the HR lifecycle. From candidate discovery to onboarding and internal mobility, organizations are now experimenting with automation, personalization, and intelligent workflows that aim to improve both productivity and employee experience. One theme that runs throughout our discussion is how AI adoption in HR varies dramatically depending on geography, regulation, and industry. In Europe, regulatory frameworks are shaping how companies deploy automation. In the United States, state-level policies introduce additional complexity. Meanwhile, organizations across Asia are often approaching AI with entirely different priorities. As a result, many global companies are experimenting carefully, introducing AI into specific business units or regions before rolling it out more broadly. We also talk about a challenge that has caught many HR teams by surprise: the growing issue of fraudulent candidates and identity manipulation in the hiring process. As job applications become easier to submit and remote work expands global talent pools, organizations must rethink how they validate candidate identity and credentials. Mahe explains how AI-driven fraud detection tools can help highlight suspicious patterns while still keeping humans in the loop for final decisions. Another important point raised in the conversation is the need to preserve humanity in the workplace while introducing intelligent automation. While AI can dramatically improve efficiency across recruiting and workforce planning, Mahe believes HR leaders must be careful to ensure technology strengthens human potential rather than reducing people to data points in a system. Looking ahead, we discuss how organizations can begin adopting AI responsibly by starting small, focusing on high-impact areas, and building guardrails that reflect regional regulations and company culture. For many companies, the most successful path forward will involve testing AI within specific workflows, measuring outcomes quickly, and scaling what works. So as artificial intelligence becomes a central part of hiring, workforce planning, and employee development, the big question for leaders is this. Can organizations use AI to create faster, smarter talent decisions while still keeping people at the center of the workplace experience?

How does a CISO turn cybersecurity from a technical conversation into a business conversation that boards actually care about? In this episode of Tech Talks Daily, I sit down with Thom Langford, EMEA CTO at Rapid7 and a former CISO, to explore what he calls the second phase of cybersecurity leadership. For years, the industry worked hard to secure a seat at the boardroom table. In many organizations, that mission has largely succeeded. But as Thom explains, gaining access was only the first step. The real challenge now is communicating security in a way that drives meaningful business decisions. Thom shares why many CISOs still approach board conversations in the same way they did a decade ago, even though boardroom awareness of cybersecurity has changed dramatically. Today, many boards include members with cybersecurity knowledge or direct security experience. That means security leaders can no longer rely on technical jargon, complex frameworks, or compliance language to make their case. One of the most interesting insights from our conversation is the disconnect between how CISOs frame risk and what boards are actually focused on. While security teams often lead with risk reduction, boards tend to think in terms of revenue growth and operational costs. Thom argues that security leaders must learn to translate cybersecurity into the language of profit and loss if they want their message to resonate at the executive level. We also explore how traditional security tools such as risk frameworks, audits, and compliance standards can sometimes create distance rather than clarity in board discussions. Instead of helping executives understand security priorities, these models can obscure the real question boards are trying to answer. How secure are we, and what does that mean for the business? Another area we discuss is the growing role of tabletop exercises. Thom explains why these simulations are becoming one of the most effective ways for CISOs to demonstrate the real-world impact of security decisions. By walking executives through a realistic incident scenario, leaders can see how security, operations, legal teams, and business priorities intersect during a crisis. Looking ahead, Thom believes the most successful CISOs will increasingly need to think like business leaders rather than purely technical specialists. Communication skills, relationship building, and understanding the organization's financial priorities may prove just as important as deep technical expertise. So if cybersecurity leaders have already earned their place in the boardroom, the next question becomes much more interesting. Are they speaking the language the board actually understands, or are they still trying to solve business problems using only security vocabulary?