AI Growth "Astounds" As 88% Of Firms Adopt GenAI
The AI revolution, is a mere two years old and shows no sign of abating.
S&P and WEKA are “astounded” by the uptake of GenAI by an “astonishing 88% of companies surveyed in an AI revolution that isn’t two years old yet.
The authors are correct, we should all step back, breathe deeply, and be astounded.
Anyone who played with the first version of ChatGPT, as I did back in November 2022, would be hard-pressed to predict the scale of the revolution it kicked off.
Some 33% of organizations already have enterprise-scale AI deployed, although scaling AI applications is a big challenge, with poor data foiling many projects.
The survey also reveals that AI’s benefits aren’t shared evenly. “AI Inequality” (see below for more on this phenomenon), is real with only 10% of companies qualifying as “AI leaders” and 90% as “AI challengers.”
👉TAKEAWAYS
🔹 AI Applications Are Increasingly Pervasive In the Enterprise
33% of survey respondents have reached enterprise scale, with AI projects being widely implemented and driving significant business value, up from 28% last year.
North America leads in enterprise AI adoption, with 48% of North American respondents indicating that AI is widely implemented, compared to APAC (26%) and EMEA (25%).
Product improvement and operational effectiveness are key investment drivers, with organizations leveraging AI to improve product or service quality (42%), target increased revenue growth (39%), improve workforce productivity (40%) and IT efficiencies (41%), and accelerate their overall pace of innovation (39%).
🔹 Generative AI Has Rapidly Eclipsed Other AI Applications
An astonishing 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%).
Generative AI adoption is exploding: 24% of organizations say they already see generative AI as an integrated capability deployed across their organization. 37% have generative AI in production but not yet scaled. Just 11% are not investing in generative AI at all.
🔹 Many AI Projects Fail to Scale — Legacy Data Architectures Are the Culprit
On average, organizations have 10 AI projects in the pilot phase and 16 in limited deployment, but only six are deployed at scale.
Data quality is the top challenge when moving AI projects into production.
The most frequently cited technological inhibitors to AI/ML deployments are storage and data management (35%)—significantly greater than computing (26%), security (23%), and networking (15%). This is evidence that weak data foundations impede many organizations’ AI projects.
🔹 GPU Availability Continues To Be Constrained, Shaping Infrastructure Decision-Making
Four in 10 organizations suggest access to AI accelerators is a leading consideration in their infrastructure decision-making, and 30% cite GPU availability among their top three most serious challenges in moving AI models into production.
🔹 Concerns About AI’s Environmental Impact Persist But Are Not Slowing Adoption
👊STRAIGHT TALK👊
Astounding is a big word, but when it comes to AI adoption, it is well deserved. We are living through genuinely miraculous times that show no sign of abating.
It shouldn’t be surprising that when looking at the top GenAI adopters, financial services and banking do not make the list.
Healthcare, government, and media are the top AI users. That financial services are surpassed even by the government, which is not known as a fast adopter of any technology, should raise eyebrows.
That banks don’t make the cut can be blamed on their being heavily regulated and risk-averse. Both will keep banks away from the “bleeding edge” of technology.
Interestingly the reports claims that AI leaders use a wider range of objectives to drive their AI strategies. Not putting all of their eggs in one AI basket increases the chances of developing impactful AI and increases the number of stakeholders.
Banks should take note of this sage advice but it won’t help them if they opted to save money and not build a robust data architecture.
Astounded? I am.
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