According to a survey by Gartner, respondents have reported 15.8 percent revenue increases, 15.2 percent cost savings and 22.6 percent productivity improvement, on average, after deloying generative artifficial intelligence.
One suspects we should take those quantifiable results with a bit of skepticism, as most of the returns from GenAI are indirect and hard to measure.
Nevertheless, some argue that early adopters already are seeing revenue upside. A global survey of mid-market and enterprise firms conducted on behalf of Google Cloud suggests that 74 percent of organizations surveyed are currently seeing return on investment from their generative artificial intelligence investments.
Furthermore, 86 percent of respondents with GenAI in production mode claim annual revenues have climbed about six percent as a result.
As with any survey of respondent attitudes, there is room for disagreement. Respondents might simply be inferring AI-driven growth when other forces are at work. Since many of the reported use cases deal with operations, contributions to revenue might often be estimates.
Also, it might be the case that top-performing firms are most likely to be putting GenAI into use at scale. In other words, the top performers grow revenues more effectively, as a rule, and might be able to deploy new technologies more effectively as well.
What we can probably say is that some firms supplying infrastructure, such as Nvidia, and some firms offering AI consulting, already can claim revenue boosts from AI. Accenture, for example, says its AI revenues for the first six months of 2024 were $2 billion.
Boston Consulting group is projecting 20 percent of its 2024 revenue, and 40 percent of its 2026 revenue, will come from AI integration projects. IBM’s consulting arm has also made more than $1 billion from generative AI from WatsonX and generative AI, since inception.
There’s a reason increasing use of generative and other forms of artificial intelligence is linked to data center capacity: model training is getting more compute intensive. So large language model training costs are growing.
Still, generative AI costs are significant, both to create models and train them.
A Gartner survey of 822 business leaders, conducted between September and November 2023, suggests that various generative AI projects cost between $5 million to $20 million. But that might not be the biggest impact, as costs for inference operations (asking questions, getting answers) could run between $8,000 to $21,000 per user.
For a 1,000-user firm, that might suggest $8 million to $21 million annually in inference operations.
All that noted, it might also be the case that some industries and use cases are more likely to be able to create direct revenue, though virtually any industry might claim indirect revenue benefits from any form of AI.
To the extent AI is the next general-purpose technology, as was the internet, we could ask the same questions about near term return from internet investments.
How many firms will see near-term and quantifiable results from their capital investments and operating expenses directly related to GenAI? Perhaps anot so many.
