Key insights for enterprises to overcome challenges

Enterprises need multidisciplinary AI and Ethics teams

According to GlobalData, the implementation of generative AI poses several challenges for enterprises, requiring them to address ethical considerations and navigate complexities. Challenges include identifying suitable use cases, vendor selection, and addressing concerns regarding privacy, content generation, copyright infringement, and data leakage. To ensure responsible and ethical implementation of generative AI technologies, enterprises need multidisciplinary AI and Ethics teams.

Generative AI, also known as gen AI, offers the potential to perform tasks that existing AI applications have not yet mastered, such as writing code, generating training data, or creating natural-sounding text. This opens up various potential applications across industries. However, enterprises must evaluate the large language models (LLMs) available and determine which model works best for their specific use case, whether multiple LLMs are required for different applications, and the level of customization needed.

Cloud giants are seeking to attract low-coders and non-coders to their platforms, using advanced AI services to eliminate complex coding requirements and simplify the deployment of new app architectures into production. This trend adds to the implementation challenges, as enterprises need to choose the right cloud platform and take advantage of the AI services provided.

Gen AI presents its own unique set of hurdles related to ethics and responsible AI, beyond the challenges encountered with previous AI deployments. The technology may produce incorrect or misleading information, leading to unearned confidence due to the authoritative presentation of results. There are concerns about privacy, generation of inappropriate or malicious content, and potential copyright infringement. Data leakage is also a significant concern.

To address these challenges, enterprises should establish multi-disciplinary AI and Ethics teams that evaluate new AI use cases and ensure adherence to corporate ethical standards. These teams can help mitigate risks and ensure responsible and ethical implementation of generative AI technologies.



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