Tech Talks Daily

Did you know that when many people hear "Orange," they still ask if it involves SIM cards? That was the perfect place to begin my conversation with Sahem Azzam, President for IMEA and Inner Asia at Orange Business.

Once we cleared that up, it opened the door to a much richer story about what enterprise innovation looks like across one of the fastest-moving regions on the planet.

Sahem joined me from Dubai, a city that has become a living case study for what happens when a region refuses to think small. As we compared notes from Gitex Global, it became clear that what is happening across the Middle East is not a short burst of enthusiasm.

It is a deliberate long-term shift driven by young populations, bold government ambition, and a willingness to adopt new technologies before anyone else. Sahem explained how this appetite for speed is shaping the region's digital transformation and how Orange Business is supporting it through cloud, connectivity, cybersecurity, digital integration, and large-scale smart city programmes.

He shared practical stories that peeled back the curtain on cognitive city design, energy optimisation, and the pressure on enterprises to simplify sprawling hybrid IT environments. What stood out was how often the conversation returned to value. Better user experiences, lower costs, and new revenue paths.

Everything Orange Business builds must deliver one of those outcomes. Sahem talked through platformization, why unified infrastructure matters, and how enterprises can reduce complexity in an age where cloud, security, networking, and AI all collide at once.

We also discussed the growing focus on responsible AI and the shared need for transparency. Sahem spoke about data ownership, trusted models, and the careful guardrails that must sit behind every AI deployment.

The rise in cyber threats is making this more important than ever, and he offered a candid look at how Orange Cyberdefense approaches modern security through an integrated view of infrastructure, operations, and risk.

What gave this conversation a personal edge was Sahem's final reflection on learning. After years at Stanford, London Business School, and Harvard, he still sees human experience as the most valuable teacher.

Listening to people, sharing problems, comparing perspectives. Events like Gitex remind him that optimism is contagious and that the future of the region will be shaped by collaboration as much as technology.

If you want a grounded view of digital transformation from someone living it every day, this conversation is a rare window into both the opportunities and the tension behind innovation at scale. Have you seen the same momentum in your own region, and how do you stay ahead of the pace of change? I would love to hear your thoughts.

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Did you ever walk into a conference session thinking you were ready for the week, only to realise the announcements were coming so fast that you almost needed an agent of your own to keep up? That was the mood across Las Vegas, and it was the backdrop for my conversation with Madhu Parthasarathy, the general manager for Agent Core at AWS.

He has spent the week at the centre of AWS's wave of agentic AI news, working on the ideas that are already moving from keynotes and demos into the hands of real enterprise teams. Sitting down with him offered a rare moment of clarity among the noise, and his calm take on what actually matters helped bring the bigger picture into focus.

Madhu talked through the thinking behind Agent Core and why he believes 2026 will be the year enterprises finally begin shifting from prototypes to production-scale agents. He walked me through the two areas customers keep coming back to, trust and performance, and why the new policy framework and agent evaluations could remove long-standing barriers to deployment.

His examples were grounded in real behaviour he is seeing inside large companies, whether that is internal support workloads, developer productivity, meeting preparation, or customer-facing flows designed to reduce the friction between intent and outcome.

We also explored the deeper shift introduced by Nova Forge, including the idea of blending enterprise data with model checkpoints to create domain-specific agents that can work with greater accuracy and context. Madhu explained why there will never be a one-size-fits-all model and how choice remains central to AWS's agentic AI approach.

My guest also reflected on how infrastructure changes, such as Trainium three ultra servers and expanded Nova model families, are shaping the pace at which companies can experiment, evaluate, and adopt emerging capabilities.

Trust surfaced again and again in our conversation. Madhu was clear that non-deterministic systems also introduce concerns, which is why action boundaries and guardrails are becoming as important as model quality. He described the excitement he is seeing from customers who now feel they have workable ways to give agents responsibility without handing over the keys entirely.

As he put it, this is the moment where confidence begins to grow because the guardrails finally meet the expectations of enterprise leaders.

