Tech Talks Daily

In this thought-provoking conversation, I'm joined by Kate O'Neill, author, speaker, and founder of KO Insights, to explore how leaders can make meaningful, human-centered decisions in a time of rapid AI adoption and organisational change.

Known as the "Tech Humanist," Kate has helped everyone from Google and IBM to city governments navigate the evolving relationship between emerging technologies and the human experience.

We discuss what it really means to create a future that aligns tech innovation with human values. Rather than racing ahead with AI for its own sake, Kate argues that every organisation needs to start by clearly articulating its strategic purpose in three to five words. From there, technology should be implemented to serve people, not just processes.

Kate shares insights from her latest book What Matters Next, including how to define ethical acceleration, how to lead through tech transformations without breaking cultural trust, and why asking "should we?" is more important than "can we?" when it comes to deploying agentic AI. She also breaks down why traditional efficiency metrics fall short when applied to human work, and how business leaders can shift towards more meaningful measures of success.

We also reflect on real-world case studies, from Apple's design clarity to Duolingo's missteps in announcing its AI-first strategy. And in a world where many are using AI to automate hiring, content creation, and even strategy, Kate reminds us that wisdom, nuance, and empathy are still irreplaceable.

So what does ethical acceleration look like in practice? And as businesses lean into automation, are we keeping the human experience at the centre or just chasing what's technically possible?

I'd love to hear your take on this. Is your organisation leading with purpose or with pressure? Let's continue the conversation.

In today's episode of Tech Talks Daily, I sit down with Ed Nabrotzky, CEO of Dot AI, to find out how a new generation of asset tracking technology is reshaping what's possible in logistics, operations, and enterprise strategy. Ed brings decades of experience as an executive and innovator in RF and IoT systems, and now leads Dot Ai at the intersection of artificial intelligence, patented hardware, and cloud-powered platforms.

With global supply chains facing continued pressure from disruptions, tariffs, and rising customer expectations, Dot Ai is offering something many enterprises are still chasing: real-time visibility with context. But what sets Dot AI apart from other asset tracking providers is its full-stack approach, which combines AI, RFID, Bluetooth, and proprietary hardware to deliver predictive insights across the entire supply chain.

During our conversation, Ed explained how Dot AI's model moves visibility from a passive reporting tool to an active intelligence layer for the business. He also shares the story behind their recent $175 million distribution agreement with Würth Industries, and what that level of demand signals about where the market is headed.

We also explore the company's upcoming product rollouts, including ZiM Bridge, new IoT trackers, and a cloud platform built to scale. But what stands out most is Ed's broader vision for the sector. Drawing from past ventures and academic research, he reflects on what it takes to build resilient, tech-forward operations in a world increasingly shaped by automation, connectivity, and real-time data.

How do we turn all that complexity into something simple and actionable? And what can other founders, tech leaders, and supply chain decision-makers learn from the Dot AI playbook?

As always, I'll leave you with a question. As AI increasingly penetrates the physical world, are we doing enough to make our systems not only more innovative but also more transparent and accountable? Let me know your thoughts.

What happens when you bring together a practicing monk, a seasoned tech entrepreneur, and an AI education pioneer? In this episode of The Tech Talks Daily Podcast, I sit down with Prashant Raizada, the driving force behind Lumi Network, to explore how we can build a workforce ready for the AI era, not just in skills, but in mindset and purpose.

Prashant shares how his journey from global banking and McKinsey to founding five startups worth over $2 billion eventually led him to education technology. But Lumi is not just another edtech venture. It's a mission-driven platform focused on upskilling the current and future workforce through human-AI collaboration, regional transformation initiatives like the Scale Up North East campaign, and a clear-eyed view of what education should look like in a rapidly changing world.

We talk about the need to rethink our approach to curriculum design, the limits of traditional university models, and why collaboration between industry, academia, and government isn't just helpful, it's urgent. Prashant explains why AI isn't a threat to humans but an amplifier of our abilities when used with intention. And he shares how Lumi's "Quest" programme trains participants in collaborative problem-solving while subtly building the 12 essential skills for thriving in an AI-powered economy.

