When I talk to clients, almost everyone tells me the same thing: “We already have a data platform, we are data driven.” On paper, that is usually true. In reality, it is more complicated.

What I see in large organizations, especially in pharma and other regulated industries, is a patchwork. Some business areas run on very modern stacks, with cloud data lakes, catalogs, and self-service. Other areas still rely on platforms that have grown over ten or twenty years, with heavy custom logic and dozens or hundreds of point integrations.

Those legacy platforms often still work and can support some AI use cases, but they may slow you down when you try to implement your AI strategy because of technical debt and technology limitations. If your goal is to build an AI data platform that makes AI part of everyday decision making, then the quality, structure, and accessibility of your data platform become critical. In my view, that is where the idea of a modern data platform really starts.

Why Having a Data Platform Is No Longer Enough

A modern data platform is not just a collection of tools. It is an ecosystem designed to help people across the enterprise make decisions quickly and confidently based on data they can trust.

The goal is simple to describe and hard to achieve: unify data ingestion, governance, and accessibility in a way that supports very different business needs. Commercial teams want near real-time insights, manufacturing needs reliable and auditable data for compliance, HR deals with highly sensitive personal information, and so on.

Most organizations already have something they call a data platform. The challenge is that it rarely looks the same across the enterprise. It is common to see:

• One domain running on a cloud lakehouse with curated data products.
• Another domain using an on-premises warehouse that predates most of the current team.
• A long tail of local solutions, extracts, and spreadsheets built to compensate for gaps.

From a distance this may look like a single platform. Up close, you see different technologies, different standards, and different user experiences. That fragmentation slows you down. It makes integration harder, increases costs, and creates a situation where your AI strategy is only as strong as the weakest piece of the landscape.

What Makes a Data Platform Modern

So what actually makes a data platform modern and not simply “new infrastructure in the cloud”?

The first aspect is architectural flexibility. Traditional warehouses are built around schema on write. You decide upfront how the data should look, then you load it. That approach is still valuable for stable, reporting-heavy use cases, but it becomes restrictive when you want to ingest new sources quickly or support experimentation.

Modern data lakes and lakehouses shift part of that decision to schema on read. You can store raw data in different formats, then apply structure when you use it. This makes it much easier to onboard sources at different levels of maturity, or to support multiple use cases on the same underlying data without redesigning everything every time.

The second aspect is support for different types of data. AI workloads rarely operate on perfect relational tables alone. They need structured data, semi-structured data such as JSON or XML, and unstructured content such as documents, text, or images. A modern platform is built to handle all of that, not as exceptions but as standard input.

The third element is ownership. In older models, there is usually one central team responsible for almost everything that happens in the warehouse. Business rules are embedded deep in ETL jobs, and every change request has to go through the same narrow funnel. This does not scale when the number of use cases grows.

Modern platforms are increasingly influenced by data mesh principles. Data is treated as a product, and ownership is shifted closer to the domain. HR owns HR data products, supply chain owns supply chain data products, and so on. These teams define what “good” looks like for their area, maintain the business logic, and are accountable for quality. The platform team provides standards, tooling, and guardrails.

The fourth area is governance, observability, and security. This is where tools such as Collibra, or similar catalogs, and cloud platforms like Snowflake play a big role. You want a single place where people can see what data exists, how it is defined, where it comes from, and who can access it. You also want fine-grained access control and masking for sensitive attributes, combined with monitoring that shows how fresh and reliable each data product is over time.

The last piece is self-service. A platform is only useful if people can actually work with it. Modern data platforms are built so that analysts, data scientists, and power users can find and request the data they need, prepare it, and combine it without opening a ticket for every step. Active metadata, search, and intuitive interfaces are just as important as raw processing power.

When these elements come together, the platform stops being a back-office system of record and starts to act as an enabler for AI and advanced analytics at scale.

AI as a Catalyst for Modernization

Artificial Intelligence has changed the conversation. In the past, organizations could live with “good enough” data quality because a human would always sit between the data and the decision. People know the quirks of the systems they use. They know which fields can be trusted, which reports are a bit off, and how to compensate.

AI does not have that context. If the input data is incomplete, inconsistent, or not available at the right time, the model will simply reflect that. There is no human intuition to correct for it.

That is why the state of your data platform (and your overall AI and data platform strategy) has such a direct impact on AI initiatives. The numbers tell the story:

Research shows that 78% of enterprises struggle to integrate AI with legacy systems, and they cite technical complexity and data silos as major barriers. In other words, the effort goes into wiring modern AI into old platforms instead of improving the underlying data foundation.

