ETR's State of Generative AI Survey 

ETR recently conducted its latest survey on generative AI and large language models (LLMs), focusing on budget plans, evaluation and production rates, anticipated ROI, and generative AI vendors. The survey received responses from 1,768 IT decision makers, with nearly a quarter of respondents representing Global 2000 organizations. Large and North American enterprises were the most common size and regional subsamples, and the IT/TelCo, Services/Consulting, and Financials/Insurance industries collectively represented 52% of respondents. By job title, roughly half of citations came from non-executive managers, while an equal 25% came from C-Suite and Practitioners. While this summary provides some key insights, the complete survey results are available on the ETR Platform and deserve closer examination.

Production of Gen AI use cases continues to grow, with three-quarters of respondents already utilizing the technology, up from 60% a year ago. Less than 20% of respondents say that their organization is NOT evaluating generative AI / LLMs, a number that has consistently fallen each survey period. Text and data summarization, followed by marketing and sales content writing, and code generation and documentation, are the most evaluated use cases, though evaluations have plateaued for each since April 2024. Collaboration improvement, a debut use case in our survey, also registers strong evaluations. In this survey period, we split the previous use case of “support” into internal and external-facing customer support, resulting in a disparity that largely favored using Gen AI more for internal employee support than for customer-facing functionalities.

As seen below, 75% of respondents that indicated their organization is/has evaluated generative AI/LLM use cases are already leveraging the technology in a production environment; this rate has quickly grown from 60% twelve months ago. Text and data summarization, along with collaboration improvement, are the two most-cited use cases, while writing marketing content comes in third. Again, ETR split the previous use case of “support” into External customer support and Internal employee support with data that strongly favors internal production use cases and infers continued trepidation for customer-touching applications.

Another key takeaway from this survey is that nearly one-quarter of respondents are still unsure when their organization will realize ROI on its generative AI investment. This may signal a lack of clarity around effective use cases or measurable metrics for the still-nascent technology. A quarter of those who could estimate an ROI timeline said between 7 to 12 months after initial deployment, and 35% said sooner. An increasing proportion of respondents expect more than a year before ROI is realized (17%), while those expecting less than a year have contracted. One interesting disparity in our demographic cuts was that C-Suite respondents are more bullish and more certain about ROI than the actual Practitioners deploying the Gen AI technology, who are more unsure and see longer paths to ROI.

In gauging an organization’s willingness to pay for an existing vendor’s embedded generative AI offerings, Information Security and Productivity were only two use cases that garnered more than 18% of respondents scoring a 5 (extremely willing) on a scale of 1 to 5. The other listed use cases had similar average rankings, although chatbot support and search stood out, with nearly 17% of respondents ranking the willingness to pay incremental costs as 5 out of 5. Meanwhile, IT asset management ranked lowest in this analysis. When isolating to Management-level respondents, Productivity moved into a tie with Information Security (see below).

ETR's most recent July 2024 quarterly State of Generative AI survey (N=1768) provides more insights into the evolving landscape of generative AI and LLM adoption among organizations. The survey also details evaluation and production rates, use cases, barriers to enabling Gen AI, buy vs. build rates, willingness to pay for embedded offerings, areas of technology most ripe for Gen AI integrations, retrieval augmented generation plans (RAG), and more. ETR subscribers can access the full survey results or read the report on our research platform. If you're not yet a subscriber but want access to this report, please get in touch with our service team or utilize the free report form in this article. Of course, if you'd like to access everything that ETR has to offer, you can start with your own platform access today.

Enterprise Technology Research (ETR) is a technology market research firm that leverages proprietary data from our targeted IT decision maker (ITDM) community to provide actionable insights about spending intentions and industry trends. Since 2010, we have worked diligently at achieving one goal: eliminating the need for opinions in enterprise research, which are often formed from incomplete, biased, and statistically insignificant data. Our community of ITDMs represents $1+ trillion in annual IT spend and is positioned to provide best-in-class customer/evaluator perspectives. ETR’s proprietary data and insights from this community empower institutional investors, technology companies, and ITDMs to navigate the complex enterprise technology landscape amid an expanding marketplace. Discover what ETR can do for you at www.etr.ai