Businesses have spent the past several years investing heavily in AI, and it’s easy to see why. New tools are entering the market constantly, making it easier to generate content, analyze information, automate workflows, and build AI-powered applications.
Getting started with AI has never been easier. Getting reliable results is another story.
As organizations move beyond experimentation and begin putting AI to work across their operations, many are running into challenges that have little to do with the technology itself. Instead, they’re discovering that their data isn’t always as accurate, accessible, or consistent as they thought.
And while an AI solution can be deployed relatively quickly, fixing years of data issues takes time.
The reality is that AI is only as useful as the information behind it. Without a strong data foundation, even the most sophisticated tools will struggle to deliver reliable results.
Most organizations have spent years accumulating data across different systems, departments, and applications. Customer information lives in multiple platforms, teams maintain their own reports, and definitions change as business needs evolve. Meanwhile, legacy systems continue operating alongside newer technologies.
People have learned to work around these inconsistencies, often without realizing how much knowledge they've accumulated along the way.
Someone in finance knows which report has the most accurate revenue numbers. A sales leader understands why two systems show different customer counts. An employee who's been with the company for ten years knows about exceptions and processes that were never formally documented.
That context matters, but much of it exists outside the systems AI relies on.
When an AI application pulls information from multiple sources, it doesn't inherently know which numbers are correct or why two reports might disagree. Unless that context has been built into the system, it can produce an answer that looks reasonable but doesn't reflect how the business actually operates.
This is often when organizations start noticing problems with their data. Issues that employees have worked around for years suddenly become much harder to ignore when AI is expected to interpret that information and act on it.
AI didn't create those inconsistencies. It just made them more obvious.
Businesses may talk about analytics, predictive models, and AI as separate initiatives. But in reality, they're closely connected.
The dashboards executives use to understand performance, the analytics teams use to identify trends, the predictive models used to forecast outcomes, and the AI applications being deployed across the business all rely on the same underlying asset: data.
Anyone who remembers dial-up internet and Netscape might also remember the phrase "garbage in, garbage out."
The technology has changed dramatically since then. The principle hasn't.
Data quality is often where the conversation starts. It isn't where it ends.
Businesses need accurate and current information. Duplicate records, missing fields, poor formatting, misspelled values, and blanks are just some examples of everyday issues that create challenges for both humans and machines.
In many cases, the problem isn't that the data is wrong. It's that teams aren't completely sure what it means, where it came from, or whether everyone is using it the same way.
That's the role of governance.
A customer record may be accurate, complete, and up to date, yet still create confusion if marketing, sales, and customer service each rely on a different version of the record or define key attributes differently.
Data quality ensures information is accurate. Governance ensures it's understood, owned, and used consistently.
Together, they create the data trust that analytics, forecasting, and AI depend on. You need both.
As the great Frank Sinatra sang in Love and Marriage, when it comes to data quality and governance, “you can’t have one without the other.”
As AI capabilities continue to improve, we will see:
We should expect that businesses will continue to gain access to increasingly sophisticated systems capable of answering questions, generating insights, and executing tasks on their behalf.
But none of those advancements eliminate the need for trusted data. If anything, they make it more important.
Because in the end, AI doesn't replace the need for a strong data foundation. It depends on it.
At Concord, we help organizations build that foundation by improving data quality, strengthening governance, and connecting the systems that power analytics and AI. Whether you're modernizing your data environment or looking to get more from your AI investments, we can help you take the next step.
Not sure on your next step? We'd love to hear about your business challenges. No pitch. No strings attached.
