AI Won’t Fix a Data Problem. It’ll Crank It Up to 11.

Why data maturity has to come before your AI strategy, and what to do about it.

I’ve been thinking about data a lot lately. Every organization has been amassing mountains of data by using various products, systems, and tools for many years. At the same time, a lot of folks are excited about how AI can impact their organizations. But I’m seeing a trend where there is an important piece of the puzzle missing.

Since pretty much the dawn of time, we’ve heard the phrase “garbage in, garbage out”. So when bad data is fed into a reporting tool, it produces a bad report. And typically, bad reports usually get caught and eventually corrected. Someone would notice something that didn’t add up and investigate before a decision got made.

But today, AI removes (or masks) that buffer.

Feed your AI model a flawed dataset, and it doesn’t know any different. It happily produces polished, confident, well-structured output built off the bad inputs. By the time anyone realizes something is off, the decisions have already been made. Some of those decisions may have been autonomously made with an AI agent. You’re essentially given faster wrong answers and more confident bad decisions.

So I want to talk to you about what we call data maturity for your organization and what it means to your AI-driven future.

Where This Data Actually Lives

All the data that’s in play is sitting in your alphabet soup of datastores: your CRM, AMS, LMS, ERP, and HRIS. It’s also scattered across Google Sheets, Excel spreadsheets, and accounting software (Xero, QuickBooks, Sage, etc.). It’s usually sitting in various different places at once.

Customer or member records in one system. Raw material costs and supplier data in another. Financial data in a third. Learning and certification activity somewhere else entirely. Often, these all have separate logins and their own definition of what counts as “active.” None of it was built to talk to the others, because none of it was built with a centralized system that needs all of it (like an AI system) in mind. It was built to run its own department.

That isn’t necessarily a failure, but just how software has evolved within organizations. It means the data that AI would need to make a good decision is usually scattered, duplicated, or contradictory across systems that were never designed to agree with each other.

What Happens if You Take a Shortcut?

Skipping this crucial data cleanup step doesn’t just mean you’re missing out on the Data Maturity party. It means you’ll never be able to truly trust what your AI outputs are providing. You still get outputs, but they’re a trap. The AI doesn’t stall out, throw an error, or know any better. It gives you an answer, and the answer looks pretty good.

But it’s built on whichever version of the truth it happened to find first. A member who renewed gets flagged as at-risk because the CRM update never synced. A learner who completed certification gets left off a re-engagement list because the LMS data never made it into the picture. Someone downstream trusts the output because it came from “the AI,” and a decision gets made on a foundation nobody actually checked.

Skip the data work, and you’re just deferring the cost, usually to a point where it’s more expensive and harder to trace back to the source.

What’s Success Look Like?

Ok, enough doom and gloom. Achieving success is less dramatic than you’d expect.

Success looks like being able to ask a real question, “which of our clients are actually at risk this renewal cycle,” and getting one answer instead of three conflicting ones depending on which system you pulled from. It looks like your team trusting the output enough to act on it without a manual sanity check first. It looks like AI recommendations getting better over time instead of needing to be re-explained every quarter because the underlying data shifted again.

In the end, success isn’t always a shiny new tool but sometimes the confidence in what the tool is telling you.

The Data Maturity Journey

Data maturity is more of a journey than a destination. Here are the four stages to weigh against where you’re at on the journey:

Reactive

Data lives in silos, spreadsheets, and people’s heads. Reporting is manual, backward-looking, and never proactive. Institutional knowledge walks out the door when people do.

Structured

Systems exist but don’t talk to each other. Data is being collected, but it isn’t connected. You can generate reports, but you can’t fully trust them. Reports only pull data from their specific platform or tool.

Integrated

Sources are interconnected, so there’s a single source of truth. You can ask real questions and get reliable answers.

Intelligent

Data intelligence is provided in a predictive, automated, and personalized manner. This is where AI compounds value.

Most of the organizations we work with sit somewhere between Reactive and Structured. The AI tools being sold to them are designed for Integrated and Intelligent environments.

Not All AI is the Same

Generative AI tools like your favorite chatbot are productivity layers operating horizontally. They’re widely accessible, genuinely useful, and relatively low-risk. They operate on public knowledge, and they don’t demand the same data maturity as the tools we’ll talk about next.

Vertical AI is a different animal altogether. Technologies like a true retrieval-augmented generation (RAG) pipeline, private language models, AI built into custom applications and workflows are tools that operate on your data, records, and systems. This is where your scalable competitive advantage gets built and where data maturity is a must.

It’s a Way of Life

Getting your data connected and trustworthy isn’t a project with an end date. It’s an entirely different way of working with your existing systems.

Systems get replaced, and new tools get added. Someone builds a workaround spreadsheet because a form was missing a field, and six months later, that spreadsheet is a critical component. Without ongoing governance, “living” documentation, and a habit of checking that your systems still agree with each other, the same data fragmentation creeps back in.

Real governance means someone is responsible for the data along with the tools that touch it. It means new systems get evaluated for how they connect before they get implemented. It means revisiting the pieces periodically instead of assuming that the work you did once will hold forever.

Strategy > Tools

We recommend against starting with picking a tool. Begin with getting honest about where your organization actually is in your data maturity journey.
That means taking a clear look at data infrastructure, mapping systems and workflows, and building a roadmap that organizes the foundational work before anything else gets added on top.
At Mile6, this is exactly what our Compass™ service is designed to do. It’s a custom, strategic, milestone-based strategy engagement that tells you what you actually have, what it would take to reach the next level, who owns what, and what AI can realistically deliver once you get there.

What’s Next

Once that picture is clear, the conversation shifts. Compass is where we help you figure out what an actual AI strategy built on the systems, gaps, and opportunities we just spent time understanding together looks like for your organization. That greatly reduces the risk compared to picking a tool off a vendor’s website and hoping it fits.

The Sequence is The Strategy

To get impactful, long-lasting value from AI over the next several years, you don’t need to be the first to adopt it. You need to adopt it when the data is ready.

Data maturity first and AI strategy second. Reverse it, and you’ve got a faster, more confident way to make the wrong decisions.

If you’re not sure where your organization is at in your data maturity journey, don’t worry! You’re just at the beginning, and we’re happy to help.

Tim Haak

Founder & VP of Innovation

Tim Haak, Mile6 Founder, is an entrepreneur and internet veteran. In the mid-’90s, Tim began creating websites for local businesses in Central Pennsylvania. This was the birth of Mile6 in 1996. Tim brings innovation and compassion to everything he does. For over 25 years, Tim has successfully navigated tech landscape changes and the evolution of the workplace. As a day-to-day leader at Mile6, Tim keeps a firm pulse on team culture and client happiness. Tim is always approachable, always friendly, and always willing to engage with friends, family, and clients. Tim lives in Elizabethtown, PA with his wife and two daughters, and two dogs.