Overall, the 2023 ML Insider revealed that AI maturity has not changed significantly over the last few years. The rise in GenAI technology has shifted the industry in some ways, but organizations are slow to adopt it. 

Despite the hype, only 25% of organizations have deployed any genAI models to production in the past year. The survey found that infrastructure is ranked as the largest barrier to productionizing LLMs and that 84% of respondents admit their skills need to improve due to the increased interest in LLM adoption.  

The survey reveals a myriad of challenges that might be causing a slow adoption of LLM technology in businesses such as lack of knowledge, cost, as well as compliance which you can learn more about in the full report. 

84% of respondents admit that their skills need to improve due to the increasing interest in LLM adoption, while only 19% said they have a strong understanding of the mechanisms of how LLMs generate responses. 

Additionally, respondents ranked compliance & privacy (28%), reliability (23%), the high cost of implementation (19%), and a lack of technical skills (17%) as the greatest concerns with implementing LLMs into their business. Nearly half of respondents see infrastructure as the biggest technical challenge to productionizing LLMs.  

However there is no doubt that GenAI is having an impact on the industry. Compared to 2022, the use of chatbots/virtual agents has spiked 26% and translation/text generation is up 12% in 2023 as popular AI use cases. That could be due to the rise in LLM technology in 2023 and the advance in generative GenAI technology. 

The full 2023 ML Insider report also reveals more about the top challenges organizations are facing when it comes to executing ML programs, how long AI teams spend from experimentation to production, popular AI tool rankings, cloud cost estimations and more.