The State of Generative AI at Enterprises

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Along with ChatGPT, the conversation around generative AI has exploded this year. 

According to the early results of a survey by VentureBeat, more than half (54.6%) of organizations are experimenting with generative AI on a small scale

However, most organizations surveyed, 81.8%, have no plans to increase spending on this technology. Enterprises are taking a cautious and measured approach to adopting new generative AI tools. The VentureBeat survey found that top reasons included:

  1. Limited talent and/or resources (36.4%)

  2. Insufficient support from leaders or stakeholders (18.2%)

  3. Unclear/Undefined use cases (13.6%)

  4. Low ROI or competing priorities (13.6%)

From respondents, the largest use cases were for natural language processing (NLP)-related tasks like chat and messaging and content creation (32%). 

Enterprises seem to see the value in generative AI, but likely find themselves overwhelmed by the options and possible uses—not to mention the available tools—for deploying the new technology. Even the two top categories of respondents’ uses—NLP and content creation—could encompass thousands of different use cases. 

Like AI and other emerging technologies in general, there are endless possibilities for enterprises to use the technology to be more efficient, improve safety, better serve customers, and more. But identifying those use cases, spinning up proofs of concept, scaling projects to department or enterprise level, and measuring results takes time. 

For now, many individuals within enterprises may be using generative AI via free tools like ChatGPT. But the enterprise-wide landscape has a while to go to prove value, establish best practices, and gain trust for vendors. 

Generative AI in enterprises is still generating, but the picture looks promising. 

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