Hidden cost of wrong AI implementation

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By now, at an individual level, we all have experienced AI going wrong

– chatbot going off-script,

– voice assistant misunderstanding us

– image generation tool writing “Welcmoe” instead of Welcome on the image

While these are frustrating, the real danger lies in enterprise AI implementation.

Take these examples:

– A sales forecasting model that overestimates demand?

(Your client might overstock and waste millions.)

– A fraud detection system with too many false positives?

(Your client could lose customers due to blocked transactions.)

– An LLM-based tender assistant that hallucinates legal terms?

(Your client might submit a non-compliant bid, or worse, face legal trouble.)

Here, wrong AI doesn’t just annoy people, it erodes the trust of your customers, damages your reputation and breaks your business.

So, what should we do?

Check for acceptability of an “imperfect AI”

For example – Recommendation engine

– It does not show you the video you want to watch, or

– It shows you the video you do not want

Either way, you do not have problems

So, before implementing AI, consider whether your users are comfortable with imperfect AI.

Because only those use cases are actually “implement-able” and deliver real value else be ready for more harm than good.

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If you have any queries or suggestions, share them with me on LinkedIn – https://www.linkedin.com/in/nikhileshtayal/

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