We closed with the topic many people have been whispering about all week, modernization. Madhu reflected on AWS Transform, the push to help organisations move away from legacy architectures far faster than before, and the impact that agentic systems will have as they support full stack migrations across Windows environments and custom languages.

Madhu cuts through the noise with a grounded view of reliable autonomy, multi-agent orchestration, policy-driven safety, and the shift toward agents as true collaborators.

The question now is where you see the biggest opportunity. How might these agent-based systems change your workflows, and what would it take for you to trust them with the tasks you never seem to have time for? I would love to hear your thoughts.

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Have you ever wondered how an idea that begins with two friends in a pub ends up shaping conversations about health all over the world? That was on my mind as I met Graham Link & Timothy Gnaneswaran from Movember on the show floor at AWS re:Invent.

Their story has grown far beyond the mustache that everyone recognises. What started with a simple gesture of support has become a movement that now reaches millions, raises vast sums through a global fundraising platform, and backs projects focused on prostate cancer, testicular cancer, mental health, and suicide prevention. Hearing them describe how that original spark grew into something this wide and long lasting gave the conversation a real sense of depth.

Recording in the middle of re:Invent added its own flavour. AI news filled the halls, yet Timothy and Graham were there speaking with engineers and builders about something deeply human. Their booth stopped people in their tracks, offered barbershop shaves, and created space for personal stories.

They talked openly about how Movember built its own platform to handle sixty to eighty million dollars in four weeks, how it must stay resilient every minute, and how AWS has supported them for more than a decade. They also shared how technology shapes the work behind the scenes, whether it is clinical quality registries, digital conversations tools, or new research paths that explore how AI might support healthier behaviours.

What stayed with me most was the honesty about the tensions they face. Men are still reluctant to talk about their health. Loneliness is rising. Social platforms create new openings and new barriers at the same time. They see how AI can help someone begin a difficult conversation, yet they are clear about the risks when people rely on tools that were never designed for mental health support.

They also talked about the patterns they see across different regions, the sobering statistics in the major markets where they operate, and how younger audiences now gather in gaming communities rather than traditional spaces.

Movember knows it needs technology to reach scale, but it never wants to lose the human connection at the heart of its mission. What part of their story stands out most for you, and where do you think technology can genuinely help shape the next chapter of men's health?

Did you know a single Formula 1 car produces 1.1 million data points every second from hundreds of sensors? That number alone sets the tone for this conversation with Ruth Buscombe, an F1 strategist, analyst, and F1TV presenter whose work sits at the meeting point of engineering precision and real time storytelling.

We met at AWS re:Invent in Las Vegas, and her insights into how much pressure, judgment, and creativity are wrapped inside each decision brought the sport to life in a fresh way for anyone who has ever stared at a dashboard of metrics and wondered what really matters.

This discussion goes far deeper than split times and tyre choices. Ruth explains how AWS and F1 are rethinking race strategy through real time insights and cloud compute, from TrackPulse and root-cause analysis all the way to predictive graphics that let commentary teams spot a race-defining moment before it happens.

She also reflects on the sport's changing culture, the growth of new fan communities, and the shift from old telemetry to modern systems that process millions of data points every second. Her stories from the paddock at Ferrari, Alfa Romeo, and F1TV help frame just how intense the job can be when 12,000ths of a second separate pole from second place.

There are moments in this conversation that remind us that F1 strategy is as much about human pattern recognition as it is about machine intelligence, and that the strongest engineers find ways to absorb pressure without losing their instinct.

What stood out most was how clearly Ruth links F1 to decision making in every industry. Whether she is talking about marginal gains, pattern detection, or the discipline needed to separate noise from signal, her examples make perfect sense to both race fans and tech leaders.

She shares how AWS tools allow broadcasters and engineers to interpret scenarios instantly, why the sport needed to move past manual diagnosis, and how new tools even help verify whether a driver's mistake came from a small steering slide or a split-second shift error.

Her passion is infectious and her explanations cut straight to the heart of what makes the blend of live racing and cloud computing work so well. As you listen, think about how your own team makes choices under pressure and ask yourself one last question. If you were in the garage making a call with the whole world watching, which signals would you trust and how fast could you act?

