The Unglamorous Work Behind Enterprise AI

As AI moves from pilots to decisions, businesses prioritise data quality with checks, context and human review to reduce errors and make automation more dependable.

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Let’s jump right into it: By now, we have learnt that AI tools can only act on the information they receive. As companies move to tools that assess creditworthiness, support clinical workflows or execute tasks, data quality is becoming a condition for safe deployment when utilising AI agents.

The challenge goes beyond correcting typos. Data can be stale, duplicated, inconsistent or incomplete, and AI agents may lack the organisational context needed to use technically accurate information. The issue is whether companies can trust data for each decision, and what can be done to ensure that happens, along with its impact to the organisation. Let’s explore the realm of data quality for enterprise AI in this edition of The AI Shift….

Bad Data Costs Enterprises  

When it comes to AI, the equation is rather simple: Data quality determines whether a system is interpreting reality or merely processing flawed inputs consistently. 

A missing transaction feed, for example, can make a customer appear financially weaker than they are. If a model treats the gap as negative behaviour, its output may look confident while resting on a faulty premise. 

As Srijan Nagar, cofounder of FinBox, an AI-native digital lending platform, puts it, model confidence should not be confused with confidence in the data.

As Srijan Nagar, cofounder of FinBox, an AI-native digital lending platform, puts it, model confidence should not be confused with confidence in the data.

The consequences vary by use case, but the underlying risk is the same: an error can travel downstream and shape a decision. Across healthcare, fintech and enterprise software, the quality of data is proving to be a big bottleneck for AI systems. 

In fintech, an incorrect identity signal may let fraud through or deny a genuine customer an account or loan. In healthcare, a missing result or duplicated record can distort the information presented to a clinical professional. In both, the problem is not just a model’s accuracy in isolation, but the quality and coverage of the evidence it receives.

The risk extends beyond structured records because that’s not how enterprise data is stored; it often sits in documents, emails, archives and business applications. Even structured data may mislead an AI agent if it is outdated, lacks context or is not authorised for the user. 

And when an........

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