Adopting large language models (LLMs) such as ChatGPT in an enterprise is challenging. In this article, we’ll discuss some key challenges enterprises face when working with LLMs and, most important, how these blockers can be released.
Data Privacy and Security Concerns
One of the biggest challenges around LLMs revolves around data privacy and security. These models are trained on massive amounts of public data. For an LLM to become helpful in the enterprise context, they need to be retrained on often sensitive information such as personal data, financial information, and confidential business information.
To mitigate these concerns, enterprises must ensure that their data is adequately secured and that the models are not accessing or using sensitive information without permission. This requires implementing robust security measures such as encryption, data masking, and access controls.
Integration with Existing Systems:
Another challenge enterprises face with LLMs is integrating them with existing systems. LLMs can generate vast amounts of data, which can be difficult to manage and integrate with existing systems. Enterprises need to ensure that the data generated by the models is properly stored and managed and can be easily accessed and integrated with existing systems such as databases and analytics platforms.
Cost:
Large language models can be costly regarding hardware and software costs. Enterprises need to ensure that they have the budget to purchase and maintain these models and the infrastructure to support them. This can be a significant challenge, especially for small to medium-sized enterprises.
Skills Shortage:
Another challenge related to LLMs adoption is the current skills shortage. A lack of talent with expertise in these models makes it difficult to implement and use them effectively. Enterprises must invest in training and development programs to ensure that their teams have the skills to use these models effectively.
Bias and Halluzination:
LLMs can be biased due to the data they are trained on, which can lead to incorrect results. Enterprises must ensure that their models are trained on unbiased data and that the predicted results from the LLM are corroborated against actual data in the enterprise.
In conclusion, while large language models offer significant potential benefits to enterprises, several challenges must be overcome to adopt them effectively.
PS. I’ll dive deep into those topics in two upcoming webinars about the usability of LLMs for enterprises. See you there!
- US/EMEA session. March 8th. https://squirro.com/events/generative-ai-for-enterprise-how-to-tame-a-stochastic-parrot
- APAC session. March 15th. https://squirro.com/events/generative-ai-for-enterprise-how-to-tame-a-stochastic-parrot-apac/
