New Data Unveils Realities of Generative AI Adoption in the Enterprise

Large language and image AI models, also referred to as generative artificial intelligence (GenAI), have been the big story of 2023. It has opened a new set of opportunities for professionals and businesses. While most businesses acknowledge the potential for GenAI, however, there are also some concerns about its use. In enterprises, we’ve seen a wide range of opinions about GenAI, ranging from wholesale adoption to severely restricted or even forbidden use. 

O’Reilly, the premier source for insight-driven learning in technology and business, recently conducted a study of more than 2,800 technology professionals from various industries around the globe to uncover the realities of GenAI use for enterprises. The findings of the report reveal the rapid rise of GenAI, bottlenecks to AI adoption, and the skills needed to move forward with these technologies. 

According to Mary Treseler, chief content officer at O’Reilly, GenAI offers a lot of opportunities for enterprises however “Without the proper talent in place to manage it, this rapidly evolving technology can quickly outpace enterprise resources. As this groundbreaking report unveils, we are far from reaching the peak of what generative AI can achieve, and organizations still have time to invest in the critical skills development required to be at the forefront of the AI revolution.” 

One of the key findings of the report is that GenAI has seen rapid adoption, more than any other technology in recent times. Two-thirds (67 percent) of companies are currently using GenAI and over a third (38 percent) have been working with AI for less than a year. This is contrary to Gartner, who reported that AI is close to reaching the peak of its inflated expectations. The result of the O’Reilly report indicates there is plenty more headroom. 

The O’Reilly report shows that 54 percent of AI users believe that AI tools will lead to better productivity, but only 4 percent believe that it would result in lower head counts. The most commonly used applications for AI include programming (77 percent), data analysis (70 percent), and customer-facing applications (70 percent). 

The increased GenAI adoption has enabled enterprises to train models more easily and deploy more complex applications on those models. Even with the rapid adoption, many enterprises are still in the early stages with 18 percent of respondents reporting having applications in production. 

While 23 percent of respondents are using one of the GPT models, enterprises are also building on top of open-source models. This indicates an active and vital world beyond GPT. 

(Alexander Supertramp/Shutterstock)

Enterprises are facing multiple bottlenecks that are restricting faster adoption. Chief constraints are the challenges in identifying appropriate use cases (53 percent), followed by legal issues, risk, and compliance (38 percent). 

While the accelerated adoption of GenAI has created a demand for technology workers, a significant skill gap remains. The most needed skills include AI programming (66 percent), data analysis (59 percent), and AI/ML operations (54 percent). Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing.

“The adoption of generative AI is certainly explosive, but if we ignore the risks and hazards of hasty adoption, it is certainly possible we can slide into another AI winter,” said Mike Loukides, vice president of content strategy at O’Reilly and author of the report. “By taking a pragmatic approach versus rushing into production, investing in training and resources, and thinking creatively about how to put AI to work, enterprises have an enormous opportunity in front of them. As the report concludes, ‘AI won’t replace humans, but companies that take advantage of AI will replace companies that don’t.”

The O’Reilly report is further proof that enterprises are optimistic about GenAI’s future. However, there are some concerns related to security, bias, correctness, and fairness. Some early adopters who ignore these risks are likely to suffer consequences. To close the AI skills gap, companies will have to invest heavily in training for both software developers and AI users. White AI won’t be replacing humans anytime soon, those who can integrate AI into their work will benefit the most. 

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