How ROT Data Decreases AI Effectiveness + Increases Risk

AI Is Only as Good as the Data It Learns From

It was clear from ARMA and other conferences this fall that AI remains the current “data frontier” that organizations are navigating. Simultaneously, firms are realizing that they hold ever-increasing amounts of data that must be governed (but often isn’t). What many are just understanding for the first time - or learning the hard way - is that adopting AI effectively is predicated on well-controlled data. Meaning governance is now a necessary precursor to AI that provides value and a return on investment. 

One of the largest hurdles to well-controlled data is ROT data, or data that is redundant, obsolete, or trivial. Firms of every type and size are often drowning in ROT data with two main effects. One, burgeoning storage costs as their need for cloud space grows. And two, AI that trains on noise, rather than high-value, accurate documentation. Thus two areas of concern have become linked by one solution: effective data governance. 

The Realities of Data Rot

Rot data is the dust of the digital space. It accumulates like clockwork, seemingly minutes after you’ve just cleaned. And organizations can treat it with the same air of inevitability - and perhaps defeat. Believing it’s a natural part of complex work, they resign themselves to a large data footprint. Consequently, much of what occupies expensive cloud storage or clutters shared drives no longer serves any operational, legal, or analytical purpose. 

Data rot typically appears in three forms: 

• Redundant data. Multiple versions of the same file, drafts saved under slightly different names, or duplicates created as teams email documents back and forth. In legal environments especially, it’s common to see six or eight versions of a brief with none clearly labeled as the final. 
• Obsolete data. Records kept long after retention schedules expire, outdated work product, or content tied to closed matters or inactive clients. Obsolete materials persist because no automated process exists to remove it, and no one wants to risk deleting something for fear it’s actually important. 
• Trivial data. Personal files, scratch documents, accidental uploads, and other content irrelevant to business operations. These files accumulate silently because cloud systems make storage feel unlimited (especially to those not paying the tab). 

Data rot grows fastest in environments where information lives in too many places. This creates what FiT leaders describe as “documents in the wild”: content that exists outside any centralized governance structure. 

Cultural behavior is another contributing factor. Knowledge workers - like attorneys, educators, and administrators - tend to hoard information. They accumulate versions, save drafts everywhere, and rarely delete anything. Over time, this behavior leads to expanses of low-quality data, making it harder for organizations to identify what is valuable, what is compliant, and what should be removed. In other words, what begins as a minor inconvenience becomes a structural issue that significantly undermines AI readiness, tech budgets, and audit preparedness. 

Rot and Risk: Data Rot Undermines Compliance 

Before anyone outside Silicon Valley was even thinking about AI tools, data rot posed serious governance and compliance risks. Over-retention, inconsistent policy enforcement, and hidden data sources (like the rise of Slack) all increase an organization’s exposure. AI has simply amplified those weaknesses in several ways. 

1. Over-retention increases legal and security risks. 

When redundant, obsolete or trivial data lingers in multiple storage areas, organizations unintentionally retain information that should have been defensibly disposed of. That excess content often includes sensitive data, creating greater vulnerability in the event of a breach, audit, or discovery request.  

2. Inconsistent policy enforcement erodes defensibility. 

Even with well-written schedules, many organizations can’t prove they’re consistently applying their policies because the plethora of storage spaces - DMS, SharePoint, OneDrive, legacy systems, and more - make it nearly impossible for humans to track it. It’s why governing across repositories has become one of the hardest challenges firms face today and why automated, software-run policies have become so critical. 

3. ROT makes audits, legal holds, and investigations slower and more error-prone. 

When data is dispersed and duplicative, teams struggle to identify the final or most critical version. This increases the likelihood of over-producing and underproducing documents during discovery. It also makes legal holds more difficult to execute. Environments rife with ROT data lack the visibility needed to confidently freeze all relevant data. 

