Big Data LDN Reflections on Where Enterprise AI is Heading
Updated: Sep 30
By Amol Malpani, Co-Founder and CTO
Big Data LDN 2026 gave us a useful snapshot of how differently enterprises are progressing with AI. Over two days of conversations with data and AI leaders, we met organisations at very different points in their AI journey, from those still strengthening their data foundations to those already working through the challenges of AI at scale.
For Cloudaeon, it was also the first opportunity to put AI Hub in front of people and demonstrate it working. After months of building, testing and refining it, being able to show real governance scenarios made the conversations far more tangible. And those conversations gave us a few things to think about.
AI Maturity Looks Different Across Enterprises
At the stand, we met organisations at almost every point in their AI journey. Some were still focused on getting their data foundations right. Others had started experimenting with one or two AI use cases or pilots. At that stage, many things can still be managed individually, so questions around governance and repeatability don't always feel urgent.
Then we spoke to organisations further along. They were already dealing with multiple AI initiatives, different technologies and platforms, governance requirements and the challenge of creating a consistent path into production. The difference was noticeable. For organisations further along, the problem AI Hub is designed to address was immediately recognisable. They were already thinking about how to control AI activity, manage approvals, capture evidence and create a repeatable approach across multiple use cases.
That led to one of our clearest takeaways from the event:
The need for AI governance doesn't suddenly appear clearly when an organisation starts using AI. It becomes much more visible as AI begins to scale. However, putting the right governance in place from the start can make it much easier to manage that complexity as AI grows.
Moving Beyond the Model
As AI adoption grows, the conversation is not limited to which model to use. Identity and access, data, tools, permissions, approvals, cost, evidence and production operations all become part of the picture. An organisation may be able to manage these considerations separately for a small number of pilots. Doing the same across a growing portfolio of AI applications and agents is a different challenge. This is where AI Hub naturally fitted into the conversations at Big Data LDN. The data and AI leaders could see what happens when an AI action is allowed, when it is blocked or when human approval is required.
AI Hub Reflects Our Own Journey
We are an engineering-led company and AI Hub has come from that experience. We didn't start with an idea for a solution and then look for a problem. Much of what we've built reflects challenges we've encountered while working with large enterprises over the years. AI Hub is an extension of that work, where we turn experience from real enterprise delivery into reusable IP and a repeatable capability. For organisations with ambitions to scale AI, putting the right governance in place early can make it easier to scale without adding complexity later.
That is where we see AI Hub becoming increasingly relevant, right from the beginning of an organisation's AI journey and as it scales.
Our perspective is also shaped by the technology environments our customers already use. Microsoft and Databricks are important pillars and our thinking isn't about replacing those platforms. It's about helping organisations operate AI consistently across their existing technology environment.
More Than a Launch
There was a lot of work behind those two days. Engineering, Data & AI, Sales and Marketing all contributed to getting AI Hub and the stand ready. Seeing the solution demonstrated made that effort worthwhile. We also had the chance to reconnect with old colleagues, meet new people, speak with partners and have conversations we hadn't expected.
But the most valuable part was the range of perspectives we heard. Big Data LDN reinforced that there isn't one AI journey. Organisations are moving at different speeds and dealing with different challenges.
“We went to Big Data LDN to put AI Hub in front of the market. We came away with something equally valuable, a clearer view of where organisations are in their AI journey and how the operational challenges evolve as they move from pilot to production. The conversations reinforced our belief that these challenges require strong engineering foundations and practical enterprise experience. That is what we've built into AI Hub, a practical, repeatable approach to governing and operating AI as organisations scale.”
- Amol Malpani, CTO, Cloudaeon
Missed our stand at Big Data LDN? See AI Hub in action and explore what governed AI at scale looks like. Watch the demo now.
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