The Unglamorous Work Behind Enterprise AI

by · Inc42

SUMMARY

  • 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.