Tech Talks Daily

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?

What if the reason most AI projects fail has less to do with the technology and more to do with how the work itself is designed?
Recording live from Qlik Connect, I sat down with Nick Magnuson, Head of AI at Qlik, for a conversation about the gap between AI ambition and operational reality. Because while many organizations are still focused on models, tools, and the race to deploy new capabilities, the real challenge often sits somewhere much less glamorous. Workflow design, trusted data, and making sure AI fits the way a business actually runs.
Nick brings more than two decades of experience in machine learning and predictive analytics, and in this conversation, he shares why so many AI initiatives fail before they ever create value. His view is refreshingly direct. Most failures are not technology failures at all. They are workflow failures, where teams try to force AI into the business without first understanding the outcomes they are trying to achieve.
We also explore the rise of agentic AI and what it means when systems move from generating insights to taking action. Nick explains why governance becomes even more important in that world, how organizations can balance speed with control, and why trusted data has to move beyond being "good enough for reporting" to becoming reliable enough for decisions and automated execution.
There is also a strong discussion around openness, portability, and the growing risk of vendor lock-in. As enterprises build more complex AI ecosystems, flexibility is becoming a strategic advantage, especially for organizations trying to scale without creating expensive dependencies they will regret later.
For mid-market businesses with limited resources, Nick also shares a practical path to production. A reminder that operationalizing AI does not require massive teams or unlimited budgets, but it does require clarity, discipline, and a focus on the right problems first.
So as the next wave of enterprise AI moves from experimentation to execution, what will separate the organizations that scale successfully from those still stuck in pilot mode? And are we asking the wrong questions by focusing on more AI, instead of better AI?
Join me for a thoughtful conversation from the heart of Qlik Connect, and let me know your view. Is workflow design the missing piece in your AI strategy?

What does it really take to turn AI from a flashy experiment into something that creates measurable business value?
In this episode of Tech Talks Daily, I sat down with Angela Virtu from American University's Kogod School of Business to talk about what business leaders should actually be paying attention to as AI moves into a new phase in 2026. This conversation goes far beyond the usual headlines about bigger models and faster tools. 
Angela brings a rare mix of academic leadership and hands-on startup experience, which means she understands both the technical side of AI and the hard business questions around adoption, trust, and ROI.
One of the most interesting parts of our discussion centered on how American University's Kogod School of Business became one of the first AI-first business schools. Angela shared how that shift was never really about chasing hype. It was about recognizing a real change in the workplace and preparing students for jobs, workflows, and expectations that are already being shaped by AI. 
From faculty training to culture change, she explained how transformation only works when leadership is willing to support experimentation and accept that some ideas will fail before the right ones take hold.
We also spent time unpacking where businesses stand right now in the AI adoption cycle. After years of pilots and proof-of-concept projects, many companies are under pressure to show results. Angela offered a refreshingly honest take on why so many AI projects stall and why adoption alone is a weak metric. Instead, she argued that companies need to tie AI initiatives to clear business problems and existing KPIs. Whether that means customer support resolution times, employee productivity, or operational efficiency, the point is simple. AI needs to earn its place.
Another thread running through this episode is governance. As AI becomes more deeply embedded inside organizations, the conversation is shifting toward oversight, accountability, and trust. 
Angela explains why the strongest governance models are often shared across the company rather than locked inside one team. She also discusses the need for closed systems, stronger communication, and honest disclosure when businesses use AI in customer-facing environments. That part of the conversation feels especially timely as more brands try to balance innovation with customer expectations.
We also looked ahead at what is coming next, from model orchestration and vertical AI to the rise of physical world models and even the possibility of AI agents becoming a customer audience in their own right. It is one of those episodes that will give business leaders, technologists, educators, and curious listeners plenty to think about.
If you are trying to understand where AI strategy is headed in 2026, and how to separate real value from noise, this episode is for you. What did you make of Angela's views on governance, ROI, and the next phase of AI adoption, and where do you think businesses are still getting it wrong? Share your thoughts with me.
Useful Links:
Connect with Angela Virtu
Kogod School of Business
Visit the Sponsors of Tech Talks Network and learn more about the NordLayer Browser.

What actually separates AI that delivers real value from AI that never makes it past the demo stage?
Recording live from Qlik Connect, I sat down with Ryan Welsh, Field CTO of Generative AI at Qlik, to get a grounded, practitioner-led view of what it really takes to make AI work inside a business. While the industry has spent the past few years racing to experiment, build, and deploy new capabilities, many organizations are still struggling to turn that progress into capabilities people use every day.
In our conversation, Ryan cuts through the noise and explains why so many AI initiatives fail. Not because the models aren't powerful enough, but because they're not designed to fit into real workflows. He shares why context is far more than just a buzzword and how getting the right data, in the right place, at the right time, enables AI to deliver meaningful outcomes.
We also explore the growing shift toward agentic AI and the responsibilities that come with it. From designing systems that can act autonomously while remaining under control to understanding where humans need to stay involved, Ryan offers a practical view of how organizations can move forward without introducing unnecessary risk.
There's also a refreshing honesty around where we are right now. After a wave of investment and expectation, many companies struggled to see immediate value from AI. But as Ryan explains, that period is changing, with more organizations finding ways to scale what works and move beyond isolated use cases.
So, as businesses look ahead, what does it really take to move from experimentation to execution? And are we focusing too much on building more AI rather than the right AI for how our organizations actually operate?
Join me for a candid conversation from the heart of Qlik Connect, and let me know your thoughts. Are you seeing AI deliver real outcomes in your business, or is it still stuck in the demo phase?
Useful Links
Connect with Ryan Walsh on LinkedIn

Learn more about Qlik.

Follow on Twitter, Facebook, and LinkedIn

 
Visit the May Sponsors of Tech Talks Network and learn more about the NordLayer Browser.