AI adoption has exploded across business functions, from dashboards to copilots embedded in daily tools. But while usage grows fast, the supporting skillset hasn’t caught up.
According to DataCamp’s 2025 report, nearly 70% of leaders now consider AI literacy essential, but half still report major data literacy gaps. This disconnect is more than a training issue. It’s a risk. When employees can’t interpret or challenge data, AI tools become liabilities instead of assets. In this article, we explore why AI success hinges on data fluency and how organizations can build the right foundation for long-term value.
This disconnect matters because AI doesn’t make decisions on its own. It reflects the data it’s fed and the logic it’s asked to follow. If business teams can’t read or challenge the data inputs, they can’t assess whether the outcomes are valid. That’s not just a productivity issue, it’s a risk.
AI works best when people know how to use it. And right now, too many teams are using powerful tools without the skills to wield them safely or effectively.
Poor Data Skills Turn AI Into a Liability
The report offers a sobering reality check. As companies push AI deeper into operations, the results are mixed, at best. One in three leaders has already seen AI generate hallucinations or false answers. Another third say their outputs are showing the signs of bias. A quarter say quality has dropped since introducing AI into the workflow.
What’s going wrong isn’t the tools. It’s the application. In one example, a marketing team used AI to segment customers, relying entirely on generated clusters without validating the logic or data assumptions. Campaigns were built around these faulty segments, and failed. The fallout wasn’t just technical; it damaged customer trust and wasted budget.
This lack of foundational understanding also affects people on a human level. Roughly 15% of leaders link poor data skills to burnout and attrition. When employees are asked to make critical decisions based on AI tools they don’t understand, stress builds fast. It creates the illusion of empowerment while quietly eroding confidence and clarity.
AI doesn’t eliminate the need for human oversight, it amplifies it. If your teams can’t question the logic, check the source, or understand the assumptions, they’re not empowered. They’re exposed.
Adoption Is Stalled in the Teams That Need It Most
You’d expect that AI would be flourishing across all departments by now, especially in areas like finance, sales, and operations where efficiency is crucial. But adoption data tells a different story.
Technical teams are far ahead. IT departments lead the pack with 60% AI adoption. Data and analytics teams follow closely behind, along with R&D. But business-critical functions are trailing. Marketing and operations sit at 34%. Customer support is at 33%. Finance at 31%. Sales at just 25%.
These are the very teams that could gain the most from AI-enabled insights, forecasting, and automation. So what’s holding them back?
The barriers are human and structural. More than a third of leaders say their teams don’t see a clear use case. A third also say employees lack the training. A quarter find the tools too complex. And 32% cite outright resistance. These aren’t insurmountable problems, but they do require a new approach. One where AI isn’t dropped in from above, but woven into the business in a way that makes sense to the people using it.
To scale AI in business, you have to start with the human architecture. Not just new tools, but new thinking, processes, and support.
When Data Literacy Improves, Everything Else Does Too
The good news? When companies do invest in AI and data literacy, the results are outstanding. Nearly every organization with a mature training program reports faster decision-making. Most report revenue growth. Many are seeing meaningful cost reductions and stronger innovation cycles.
The key isn’t just teaching people about AI. It’s tying that knowledge to real-world outcomes. Take Rolls-Royce, for example. They trained engineers in Python not as a generic upskilling initiative, but to solve a specific problem: speeding up design cycles. That focus led to a 100x improvement in output. Why? Because the training was tied to business value, not just technical fluency.
It’s not about turning everyone into data scientists. It’s about giving teams the fluency to understand, challenge, and improve how AI supports their work. When that happens, employees don’t just use the tools, they improve them.
Data literacy isn’t an abstract goal. It’s a business capability that drives ROI.
How We Help: Turning Strategy Into Adoption
At Centida, we work with finance, planning, and operations teams that want to go beyond dashboards and bring AI into daily decision-making.
That means we don’t start with the model, we start with the use case. What’s broken in your current planning cycle? Where are your reports lagging behind reality? Where could an LLM add context or automation without losing control?
Once we know where AI fits, we build the skills to match. We offer tailored data literacy programs, focused not on theory, but on helping your team ask better questions, work with better data, and trust the systems they’re using. From there, we integrate or fine-tune the AI models themselves and build writeback workflows that keep your people in the loop.
Because to make AI work, you need both systems and people aligned.
You Can’t Build AI Success on Weak Foundations
AI is changing how business works, but not always for the better. Without data literacy, teams make mistakes they can’t explain, and leaders make decisions they can’t defend.
That’s why the smartest companies in 2025 aren’t just adopting AI. They’re building data-first cultures where tools support people, not the other way around.
So, before you ask what’s the best AI tool for your team, ask something simpler: do they understand the data that tool runs on?