Data Reality Check: Why AI Demands a New Standard for Data

In my last article, I explored how AI needs a solid foundation - a base of reliable, structured, and accessible data - to deliver real business value. For many this is part of a wider reality check when it comes to data.

AI is forcing the issue. For years, organisations have been hoarding data under the assumption that “more is better.” But AI doesn’t just want more data - it wants better data. And it’s exposing every weak point in how companies store, structure, and secure that information.

Here are four critical truths AI is revealing about your data estate:

1. Provenance Matters

AI doesn’t just consume data - it inherits its traits. If your data’s origins are murky or unverified, AI outputs will reflect that ambiguity. Provenance is now a prerequisite, not a luxury. It answers a fundamental question: Can we trust where this data came from?

2. Classification Isn’t Optional

As AI becomes embedded across workflows, it can inadvertently surface the wrong data to the wrong users. Without robust classification, sensitive data can cross boundaries it shouldn’t. This isn’t just a governance issue - it’s a reputational and legal risk.

3. Bias Will Out

Many AI initiatives train on historical data assuming it’s neutral. But AI is a mirror. If your data reflects biased hiring, skewed customer treatment, or legacy process inefficiencies - the model won’t fix them. It will scale them. Quickly.

4. Data Ages, Fast

In fast-moving markets, data relevance can shift in months or even weeks. What was true last quarter may be dangerously misleading today. AI needs freshness as much as it needs volume.

From Readiness to Accountability

In my previous article, I laid out a framework for AI data readiness: availability, trustworthiness, and usability. The next phase is accountability. AI initiatives must operate under the assumption that bad data equals bad outcomes. That means:

• Implementing clear data lineage tracking

• Revisiting data governance structures

• Actively identifying and removing sources of bias

• Embedding data quality metrics into AI performance reviews

What I’m Doing at Crossfuze

At Crossfuze, I’m focused on spotlighting these issues through the AI Readiness Assessments I'm running with clients. As part of each engagement, I work with organisations to uncover where their current data practices on ServiceNow might hinder AI performance and where meaningful improvements can be made.

I’m not just scanning for surface-level data quality; I’m helping clients examine the foundational structures that will determine whether their ServiceNow AI initiatives scale or stall. The goal is to move organisations from blind AI optimism to informed, deliberate adoption. That's the only way to see business value from investing in AI.

Takeaway

Many organisations are moving from seeing data readiness as an afterthought to a full data reality check. AI is not forgiving, it won’t politely ignore the mess. It will magnify it.

The organisations that come out ahead will be those that treat data as a strategic asset, not just a technical one. Because when AI fails to deliver, it’s rarely the model that’s to blame. It’s the data.