What special considerations are needed when implementing Generative AI tools at the enterprise level?

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Chief Strategy Officer, Self-employed
Focus on solving for the use cases and business requirements instead of getting caught up in the hype. Do not be fooled by a great POC. There is a stark difference between how a Generative AI tool is perceived in the lab versus when it is finally rolled out to your employees and customers and integrated with existing workflows at an enterprise level. 


Business and technology leaders will need to collaborate and strategize on the best approach which is ‘human-centric’ : 

Focus on and address questions of responsible and ethical use of AI (e.g., data bias, proprietary information, etc.)
Decide whether you will use large publicly available models with some APIs and consideration towards cybersecurity or initiate smaller very niche models for specific use cases which are fine tuned to meet your requirements.
Include the team by educating and clarifying that this implementation will augment but not replace human effort. The idea is to empower the employees and serve the stakeholders better more effectively and efficiently.
Chief Strategy Officer, Self-employed
Would like to know the views of other leaders in the community about the issues faced and mitigating actions undertaken in their respective field of work ? 
Associate Director of Data Science & Analytics in Healthcare and Biotech, 10,001+ employees
Cost. Training these models is extremely expensive. A large internal LLM can cost tens of millions of dollars in compute. And that is assuming your organization has the proper infrastructure to support it, and the right expertise to optimize it. 

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