Is Your Data Ready for Generative AI?

Author

Andreas Obermair

Share Insight:

The buzz around Gen AI is impossible to ignore and businesses across almost every industry are eager to tap into its potential to enhance operations, boost innovation, and deliver personalized customer experiences. But as organizations rush to implement these powerful tools (at great cost), one critical question often gets overlooked: Is your data ready?

A recent Harvard Business Review Analytic Services survey highlights the gravity of this challenge. Among 646 professionals involved in data decision-making, 39% cited data issues as the top challenge in scaling up Gen AI. Moreover, 30% of organizations that started but later stopped Gen AI projects pointed to data challenges as the primary reason for abandoning their efforts.

These figures reflect a reality I’ve seen time and again: while Gen AI offers enormous promise, it’s only as good as the data it’s trained on.

Maximizing ROI

When it comes to automation, the goal is always to get the most bang for your buck, and Gen AI projects are no exception. A well-trained AI model, powered by clean, relevant data, is key to reducing mistakes. The fewer the errors—whether it’s wrong predictions, flawed designs, or inaccurate outputs—the less time you’ll spend fixing them. And as the saying goes, time is money!

But it’s not just about minimizing mistakes. High-quality data also helps cut down on unnecessary redundancy and leads to smarter decision-making. With better insights, you can use your resources more effectively. All of this adds up to a stronger return on investment, ensuring that your investment in Generative AI truly pays off.

According to the Harvard survey, the key difference between business leaders who have several established Gen AI use cases and those with less or none is their data readiness. Leaders have invested in data foundations that are well-prepared for Gen AI integration. In fact, 56% of these leaders are focusing on improving their data to scale up their AI efforts, setting them up for greater success.

Start with Your Data

At Roboyo, we explored this very topic in our Guide Through the Hype Report last year. My advice then, and now, is simple:

“Start with your data governance and AI strategy before you spend time and money trying out the latest Gen AI tools.”

Why? Because without clean, accurate, and relevant data, Gen AI will deliver only generic outputs—not the tailored insights and solutions your organization needs. To unlock its potential, you must first address the foundation: your data.

The Road to Readiness

Preparing your data for Gen AI isn’t just a technical task, it’s a strategic and cultural transformation. Here’s how to begin:

1. Prioritize Data Governance
   Start by setting clear processes and assigning data ownership roles to make sure your data is consistent, high-quality, and compliant. Data quality frameworks and validation tools—especially those with automation to catch errors early—are key to keeping things on track.
2. Reframe How Your Organization Thinks About Data
   Set up a change management program to help re-train your organization. This could be as simple as offering basic training, getting leadership on board, or encouraging more departmental collaboration. The key is to help everyone see that data is the backbone of AI, and that building a data-driven culture together is crucial for long-term success.
3. Keep Humans in the Loop
   While Gen AI can automate and generate insights, human oversight around data is critical to refine and contextualize outputs. AI works best when paired with human judgment.
4. Start with a Clear Strategy and Use Case
   Before diving into Gen AI, define a specific use case and establish a data strategy, ideally led by your Chief Data Officer or IT department. While experimentation is valuable, starting without a clear plan for your data often leads to wasted resources and missed opportunities.
5. Consider the Value of a Data Platform
   You don’t need a dedicated data platform to make Gen AI work but having one can make a huge difference in how efficient, scalable, and effective your results are. As we’ve already mentioned in this blog, data is the backbone of Gen AI. It’s what powers the ability to generate meaningful, contextually relevant, and creative outputs. To get the most out of it, the right data platform is essential. It should give you easy access to diverse, high-quality, and well-structured datasets, that are clean, unbiased, and representative of the problem you’re trying to solve.

The Future of Gen AI

Data challenges should never deter you from exploring Gen AI; rather, they should serve as a wake-up call. The success of any AI initiative depends on the quality of the data it relies on.

At Roboyo, we’ve seen firsthand how businesses can overcome these challenges by focusing on the fundamentals. A strong foundation of clean, structured, and well-governed data enables Gen AI to go beyond generic results and deliver the tailored solutions that organizations truly need.

So, before you leap into the latest Gen AI tools, take a step back. Assess your data readiness, establish robust governance, and align your AI projects with a clear strategy. Gen AI’s potential is immense—but to harness it, you must start with your data.

Ready to take your Gen AI projects to the next level? Together, let’s build a data foundation that sets your organization up for success. Book a quick call with our team today.

Related content

Why Governance Breaks Before AI Agents Do

AI agent pilots stall in production not because technology fails, but because decision ownership, account…

How to Tell If Your Current Automation Estate Can Support Agents

Many automation estates perform well under human supervision but struggle when AI agents act independentl…

3 Layers Enterprises Must Have Before Agentic Autonomy Is Safe

Many enterprises introduce agentic AI before they are ready. This article explains the 3 layers required …

Why Even Mature Automation Programs Fail the Agentic Readiness Test

Even mature automation programs often fail when autonomy is introduced. This article explains why bot suc…