The 2026 edition of Big Data LDN promised that visitors would get to see the “good, bad and ugly” when it comes to data, analytics and AI. All three traits were on display when your author and 15,000 or so other attendees descended on London’s Olympia for a recent conference, with talks from multiple business leaders. The challenge was trying to differentiate which was which.
There was inevitable gimmickry, with swathes of robotic dogs and flying drones gracing the exhibition hall. However, the tone of the event (which your author has visited most years since 2019) was markedly more serious than in the past. Presentations on topics such as token costs, governance and agent monitoring featured prominently on the agenda. AI inevitably dominated throughout with one speaker aptly noting that “just because we can use AI, does not mean we should.”
Agents, customer-facing generative AI and the very latest models were front of mind when it came to ‘hot’ topics that everyone seemingly wanted to discuss. By contrast, governance, regulation, compliance and oversight were the subjects where there was a grudging recognition of their growing importance. People are rushing to build and deploy agentic workflows, even if they are not mature at doing so perhaps best represents the state of the world.
Data and AI are moving faster than governance can keep up might be another way of framing the debate. This is the path that most technological shifts take, even if it does throw up adverse consequences. Beyond the risk of agents going rogue (see our recent post), one speaker highlighted how “bad data can create alternate realities.” Imagine 17 different sets of customer data – none of which could be trusted – or an agent recommending a product that did not even exist. These were the experiences of a consumer health company we heard speak. The solution: AI readiness is not “bought” – but, as we were told, it’s “built”, bottom-up and using credible foundations.
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Originally Posted October 6, 2026 – The good, the bad and the ugly
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