The Hidden Cost of Poor Data Quality in Enterprise AI

Imagine an AI assistant helping a customer service team decide whether someone qualifies for a refund. The answer arrives quickly. It cites the customer’s purchase, account status and previous support history. It sounds reasonable. So the employee follows it. What they can’t see is that the account record is three weeks out of date. 

A recent payment never reached the system the assistant searched. The recommendation is wrong, but nothing crashes. There’s no error message or warning telling the employee to stop. The problem only becomes visible when the customer complains. Someone then has to investigate the account, correct the decision, update the records and explain what happened. 

Other employees may start checking every recommendation manually, just in case. When a normal application fails, it’s usually pretty obvious. It throws an error. It slows down. It refuses to load, usually when you’re presenting something to people with job titles that make the room tense.

Poor data quality in enterprise AI behaves differently. The system can keep working while the cost appears somewhere else. IBM reports that more than a quarter of organisations estimate they lose over US$5 million each year because of poor data quality. Seven per cent estimate losses of at least US$25 million.

Those losses aren’t limited to fixing records. They spread into implementation, cloud use, employee time, business decisions, compliance work and customer trust.

What Poor Data Quality Costs Enterprise AI

Poor data quality in enterprise AI means the information available to an AI system isn’t sufficiently accurate, complete, current, consistent, representative or contextual for the task it’s expected to perform.

The important part here is the task. Data doesn’t have to be perfect in every possible sense. It needs to be suitable for the decision, prediction, search or action being performed. An old customer address may be perfectly adequate for analysing long-term regional buying patterns. It could be dangerous if an AI agent uses it to arrange an urgent delivery.

A document may contain correct information but lack the metadata showing which version is current. Two departments may hold complete records but use different definitions of an “active customer”. A model may have learnt from representative historical data, only to receive incomplete live data once it enters production.

This is why a single accuracy score won’t show the full cost of poor data quality. It can tell leaders something about model performance under specific test conditions. It can’t reveal how many employees are checking the output, how much extra processing the system needs, or how often a delayed answer changes the value of the decision.

Precisely and Drexel University’s LeBow College of Business found that only 12 per cent of surveyed organisations considered their data sufficiently high-quality and accessible for AI.

The consequences tend to appear across five connected areas: building the system, running it, using it to make decisions, managing the risks it creates and convincing people to trust it.

None of these costs stays neatly inside the data team.

Build costs appear before the system creates value

Every AI project requires some data preparation. The trouble begins when teams discover much later than expected that sources are incomplete, definitions conflict or nobody can confirm where important records came from.

Data engineers then spend more time finding, cleaning, matching and labelling information. Integration work has to be repeated because one source uses a different format or identifier. Evaluation takes longer because results keep changing and the team can’t tell whether the model, prompt or underlying data caused the difference.

Sometimes the project needs to be retrained. Sometimes specialist consultants are brought in after the original budget has already been approved. And sometimes the intended launch date passes while competitors, customers or internal priorities move on.

Gartner predicts that through 2026, organisations will abandon 60 per cent of AI projects that aren’t supported by AI-ready data.

In financial reporting, these costs may appear as implementation delays, consulting fees or scope changes. The original data problem disappears into the project overrun.

Run costs continue after deployment

Deployment doesn’t end the expense.

A retrieval-augmented generation system, or RAG system, searches approved company information before asking a language model to form an answer. When its documents are poorly organised or described, the system may retrieve irrelevant material or miss the best source altogether.

Teams often compensate by sending more documents to the model, increasing the context window or running several searches instead of one. That means more model calls, more processing and more cloud consumption. Employees may also rewrite prompts repeatedly because the answer doesn’t feel reliable. 

They check source documents themselves. They keep old spreadsheets available as a backup or continue using the manual process that the AI system was meant to replace. At that point, the organisation is paying for both workflows.