We also tackle the bigger picture: why education has often been overlooked in AI investment conversations, and what needs to change for the sector to finally unlock the $10 trillion opportunity ahead of it. Prashant doesn't shy away from the challenges but makes a compelling case for why the UK could become a global exporter of a scalable, human-centric education model.

So, what would it take to scale this nationally? And if we're serious about upskilling 7.5 million people by 2030, who's going to deliver it? Prashant believes startups like Lumi could be a central part of the answer and the time to act is now.

Could meaningful AI education be the missing piece in our national skills strategy? And what does real collaboration between humans and machines actually look like in practice? Let us know your thoughts after listening.

Is innovation failing because we're starting in the wrong place?

In this episode of Tech Talks Daily, I'm joined by Colin Scott, Senior Vice President of DownSelect at Innventure, to challenge the default tech-first mindset that dominates R&D and corporate innovation. Instead of asking what a technology can do, Innventure flips the question: what real-world problem needs to be solved? It's a market-first philosophy, not a pitch deck philosophy, and it's led to some fascinating outcomes.

Colin walks us through Innventure's DownSelect process, a trademarked, multi-stage approach for evaluating early-stage innovation through the MATCH framework: Market, Advantage, Timeline, Capital, and High Value.

The result? Out of over 150 evaluated opportunities, just four companies have cut, including PureCycle Technologies, AeroFlexx, and Rafinity. That 5 percent conversion rate isn't a bug; it's a feature. It's the design. Colin explains why prioritising unmet strategic needs over flashy prototypes leads to better long-term outcomes for customers, partners, and the technology itself.

We also explore how Innventure fosters creative tension inside teams through something they call "Tigger Time," a framework that assigns roles for optimists and sceptics to surface risks and possibilities early in the process. It's a smart way to turn internal disagreement into forward momentum.

So, how do you approach innovation in your organisation? Are you starting with the technology or the need? Please let me know your thoughts after listening.

When AI enters the boardroom, it tends to arrive with big promises: productivity, automation, efficiency. But for Francesco Brenna, Global Leader of AI Integration Services at IBM Consulting, the real opportunity isn't just faster workflows—it's rebuilding how business gets done from the ground up. In this episode, recorded in the heat of a New York summer, Francesco joined me to unpack what agentic AI really means for enterprise leaders and why "doing AI right" is about more than picking the latest model.

We began by breaking down the term agentic AI, which Francesco defines as the shift from passive assistants to intelligent agents that can actually execute work, not just suggest how to do it. That might sound subtle, but it's a huge leap. And it's not one companies can take by simply layering AI on top of broken or inefficient processes. Instead, IBM is helping its clients rethink entire workflows, starting not with the tech stack, but with the business outcome.

Francesco explains why data readiness is still the number one challenge. While many companies have talked about modernizing their data foundations, few have done it in a way that supports grounded, contextual, reliable AI agents. He introduces the idea of "data products" as a way to anchor agent behavior in the right context, feeding into IBM's three-layer model: user experience, orchestration, and data.

We also explored the growing role of standards like Model Control Protocol (MCP), which could make secure integration with legacy systems more realistic at scale. Francesco highlights how IBM is addressing access control, security, and governance to ensure agentic systems are not only powerful but also trustworthy and accountable.

There's plenty here for enterprise leaders wondering how to move AI projects out of pilot mode. From real examples in customer service, insurance, and pharma, to IBM's internal strategies for employee upskilling, Francesco shares what early success looks like and why hackathons, hands-on experience, and human-centered design are critical.

Voice has always been a powerful way to connect with customers, but until recently, voice AI struggled to deliver the kind of seamless experience we associate with great service. That is starting to change. In this episode, I catch up with Sam Richardson from Twilio to discuss the renewed momentum behind voice AI and what it means for the future of customer experience.

Sam explains why voice is gaining relevance again. It is not just because the technology has improved, but because customers still prefer natural conversations when solving problems.

