I have friends in corporate who are some of the smartest people I know.
We’re talking senior leaders at major financial institutions. People running special projects right now on how to bring AI into organizations with tens of thousands of employees, decades of legacy systems, and more acquired companies than they can count.
And when we talk (really talk, not the polished version they give in meetings), here’s what I keep hearing:
“Stacie, we don’t even know where our data lives.”
That sentence stops me every single time. Because these are not junior employees who got handed a project they don’t understand. These are experienced, brilliant leaders. And they are staring down one of the most complicated technology transformations in business history while sitting on a foundation that was never designed for what they’re being asked to do.
Here’s what happened. Over the years, big companies acquired other companies. Those acquisitions came with their own products, their own platforms, their own data structures. And for a long time, the approach was: leave them where they are. Leave that company in Arkansas. Leave that one in Colorado. Leave that one in North Carolina. Band-aid it together well enough to make the deal size bigger, add it as a line item in a bundled package, and move on.
It worked. Until now.
Because now the mandate is AI. And AI needs a data lake. Not a collection of disconnected puddles sitting in different states: a unified, structured, trustworthy body of data that the whole organization can actually draw from.
And when you’ve spent twenty years duct-taping acquisitions together across multiple geographies, building that data lake is not a six-month project. We’re talking five to eight years. Maybe more.
Here’s the part that keeps me up at night on their behalf: in the age of AI, the goalpost moves constantly. What you’re building toward today, the vision you’re executing against right now, could be archaic by the time you get there. The technology is moving that fast. You’re building toward a target that is actively shifting underneath you.
That is a genuinely hard problem. And I have enormous respect for the people trying to solve it.
But here’s why I do the work I do.
I don’t work with Fortune 500 companies. I work with mid-market B2B companies in the $30M to $50M range. And the reason I love this space (genuinely love it, not just as a business decision but as a personal conviction) is this:
You have the same problem. At a fraction of the scale.
Your data is probably siloed too. You’ve probably got systems that don’t talk to each other, fields that nobody fills out, automations that were built for a business model you don’t even run anymore. You’ve got your own version of Arkansas sitting somewhere in your CRM, and you’ve been leaving it alone because it worked well enough.
But here’s the difference: you are nimble. You can make changes fast. What takes a Fortune 500 company five to eight years to fix, we can get a mid-market company through in one to two years. Sometimes less, depending on how clean your starting point is.
And in the age of AI, that is not a small advantage. That is a massive competitive edge.
The companies that fix their data foundation now are the ones that will be able to actually use AI the way it’s meant to be used, not as a tool that makes your garbage faster, but as a system that surfaces real patterns, predicts real outcomes, and gives your leadership team real intelligence to act on.
That is the difference between scaling chaos and scaling clarity.
This is the personal reason behind the work for me. My whole philosophy comes down to one thing: work smarter, not harder.
For years, I watched brilliant marketing and sales leaders, people with decades of experience and genuinely great instincts, grind themselves into the ground trying to compensate for broken systems with sheer effort. More hours. More reports. More meetings. More manual workarounds.
They were working so hard. And the system was eating every bit of it.
That is not a people problem. That is a foundation problem. And no amount of hard work fixes a broken foundation. You fix the foundation, and then the hard work actually goes somewhere.
That’s what the August episode of C-Suite Strategies is about. We go deep on structured data, unstructured data, data lakes, and why AI will make your problems significantly worse if you skip the foundational step.
If any of this is landing for you, if you heard yourself in the friends I described, or in the mid-market version of this problem, this episode is for you.
Connect With Stacie
- LinkedIn: https://www.linkedin.com/in/staciesussman
- Substack: https://staciesussman.substack.com/
- RevUp Advisory: https://www.revupadvisory.com
