Unstructured Data Is Blocking Enterprise AI Deployment—How Companies Can Untangle Themselves

Healthcare startups say unclear regulations are stuffy AI innovation in the sector. Of course, such precautions are necessary in the healthcare industry, where it is literally a matter of life or death. But it makes less sense slow implementing artificial intelligence in enterprises using software as a service – an area that is not limited by formalities like other sectors.

So what’s holding businesses back from adopting AI to streamline and optimize processes? The primary culprit is the mountains of chaotic data that accumulate as companies grow and add new tools and products. In this article, I’ll dive into how chaotic data is blocking AI innovation in the enterprise and explore solutions.

Welcome to the data jungle

Let’s start by looking at a common data challenge that many modern businesses face. Initially, when businesses offer a limited range of products, they typically have clean revenue data that is stored in a single system. However, as they expand their offerings and adopt a range of revenue models, things quickly become chaotic.

For example, a company might start with a one-time purchase model but later introduce additional options, such as subscriptions or usage-based pricing. As it grows, it will likely diversify its sales channels as well. A company that starts with 100% product-based sales may eventually realize that it needs help from its sales teams to increase sales, cross-sell, and acquire larger customers.

During rapid growth stages, many companies simply layer new sales systems on top of their existing ones. They will equip themselves with different SaaS tools to manage every different traffic, pricing model, buying process, etc. It’s not uncommon for a company’s marketing department alone to have 20 different SaaS tools with 20 different data silos.

So while companies typically start with clean, integrated data, growth causes data to quickly spiral out of control, often before companies recognize it as a problem. Data becomes siloed across billing, fulfillment, customer service, and other systems, meaning companies lose global visibility into their internal operations. And unfortunately, manual data reconciliation is often so labor-intensive and time-consuming that insights can be outdated when they’re ready to use.

AI won’t fix your messy data

We’ve had a few potential customers ask us, “If AI is so great, can’t it just solve this data mess for us?” Unfortunately, AI models are not a panacea for this data problem.

Current AI models require clean data sets to function properly. Companies that rely on disparate sales flows, SaaS platforms, and revenue processes inevitably accumulate fragmented and fragmented data sets. When a company’s revenue data is spread across incompatible systems that can’t communicate with each other, AI can’t make sense of it. For example, what’s labeled “Product” in one system may be very different from “Product” in another system. This subtle semantic difference is difficult for AI to identify and would inevitably lead to inaccuracies.

Data must be properly cleansed, contextualized and integrated before AI is the name of the game. There’s a long-standing misconception that data warehousing offers a one-size-fits-all solution. In reality, even with a data warehouse, data still needs to be manually refined, labeled, and contextualized before companies can use it to create meaningful analytics. In this way, there are parallels between data warehousing and AI, as companies need to get to the source of messy data before they can reap the benefits of either tool.

Even when data is contextualized, it is estimated that AI systems still hallucinate At least 3% time. But corporate finance—where even a decimal point in the wrong place can have a knock-on effect, disrupting multiple processes—requires 100% accuracy. That means human intervention is still necessary to verify the accuracy and consistency of data. Premature AI integration could even create more work for analysts, who must devote additional time and resources to correcting these hallucinations.

Error in data

However, there are several solutions to the problem of the proliferation of SaaS solutions and the resulting data mess.

First, companies should regularly evaluate their technology stack to make sure that each tool is absolutely essential to their business processes, and not just adding to the data clutter. You may find that there are 10 or even 20+ tools that your teams use every day. If they truly deliver value to your departments and the business as a whole, don’t get rid of them. But if disjointed, siloed data is disrupting your processes and intelligence gathering, you need to weigh their benefits against moving to a lean, unified solution where all data is stored in the same tool and language.

At this point, companies face a software dilemma: All-in-one tools may offer data consistency, but likely less precision in specific areas. A middle ground is for companies to look for software that offers a universal object model that is flexible, adaptable, and seamlessly integrated into the overall ecosystem. Take Jira from Atlassian, for example. This project management tool operates on an easy-to-understand and highly extensible object model, making it easy to adapt to different types of project management, including Agile Software Development, IT/Helpdesk, Marketing, Education, and so on.

To navigate this trade-off, it’s crucial to map out the metrics that matter most to your business and work back from there. Identifying your business’ North Star and aligning your systems to it ensures that you’re designing your data infrastructure to deliver the insights you need. Instead of focusing solely on operational workflows or user experience, consider whether your system contributes to non-negotiable metrics, such as those critical to strategic decision-making.

Ultimately, it will be the companies that invest the time and resources to clean up the data mess they find themselves in that will be the first to discover the true potential of AI.