You’ve been here before. The team has just migrated to a shiny new platform, and everyone is excited until lead routing goes sideways, and a campaign mistakenly targets the wrong segment. A few fingers point toward the vendor, a few toward IT. Then someone at the table says what everyone is thinking: “Isn’t AI supposed to fix this stuff?”
And that’s the moment you know it’s going to be a long quarter.
The truth is, AI can be one of the most powerful assets in your tech stack. Done right, it becomes your co-pilot—sorting, predicting, flagging issues before they escalate. But that only happens when AI is deployed in the right conditions. Most businesses don’t realize that AI isn’t magic. It’s math. It’s rules. It’s logic applied to any data you provide. And if the data is inconsistent, incomplete, or contradictory? That’s exactly what the AI will learn to trust…and replicate.
Too many instances of AI ineffectiveness are already being reported, AI isn’t failing you. Your data is.
There’s a common misconception that AI will “figure it out” later, that you can roll out automation first and clean things up after. But AI doesn’t work that way. It can’t invent the context you never gave it. It can’t infer your naming conventions, your account hierarchies, or your regional segmentation logic. It doesn’t know that “V.P. of RevOps” and “Vice President, Revenue Operations” are the same role unless you teach it.
When AI underperforms, it’s usually because it was used reactively rather than strategically. That’s what leaders are really saying when they throw up their hands and ask why it’s not delivering; they’re realizing too late that AI only amplifies what it’s fed.
If you haven’t established structure, clarity, and standards, AI won’t either. The model’s job is to run with whatever it’s given. Which means if you feed it dirty data, all you’re doing is adding horsepower to chaos.
You’re moving into a new CRM or ERP, and the thinking goes, “Let’s just move everything over and clean it up once we’re settled.”
But that’s like moving into a new house without unpacking boxes from the last three apartments. Sure, you’ve got a new address, but now all your old clutter is in a different ZIP code. And worse, your new system is smarter, faster, and more automated. Which means those problems? They scale fast.
A SaaS migration isn’t a relocation. It’s an inflection point. It’s your best chance to evaluate your data, identify what’s broken, and reset the foundation before you let AI—or any automation—into the room.
Skip that step, and you’re just transferring the dysfunction from one tool to another. Dirty data in, broken processes out. Now with the added benefit of real-time delivery.
At Datagence, we have found that a smarter approach starts with treating your migration like an opportunity. Here’s what we recommend:
Do extract, validate, and enrich your data before it ever touches your new platform. Think of it like quality control at the factory, if you wouldn’t ship a product without testing it, don’t ship data without cleaning it.
Don’t assume AI will clean it for you. It won’t. AI doesn’t know your business context or logic unless you define it. That’s your role, not the model’s.
Do define your data quality standards upfront. Decide what counts as “clean” and what doesn’t. Clarify which fields matter most, what completeness means in your context, and what levels of accuracy your teams require to function.
Don’t let AI run without expert oversight. If you wouldn’t put a new hire in charge of system architecture without training, don’t give a machine that level of control. AI is only as strategic as the people who guide it.
Do work with a team that has done this before. The right partner will bring pre-trained models, tested logic, and proven frameworks so you’re not trying to reinvent enterprise-grade data practices with ChatGPT and a spreadsheet. (Hint, we can help you here.)
And most of all, remember, your next system won’t save you from your last one.
If the data underneath is messy, the tools on top won’t matter. In fact, the more advanced your stack, the more damaging bad data becomes. AI can absolutely transform your business, but only when it’s built on structured, governed, and unified inputs.
Before your next migration, ask the hard question: Are you moving value, or just moving problems?
Talk with Datagence pros before migrating to ensure you avoid transferring dirty data into your next system. Click here to chat with a data expert.
