How are businesses supposed to grow when technology is moving faster than regulation, customer expectations keep shifting, and AI is changing the rules in real time?

In this episode, I sat down with Derya Matras, Vice President of EMEA at Meta, to talk about what growth really looks like for businesses operating in Europe, the Middle East, and Africa right now.

This was a fascinating conversation because it went far beyond the usual talking points around AI and advertising. Derya brought a broader view of the pressure many businesses are under today, from macroeconomic uncertainty and political complexity to changing consumer behavior, tighter margins, and the need to adapt to a world where AI is now part of everyday decision making.

What really stood out to me was her point that this moment is about far more than adopting new tools. It is about culture, leadership, and having the discipline to know what you are actually trying to achieve.

Derya spoke about the importance of having a clear North Star goal, getting the foundations right, and making sure businesses are not simply adding AI into broken systems or unclear strategies. Because as she put it, AI can make everything more powerful, but it can also amplify mistakes. That is such an important point, especially at a time when so many companies are racing to show they are doing something with AI without always knowing what success should look like.

We also explored how Meta sees its role in supporting growth across Europe's digital economy. Derya shared insights into how Meta's platforms are helping businesses of all sizes reach customers in ways they simply could not do on their own.

For large companies, that may mean better measurement, faster optimization, and more personalized engagement. But for smaller businesses, the stakes can be even higher. She shared examples that brought those numbers to life, including entrepreneurs using Instagram and WhatsApp to reach global markets, support their families, and create jobs in ways that would have been out of reach just a few years ago.

Another part of the conversation I found especially interesting was the tension between innovation and regulation in Europe. Derya was honest about how complicated and fragmented the environment has become, and how that complexity can slow progress or delay the rollout of new products.

At the same time, she made a strong case that Europe still has a real opportunity ahead if it can find the right balance. That balance matters not only for big tech companies, but for startups, small businesses, creators, and the wider economy that increasingly depends on digital tools to compete and grow.

We also talked about creativity, measurement, AI assistants, wearables, and even how these technologies are beginning to shape life at home as much as at work. It all made for a conversation that felt very current, but also deeply practical.

So as AI becomes woven into advertising, business operations, and everyday life, are organizations truly building the foundations they need to benefit from it, or are they still chasing the next shiny thing? And what do you think Europe needs to get right to make sure innovation and opportunity can keep moving forward?

What happens when AI ambition starts moving faster than the infrastructure built to support it?

In this episode, I spoke with Lee Caswell, SVP of Product and Solutions at Nutanix, about the latest Enterprise Cloud Index and what it tells us about where enterprise IT really is right now. There is no shortage of AI headlines, product launches, and promises about what comes next, but this conversation gets behind the noise and into the operational reality that many business and technology leaders are now facing. As Lee explained, AI is not arriving in isolation. It is pulling containers, data strategy, hardware decisions, governance, and application modernization along with it.

One of the biggest themes in our conversation was the growing link between AI workloads and container adoption. Lee made the point that applications still sit at the top of the org chart, and infrastructure exists to serve them.

As more AI-enabled applications are built by developers who favor containers and Kubernetes-based environments, enterprises are being pushed to rethink how they support those new workloads.

We talked about why containers are becoming such an important part of modern application strategy, how they help organizations handle distributed AI use cases, and why many businesses are trying to balance speed and flexibility without giving up the resilience and control they have spent years building into their infrastructure.

We also spent time on the less glamorous side of AI adoption, but arguably the part that matters most. Shadow AI, data sovereignty, unpredictable token costs, and infrastructure readiness are all becoming board-level issues.

Lee shared why so many organizations are realizing that AI cannot simply be layered onto existing systems without deeper changes underneath. New hardware, new software, new governance models, and a more consistent approach across edge, on-prem, private cloud, and public cloud environments are all part of the picture now.

What I enjoyed most about this conversation was that it never framed AI as magic. It framed it as work. Real work that demands better architecture, sharper oversight, and faster decision-making from IT teams that are already under pressure.

So if your organization is racing to adopt AI, are you also building the foundation needed to support it responsibly, and where do you think the biggest risk sits right now? Share your thoughts with me.

How far can we trust research that is generated without asking a single human being?

In this episode, I sat down with Jordan Harper from Qualtrics to unpack one of the most talked-about developments at the Qualtrics X4 Summit, synthetic research. It is a topic that sparks curiosity, excitement, and a fair amount of skepticism in equal measure. And honestly, that tension is exactly why this conversation matters.

Jordan brings a rare mix of scientific thinking and real-world technology experience, which makes him well placed to cut through the hype. We explored what synthetic panels actually are, and just as importantly, what they are not.

While many assume this is simply about asking a large language model for answers, the reality is far more nuanced. The approach Jordan and his team are building is grounded in how humans respond to surveys, trained on vast datasets to reflect the inconsistencies, biases, and unpredictability that make human insight valuable in the first place.

What stood out throughout our conversation was the idea that synthetic research should be seen as additive rather than a replacement. It offers speed, flexibility, and the ability to test ideas quickly, but it does not replace the depth and lived experience that only real people can provide. In fact, some of the most interesting insights come from comparing synthetic responses with human ones, revealing patterns, biases, and even blind spots in traditional research methods.

We also got into the practical side of things. From controlling for issues like survey fatigue and social desirability bias, to experimenting with question design in ways that would be difficult with human respondents, synthetic research opens up new ways of working. At the same time, it raises important questions about validation, trust, and where to draw the line when decisions carry real-world consequences.

For me, this episode is about perspective. In a world where AI is accelerating everything, it can be tempting to look for shortcuts. But as Jordan explains, the real value comes from using these tools thoughtfully, alongside human insight rather than in place of it.

So as this technology continues to evolve, how should researchers and business leaders strike that balance? And where could synthetic research help you ask better questions before you make your next big decision?