Data is an important part of modern business, but collecting data isn’t enough. You need to find ways to unify and understand that data if you want to maximize its value. That starts with organization. If leaders want actionable takeaways from their data, they need it to be well-structured, clean and connected.

This is more than a benefit. It is often an important part of the data management process. Unorganized data often sits unused in data lakes, and if a company tries to use it in that format, it can become a two-edged sword. Fragmented data can contribute to incorrect assumptions and misleading takeaways.

This is why many companies are shifting to smarter tech stacks. They are using AI to do two things. The first is to unify disparate data across their operational ecosystems. The second is to use that organized information to quickly and continuously identify actionable insights from their proprietary information.

To understand the impact of data, it’s important to differentiate between data and information. Data is a term that refers to raw elements and unprocessed facts. This could be anything from text and visual content to numbers and symbols. Data can be both qualitative and quantitative, but either way, on its own, you can’t do much with it.

Information is what you get when you process data. This means things like structuring, organizing and analyzing. When that happens, you turn data into information that you can use to inform decisions.

The issue with many companies, then, isn’t a lack of data. Modern tech stacks produce large quantities of data. But when that is left sitting, unused and misunderstood, it leads to data lakes. That is when data is left in a giant, unorganized bucket. The problem becomes even worse when you have data siloes.

Data siloes are small data lakes that are compartmentalized by things like departments and tools within a company. An accounting data silo, for instance, might have good information to inform a marketing decision. But if it isn’t processed alongside marketing and other data within a company, that siloed data may remain incomplete and less effective.

The issue of data siloes isn’t theoretical. It is currently a significant concern in many businesses. In 2025, a leading AI-powered customer service platform reported that just 22% of leaders say their teams actually share important data across their companies well. That means more than three in four employees are contributing to the data siloing issue. This is where AI may be able to help.

There is a lot of data in most business systems these days. The 2026 estimate is 175 zettabytes across the global datasphere. (A zettabyte is one sextillion bytes or around a trillion gigabytes.) So basically, there’s a lot of data out there at this point. And while there is a tiny fraction of that data in a single company’s data siloes, it doesn’t change the fact that there is still a significant amount of potential information. This makes a human solution to collating and organizing that data difficult. Humans have practical limitations that can make it difficult to keep up with the amount of sharing, synthesizing and analysis required to unsilo data and help use it across departments and companies.

This is one area where AI can make a difference. A machine solution may be better equipped to manage this isolated data issue. AI has the ability to act as a connective layer between siloed legacy systems within a company. It can review information from large tech stacks and databases much faster than a human worker.

This means a well-trained AI tool can offer more than consumer-facing entertainment value, as is often the perspective. Companies are repurposing it to add practical value to daily operations by helping to discover important but disorganized data within their operational systems.

Of course, connecting data is just the first part of the process. Bringing it together doesn’t organize it. It just brings a general awareness to data that was previously separate and fragmented. But now that we’re a few years into the AI era, many of the artificial intelligence tools available to businesses can go further.

They can also map and synchronize information, bringing data into a central location that can serve as a centralized source of information for an enterprise. This can centralize, cross-check and draw conclusions from all data within a company, not just one area. This process of turning data into actionable information can be faster than traditional methods. It is also a continuous process that AI can perform over time, not just in a single snapshot.

This advantage is why it’s estimated that 60% of organizations will have AI-driven data integration tools this year. That represents a 40% jump in just two years. DeepAuto.ai is a good example of what this new, business-savvy approach to AI looks like in practice. The enterprise-level artificial intelligence startup is a business efficiency platform designed to help users deploy a collaborative AI agenda that can look for operational gaps, inefficiencies and potential growth opportunities.

This kind of operational advantage would be difficult to achieve if AI was not capable of rapidly providing a broader view across departments and systems within a company. The enterprise transformation platform, and others like it, are helping organizations integrate disparate datasets that were previously siloed.

It’s important to note that this unified approach doesn’t just bring previously hidden data into the light. It helps business leaders to turn that data into actionable information. This has the effect of giving leaders more of the insights they hoped to gain when they invested in data-collecting tools in the first place.

The problem for many leaders when it comes to AI is that they expect to completely replace costs or even entire positions with AI-powered tools.  Many company leaders are finding ways to use AI not to cut out key functions or individuals but to boost operational efficiency.

Most artificial intelligence solutions don’t yet have the capacity to work in place of humans. Instead, the best tools are coming alongside their human counterparts and bridging gaps that were previously difficult to address.

One of the clearest examples of these gaps is turning siloed data into actionable insights. It is a practical and accessible AI-powered solution that many leaders are already using to improve internal knowledge with proprietary data that may help them stay ahead of their competitors.