The Intractable Data Problem: How AI Coworkers Can Untangle Disjointed Data

In our journey to create AI coworkers for finance teams, we started with a massive but deceptively simple problem: data. Specifically, we were determined to address the vast amounts of critical business data trapped in disconnected systems, which requires enormous manual effort to bring together for any end-to-end task.?

At first glance, it may seem like an issue of "just putting data together," but the reality is far more complex. Whether it's customer information, contracts, invoices, or bills, enterprises are sitting on mountains of data that remain fragmented across systems and formats. Despite the technological advances of the last decade, the ability to seamlessly bring this data together to generate value is still a distant dream for many companies, and the toll on efficiency and accuracy is staggering.?

Over the past few months, we had countless conversations with enterprises and VCs, learning about their most pressing needs and where investments are happening in the industry. One post from Allie K. Miller captured our learnings perfectly:

?Silicon Valley severely underestimates the pain of taking disjointed or unstructured data (ex: client info, contracts, invoice, term sheet, maintenance checklists) and putting it into a structured format.?

And then when I talk to enterprises, it’s one of the top things they need.

In our research, this insight has been evident. Enterprises are desperate for solutions to unify their data to get tasks done or gather hidden insights, yet most tech solutions fail to grasp the complexity of the problem.?

In Silicon Valley, disjointed and unstructured data is often dismissed as a “solved” or boring problem—but this couldn’t be further from the reality.

Why Data Disconnection is an Urgent Problem

Disconnected data doesn’t just slow down operations; it impedes growth, leads to costly errors, and prevents companies from making timely, informed decisions. Imagine a finance team tasked with reconciling payroll contributions, managing payroll accurately, or calculating gross margins. Each of these tasks requires pulling information from multiple systems, spreadsheets, and documents that don’t seamlessly interact with each other. The result? Hours, if not days, of manual data collation, cross-checking, and reformatting.

This process is more than just tedious—it’s expensive. Skilled employees end up spending their time on repetitive, low-value tasks instead of focusing on strategic, high-impact work. Moreover, manually handling data introduces errors, which can cascade into significant financial or compliance issues, putting the organization at risk.

Many companies try to solve this by building a unified data store, but this solution often falls short. Integrating diverse data sources and maintaining quality requires massive investments, and even then, these stores rarely deliver the expected value, often becoming yet another disconnected data silo.

Enter AI Coworkers: Automating the Mundane, Empowering the Strategic

Our vision with LuminaData’s AI coworker is to eliminate this tedium by automating the manual tasks finance teams currently tackle in Excel and other disconnected systems.?

Imagine an AI coworker who can handle the heavy lifting of data integration and formatting, allowing teams to spend their time on high-impact tasks.

Take, for instance, a common task like gross margin calculation. Traditionally, this would involve pulling numbers from multiple files, entering them into a spreadsheet, and painstakingly checking each entry. With an AI coworker, you can instruct your agents so that? the data flows directly from source systems into a structured report, with minimal human intervention. Reconciliation processes that used to take hours can be completed in minutes.

Why Silicon Valley Has Overlooked This

Silicon Valley’s investments often seem to chase what’s flashy—sleek apps, cutting-edge algorithms, and AI that mimics human thinking in sci-fi-esque ways. But foundational, labor-intensive data challenges often lack that initial wow factor. As Allie Miller highlighted, Silicon Valley frequently underestimates the pain of disjointed data.?

The truth is, most companies don’t need futuristic AI that “thinks” like a human; they need practical AI that acts as a reliable coworker, taking on repetitive tasks that drain time and resources. This oversight has left a significant gap in the market. Enterprises aren’t looking for AI that dazzles—they want AI that understands their pain points and provides real, tangible value by making data manageable, accessible, and actionable.?

That’s exactly what we need to deliver.

Looking Forward: The Future of Data Integration and Automation

As we move forward, we envision a future where finance teams are liberated from tedious manual tasks and can focus on strategic, impactful work. You don’t need to make costly investments or completely overhaul your systems to achieve this—start integrating AI into your workflows, wherever you are.

The intractable data problem isn’t insurmountable—it just requires a shift in perspective. With AI coworkers designed to tackle data challenges head-on, we can finally bridge the gap between disjointed data and seamless, efficient workflows.?