AI Is Not the Problem — Siloed Data Is

Marketers may be falling for AI like it’s a new love interest — full of promise, possibility and breathless potential. But behind the scenes, the mood is very different. For those tasked with actually deploying AI and machine learning tools in real-world business environments, the number one challenge is clear: fragmented, siloed data.

New research from Confluent, based on a survey of 550 UK tech leaders, confirms what many of us in the industry already know: the hype around AI is clashing hard with the reality of outdated data infrastructure.

When asked what’s standing in the way of AI adoption, 67% of UK decision-makers pointed to siloed data as the biggest barrier. That was followed closely by issues such as unclear data lineage, quality concerns, and the lack of internal expertise needed to manage complex AI workflows. Meanwhile, half of respondents said their organisation lacks the infrastructure to process data in real time — a fundamental requirement for modern AI tools to be effective.

We weren’t surprised by any of this. If anything, we see it playing out even more starkly in practice. For almost every client we work with — regardless of sector, size or ambition — the main obstacle to realising the value of AI isn’t the technology. It’s the data.

A case in point

One national retailer came to us convinced that their AI investments were underperforming.  They had a modern Martech stack, a loyalty programme with millions of members, and strong executive buy-in.  But nothing was landing.

When we mapped their environment, the issue was clear: over 200 customer data sources, fragmented across departments.  CRM data lived in one system, online behaviour in another, and store transactions weren’t connected at all.

We started by running a data hygiene check, unifying key datasets, and standardising taxonomy across teams.  Within six weeks, they could finally see a single view of the customer – and within 12 weeks, they had launched their first truly personalised campaign.

The results?  A 28% uplift in email conversion and a clear roadmap to automated churn prediction.”

The Illusion of Readiness

Many businesses think they’re ready for AI because they’ve invested in the tech stack — whether that’s analytics platforms, cloud tools, or off-the-shelf AI models. But scratch the surface and the cracks start to show. Data is scattered across departments, stored in incompatible formats, or locked away in legacy systems with no clear ownership or governance.

And here’s the harder truth: even when businesses have the right tools, they often don’t know how to use them fully. We frequently see underutilised platforms, missed capabilities, and disconnected martech stacks gathering dust — not because the tech is bad, but because the organisation hasn’t built the connective tissue between systems, strategy, and people.

Without a clear plan for integration, a shared understanding of data flow, or a framework for activating insights, even the best-in-class tools can become shelfware.

Going Back to Basics

The solution isn’t more AI — it’s better data strategy. And that starts with some unglamorous, but essential work:

• Mapping where your data lives

• Standardising formats and taxonomies

• Cleaning and de-duping records

• Breaking down departmental silos

• Establishing ownership, governance, and flow

  
  

This is the work that gets overlooked. But it’s the work that determines whether AI delivers real business value — or becomes just another line on a strategy deck.

The report notes that a growing number of leaders are turning to data streaming and integration platforms to try and connect the dots. But, in our experience, that’s just a sticking plaster and only part of the answer. Moreover, it will only work if the data flowing through those pipelines is fit for purpose — accurate, timely, consistent, and governed.

From Chaos to Confidence

We do what it says on the tin: put data to work — not in theory, but in practice. We specialise in uncovering hidden silos, improving data hygiene, and getting teams aligned around data that’s ready for AI. Not speculative AI. Not “proof-of-concept” AI. But real, applied AI that solves actual problems — from customer personalisation and churn modelling to operational forecasting and automated insights.

If your organisation is excited about AI but frustrated by a lack of results, you’re not alone. The truth is, the problem usually isn’t AI. It’s the data it’s being asked to work with — and the untapped tech stack gathering dust in the corner.

Get the foundations right, and everything else becomes possible.