There is no one large language model (LLM) to rule them all, at least not according to enterprise IT leaders surveyed by IBM. That finding is part of a new report released today by the IBM Institute for Business Value, titled “The CEO’s Guide to Generative AI: AI Model Optimization.” The report is based on a survey of U.S.-based executives collaborating with Oxford Economics. According to IBM, the report aims to provide CEOs with actionable insights to make informed decisions about AI implementation and optimization within their organizations. It also provides its fair share of interesting views on how enterprise AI adoption is actually rolling out in the real world. Key findings from the report include:

• Model specialization: The study debunks the myth of a universal AI model, emphasizing the need for task-specific model selection.
• Model diversity: Organizations currently use an average of 11 different AI models and project a 50% increase within three years.
• Cost barriers: 63% of executives cite model cost as the primary obstacle to generative AI adoption.
• Model complexity: 58% cited model complexity as a top concern.
• Optimization techniques: Fine-tuning and prompt engineering can improve model accuracy by 25%, yet only 42% of executives consistently employ these methods.
• Open model growth: Enterprises expect to increase their adoption of open models by 63% over the next three years, outpacing other model types.

“From what I see, enterprise technology leaders are very well educated about the types of models available today and understand that for their specific use cases, each model would have their strengths and limitations,” Shobhit Varshney, VP and senior partner at IBM Consulting told VentureBeat in an exclusive interview. “But other C-suite leaders are still catching up and learning what LLMs can do and can’t do, and generally think of one large gen AI model that can handle different tasks.”

Full study by IBM : Cost and model complexity remain barriers to enterprise AI.