According to Twilio's research, more than half of customers want to know when they are speaking to a bot, while nearly half do not mind as long as their issue gets resolved. That balance is important. It is not about deception. It is about resolving problems efficiently without losing the human touch.

We talked about the balance between automation and empathy. Sam emphasizes the importance of escalation paths. When a customer is frustrated, repeating themselves, or directly asking for a person, they should be able to reach one. Without that, companies risk creating what he calls "reality privilege," where only premium customers receive human support. Voice AI should serve everyone, not create unfair divides.

Sam also shared how Twilio is helping businesses adapt without replacing everything. Using API-based tools, companies can integrate voice AI into existing systems. That flexibility matters, especially since 96 percent of Twilio customers are building custom customer experience solutions to fit their specific environments. This is especially relevant in industries like hospitality, retail, and automotive.

The early results are promising. Some companies are seeing a 60 to 70 percent increase in call containment and a noticeable drop in contact volume. Customer satisfaction scores have not suffered. Still, Sam is realistic. Long-term impact takes time to measure. The key is testing thoroughly, choosing the right solution, and tracking how customers actually feel.

When we talk about cybersecurity, it's often easy to think in terms of firewalls, passwords, and high-profile breaches. But what happens when the vulnerability isn't within your own systems but somewhere deep in your third or fourth-tier supply chain? In this episode, I spoke with Ben Edwards from Bitsight about the unseen infrastructure propping up much of the global digital economy and the new risks emerging from it.

Our conversation begins by challenging the assumption that larger technology providers are automatically safer. Bitsight's research reveals that scale often introduces complexity and a larger attack surface, which can make it even harder to stay secure. In fact, UK supply chains are now around 10 percent larger than the global average, reflecting a more advanced digital economy but also introducing more room for hidden weaknesses.

One of the most sobering parts of the discussion focused on geopolitics. Around 30 percent of UK and US supply chains rely on Chinese military-linked companies like Huawei and China Telecom. That's not just a cybersecurity concern. It's a geopolitical time bomb. Ben broke down the ripple effects that potential restrictions or bans could have, including costs, infrastructure overhauls, and widespread operational disruption.

Then there are the "hidden pillars," smaller vendors like Aptiv and Yardi, which may not be household names but play disproportionately influential roles in sectors like aerospace, education, and real estate. Their obscurity makes them dangerous single points of failure, especially when regional dependencies form without anyone noticing.

The bottom line? End-to-end supply chain visibility remains elusive. Shadow IT, employee workarounds, and a constantly shifting tech landscape mean organizations must approach cybersecurity as an ongoing process, not a checklist. Ben urges companies to continually assess the criticality of their providers and, just as importantly, understand their own role in others' ecosystems.

If you're curious about how internet balkanization, AI, and outsourcing are shaping the next phase of cybersecurity strategy, this episode will give you a lot to think about. Y

IT support is changing fast, and the old service desk model is starting to feel like a relic from another era. In this episode, I spoke with Samuele Gantner, Chief Product Officer at Nexthink, about what comes next and what companies need to do now to prepare for it. With Nexthink sitting at the forefront of Digital Employee Experience (DEX) technology, Samuele offers a grounded, thoughtful look at what a "ticketless" future might look like.

One of the boldest predictions shared is a 90 percent reduction in IT support tickets by 2027. That is not marketing spin. It's a vision rooted in practical shifts toward proactive support, real-time observability, and AI-powered automation. Samuele explains how organizations can move away from reactive models by embracing intelligent agents that solve problems before a ticket is even raised. Instead of asking employees to fix things themselves, IT support is quietly transforming into something far more sophisticated and intuitive.

We talked about what drives this change, from millennial and Gen Z expectations to the technological leap enabled by large language models and automation. Samuele breaks down why old attempts at self-service failed and how today's AI agents are finally equipped to handle objective complexity with empathy, scale, and context.

