Context and culture - the foundations for AI success
About this session
Context and culture are the two variables that decide whether AI in an organisation compounds good work or compounds confusion: Stanford's Digital Economy Lab published a study called "The Enterprise AI Playbook," looking at 51 internal AI transformations to explain why overwhelming majority of enterprise GenA I investments show no measurable return.
They found that companies using the same technology and the same use cases still had transformation timelines ranging from weeks to years. They ruled out the tools as the cause and said the variable was org design and culture: How ready it was to change. Whether its processes could absorb a new way of working. Whether leadership was actually behind it. Whether the culture tolerated failed attempts along the way. were they able to change their operating model around the AI Culture is the judgment that decides what to do with what the system produces. Taste is experience plus strategy plus human intelligence, and it forms through the act of making: designing, shipping, getting critiqued, getting it wrong in ways you can eventually name. That's what lets someone direct an AI system with precision. It's also what lets a team catch confident nonsense before it ships. Culture is what makes AI transformations work - I will talk about how we are building this culture at contentsquare and give advice on what is working, and how to make your culture work for you
Context is the structured, current account of what's actually true in a business: the data model, the source of truth, the decisions already made, the constraints that haven't changed. AI amplifies whatever context it has access to. Rich context makes an AI system's output something a team can trust and reuse. Thin or scattered context makes speed the fastest way to spread a wrong answer. Contentsquare's AIx group is building a context layer that works the same whether the tool is Claude, Copilot, or whatever comes next. Governance is built into the layer itself: who wrote something, what stage it's at, whether it's been signed off. Context delivers better, faster and cheaper AI outputs.
And I will talk about the interplay of both and how to create a culture that build context
I'll walk through how the two work together in practice: a five-stage prototyping model that puts a human judgment gate between an AI-generated hypothesis and anything that ships, and a front-matter governance model that stops "looks true" from becoming policy. Attendees leave with the five-stage model and the front-matter governance approach, plus one question to bring back to their own teams: when someone hands you AI-generated work, can they tell you exactly what's wrong with it, or only that something feels off?
Speaker
Key takeaways
- Why most AI iniatives add no value and what you can do differently
- Why tools are not enough - you need to build culture too - here is how
- How successful context layer to power your AI