Useful Links:

• Connect with Ruth
• Sign up to Ruth's Newsletter
• AWS Insights

Have you ever wondered how a company with nearly a million associates across continents keeps everyone learning, aligned, and prepared for constant change? That question sat at the heart of my conversation with Victor Arguelles, the VP of Global Learning Design and Development at Marriott International.

Victor began his career as a high-school educator, and it is clear that this early experience shapes his entire approach to enterprise learning. He brings the empathy and discipline of the classroom into a global operation where cultural nuance, business complexity, and operational scale collide every day.

Across our conversation, Victor opens up about what digital transformation in learning actually looks like behind the curtain at Marriott.

Rather than focusing on tools alone, he explains how mindset, process, and cultural confidence dictate success. He talks about the delicate balance between global standardization and local relevance, and how Marriott validates learning experiences to understand how change will feel for associates before any deployment begins. It becomes clear that the company's commitment to people first is not a slogan, it is the foundation of the entire learning strategy.

Victor also shares how Marriott is using partners and platforms to reimagine training in a way that fits into the flow of work. He describes how digital adoption tools have reduced training seat time by as much as 60 percent and given associates real support inside the tools they use every day. This shift has created confidence, improved performance, and given teams more time with guests, which he considers the most meaningful return on investment of all.

Looking ahead, Victor reflects on the role AI will play in learning, from measurement to content creation, and how emerging tools could eventually provide adaptive, contextual support in real time.

If you are a tech or business leader trying to understand how large enterprises truly modernize learning, this conversation offers a grounded and human view of what it takes. And as Victor looks toward 2026 and beyond, he shares why he believes the next wave of learning innovation will be shaped by AI, data, and a deeper understanding of behavior inside the flow of work.

What stood out to you in his approach, and how do you see the future of enterprise learning evolving? I would love to hear your thoughts.

Have you ever wondered how an industry known for delays and uncertainty suddenly starts operating with the pace of a tech company? That thought stayed with me as I spoke with Eppie Vojt, the Chief Digital and AI Officer at West Shore Home. His team is bringing applied AI into home remodeling in a way that feels practical, grounded, and surprisingly human.

Eppie explains how a strong data foundation allowed them to introduce agentic systems without the usual chaos. Those systems now handle scheduling, permitting, forecasting, and communication in the background. The result is a level of certainty that customers rarely experience in remodeling. When someone signs a project, they already know the installation date. Hours of operational work happen silently, and that alone changes the entire experience.

We also talk about the culture that made this possible. Instead of forcing new tools onto teams, leadership encouraged small experiments and curiosity. That simple move flipped the mood internally. Departments began approaching Eppie with ideas rather than waiting to be pushed. The rollout was gradual, giving people time to shift into more valuable work without fear or disruption.

Looking ahead, Eppie sees huge potential in letting customers start their journey in different ways. Tools like photogrammetry and digital twins could help people get early pricing guidance without a full in-home visit. It reflects a bigger change across physical industries as AI becomes something that quietly supports accuracy, safety, and convenience.

If you care about real AI adoption rather than hype, this one offers a clear view into what works. I'd love to hear what stood out to you after listening.

Useful Links

• Connect with Eppie Vojt on LinkedIn
• Learn more about West Shore in this video

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What does it really mean to run a company that aims to be "good" before it ever thinks about becoming "great"? That was the question sitting with me as I sat down with Appfire's CEO, Matt Dircks. The conversation took us straight into the heart of modern leadership, purpose, and the realities of running a global SaaS business during a period of change.

Matt has led organisations through rapid growth, mergers, cultural resets, and shifting market expectations. What stood out in our discussion was how open he is about the parts of leadership that are messy. He talked about transparency, dealing with hard decisions, and the challenge of building a culture where people feel safe enough to be honest without losing accountability. His philosophy is grounded in something simple. You cannot scale trust unless you behave in ways that earn it every day.

