In the world of AI, things are moving fast, and organisations that effectively apply new AI solutions will race ahead of their competitors. Those who hesitate will fall behind.

When I work with enterprise executives to deploy AI-enabled products, their biggest challenge is their own Enterprise AI Fears.

There are three big ones:

• Garbage In – Garbage Out
• Security and Data Risk
• Is my team capable of handling AI?

In this series of articles, I’m going to break down these Enterprise AI Fears and present ideas for how to overcome them. Let’s start with the first fear I always run into: Data Quality.

Garbage In – Garbage Out (GIGO)

If you’ve worked in enterprise data, analytics, or IT, you’ve heard the warning: “Garbage In – Garbage Out.” The hidden meaning is that incorrect or inaccurate data could drive poor business decisions.

GIGO is a real concern, but with the right data cleaning and validation technology, these challenges can be overcome. Advanced AI tooling can flag suspicious data, suggest fixes, and provide human oversight.

For example, Optii runs AI/ML tests on customer data to detect anomalies like incorrect Minimum Order Quantity (MOQ) settings. These are flagged as “Data Alerts” in the Optii Data Explorer with recommended solutions for users to review.

When data is missing, Optii uses machine learning predictions to suggest values, which are then validated by human operators. If a missing value is business-critical, it’s flagged for further investigation.

Many assume that any “garbage” in data invalidates all results, but well-constructed probabilistic AI is robust against modest noise. At Oii, we assume some level of noise in all customer data—because perfect data doesn’t exist.

The cure for GIGO is analytical scrutiny and automated data cleaning, paired with human oversight. With the right AI tools, enterprises can overcome this fear and trust their AI-driven decisions.

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