He also shares examples that bring this future to life, including a retailer that avoided a costly outage through predictive alerts, a healthcare provider saving millions through experience-driven device refreshes, and an energy firm preventing disaster in a virtual desktop environment. These are not theoretical case studies. They are live proof that DEX is not just buzz. It works.

We also explored what skills IT teams need to thrive in this shift. It is not all about tech. Emotional intelligence, cross-functional collaboration, and a proactive mindset are just as important. Whether you're a CIO or a support engineer, there is something here that will challenge how you think about IT's role in the business.

AI is racing ahead, but for industries like life sciences, the stakes are higher and the rules more complex. In this episode, recorded just before the July heatwave hit its peak, I spoke with Chris Moore, President of Europe at Veeva Systems, from his impressively climate-controlled garden office. We covered everything from the trajectory of agentic AI to the practicalities of embedding intelligence in highly regulated pharma workflows, and how Veeva is quietly but confidently positioning itself to deliver where others are still making announcements.

Chris brings a unique perspective shaped by a career that spans ICI Pharmaceuticals, PwC, IBM, and EY. That journey taught him how often the industry is forced to rebuild the same tech infrastructure again and again until Veeva came along. He shares how Veeva's decision to build a life sciences-specific cloud platform from the ground up has enabled a deeper, more compliant integration of AI.

We explored what makes Veeva AI different, from the CRM bot that handles compliant free text to MLR agents that support content review and approval. Chris explains how Veeva's AI agents inherit the context and controls of their applications, making them far more than chat wrappers or automation tools. They are embedded directly into workflows, helping companies stay compliant while reducing friction and saving time. And perhaps more importantly, he makes a strong case for why the EU AI Act isn't a barrier. It's a validation.

From auto-summarising regulatory documents to pulling metadata from health authority correspondence, the real-world examples Chris offers show how Veeva AI will reduce repetitive work while ensuring integrity at every step. He also shares how Veeva is preparing for a future where companies may want to bring their LLMs or even run different ones by geography or task. Their flexible, harness-based approach is designed to support exactly that.

Looking ahead to the product's first release in December, Chris outlines how Veeva is working hand-in-hand with customers to ensure readiness and reliability from day one. We also touch on the broader mission: using AI not as a shiny add-on, but as a tool to accelerate drug development, reach patients faster, and relieve the pressure on already overstretched specialist teams.

Chris closes with a dose of humanity, offering a book and song that both reflect Veeva's mindset, embracing disruption while staying grounded. This one is for anyone curious about how real, applied AI is unfolding inside one of the world's most important sectors, and what it means for the future of medicine.

What does modern cybersecurity look like when you're leading a federally regulated financial institution serving Canadians from coast to coast?

In this episode, recorded at Cisco Live last month, I sat down with Shawn Spurko, VP of Information and Cybersecurity at Innovation Federal Credit Union. Based in Swift Current, Saskatchewan, but now operating across the country, Shawn Spurko has played a key role in building a security posture that treats laptops as offices and supports a workforce no longer tethered to physical locations.

Shawn Spurkoshares his journey from service desk and web design to becoming a cybersecurity leader, and offers a refreshingly grounded view of how to approach regulation, zero trust, and digital maturity. He explains how Innovation CU's move from a provincial to a federal charter transformed their compliance obligations and how solutions like Cisco Secure Access enabled them to scale security seamlessly for a hybrid workforce.

We explore why transparent, user-friendly security is no longer optional and how mature implementations of SWG, ZTNA, DLP, and management tunnels are quietly solving problems before users even notice them. Shawn Spurkoalso opens up about the practical realities of working with tools that are constantly evolving, and how his team reviews every new feature not just for capability but for how it maps to regulatory controls like Canada's OSFI B-13.

This episode is a masterclass in modern cyber strategy, but it's also a reminder that the goal isn't complexity, it's making things work, everywhere, all the time.

So, as the cybersecurity landscape continues to shift and regulators tighten expectations, how are you designing systems that work for both users and auditors? And what lessons can we all learn from financial services as we navigate this new world of anywhere access?