We explored how Appfire is evolving beyond its acquisition roots, expanding from Atlassian aligned tools into cross platform solutions that support enterprises across Microsoft, Salesforce, GitHub and more. Matt explained why the company is investing heavily in new AI native products and why being close to customers is becoming a priority as their needs become more complex. He also shared how openness, active communication, and a willingness to be challenged guide the way he leads through uncertainty.

The more we talked, the clearer it became that Appfire's next chapter is a blend of product innovation, cultural maturity, and a renewed focus on service. Matt's story offers a useful lens for anyone wrestling with questions about values, growth, and the human side of technology. What does a "good company" look like in practice, and how does that shape the road to long term success?

I'd love to hear what resonated with you, so let me know your thoughts.

• Connect With Matt Dircks on LinkedIn
• Learn more about Appfire
• The No Asshole Rule: Building a Civilized Workplace and Surviving One That Isn't by Robert I. Sutton
• Range: Why Generalists Triumph in a Specialized World by David Epstein

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What happens when AI adoption surges inside companies faster than anyone can track, and the data that fuels those systems quietly slips out of sight? That question sat at the front of my mind as I spoke with Cyberhaven CEO Nishant Doshi, fresh from publishing one of the most detailed looks at real-world AI usage I have seen. This wasn't a report built on opinions or surveys. It was built on billions of actual data flows across live enterprise environments, which made our conversation feel urgent from the very first moment.

Nishant explained how AI has moved out of the experimental phase and into everyday workflows at a speed few anticipated. Employees across every department are turning to AI tools not as a novelty but as a core part of how they work. That shift has delivered huge productivity gains, yet it has also created a new breed of hidden risk. Sensitive material isn't just being uploaded through deliberate actions. It is being blended, remixed, and moved in ways that older security models cannot understand. Hearing him describe how this happens in fragments rather than files made me rethink how data exposure works in 2025.

We also dug into one of the most surprising findings in Cyberhaven's research. The biggest AI power users inside companies are not executives or early career talent. It is mid-level employees. They know where the friction is, and they are under pressure to deliver quickly, so they experiment freely. That experimentation is driving progress, but it is also widening the gap between how AI is used and how data is meant to be protected. Nishant shared how that trend is now pushing sensitive code, R&D material, health information, and customer data into tools that often lack proper controls.

Another moment that stood out was his explanation of how developers are reshaping their work with AI coding assistants. The growth in platforms like Cursor is extraordinary, yet the risks are just as large. Code that forms the heart of an organisation's competitive strength is frequently pasted into external systems without full awareness of where it might end up. It creates a situation where innovation and exposure rise together, and older security frameworks simply cannot keep pace.

Throughout the conversation, Nishant returned to the importance of visibility. Companies cannot set fair rules or safe boundaries if they cannot see what is happening at the point where data leaves the user's screen. Traditional controls were built for a world of predictable patterns. AI has broken those patterns apart. In his view, modern safeguards need to sit closer to employees, understand how fragments are created, and guide people toward safer workflows without slowing them down.

By the time we reached the end of the interview, it was clear that AI governance is no longer a strategic nice-to-have. It is becoming a daily operational requirement. Nishant believes employers must create a clear path forward that balances freedom with control, and give teams the tools to do their best work without unknowingly putting their organisations at risk. His message wasn't alarmist. It was practical, grounded, and shaped by years working at the intersection of data and security.

So here is the question I would love you to reflect on. If AI is quickly becoming the engine of productivity across every department, what would your organisation need to change today to keep its data safe tomorrow? And how much visibility do you honestly have over where your most sensitive information is going right now? I would love to hear your thoughts.

Useful Links

• Connect with Cyberhaven CEO Nishant Doshi on LinkedIn
• Learn more about Cyberhaven

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Why does it feel as though every headline about the future of work points to AI pushing entry-level roles off a cliff? That question stayed with me as I sat down with Robin Adda, a long-time learning and development leader, bestselling author, and one of the most balanced voices I have heard on skills, technology, and the workplace. Robin argues that AI can protect white-collar roles rather than erode them, and hearing him explain why immediately shifted the tone of the conversation.

