What happens when the biggest breakthrough in AI isn't a flashy new tool, but finally getting rid of 450 spreadsheets?

Recording live from Qlik Connect, I sat down with Ed Dunger from HelloFresh to talk about what operational transformation actually looks like inside one of the world's most complex supply chain environments. Because when your business depends on forecasting demand, managing perishable food, coordinating deliveries, and making sure customers receive the right box at the right time, small inefficiencies quickly become expensive problems.

Ed leads operational technology and analytics enablement across global teams at HelloFresh, covering everything from forecasting through to final-mile logistics. In this conversation, he shares how the company moved away from hundreds of disconnected Google Sheets and manual processes toward a near real-time, data-driven operating model that gives teams faster, clearer, and more reliable decision-making.

We talk about the practical reality of replacing over 450 spreadsheets, building trust in the data, and creating systems that operational teams actually want to use. Ed explains why this was a two to three year journey rather than an overnight transformation, and how early wins, like predicting waste before it happened, helped build confidence across the business.

We also explore how HelloFresh is using predictive AI to improve exception management when deliveries fail. From triggering recovery boxes faster to improving customer communication when something goes wrong, the focus is not on AI for the sake of AI, but on solving real problems that directly affect customer experience.

There is also a valuable lesson here for any business trying to move from experimentation to operational reality. Start small, build trust gradually, and focus on solving one problem well before trying to transform everything at once.

So as more organizations race to adopt AI, are we sometimes overlooking the simple operational fixes that create the biggest impact? And is real transformation less about the technology itself, and more about how people learn to trust it?

Join me for a practical and honest conversation from Qlik Connect, and let me know your thoughts. Are you still managing around old processes, or are you building systems people can truly rely on?

How do you turn complex regulatory data into something customers can actually use, trust, and act on?

Recording live from Qlik Connect, I sat down with Robin Astle, Head of Qlik Analytics at Reconomy Group, to explore how data is becoming far more than an internal reporting tool. In Robin's world, it has become a product in its own right, helping some of the world's largest retailers manage compliance, reduce costs, and make smarter sustainability decisions.

Robin works across Valpak, a business at the center of environmental compliance and packaging regulation, supporting over 100 enterprise customers across the UK, Europe, and the US. From packaging taxes and recycling targets to government submissions and sustainability reporting, the amount of data involved is enormous, and the stakes are high.

In our conversation, Robin shares how the Valpak Insight Platform evolved from manual SQL extracts and spreadsheets into a fully scaled cloud-based analytics platform ingesting millions of rows of data every day. We discuss how that transformation helped reduce onboarding from weeks to days, created up to 90% time savings on CSR and analytics requests, and helped customers reduce compliance costs by up to 15%.

We also explore the launch of PackChat, which uses natural language queries to help customers interact with compliance and packaging data without needing deep technical knowledge. Robin explains why context is everything when dealing with environmental regulations, and why building trust in the data model is essential before AI can deliver real value.

There is also a bigger conversation here around how businesses can use data to serve customers directly, not just support internal teams. From OEM partnerships and cloud automation to scaling AI-powered services across global markets, Robin shares what it takes to turn data into a revenue-generating service.

So as more organizations look to unlock value from the information they already hold, are we still thinking too narrowly about what data can do? And could your greatest untapped product actually be the data sitting inside your business today?

Join me for a fascinating conversation from Qlik Connect, and let me know your thoughts. Are you still using data for reporting, or are you starting to think about it as a product?

How do you turn powerful AI technology into something customers actually trust, adopt, and use?

Recording live from Qlik Connect, I sat down with Mary Kern, Vice President of Analytics Product Go-To-Market at Qlik, to explore one of the most overlooked challenges in enterprise AI today. Not building the technology, but making it real for the people expected to use it every day.

Because while AI innovation is moving at incredible speed, many organizations are still struggling with a much more practical question. How do you move from exciting product announcements and pilot projects to real adoption, measurable outcomes, and business value?

In our conversation, Mary shares how Qlik is approaching that challenge by shifting the focus away from shiny features and toward outcomes that matter. We discuss why agentic AI is creating so much excitement, why customers are often much closer to operationalizing it than they realize, and how years of investment in data quality, governance, and analytics are now becoming the foundation for what comes next.

We also talk about the growing importance of trusted data and context, especially as AI moves from generating insights to influencing decisions and actions. Mary explains why simply adding a large language model on top of existing systems rarely works, and why organizations need to think more carefully about how AI is trained, governed, and integrated into the environments where people already work.

There is also a refreshingly honest conversation around cost, experimentation, and imperfection. Mary makes the case that organizations should start now, even if the data is not perfect, because using AI often reveals where the real gaps are and what needs to improve next.

So as businesses look ahead to the next 12 months, what will separate those who successfully scale AI from those still stuck in pilot mode? And are we spending too much time talking about the technology, and not enough time understanding how people will actually use it?

Join me for a candid conversation from the heart of Qlik Connect, and let me know your thoughts. Is your organization closing the gap between AI capability and real adoption, or is that still the biggest challenge?