Large Language Models: Opportunity, Risk, and a Path Forward

Successful adoption of enterprise language models will need to overcome significant challenges to effectively, responsibly and securely train them.

At a time when the hype surrounding Open AI’s ChatGPT has prompted 45% of executives to increase their investments in artificial intelligence (AI)*, the new research Large Language Models: Opportunity, Risk, and a Path Forward reveals that more than one-third of enterprises (37.1%) are already planning to train and customize language models to meet their business needs.

A significant majority of enterprises (78.5%) realize that the efforts required to effectively train a usable and accurate enterprise-specific language model is a significant undertaking which will require dedicated resources and budget. Almost three-quarters of enterprises surveyed have budget or are discussing adding budget to support large language model (LLM) adoption.

“Enterprise specific language models with a human-centered approach are part of the future,” says Marco Varonefounder and CTO of“Business natural language use cases always require some degree of domain-specific training applied to existing proprietary or open-source LLMs. Specific enterprise models can be smaller, more efficient, faster and less resource-hungry while still maintaining high performance. Having subject-matter experts monitoring and refining data and inputs throughout the process ensures accuracy, transparency and accountability.”  

The study shows that while only a few surveyed favor a LLM training moratorium (21.2%), the majority of AI professionals and practitioners (70.6%) point to the need for commercial and malicious use in AI regulations. Top adoption challenges include: data privacy and security (73.1%), accuracy and quality for production model deployment (51.2%) and knowledgeable resources on how to build and train LLMs (40.7%).

For companies that are prioritizing AI transparency and responsibility, generative AI and LLMs may bring real risks for their ESG objectives and performance, with truthfulness (69.8%), bias (67.3%) and leaks of proprietary data (62.6%) as the top concerns.

Regardless of the direction an organization chooses, basic AI data governance principles still apply to generative AI and LLMs. While 24.0% of survey respondents indicate that further restrictions need to be put in place to test LLMs and provide clear communication of applied policies, 38.8% feel some additional degree of freedom should be encouraged, and 34.3% cite that current principles are adequate.

Download the’s e-book Large Language Models: Opportunity, Risk, and a Path Forward.

* Gartner Poll Finds 45% of Executives Say ChatGPT Has Prompted an Increase in AI Investment – May 3, 2023, link

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