4. AI data compliance raises the stakes. 

Emerging AI guidelines and internal risk standards mean organizations must know exactly which data is used to train models. As these guidelines become laws and those laws continue to shift, traceability will only matter more. ROT makes this nearly impossible, however. It’s one thing if trivial documents slip into training datasets, it’s another if privileged or regulated data is unintentionally included. 

Building AI tools that are effective and legal has become one more way in which governance is cross-functional in organizations. And upped the ante on solving ROT data. 

ROT and AI: Bad Data = Bad Models 

One reason data rot is such a slippery problem to get ahold of is that its negative impact won’t arrive for some time, making it feel less urgent. Not so when you begin training or deploying AI. Now the problem is immediate. Because even sophisticated models struggle when underlying datasets are cluttered with duplicates and irrelevant information, the model absorbs the noise - and the results reflect it. 

Organizations want AI that is accurate, cost-effective to train and deploy, up-to-date, and secure. ROT data puts all of that in jeopardy. 

For starters, data rot pollutes your training data with low-value or inaccurate inputs. This kind of contamination leads to weak reasoning, unreliable recommendations, and output that does not match current organizational standards. 

ROT data also dramatically increases training time and overall costs. Training AI is resource-intensive. When ROT inflates a dataset, the model spends time crawling content that adds no value, consuming computing power and budget without improving accuracy. This slows deployment as it leads to manual reviews that impact momentum and undermine confidence at the executive level. Increasing costs and negative scrutiny harm AI initiatives. 

Data rot also introduces bias and outdated thinking. Because the AI is trained on obsolete documents that reflect old policies, arguments, and business practices, it embeds these patterns into the new systems, reinforcing approaches organizations have already moved beyond. 

And lastly - and most dangerously - ROT creates compliance vulnerabilities. If untracked or noncompliant data makes its way into training sets, the organization may inadvertently expose privileged content, regulated information, or personal identifiers. 

How to Reduce ROT and Prepare AI-Ready Data 

The good news is that organizations don’t need massive datasets to build excellent AI tools. They do need clean data, however, that is governed, current, and aligned with their retention policy. And that can feel like bad news to some firms. Training with the right data starts with five steps. 

1. Surface ROT wherever it lives. Seems like a no-brainer, but you can’t fix something you can’t find. And many organizations underestimate just how spread out their rot data is. FiT’s platform was built specifically to uncover this fragmentation, and give organizations a unified view of their actual data landscape.
2. Deduplicate and classify automatically. Manual review of thousands of documents is impractical. Automation is the only way to realistically complete the process. FiT applies automated classification to ensure ROT is surfaced, high-value content is preserved, and AI training datasets remain clean and accurate.
3. Enforce retention and defensible disposition. Most ROT exists simply because no system was enforcing policy. FiT’s configurable workflows make those rules enforceable across all connected repositories - without custom code and without relying on end users to delete anything manually.
4. Strengthen integrations to prevent ROT from re-accumulating. Broken or shallow integrations between repositories are one of the biggest drivers of data rot. When systems fail to communicate, content is duplicated, orphaned, or misclassified. FiT’s durable, actively monitored integrations ensure policy enforcement remains stable, even as APIs or external systems change.
5. Build predictable, repeatable data-hygiene workflows. AI readiness requires ongoing governance and shouldn’t be looked at as a one time job. FiT’s “three-click” workflows make data hygiene operational rather than aspirational. Teams can run periodic reviews, disposition cycles, and compliance checks consistently, without technical expertise.

The outcome of following these five rules is a smaller, cleaner, more compliant dataset that allows your AI initiatives to thrive. 

The Bottom Line 

Data rot is a pervasive problem for almost every industry, but industries with many knowledge workers - like law, education, and government - outpace them all. And when it comes to cloud storage costs and AI effectives, the phrase “the more data the better” does not apply. The organizations that will see real value from AI are the ones investing in clean data: governed, deduplicated, classified, and aligned to policy. With the right workflows and technology, AI becomes safer, faster, and far more effective.

To build an AI program you can trust, start by eliminating the data you can’t. FiT makes that process simple, consistent, and scalable across every repository. Book a demo today. 

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