By Alistair Barr
Author of the Tech Memo newsletter
New
Every time Alistair publishes a story, you’ll get an alert straight to your inbox!
By clicking “Sign up”, you agree to receive emails from Business Insider. In addition, you accept Insider’s
Terms of Service and
Privacy Policy.
Follow Alistair Barr
- Enterprise AI adoption shows signs of slowing, despite strong results from Big Tech.
- RBC analysts note a recent decline in US businesses paying for AI services.
- Factors include unmet productivity gains, pilot fatigue, and limited transformative apps.
A powerful new technology that helps companies grow faster with fewer employees. This is part of the promise of AI, and it's been catnip to executives. What could go wrong?
Corporations have jumped on the generative AI bandwagon in droves over the past couple of years, adding a huge extra dose of demand beyond consumer usage. Now, this enterprise trend may be showing early signs of slowing down.
In a recent note to investors, RBC Capital Markets analysts signaled a potential pause in enterprise adoption of AI, even as Big Tech continues to report strong AI-driven results.
RBC analysts, led by Rishi Jaluria, wrote that the incredibly strong AI demand cited by Microsoft, Amazon, Meta, Oracle, and Google largely reflects spending on model training, deployment, and AI-native firms — not a broad-based surge across traditional enterprises.
They pointed to new data from Ramp's Fall 2025 Business Spending Report, showing the share of US businesses paying for AI services slipped slightly, from 44.5% in August to 43.8% in September.
That change may seem modest, but it marks the first measurable pullback since enterprise AI adoption began accelerating in 2023, according to Ramp data they shared via this chart.
The RBC analysts outlined three potential reasons for the slowdown:
- The Productivity Paradox: Many enterprises haven't yet realized the productivity gains promised by AI. Without coordinated improvements across workflows, efficiency gains remain isolated or marginal.
- Pilot Fatigue and Privacy Concerns: Some companies have paused AI pilots due to overblown expectations and concern about data governance and privacy.
- Limited "Killer Apps": While use cases such as coding, customer service, and marketing are proving valuable, other sectors, such as healthcare and supply chain, have yet to see transformative results.
RBC remains cautiously optimistic. As this technology matures and more "contemplated" use cases become practical deployments, the analysts expect another step-function increase in enterprise AI demand — a rebound that could require yet another round of large AI infrastructure investment.
Sign up for BI's Tech Memo newsletter here. Reach out to me via email at [email protected].
