Overall Understanding Of AI Necessary For Consumer Goods Companies, Notes GlobalData

By Dayeeta Das
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Overall Understanding Of AI Necessary For Consumer Goods Companies, Notes GlobalData

Consumer goods companies need to understand the technical, financial and organisational requirements of any AI application to reliably assess the level of risk that that application represents, according to GlobalData.

The data and analytics firm noted that while artificial intelligence and generative AI have developed rapidly over the last 18 months, the benefits and costs of AI applications are poorly understood by many in the industry.

GlobalData further added that consumer goods companies need to fail – and fail fast – in their AI initiatives to gain this understanding.

Rory Gopsill, senior consumer analyst at GlobalData, commented, “Understanding these risks will enable consumer goods companies to fail early and safely, and to learn from that failure. This will equip them with the knowledge to implement AI in a way that is safe and profitable.

“Fostering a culture of transparency around the risks of AI will help drive industry application and protect consumer goods companies and customers from the potential pitfalls of this evolving technology.”


Risk Assessment

Adopting AI can pose real financial and security risks, and AI training can prove very expensive, especially if the task being automated is complex and requires advanced AI.

Moreover, if an AI application requires training data that is commercially sensitive or confidential, a company may choose to train the AI application in a private cloud environment, rather than a less secure public cloud.

Purchasing and maintaining the necessary IT infrastructure for the same would be very expensive and organisationally demanding, GlobalData added.

Gopsill added, “Consumer goods companies need to be aware of these – and other – risks when choosing to develop AI applications. If they are not, their AI initiatives could fail, with serious consequences.

“For example, sensitive data could be exposed, development costs could outweigh the application’s benefits, the quality of the AI application could be diminished, or the project could simply never get finished.”

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