'We can now see a direct correlation forming with those with a higher degree of AI maturity and increased revenue, operating efficiencies, and faster time to market for product innovation'
Generative artificial Intelligence (GenAI) is now more widely implemented in global organizations compared to other AI applications, according to a recent report.
Nearly nine in 10 (88%) of organizations are actively investigating generative AI, far outstripping other AI applications such as prediction models (61%), classification (51%), expert systems (39%) and robotics (30%), report WEKA and S&P Global Market Intelligence.
Currently, nearly quarter (24%) of organizations say they already see generative AI as an integrated capability deployed across their organization. Over a third (37%) have generative AI in production but not yet scaled, and just 11% are not investing in generative AI at all.
It’s an “astonishing rate of change” that’s taken place since the onset of ChatGPT 3 and the first wave of generative AI models reached the market in early 2023, said John Abbott, principal research analyst at 451 Research, part of S&P Global Market Intelligence.
“In less than two years, generative AI adoption has eclipsed all other AI applications in the enterprise, defining a new cohort of AI leaders and shaping an emergent market of specialty AI and GPU cloud providers.”
The uneven implementation of AI across the world could potentially widen existing disparities in income and quality of life, according to a previous report from the International Labour Organization (ILO).
A third (33%) of survey respondents have reached enterprise scale, with AI projects being widely implemented and driving significant business value, up from 28% last year, according to the WEKA and S&P survey of over 1,500 AI/ML decision makers/influencers in enterprises, research organizations, and AI providers building AI technologies, products, and solutions.
North America leads in enterprise AI adoption, with 48% of North American respondents indicating that AI is widely implemented, compared to 26% in APAC and 25% in EMEA.
Overall, four in 10 organizations suggest access to AI accelerators is a leading consideration in their infrastructure decision-making.
On average, organizations have 10 AI projects in the pilot phase and 16 in limited deployment, but only six are deployed at scale.
However, concerns around AI remain.
The most frequently cited technological inhibitors to AI/ML deployments are storage and data management (35%) – greater than computing (26%), security (23%), and networking (15%).
“This is evidence that weak data foundations impede many organizations’ AI projects,” says WEKA.
Nearly two-thirds (64%) of organizations say they are concerned about the impact of AI/machine learning (ML) projects on their energy use and carbon footprint. And 25% indicate they are very concerned.
However, 42% of organizations indicate that they have invested in energy-efficient IT hardware/systems to address the potential environmental impacts of their AI initiatives over the past 12 months. Of those, 56% believe this has had a “high” or “very high” impact.
A previous recent report by Slingshot shed light on the growing disparity between employer expectations and employee use of AI in the workplace.
