The State of Enterprise LLM Preparedness & Adoption
We surveyed over 500 senior tech professionals about the status of their generative AI projects, which models and inference providers they're using and considering, their top deployment challenges, and how they expect LLMs to impact their business. This report provides a comprehensive overview of LLM adoption in the enterprise and why AI projects get stuck in experimentation.
The revolutionary power of generative AI is undeniable
Organizations are leveraging generative AI to drive efficiency and productivity by automating time-consuming tasks and workflows
90%
of respondents
say fine-tuned LLMs would bring value to their organization
94%
of respondents
expect to run more than one model in the next two years
67%
of respondents
say they will have more than one inference provider
70%
of respondents
are not prepared to run LLM projects for enterprise solutions
Read this report to understand:
- The difference between very prepared tech teams and unprepared tech teams when it comes to initiating generative AI projects
- Why generative AI projects get stuck in experimentation
- Which foundational models are of interest or being tested by senior tech professionals
- Why enterprise tech teams expect to run more than one model and inference provider
- Priority use cases and business areas that are expected to be augmented with LLMs
This report reveals who is most prepared to fully integrate LLMs into their business applications and what they are doing to ensure success as they test and deploy multiple models and providers.
