More than eighty percent of AI projects within organizations never make it to the production phase. This isn’t a minor setback—it’s a systemic failure that no one is willing to acknowledge.
Investments in AI continue to rise. Budgets are allocated, tools are purchased, and experiments are launched. And then things come to a standstill. Not because AI doesn’t work. Not because the technology is too complex. But because most organizations start in the wrong place: they buy the tool before they’ve properly built the foundation on which that tool needs to run.
That foundation is data. And for most organizations, that foundation simply isn’t in place yet.
The Promise Versus the Reality
AI tools promise a lot. Personalized recommendations based on customer behavior. Predictive analytics that show which orders are falling behind before things go wrong. Automated reports that are ready every morning without anyone having to do anything manually. Chatbots that answer customer questions based on your product catalog and order history.
Those promises are realistic—but only if the underlying data is accurate. AI models are trained on data. They learn to recognize patterns in historical data. They make predictions based on current data. They provide answers based on the data they’ve indexed. What goes in determines what comes out. No AI model can compensate for fragmented, inconsistent, or outdated data. It only makes the errors happen faster and on a larger scale.
"There’s a common misconception: AI models can handle imperfect data on their own because they’re smart enough to filter out the noise. That’s not true."
AI models are statistical tools; they recognize patterns in the data they’re fed. If the data they’re fed is messy, incomplete, or inconsistent, they’ll recognize patterns in the messy data you provide.
What AI actually needs is the following:
- Centralized data: all relevant information in one place, rather than scattered across separate systems that are unaware of each other.
- Unambiguous definitions: one way to define a customer, one way to calculate revenue, and one way to classify an order—applied consistently across all sources.
- Clean, complete data: no missing fields in critical columns, no duplicates, and no conflicting information between systems.
- Historical depth: enough historical data for training—depending on the application, at least one to three years.
- Up-to-date data: an infrastructure that updates data frequently enough.
Gartner predicted that by the end of 2026, organizations without AI-ready data will see more than sixty percent of their AI initiatives fail.
Conclusion
It’s very tempting to start using AI because competitors are adopting it, because it’s being discussed at the executive level, or because the tools are now available for a fraction of what they would have cost ten years ago.
But an AI tool running on poor-quality data is like a Formula 1 car driving on unpaved terrain. The vehicle isn’t the problem. The track is the problem.
Organizations that invest now in a solid data foundation—including data connections, data engineering, and data quality—will be the ones that actually make AI pay off in two years.
So the question is certainly not whether AI is useful for your organization. The question is whether your data is ready to deliver maximum value in the tools you want to use.
Related articles
AI agents aren’t recommending your products? Here’s how to boost the visibility of your Magento online store
Worried that an update might break your Magento shop? Here’s how to safely update Magento extensions
What it really takes to have a product configurator developed within an organization
Three quotes, three very different prices: what an Magento B2B online store costs
Done with your current agency: here’s how the transition of your Magento management works
The license is free, but the migration isn’t: what “ Hyvä ” means for your Magento online store
A Custom Price for Every Customer: What This Means for Your Magento B2B Online Store
Cloned in just a few hours: the number of AI-generated copies of online stores is growing
Digital Product Passport (DPP): What Does This Mean for Your Product Data and PIM?
