A decade ago, businesses bought into the hype that more data—and more tools to analyze it—would necessarily lead to better decisions and competitive advantages. They poured resources into dashboards, analytics platforms, and data warehouses, often without a clear sense of how the data would solve business problems. As a result, we had to caution organizations to shift focus—prioritizing business needs over technology. It was the right advice at the time.
But times have changed, and many organizations have let their data technology lag. Now, with the rise of generative AI, the pendulum is swinging back. Companies are facing a new mandate: catch up—or risk getting left behind.
Consider a mid-sized manufacturing company—let’s call it Precision Parts. Over the past decade, Precision Parts excelled by putting business needs first. They streamlined their supply chain, reduced costs, and grew market share by focusing relentlessly on customer needs and operational efficiency. Data was used to track key performance indicators and support decisions, but the focus remained on solving business problems, not building complex technology.
For years, this approach worked. But now, Precision Parts faces a new challenge: competition is using generative AI to forecast demand, optimize production schedules, and even personalize customer outreach. Precision Parts’ aging data systems—designed for static reporting, not AI—can’t keep up. Their infrastructure struggles to integrate new data sources, deliver insights in real time, and support advanced modeling.
Precision Parts still relies on legacy systems, considered “modern” just a few years back, but now can’t reliably scale to meet the demands of modern AI applications. Without a truly modern cloud data platform, they lack the scalability, integration, and real-time processing capabilities that AI requires. Despite their success in prioritizing business goals, Precision Parts has hit a wall. To compete in this AI-driven era, they need to modernize their data technology—or risk falling behind.
Why Data Tech Is the Hidden Roadblock to AI Readiness
The explosion of interest in generative AI has companies of all shapes and sizes scrambling to integrate new capabilities while facing obstacles with outdated or misaligned data technology. After all, AI depends on high-quality, well-organized data. Fragmented systems, unreliable pipelines, and siloed data sets often create bottlenecks instead of delivering insights. They leave businesses struggling to use even the most powerful AI tools.
The hard truth is that many organizations right now don’t have an AI problem—they have a data technology problem.
Three Steps to Modernize Data Tech for AI
1. Start with Well-Defined Business Questions
Solving business problems remains paramount, but it’s only the first step in achieving AI success. While starting with clear business priorities is essential, those priorities must be matched with modern data capabilities and infrastructure to deliver results. For example, revenue forecasting may require different systems and expertise than optimizing supply chains or improving customer retention.
Starting with the tool, not the problem, can lead to expensive but ineffective systems. Grounding your strategy in well-defined business questions ensures focus. Still, success also depends on how well you align those questions with the right data, technology, and talent. Tying every data investment to a specific business outcome—like improving forecasting accuracy by 10% to reduce inventory costs or identifying at-risk customers to increase retention rates—provides a strong foundation. From there, the right infrastructure and leadership turn those questions into actionable insights and results.
2. Build Tech That Scales
The next step is ensuring the data infrastructure can support solving those problems. This means investing in systems that not only collect and store data but also make it accessible, reliable, and actionable. In a world increasingly dominated by AI, scalable data tech has become a baseline requirement.
Modern cloud platforms are becoming essential building blocks for AI success. These platforms provide the scalability, integration, and real-time insights businesses need to manage and analyze vast amounts of data. They also reduce silos by connecting data across departments, enabling organizations to streamline operations and make faster decisions.
Key Questions for Your Business:
- Can your data infrastructure scale seamlessly to handle the increasing demands of AI?
- Are your teams struggling with data silos that hinder cross-functional collaboration and decision-making?
- Does your current system allow you to integrate and analyze new data sources without significant delays or added complexity?
If any of these questions resonate, it might be time to take a hard look at your data infrastructure.
3. Appoint Strong Data Leadership
None of this works without great data leadership. A strong data leader isn’t just a technical expert—they’re someone who understands how to make decisions about data investments and align them with business goals. They bridge the gap between strategy and implementation, ensuring data becomes a source of insight and impact.
The best data leaders ask tough questions: How does this data create value? What’s the ROI of this model? Can we really trust our data? Their ability to make smart trade-offs determines whether data becomes a strategic asset—or a costly distraction.
Modern Data Tech: The Foundation for AI Success
The lesson from PrecisionParts is clear: data infrastructure isn’t just a technical problem—it’s a business problem. And it’s one that too many organizations ignore until it’s too late.
Modernizing data technology doesn’t mean shifting focus away from business problems. It means pairing sharp business strategy with data systems that can deliver real-time insights, support AI models, and scale as organizations grow.
The future of AI depends as much on architecture as it does on algorithms. Companies that recognize this and prioritize data tech and leadership will outpace their competitors.
For businesses looking to move past roadblocks and capture the benefits of AI, the time to modernize is now.