Syntra: Where people and technology meet

Why does one customer abandon the cart after twelve form fields while their neighbour buys the same product in nine seconds? In this episode we unpack what friction actually is, why we stop seeing it, and when AI is the right tool to remove it, and when it's completely the wrong one.

Why is it often harder to find your way to a task than to actually do it? In the first Syntra episode of the autumn we unpack the navigation tax in large enterprise systems, where chat actually belongs, and what happens when AI can generate a dedicated interface for every single task.

What happens when your gut feeling and the numbers point in opposite directions? In this episode we unpack what being data-driven actually means, why it is a culture question rather than a system purchase, and how to get started without launching a huge project.

What happens when the family calendar says one thing and reality says another? The same thing that happens in companies without working master data: chaos ensues. In this episode we break down master data: why one system must have the exclusive right to the truth, the difference between collecting data and owning it, and how to find the shortcuts that erode your single source of truth.

How did we get from the mail-order catalog's five data fields to today's information war over the customer? In this episode, we talk about the transformation of the product journey, why the PIM concept may have become too broad and what it takes to succeed with your product data.

Why are the systems that keep the company running dismissed as "old junk"? In this episode we break down what legacy really means, when your technical inheritance becomes a barrier to the business, and how to modernize smartly by carving out pieces instead of replacing everything at once.

What happens when AI takes over not just the execution, but the thinking behind it? In this summer special, Mitra and Glenn are joined by guest Hugo Carlén to reflect on an intense year: AI's advances, global unrest, and what it all really means for our jobs, our decisions, and us as human beings.

What happens when a project looks green in the status report but is bleeding red on the inside? In this episode, Sebastian Mildgrim unpacks the concept of Fail Fast – what it actually means, why it's so often misunderstood, and how you stop building in the dark.

What happens when your AI can't remember what you talked about? In this episode, we untangle why agents are built without memory by default, what that means for you as a user, and what you can actually do about it today.

At your company, everyone is speaking "Danish" to each other without knowing it. Words like "customer," "product" and "POC" sound perfectly clear but mean completely different things depending on who's listening. In this episode, Sebastian explains why your internal terminology, not the technology, is what determines whether you succeed with AI.

Why doesn't product search work the way it should? In this episode, we untangle who actually owns the problem, why traditional search engines don't understand your customers, and what you can do about it.

Many companies spend millions “cleaning” data that no one ever looks at while business critical data gets lost in the noise. In this episode, Sebastian explains what data quality really means and why perfectionism can be your worst enemy.

What happens when information does not add up? In this episode, we explore the impact on your business, clarify the difference between data quality and information quality, and explain how you can start automating your validation process.

In this episode, we talk about why trust in the numbers in your reports is often lower than it should be. We discuss what Data Trust means, why trust in data is the foundation for succeeding with AI, and what you can concretely do as early as Monday.

In this episode, we explore why AI solutions shouldn’t be purchased and onboarded like traditional IT systems. We discuss what it means to “recruit” an AI to your team and how that differs from bringing in a human colleague.