Garbage in, chaos out: Why AI fails on bad data

Picture this: a major bank rolls out an AI system to fast-track loan approvals. Optimism is sky-high. Automation promises to cut human bias, accelerate decision-making, and improve customer satisfaction.

But within weeks, things started to unravel. Creditworthy applicants were being denied loans. Meanwhile, applicants flagged as high-risk were somehow approved. The AI delivered decisions with absolute confidence, yet they were wrong.

The bank’s leaders quickly realized that the issue wasn’t the algorithm. It was the foundation it was built on. Incomplete customer records, mislabeled financial histories, and unstructured documents scattered across multiple systems. The AI had been asked to make wise choices on the back of broken information.

This wasn’t intelligence. It was chaos.

Cracks in the foundation

The story of this fictional bank is just one example of a larger, universal challenge. AI can only ever be as good as the data it consumes.

Think about what lives inside most corporate data environments. Terabytes of old emails, scanned PDFs, contracts from decades past, audio files, video archives, and transaction logs. Some are duplicated, many are mislabeled, and most have never been properly cleaned. Much of it is what experts call “dark data”, which is unstructured, inaccessible, or poorly governed information that no one truly sees or understands.

This is where the cracks begin. Instead of a solid foundation, organizations are asking AI to balance on shifting sand. 

Research underscores this point. Iron Mountain, in partnership with FT Longitude, surveyed 500 senior executives worldwide and found that, in the short term, organizations can improve their success rates by working with the information they already have rather than waiting to centralize every data source.

"Sometimes, people think they have to centralize all their data before they can start to use it for AI models, but that's a myth," says Narasimha Goli, Chief Technology Officer of Iron Mountain. "Instead, focus on the problem you need to solve and identify the datasets you need to tackle that issue."

When data turns against you

In the bank’s case, flawed data didn’t just cause small mistakes. It led to systemic failures:

• The model hallucinated, generating outputs that sounded logical but were based on gaps or errors.
• It reinforced bias, amplifying historical patterns buried in incomplete records.
• It introduced ethical and compliance risks, making opaque decisions that no one could fully trace or defend.

Swami Jayaraman, Senior Vice President and Chief Enterprise Architect at Iron Mountain, frames it bluntly:

“AI without good data is like a car without fuel. We need to focus on data utility and quality rather than volume and on ensuring that data is gathered with consent.”

What happened at the bank is a nightmare scenario: reputational damage, regulatory scrutiny, and a loss of customer trust. The chaos didn’t come from the AI itself but from the ‘garbage’ it was fed.

The reality check

The Iron Mountain–FT Longitude research highlights just how wide the gap is between AI ambition and reality: 

• 64% of organizations admit their AI readiness efforts don’t consistently generate value.
• 69% say data integrity and sourcing remain significant weaknesses.
• The average large organization lost $389,780 in the past year alone due to flaws in data integrity.

And yet, the survey uncovered a small group of leaders, fewer than 10% of the companies surveyed, who are consistently outpacing the rest. These organizations don’t just see occasional benefits from their data strategies; they convert information management into a sustained competitive advantage. What sets them apart isn’t the volume of data they collect, but their unwavering focus on integrity, governance, and responsible sourcing.

The message is clear. For most companies, data chaos is silently draining resources. For a select few, data discipline is fueling growth.

Losing more than trust

Returning to our bank, the cost of bad data stretched far beyond technology. Customers started questioning whether the institution was fair. Regulators began asking hard questions about explainability and transparency. Inside the boardroom, executives hesitated to rely on the insights the AI system provided.

This spiral is exactly what the Iron Mountain research warns against. Poor data integrity doesn’t just derail an AI model—it erodes trust at every level. Once that trust is lost, revenue, reputation, and competitive advantage are quick to follow.

How leaders break the cycle

So what are the leaders doing differently?

First, they are obsessed with data lineage. They track how data is generated, transformed, and used across systems. This gives them confidence that AI outputs can be traced back to their source.

Second, they are adopting “AI nutrition labels”, which clearly disclose where data comes from, how it’s processed, and whether it involves sensitive information. Much like food labels build consumer trust, these labels help explain AI decisions to regulators, employees, and customers.

Finally, leaders are investing in tools to bring unstructured data into the light. They don’t ignore messy archives or dark data troves. They use AI-powered quality control to clean and prepare them.

As Jayaraman notes:

“The way we gain trust is by showing people the data sources we use to create the AI outcomes they see in front of them.”

Turning the tide

For the bank in our story, recovery meant going back to basics, auditing its datasets, eliminating redundant or obsolete records, and creating stronger governance around unstructured data. It isn’t glamorous work, but it is essential.

And this is the paradox of AI. The magic doesn’t start with the model. It begins with the data. Organizations that invest in responsible, transparent, and high-quality data practices are the ones who will turn AI chaos into a competitive advantage. Those who don’t risk watching their systems spin into failure.

What chaos really costs

In our next blog, we’ll take a closer look at the price tag of poor-quality data and how every flaw in your information systems can quietly drain millions from your bottom line. Stay tuned! 

The journey from chaos to clarity starts with how you manage your data. Discover how with Iron Mountain’s InSight® Digital Experience Platform (DXP).