Fueling AI with Modern Data Foundations: Why Enterprises Can’t Afford to Wait
Data Is the Hidden Barrier to AI
AI has moved from the hype cycle into the boardroom agenda. Across industries, leaders are investing in machine learning models, automation, and generative AI to gain a competitive advantage. Yet many executives are left asking the same question: why aren’t we seeing the impact we expected?
The answer, more often than not, lies in the data. AI might be the engine, but data is the fuel, and for many enterprises, the pipelines are clogged. Most digital transformation efforts over the past decade prioritized customer-facing apps and workflows. They modernized the front end. However, the underlying data layer, the part that determines whether AI can truly scale and deliver, has been left behind.
This blog explores the misconceptions, pitfalls, and practical steps for building AI-ready data foundations. Along the way, we’ll look at how companies like Netflix, Walmart, and UPS have turned data modernization into a competitive advantage.
The Big Picture: Beyond Apps and Dashboards
When organizations talk about modernization, they often mean better apps, smoother workflows, and improved user experiences. Those investments are valuable, but they’re only half the story. Without addressing how data is produced, shared, and governed, AI initiatives will never make the leap from isolated pilots to enterprise-wide transformation.
The next wave of modernization isn’t about building flashier dashboards or migrating workloads. It’s about treating data as a first-class product, designed to be trusted, shared, and reused across the business.
What Most People Get Wrong About AI Readiness
There are a few common traps enterprises fall into:
Dashboards ≠ AI readiness
Digital ≠ Data
App modernization ≠ Data modernization
In other words, AI doesn’t “just work” on top of legacy systems. With the right data that is clean, connected, and contextual, AI models don’t just function; they excel. Modern data practices transform AI from a series of experiments into a strategic driver of value at scale.
Real-world example: Netflix
Netflix faced this reality head-on. To power its world-famous recommendation engine, it had to rebuild its data systems to process billions of viewing events every day.
What happened next? The app made real-time personalized content suggestions, driving engagement, satisfaction, and subscriber loyalty.
Netflix’s success wasn’t just due to its algorithms; building the data foundation to make them effective was an equally important contributor.
Where Data Modernization Stalls
Many organizations recognize the need for better data infrastructure, but modernization efforts often stall. Common roadblocks include:
Different departments use different definitions for the same metrics.
Projects move forward without “data contracts,” which are clear rules about ownership, quality, and usage.
Business and technology teams operate in silos, with little shared accountability.
KPIs are fragmented, so progress is measured locally, not across the enterprise.
It involves high costs, initiatives that look good on paper but fail to deliver in practice, and leads to low adoption
Real-world example: Walmart
Walmart avoided these pitfalls by modernizing its analytics platform. The company shifted to cloud-based systems and gained real-time visibility into its supply chain and customer behavior. This allowed store managers to make faster decisions on inventory and gave customers more personalized shopping experiences.
The lesson: Modernization involves introducing new tools, unifying definitions, aligning KPIs, and creating shared trust in data.
What Does a Composable Data Foundation Looks Like
So, what does “modern data foundations” mean?
Enterprises are increasingly adopting a composable data foundation. Think of it as “building blocks for data.”
A composable data foundation includes:
Modular, domain-owned data products
Role-based access and secure sharing
Self-service tools for business users
Central governance paired with local execution
Teams aligned to business priorities rather than IT silos
This approach makes data more agile, reusable, and trustworthy. It also reduces the friction between business and technology, making it easier to deploy AI use cases at scale.
Real-world example: UPS
UPS built such a foundation to support its ORION project, an AI-driven system that optimizes delivery routes in real time. With modernized data systems, UPS saved millions of gallons of fuel annually and cut operational costs dramatically. The target was to use technology for building a data foundation that could deliver measurable outcomes.
Non-Negotiables for AI Readiness
To succeed, enterprises must commit to a few essentials:
Data contracts to define what data is available, at what quality, and with what ownership.
Shared business definitions so every department speaks the same language.
Integrated analytics portals to make data discoverable and accessible.
Strong governance and access control to ensure security without slowing down innovation.
Observability to track the health of pipelines, detect data drift, and maintain reliable AI models.
These elements aren’t optional; they’re the foundation for any AI strategy that hopes to deliver real results.
Quick Wins That Build Momentum
The good news? Data modernization doesn’t have to be a multi-year overhaul. Many organizations see value quickly by starting small.
Some practical first steps include:
Running a Data Maturity Assessment to understand the current state (most organizations hover around 2.5 on a 5-point scale)
Deploying an Integrated Data Portal to unify reporting, enable self-service BI, and enforce consistent access policies
Launching 1–2 high-value data products tied to business pain points, demonstrating ROI early and building momentum for broader efforts
These wins prove the value of modernization and help build trust and adoption across the organization.
Remember This
If your AI strategy is failing, it’s probably your data.
If users aren’t adopting tools, check your metadata, not the UI.
If projects are dragging, the issue is often missing contracts and ownership, not missing technology.
Building for the Future
Data modernization is more than a technical upgrade; it’s a business imperative. Without it, AI strategies stall and transformation goals remain out of reach. With it, enterprises can move beyond fragmented reporting to continuous, intelligent decision-making.
The examples of Netflix, Walmart, and UPS show what’s possible when data is treated as a first-class product. They didn’t wait for AI initiatives to fail before investing in the foundation. They built systems designed to adapt, scale, and fuel growth for the future.
The message for enterprises aiming to harness AI is clear: modernize your data, or risk falling behind.