KPMG found that 49 per cent of surveyed organisations had questioned, delayed, narrowed or paused AI-agent deployments when expected costs began to outweigh the value being generated.

Poor data won’t explain every one of those decisions. But it can increase the processing, monitoring and human review needed to keep a system usable. The resulting expense may then be recorded as cloud spend, operational labour or software cost rather than an AI data quality issue.

Decision costs affect revenue and operations

Most enterprise AI systems exist to improve a decision or help someone act sooner. That benefit disappears when employees have to wait for additional confirmation. It can become a direct loss when the system gives them the wrong answer.

A false positive might flag a legitimate transaction as fraud, creating unnecessary investigation and frustrating a customer. A false negative might allow a genuine threat to pass unnoticed. An unreliable forecast can lead to too much inventory in one region and not enough in another. 

Outdated customer data can produce an offer that’s irrelevant, inappropriate or already rejected. The cost depends on the system’s reach. An AI assistant may suggest that an employee contact the wrong customer. 

A human can still notice the mistake before anything happens. An AI agent with access to customer relationship management software, email and order systems might update the record, send the message and trigger another workflow before anyone checks its work. As AI receives more authority, poor data stops being an input problem and becomes an operational one.

Risk costs grow as AI gains authority

Some incorrect decisions cost time. Others create regulatory, legal or security consequences. An AI system might use incomplete information to determine customer eligibility, prioritise a claim or recommend an account action. 

If the organisation can’t reconstruct which records contributed to the outcome, explaining that decision becomes difficult. This is where data lineage becomes important. Data lineage is the record of where information came from, how it changed and where it travelled. Without it, teams may know an AI result was wrong without being able to prove why.

Outdated identity information can also leave former employees with access they should’ve lost. Incomplete asset records can prevent security teams from recognising that a vulnerable system needs attention. Incorrect customer details can lead to complaints, refunds, legal review and weeks of remediation.

Deloitte’s 2026 enterprise AI research found that 73 per cent of surveyed organisations were concerned about data privacy and security. Forty-six per cent raised concerns about model quality, consistency and explainability, while only 21 per cent reported having mature governance for autonomous agents.

The more systems an agent can reach, the more places one defective record can influence before a person steps in.

Trust costs remain after the data is fixed

There’s another cost that rarely appears in the incident report. Once employees learn that an AI system can’t be trusted, they change how they work around it. They check every answer. They ask a colleague to confirm the result. They keep their own records because they’re more confident in the spreadsheet they’ve maintained for five years. 

Or they stop using the AI for important tasks altogether. These habits can remain after the original data problem has been corrected. The technical team may consider the incident closed because the dataset is repaired and the system has passed another round of testing. 

For the employee who made a bad decision because of it, the experience isn’t quite so easy to reset. That difference helps explain why technical recovery and organisational recovery happen on separate timelines. 

IBM’s 2025 CEO Study found that only 25 per cent of AI initiatives had delivered their expected return on investment, while 16 per cent had scaled across the enterprise. Poor data quality isn’t the only reason AI struggles to scale. But a system people feel they must constantly verify will find it difficult to create the productivity gains used to justify the investment.

Why Enterprises Struggle to See the Full Cost

Most organisations can identify individual symptoms of poor data. The harder part is seeing them together. 

• The data team sees a pipeline incident. 
• Operations sees a process that took four hours longer than usual. 
• Finance sees an unexpected increase in model and cloud usage. 
• Customer service sees more complaints. 
• Risk teams see an investigation that continues long after the original record has been corrected.

Each department is looking at a real cost. They’re just recording it under different names. AI programme reporting can make this harder. Leaders often track development spend, infrastructure use, model performance and direct return on investment. 

Those measures are useful, but they don’t always include time spent checking answers, maintaining duplicate workflows or avoiding decisions because confidence is low. When outputs deteriorate, organisations may also assume the model needs replacing. Or that employees need better prompt training. 

