What if the key to global access to high-quality education isn't policy reform or private investment, but open source software? In this episode of Tech Talks Daily, I sit down with Scott Anderberg, CEO of Moodle, to explore how one of the world’s most widely used learning platforms is quietly transforming education in ways that extend far beyond the classroom.

Scott’s journey is anything but linear. From helpdesk support in Denver to leading online education efforts across the US, UK, and Australia, his international experience has shaped a clear mission: to make education more accessible, more inclusive, and more creative. His role at Moodle aligns perfectly with that goal.

We discuss what open source really means in the context of education and why it continues to be misunderstood. Scott explains how Moodle’s global community of developers and educators contributes everything from security-tested code to deeply localised customisations that enable learning to happen anywhere, even in places without electricity. Projects like MoodleBox and the Inventorium for at-risk students in Australia reveal the power of local innovation when built on flexible, open platforms.

Scott also outlines Moodle’s measured approach to AI, focusing on what delivers actual value. Rather than embedding generative tools for novelty, they’ve released an AI subsystem that allows the community to experiment and share what works. This model not only encourages innovation but also respects the diverse regulatory, cultural, and economic environments their users operate within.

Throughout the conversation, we explore the myths that often discourage adoption of open source solutions. Security, support, and scalability are frequently raised, but Scott shows how Moodle’s global ecosystem consistently challenges those assumptions. Innovation doesn’t have to be proprietary or top-down. In fact, when communities co-create solutions, the results are often more resilient and more relevant.

We close with a powerful reflection on the importance of diversity in both tech and education. While some organisations are becoming hesitant to talk about inclusion, Scott argues that now is the time to stand firm. Education is about connection, and you cannot truly connect people if only a narrow group is included in the conversation. Open platforms like Moodle make it possible to include everyone, not just in theory but in practice.

So can open source help us rebuild education in a way that is genuinely inclusive and globally relevant? Or are there still barriers that need to be broken? I’d love to hear your thoughts. What role should open platforms play in shaping the next chapter of learning?

In this episode, I sit down with Robin Smith, Chief Technology Officer at TravelPerk, to hear how one of Europe’s fastest-growing travel platforms is blending automation with empathy in a sector built on unpredictability.

Robin shares how that continuity has shaped his view of disruption, from the rise of web and mobile booking to the rapid acceleration of cloud platforms and now AI. He explains why staying rooted in a single mission has helped him stay curious and open to change in a way frequent job hopping never could.

We explore how TravelPerk is positioning itself as a consumer-grade experience built with business-grade rigour, serving mid-market companies that want seamless travel and expense tools without compromising on policies, duty of care, or integrations. Robin unpacks why large enterprises often lag in tech adoption and how nimble, decentralised teams are setting a new benchmark for innovation in corporate travel.

The conversation dives into AI’s growing role in travel, from Yokoy’s AI driven expense automation to the company’s internal enablement strategy powered by its partnership with 9x. But Robin also reminds us of something easy to forget amid all the hype: 31 percent of travelers still pick up the phone when disruption hits. The appetite for AI is growing, but human connection isn’t going away.

We also look at the invisible complexity behind making global travel feel simple, from airline add ons and multi country compliance to supporting collaborative group travel in a post pandemic world. Robin shares why the real challenge isn’t building software, it’s hiding the machinery behind a product that “just works.”

As we round off the conversation, Robin offers advice to engineers and product leaders hoping to follow a similar path. His takeaway is refreshingly honest: ignore most advice, and fall in love with the problem. It’s not about replicating someone else's journey. It’s about staying curious and building your own.

So, will AI ever fully automate travel bookings or is the human touch still too valuable to leave behind? And what does building resilience look like when the rules of travel keep changing? Tune in and let me know what you think.

What happens when the world’s most popular AI tools become ad platforms? That’s the question I put to Aaron Goldman, CMO of Mediaocean, in a candid conversation about the future of advertising, search, and AI-native experiences.

Aaron brings over two decades of industry insight to the table, sharing his perspective on how platforms like ChatGPT are becoming the next digital frontier, not just for consumer interaction but for ad monetization. As generative AI shifts from novelty to utility, the implications for marketers are profound. Google, Meta, OpenAI, and Perplexity are all experimenting with ways to integrate advertising without disrupting user experience. But what happens when the interface you trust for recommendations also serves you tailored promotions?

We explore how AI can shape embedded ads, personalize intent-driven marketing, and even facilitate transactions within the prompt itself. Aaron also reflects on the rise of AIO (AI Optimization), what it means for traditional paid search, and how agencies must adapt to tools like Model Context Protocol to stay relevant.

This episode isn’t just about platforms and protocols. It’s about empathy, creativity, and critical thinking in a world increasingly automated by algorithms. If AI becomes your co-pilot, how do you stay in the driver’s seat?

So what do you think? Will AI advertising be a seamless leap forward or the start of a trust crisis? Let me know your thoughts after the episode.

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