3 days ago
Getting AI-Ready In A Hybrid Data World
Maggie Laird is the President of Pentaho, Hitachi Vantara's data software business unit.
If AI is the future, then data is the terrain. And most businesses are hiking in flip-flops.
We all feel the urgency. Executives know that AI has the potential to drive exponential gains in productivity, insight and market leadership. But inside most organizations, the reality looks a lot less like ChatGPT magic and more like stalled pilots, costly proof-of-concepts and growing technical debt.
Gartner predicts that through 2025, 60% of AI projects will be dropped because they are 'unsupported by AI-ready data.' What's more, 63% of organizations either lack proper data management practices for AI or aren't sure whether they have them, according to Gartner.
And it's not just about volume or speed. It's about trust, structure and the ability to bridge hybrid environments without losing fidelity or context.
Adopting AI today is a lot like installing a high-performance kitchen in a house with faulty wiring and sagging floors: flashy, expensive and ultimately unsafe. That's because AI doesn't magically fix data problems—it amplifies them.
Many of the core data challenges that AI surfaces are issues companies have faced for years. Most businesses have data scattered across multiple environments: in the cloud, on-premises or a mix of both.
Take customer retention. A company may have six streams of data tied to improving it. Some of the data is structured in columns and rows. But other critical data is unstructured—buried in email, PDFs or training videos. Most companies have mountains of unstructured data that hasn't been labeled, making it inaccessible to AI or incompatible with structured data.
If a company wants AI to detect customer retention issues or trends, it needs all the relevant data to build an accurate picture. Without it, insights are skewed and incomplete—and the resulting decisions may be wrong.
Imagine setting an AI agent loose to fix customer retention problems without reliable data. Who knows what could happen? Air Canada, for example, was recently found liable for a chatbot that gave a passenger incorrect information. The airline argued that it shouldn't be held responsible for what the chatbot said, but the court disagreed.
The bar has been raised. 'Good enough' is no longer acceptable. AI is often designed to operate without humans in the loop, which means errors can go undetected.
To get the right and best results from AI, organizations need a strong data foundation—what I call 'data fitness.'
Here are four key indicators that your organization is data-ready for AI:
Most organizations don't. Data lives in the cloud, on-prem servers, Slack threads, old Excel files and more.
Being data-fit means you've cataloged what matters, labeled what's useful and can locate the most current version of any given asset—structured or unstructured. Your platform should connect across hybrid environments without needing to copy or move the data.
To streamline this, start by clearly defining the AI use case. That makes it easier to identify what data to inventory.
AI shifts decision-making to more people who don't have 'data' in their job titles.
An analyst might know to exclude 'test region X' or adjust for seasonal bias in a report. Your AI agent won't. Neither will the product manager using a low-code interface to generate pricing suggestions.
If your data isn't clean, governed and context-aware, you risk making high-speed, AI-driven decisions based on flawed inputs. That's not just bad insights—it's a serious risk.
Different problems require different speeds. Historical data might be enough to plan next quarter's staffing, but real-time data could be essential for adjusting a flash sale or spotting inventory shortfalls.
AI-ready platforms must operate across batch, real time and streaming—sometimes all within the same use case.
Getting data-fit isn't just about cleaning up for the sake of it. It's about knowing which AI use cases matter, which data is needed and how much effort it will take to make that data usable.
Sometimes, the return isn't worth it. That's okay—clarity saves time. But in many cases, once the investment is made, follow-on projects accelerate. Readiness compounds. Future efforts don't start from zero.
AI isn't a tech project any longer, it's a business imperative. But without a solid data foundation, the tools don't matter.
A home kitchen remodel inevitably involves hard, messy work—so does good data management. AI just makes it more urgent than ever.
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