Perhaps the vendor isn’t capable enough. Perhaps the system needs more computing power. Any of those explanations could be correct. But replacing the model won’t repair conflicting customer definitions or restore missing metadata. It simply adds another cost before the original cause has been identified.

This is cost propagation. One defective or unsuitable piece of data creates several expenses as it moves through an AI system and into the business processes connected to it. Leaders need a way to follow that chain back to where it began.

How Leaders Can Measure and Contain Data-Quality Costs

There’s no universal figure that can tell an organisation exactly what poor data is costing its AI programme. The more useful approach is to identify which defects can influence important use cases, where those problems travel and what the organisation spends responding to them.

Start with the decision or action

Begin with what the AI system is meant to help someone decide or do. Define the prediction, recommendation, answer or automated action clearly. Then identify who relies on it, how quickly it needs to be available and what happens when it’s wrong. 

A low-risk writing assistant doesn’t need the same control thresholds as a system making credit, security or patient-care recommendations. An agent executing actions needs closer limits than an assistant whose work is approved by a person.

This keeps data quality management connected to business impact rather than turning it into an attempt to perfect every record the organisation owns.

Trace the data path

Next, identify the information supporting the use case and how it reaches the AI system. That includes source applications, data pipelines, transformations, metadata, retrieval tools and any systems receiving the final output. Live operational data deserves the same attention as the information used during training or initial testing.

For RAG systems, leaders should also ask whether documents were extracted correctly, divided into useful sections and labelled with enough context to retrieve the authoritative version. Lineage shows where the information travelled. Data observability helps teams monitor whether it remains complete, current and usable while the system is running.

Together, they make it easier to find where a defect entered and what it affected.

Measure the response, not only the defect

A duplicated record or missing field can be counted. That doesn’t tell leaders what it cost. Track the time spent investigating the issue, correcting outputs and repeating work. Record additional model calls, retrieval attempts and processing. Measure manual review, customer remediation, delayed decisions and compliance effort linked to the incident.

It’s also worth watching what employees do afterwards. Rising override rates, falling adoption or continued use of parallel workflows can show that the problem has outlived the technical fix. The cost of repairing data is only one part of the total. The organisation also needs to count the work required to correct what the AI did with it.

Set controls according to business impact

Not every defect needs the same response. High-impact use cases need stricter quality thresholds, named dataset owners and clear escalation routes. Human approval may be necessary when an outcome affects customer rights, financial exposure, security or regulated decisions.

Agent authority should also be reduced when the reliability of its supporting information falls below an agreed level. That might mean allowing the system to recommend an action while temporarily preventing it from completing one. 

Lower-impact issues may be tolerable for a limited period, especially when correcting them immediately would cost more than the disruption they create. The standard should follow the risk and cost of the decision, not how advanced or impressive the AI system appears to be.

Final Thoughts: The Real Cost Appears After the Data Leaves the Pipeline

The customer service assistant from the beginning may never crash. It may continue answering questions, retrieving records and helping employees move through their work. The expense begins when people have to check what it says.

It grows when the wrong decision reaches a customer, when another team investigates the cause and when employees return to the manual process because they no longer trust the system. Poor data quality isn’t one technical cost. 

It follows information into every workflow the AI touches, changing how much the system costs to run and how much confidence people place in the decisions it supports. As AI agents take on more operational work, this will become harder to treat as a problem for data teams alone. These systems won’t only summarise enterprise information. 

They’ll increasingly use it to choose and complete actions across connected applications. Leaders don’t need every dataset to be flawless. They do need to know which information supports important decisions, what happens when it’s wrong, how quickly the organisation can contain the consequences and where the resulting costs are recorded.

Enterprise AI economics will become much clearer once organisations measure the business work created by a data defect, not only the work required to clean it. As AI becomes part of more operational decisions, understanding how data, governance and infrastructure shape the result will become part of managing the investment itself. 

EM360Tech brings those connected decisions into focus through practical analysis for the leaders responsible for making enterprise technology work.