Generative AI in the enterprise has crossed from experiment to expectation. The question for most leaders is no longer whether to adopt it, but how to do so in a way that produces measurable value instead of stranded pilots. This resource summarizes what the data shows and what it means for your organization, building on the widely cited O’Reilly survey on generative AI in the enterprise.
Generative AI in the Enterprise
Generative AI has been the biggest technology story of 2023. Almost everybody’s played with ChatGPT, Stable Diffusion, GitHub Copilot, or Midjourney. A few have even tried out Bard or Claude, or run LLaMA1 on their laptop. And everyone has opinions about how these language models and art generation programs are going to change the nature of work, usher in the singularity, or perhaps even doom the human race. In enterprises, we’ve seen everything from wholesale adoption to policies that severely restrict or even forbid the use of generative AI.
What’s the reality? We wanted to find out what people are actually doing, so in September we surveyed O’Reilly’s users. Our survey focused on how companies use generative AI, what bottlenecks they see in adoption, and what skills gaps need to be addressed.
Executive Summary
We’ve never seen a technology adopted as fast as generative AI—it’s hard to believe that ChatGPT is barely a year old. As of November 2023:
- Two-thirds (67%) of our survey respondents report that their companies are using generative AI.
- AI users say that AI programming (66%) and data analysis (59%) are the most needed skills.
- Many AI adopters are still in the early stages. 26% have been working with AI for under a year. But 18% already have applications in production.
- Difficulty finding appropriate use cases is the biggest bar to adoption for both users and nonusers.
- 16% of respondents working with AI are using open source models.
- Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing.
- 54% of AI users expect AI’s biggest benefit will be greater productivity. Only 4% pointed to lower head counts.
Is generative AI at the top of the hype curve? We see plenty of room for growth, particularly as adopters discover new use cases and reimagine how they do business.
Source: https://www.oreilly.com/radar/generative-ai-in-the-enterprise/
What the Data Means for Leaders
The consistent finding across enterprise generative AI research is that adoption is broad but value is uneven. Most organizations are using generative AI somewhere, yet a much smaller share can point to durable, measurable returns. The gap is rarely the model. It is the surrounding readiness: data quality, governance, skills, and clear use cases tied to business outcomes.
How to Capture Real Value from Generative AI
The enterprises pulling ahead treat generative AI as an operating capability, not a feature. That means picking a small number of high-value workflows, putting governance and measurement in place before scaling, and investing in the AI literacy that lets people actually use the tools well. Start by benchmarking your readiness on the Elevates.AI Launchpad, then prioritize the use cases with the clearest payoff.
Frequently Asked Questions
What does generative AI in the enterprise actually look like?
In practice it spans content generation, code assistance, knowledge retrieval, customer support, and analysis. The common thread among successful deployments is that generative AI is embedded into a defined workflow with clear ownership and measurement, not bolted on as a standalone novelty.
Why do most enterprise generative AI projects stall?
They stall because the foundation is not ready. Without reliable data, governance, and AI literacy, generative AI amplifies existing dysfunction instead of fixing it. That is why readiness, not model choice, is the strongest predictor of return.
