AI and data integration

Technology

Michael Curry at Rocket Software explains why AI falls short without real-time data integration

Many enterprises sit on a wealth of organisational data, containing an accumulation of transactions, customer interactions, operational metrics, and more. This data holds immense strategic value, and when paired with artificial intelligence (AI), it has the potential to unlock powerful insights, inform critical decisions, and uncover new avenues for growth.

And yet despite its potential, the data itself is often fragmented, scattered across mainframes, cloud platforms, and distributed environments. In fact, at least 84 percent of CMOs struggle with fragmented systems in AI adoption.

This reality raises a fundamental question for IT leaders: How accessible and, more importantly, how synchronised is their data? Real-time data integration isn’t just a technical goal but rather the foundation on which meaningful AI integration depends.

Without timely, high-quality, and contextually aligned information, even the most advanced AI models will fall short. If organisations are to effectively leverage AI tools, then synchronisation across systems is imperative so that AI can be relied on to deliver accurate, actionable outcomes.

The challenges of data integration

As enterprises generate and collect more data than ever, set to exceed 394 zettabytes globally by 2028, the challenge of bringing that data together in a meaningful way is only becoming more complex. While this data explosion holds vast potential, it also creates new obstacles for IT and data teams tasked with harnessing it. Much of the difficulty lies not in a lack of data, but rather, where and how it is stored.

In most large organisations, data isn’t housed in a single, centralised system. It’s spread across mainframes, cloud platforms, data warehouses, and distributed environments, often managed separately by different departments or business units. As a result, data silos form. These isolated pockets of information may serve local purposes, but they limit the organisation’s ability to see the full picture. They also make data difficult to access and hard to relate.

Disconnected formats, rigid data pipelines, and lack of interoperability can all contribute to a fragmented view of the business. In practice, this leads to duplicated or conflicting information, incomplete records, and missed opportunities. All of this contributes to business leaders being unable to make strategic decisions based on an incomplete understanding of operations or customers.

What’s more, with a lack of visibility into where data resides, without a shared context or unified metadata framework, it’s nearly impossible to align and integrate disparate datasets in a way that supports AI or analytics.

Therefore, real-time data integration starts with addressing these foundational issues. It requires not just breaking down silos, but actively creating a synchronised, contextualised data environment that modern tools can build upon.

The rise of AI across IT operations has only increased the urgency of solving these integration challenges. Models can only generate valuable insights when they’re powered by data that’s complete, current, and consistent. And that demands infrastructure capable of keeping up with the speed and scale of modern business.

Breaking down data siloes

Eliminating data silos requires more than just the right technology, organisations instead must demand a cultural and structural shift in how they approach data.

Silos often emerge when teams work in isolation, prioritise local goals, or view data as proprietary. To reverse this, leaders must foster a culture where data is treated as a shared asset. That means clearly communicating the benefits of unified data, such as better decision-making, improved AI performance, and enhanced customer experiences, while also addressing the risks of fragmented data environments.

However, cultural change alone isn’t enough. A modern data infrastructure is key to breaking down silos. Cloud-based data lakes and warehouses now allow organisations to centralise data from legacy systems, SaaS platforms, and distributed environments quickly and efficiently. Centralisation standardises data formats, simplifies governance, and provides broader visibility across the enterprise, all while ensuring secure, controlled access.

Yet even centralised data is of limited use without integration. As business needs shift and systems evolve, integration must be ongoing. The goal isn’t just to unify data once, but to build a living framework where information stays connected and accessible, empowering AI systems and the human overseers. With the right combination of cultural change, centralised infrastructure, and continuous integration, organisations can dismantle data silos and unlock the full value of their data.

Data that matters

Artificial intelligence can only be as effective as the data it has access to. When AI models are powered by real-time, synchronised information, the impact across the enterprise is significant. Operations become more efficient, decisions more precise, customer experiences more tailored, and forecasting models more reliable. Real-time data doesn’t just enhance AI, it unlocks its full potential.

But effective AI doesn’t stop with data movement. Intelligent integration platforms can automatically discover and map enterprise data, creating a holistic view of the information, allowing AI models to focus on the data that matters. It also gives business users and IT leaders a clearer understanding of what data is available and how it’s being used, which is critical for governance, compliance, and decision-making.

As AI continues to scale across industries and use cases, the value of this real-time foundation will only grow. Because ultimately, any AI solution is only as good as the data that fuels it.

Michael Curry is President of data modernisation business unit at Rocket Software

Main image courtesy of iStockPhoto.com and vadishzainer