We’re out of the experimentation phase with AI – its usage has become an expectation, featuring strongly in the operations of businesses of all sizes. Think about how Microsoft Copilot is embedded in your workload – answering questions, writing emails, summarising documents. It’s a valuable tool and becoming more so.
That value isn’t created on its own, though – it reflects the quality of data the AI is given. If your AI is working with poor-quality data, it’s not going to help you fix problems. It’ll make them worse.
Clean, consistent, well-governed and up-to-date data is what’s required. Today, we’re going to look at five reasons why data quality is the number one enabler of successful AI adoption.
1. AI Doesn’t “Think” for Itself
AI can’t reason like humans do. This means everything AI repeats is based on the data you’ve fed into it. So, if your data contains duplicates, conflicts, or incorrect information, your AI model will treat it as correct and reuse it.
That leads to inefficiency and bad decisions. Work has to be redone, clients get fed the wrong information – it’s not a good look. The problems compound, too, with AI. What was once a small data error affecting one or two reports is now repeated at scale through multiple AI-generated outputs.
This isn’t theoretical – it has real-world impacts that cost organisations money. IBM estimates that poor data quality costs businesses US$3.1 trillion per year globally.
2. Security and Compliance Risks
2. Security and Compliance RisksTight data security needs to be part and parcel of a modern business environment. When it comes to the AI platforms you use, the quality of your data is closely tied to security.
If you’ve got unmanaged data sprawl, together with inconsistent ownership and murky permissions, applying security controls becomes difficult.
According to Microsoft security research, most data breaches are down to excessive permissions or misconfigured access. AI makes these missteps more visible, because a tool like Copilot will put information out there based on permissions. If yours are wrong, sensitive data can get shared too broadly or end up stored in the wrong place.
AI reveals risk by exposing data that should be kept hidden.
3. AI Needs Trusted Data, Not Guesswork
A well-organised SharePoint site with clear ownership and consistent naming will outperform an unstructured file repository every time. When data is organised logically and stored where it belongs, AI tools can more easily identify what’s relevant, current, and appropriate to surface. The data can be trusted because solid data governance is in place.
A tool like Microsoft Copilot relies on signals such as recency, structure, metadata, and access rights to determine what information is relevant. So if your organisational governance over your data isn’t appropriate, your AI isn’t going to deliver the value you expect it to. According to Gartner, this happens to 80% of AI-fuelled projects.
The issue isn’t the AI tool – it’s the data environment.
4. Bad Data Drives AI Hallucinations and Bad Decisions
AI hallucinations are confident but incorrect responses. It’s easy to blame these on the tool you’re using, but they are usually the result of poor data.
We’re talking data drawn from multiple versions of a policy, or from outdated procedures or incorrect documents. AI works with the data it has been given, so it will present the wrong answer with confidence.
Hallucinations aren’t random. They’re predictable outcomes of data environments that lack clear ownership and governance. Fix the data and the quality of your AI responses will improve significantly.
5. Scale Beyond Experimentation
There’s a difference between giving AI a trial and successfully deploying it across your business in the long term. That difference is always down to the quality of your data.
According to McKinsey researchers, clean data with strong governance is three times more likely to deliver measurable value. That’s not theory, that’s a solid and researched fact.
Clean, well-governed data reduces risk, improves consistency, and makes it easier to supervise AI outputs. It’s what allows AI to move from isolated use cases into everyday business processes.
What Does Good Quality Data Look Like?
If you’ve got good data, your environment is ready to leverage the power of AI to its maximum. Let’s summarise briefly what ‘good data’ means in practical terms, based on the above five points:
- Clear data ownership
- Consistent naming conventions and structure
- Little to no duplication
- Less outdated legacy content
- Appropriate classification and retention policies
- Permissions that align with business roles
These aren’t new concepts; it’s just that AI tools can really highlight their absence. Poorly performing AI can almost always be traced back to the quality of the data.
Why This Matters Now
AI tools are no longer confined to specialist teams. They’re being embedded directly into productivity platforms that staff use every day. That makes data quality a whole-of-business issue, not just an IT one.
AI will reward organisations that invest in their data foundations — and expose those that don’t.
How Smile IT Can Help Your Organisation
AI adoption succeeds when the data is clean, structured, and well-governed. That looks like this:
- Assessing data readiness before enabling AI tools
- Cleaning up Microsoft 365 environments
- Applying governance and security controls that support AI safely
- Enabling Copilot with confidence, not based on guesswork.
Data quality is the fuel that drives the big AI engine. If you have any questions or need help with the above steps in your organisation, get in touch with the Smile IT team.
When he’s not writing tech articles or turning IT startups into established and consistent managed service providers, Peter Drummond can be found kitesurfing on the Gold Coast or hanging out with his family!