From the start, Robin talks about how traditional training models have failed to keep pace with reality. Companies know the skills gap is widening, yet many still rely on broad, generic programmes that miss what people actually need. His journey toward building SkillsAssess grew out of that frustration. He realised that training without insight only scratches the surface, and employees end up going through motions instead of growing in ways that matter.

Inside organisations, the picture is even more complicated. Robin describes teams that want to move forward but have no clear road map, along with job seekers who struggle with basic digital tasks long before they reach more advanced expectations. Opportunity exists, yet people often cannot reach it because they lack a personal starting point. His work focuses on bridging that divide by giving individuals clarity and giving leaders accurate visibility into their workforce.

We also talk about the emotional weight behind all of this. Anxiety around AI is everywhere, especially for people who feel their role is drifting into uncertainty. Robin has seen organisations handle this well by focusing on clear information rather than vague reassurance. When people understand what they need to learn and why, their fear gradually shifts into something more constructive.

Another area that stood out was his emphasis on human strengths. As routine work moves to AI systems, qualities like curiosity, communication, and thoughtful decision making become even more valuable. Robin explains how behavioural profiling and tailored learning pathways can help companies build stronger teams rather than rely on technology to smooth every challenge.

By the end of our conversation, I found myself thinking differently about the future of work. Robin's perspective is grounded in decades of watching technology rise, fall, and rise again. He sees AI as a chance to rethink employability rather than fear the disruption. In his view, if we use these tools wisely, we can build a workforce that is more confident, more adaptable, and more resilient.

So here is the question I want to leave you with. If learning could finally become personal, and if AI could help people understand their own potential instead of replacing it, what would that change for you and your organisation? And how would it reshape the way you think about your career? I would love to hear your thoughts.

• Find out more at https://skillsassess.ai and by following the SkillsAssess' LinkedIn
• Listen to Robin and key industry guests on the SkillsAssess podcast - When Skills Matter
• Connect with Robin directly on LinkedIn

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Have you ever wondered what it looks like when an enterprise finally breaks free from spreadsheet-driven decision paralysis and lets AI take the wheel? That was the question at the back of my mind as I sat down with Bianca Anghelina, the founder of Aily Labs.

In our conversation, Bianca explains how her career inside large global enterprises shaped her view of the world. She saw first hand how companies could gather astonishing amounts of data but struggled to translate it into choices that actually mattered.

That friction pushed her to imagine something bolder, a decision intelligence platform that could remove the hand-stitched chaos of manual analysis and replace it with real-time clarity. She shares how she took the leap during an uncertain moment in 2020, trusting the idea that disruption often grows during difficult periods. Hearing her describe that early stage reminded me how many founders take quiet risks long before the public sees any success.

What stood out most was the simplicity of her philosophy. Every company will eventually use AI, but only the ones that rewire their culture and everyday routines will turn it into measurable value. Bianca talks about the shift from pilots to production, the widening gap between firms that run AI at scale and those still treating it as a side project, and how leaders need to rethink their role if they want to see material financial impact.

She also shares how Aily Labs uncovered a hundred million dollars in opportunities instantly for one enterprise, and how their AI agents connect previously isolated functions to solve resource allocation, supply chain shocks, and board-level scenarios in minutes instead of months.

We also look ahead. Bianca outlines her vision for fully autonomous decision-making agents and the long path toward an operating model where strategy, execution, and action flow through a single intelligent layer. Her optimism about where this can lead Fortune 500 organisations over the next five years left me thinking about how quickly boardrooms will need to adapt.

At the same time, she grounds that vision in her own story, acknowledging the mentors and supporters who helped her grow from corporate leader to founder.

If you are wrestling with the business case for AI, or trying to understand why so many firms still struggle to get past experimentation, this episode offers a clear window into what happens when AI is built into the centre of how an enterprise thinks. It is a rare mix of founder story, practical insight, and a glimpse of the future.

What part of Bianca's thinking resonates most with your own experience, and how do you see decision intelligence reshaping leadership teams in the years ahead? Let me know your thoughts.

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