Pharma 5.0

Why poor data quality has the potential to derail AI in pharma

Artificial intelligence is rapidly transitioning from a technology pipedream to an operational reality for many businesses in the pharmaceutical and medical device manufacturing sectors

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Facing tightening margins, mounting pressure to cut time-to-market and upcoming regulatory changes across the US and EU, operations leaders are racing to modernise.

And AI tools that automate complex labelling changes, streamline multilingual artwork and maintain regulatory compliance at speed are at the top of the C-suite agenda.

Why poor data quality has the potential to derail AI in pharma

Gurdip Singh, CEO of Kallik (pictured), reports.

Yet, on the factory floor, operational realities tell a different story.

Although leadership teams see AI as an instant fix for labour shortages and overcoming bottlenecks, they’re missing a vital point: they are plugging automation into a flawed foundation

Navigating the bottleneck

On manufacturing floors around the world, critical product and labelling data remains trapped in legacy silos, manual spreadsheets and disconnected departmental databases.

And, with time, the data points have become fragmented and forgotten. You might not think it, but unchecked, haphazard data is one of the biggest risks those manufacturers will face today. 

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