At the same time, 82% of organizations face data standardization and compatibility issues during AI implementation, and this can extend project timelines by up to 32 months. If every system models customers, products, or employees differently, and if there is no consistent metadata, then even the best models will produce conflicting answers.

Data quality and availability are not abstract concerns. Gartner predicts that through 2026, organizations will abandon 60% of AI projects that are not supported by AI-ready data. The absence of a modern, well-governed data platform does not just slow AI down, it puts a large share of initiatives at direct risk of being cancelled.

All of this has a real financial cost. Enterprises spend an average of 4.2 million dollars per year just to maintain legacy systems, plus 2.8 million dollars on custom middleware that tries to bridge the gap for AI compatibility. In other words, doing nothing is not free. There is a significant price for keeping outdated architectures alive and forcing new AI initiatives to work around them.

A modern data platform does not magically solve every AI challenge. What it does is remove a large class of structural obstacles so that teams can focus on the models, the use cases, and the business change, not on fighting the plumbing.

Why Modernize If Legacy Platforms Still Work

At this point the natural question is: if the legacy platform delivers the data every night and the business runs on it, why touch it at all?

From a narrow operational perspective, many of these legacy platforms are indeed stable. Reports arrive on time, downstream interfaces receive the files they expect, and business users know how to interpret them. That is why they often survive far longer than anyone originally planned.

The problem is not that legacy platforms fail in an obvious way. The problem is that they are slow to change, expensive to adapt, and difficult to integrate with new capabilities like AI. Every new requirement means more custom logic, more middleware, and more exceptions.

Modernization is not about rewriting everything from scratch. It is about standardizing and simplifying where it matters. A unified technology stack reduces the cognitive load for teams. Common patterns and components reduce duplication. Having a single, governed view of critical data assets makes it possible to reason about the enterprise as a whole instead of as a collection of disconnected systems.

Legacy warehouses do not completely block AI adoption, but they do slow it down and raise the cost. At some point, the investment in modernization becomes lower than the ongoing cost of workarounds.

Challenges and the Road Ahead

Modernizing legacy data platforms is not only a technical exercise. In my experience, the hardest problems are often organizational.

Shifting to data as a product requires real ownership in domains. Someone has to care about the quality and meaning of each data product, not just about keeping jobs running. That is a cultural change. It affects roles, responsibilities, and how teams work together.

Data democratization also needs to be more than a slogan. Giving people self-service tools without guidance or support does not work. You need communities around data, clear standards for how to document and share it, and platforms that make it easy to understand what you are looking at.

Looking forward, I expect data platforms to continue moving in the direction of usability. Infrastructure and ingestion will become more automated. The number of people who need to write low-level pipeline code every day will probably decrease. At the same time, the number of analysts and domain experts who work directly with data products will increase.

AI will accelerate that trend. It will help users navigate complex data landscapes, understand lineage and definitions, and spot anomalies. It will not replace the need for governance or ownership, but it will make it easier to work with well-designed platforms.

In that environment, the enterprises that succeed will not be the ones with the most sophisticated model for a single use case. They will be the ones that treat their data platform as a strategic asset, designed around accessibility, quality, and trust, and that are prepared to evolve it as AI capabilities grow.

FAQ

What is a modern data platform in the context of AI?

A modern data platform is a cloud-native environment that brings together data ingestion, storage, transformation, governance, and access so that AI and analytics teams can work with trusted, well-documented data. It typically includes a data lake or lakehouse, a data catalog, data quality and observability tools, and self-service interfaces that make AI-ready data available across the enterprise.

Why is a modern data platform essential for successful AI adoption?

AI initiatives depend on consistent, high-quality, and accessible data. Legacy data warehouses often suffer from silos, incompatible schemas, and limited availability, which slows down or blocks AI projects. A modern data platform addresses these issues by standardizing core datasets, improving data quality and governance, and making AI-ready data available through governed self-service rather than one-off integrations.

How does modernizing a legacy data warehouse into a data lakehouse help AI projects?

Modernizing a legacy warehouse into a lakehouse consolidates fragmented logic, reduces the number of custom interfaces, and supports new types of data such as semi-structured and unstructured content. It also enables organizations to introduce data products, active metadata, and a data marketplace, which makes it easier for AI teams to find and use the right datasets. As a result, AI projects start faster, are less dependent on bespoke pipelines, and are more likely to deliver value at scale.