Enterprise AI moves from ‘experiment’ to ‘essential,’ spending jumps 130%

A new study reveals that generative AI has rapidly transformed from an experimental technology to an essential business tool, with adoption rates more than doubling in 2024. 

The research, conducted by AI at Wharton, a research center at the Wharton School of the University of Pennsylvania, in partnership with GBK Collective, provides a comprehensive look at AI’s integration across American businesses. The research team surveyed more than 800 enterprise decision-makers across the United States, examining AI adoption patterns, investment trends, and organizational impacts. The study, titled “Growing Up: Navigating Gen AI’s Early Years,” compared data from 2023 to 2024, tracking changes in usage patterns, departmental adoption, and employee attitudes.

Key Findings:

• Weekly AI usage among business leaders surged from 37% to 72%

• Organizations reported a 130% increase in AI spending since 2023

• 72% of companies are planning additional AI investments in 2025

• 90% of leaders now believe AI enhances employee skills (up from 80%)

• Concerns about AI-related job displacement decreased from 75% to 72%

• 58% of organizations rated AI’s performance as “great”

“The most interesting things that come out of the survey is this snapshot of how corporates are feeling, thinking and implementing Gen AI, and how that is changing quite rapidly,” Stefano Puntoni, Sebastian S. Kresge Professor of Marketing at the Wharton School and co-director of AI at Wharton told VentureBeat. “This year, what we’re seeing is that people are less curious, they are more excited, they’re less scared and there is a more belief that these are tools that are going to augment human expertise.”

Investment surge for enterprise AI is a ‘gold mine’ for consultants

The research shows a dramatic increase in organizational spending on generative AI, with over 40% of companies now investing more than $10 million in the technology. This represents a significant shift from the previous year when the typical investment range was between $1-5 million.

What is perhaps even more interesting than the rise in spending, is understanding where the money is going.

“About a third of the money is spent on tech,” explained Puntoni. “But that’s actually a minority of all the money that is pouring into Gen AI.” 

The remaining investment is distributed across training and upskilling the existing workforce, onboarding new employees and consulting services. While much of the hype and news in generative AI in 2024 has been about the technology, that’s not the differentiator for many enterprises at this point.

“The technology itself is more or less a commodity. meaning, you know, my ChatGPT is as good as your ChatGPT and so the differentiation is largely going to come from the integration of the technology and business processes,” he said. “There’s no template, there’s no blueprint,  people will have to experiment and learn.”

Puntoni actually expects that consultants, at least in the short term, will be the big winners in the AI gold rush. In his view, the technology part of generative AI is increasingly becoming commoditized.

“I think we’re going to see a protracted period of experimentation, learning new business models and new ways of organizing business functions,” Puntoni said. “It’s a gold mine for consultants And I think this is not going to run out of gold anytime soon.”

Small and mid-sized companies lead the way in AI

An unexpected finding reveals that smaller organizations are currently ahead in AI adoption compared to their larger counterparts. The study defines smaller organizations as those with revenue between $50 million to $250 million and mid-sized as $250 million to $2 billion.

“We still see a difference between smaller organizations and large organizations in reported adoption, as well as less restrictive uses within the organization for experimentation,” Jeremy Korst, Partner with GBK Collective, told VentureBeat. 

Korst suggests this could lead to interesting competitive dynamics.

That is if the smaller organizations are actually able to find not only cost efficiencies and productivity, but new business models and capabilities, overall competition could increase. Korst said in that situation smaller groups might be able to compete differently and more effectively with some of their larger organizations.

What organizations should be doing now to improve enterprise AI outcomes

Despite the increased adoption, organizations face several challenges in implementing AI effectively. The study highlights issues around data governance and security, with concerns about unintended data leakage within organizations even when using enterprise-grade AI tools.

The research also indicates that while the adoption curve for generative AI has been unprecedented in its speed, organizations are now entering a more mature phase focused on practical implementation and return on investment

“I think that organizations ought to be learning, I don’t think there is a way in which you’re going to be successful in the future unless you make a concerted, serious effort to see how this technology can help you,” Puntoni said.