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Just three years ago, most companies treated generative AI like an uncertain curiosity. Today, it’s hard to find a Fortune 500 company that isn’t rethinking core processes to leverage it — momentum that’s only accelerating as 2026 approaches.
Leaders are no longer debating whether generative AI will matter; they are racing to determine how to operationalize it. That shift was the topic of my conversation with Wharton marketing professor Stefano Puntoni, co-director of the Wharton Human-AI Research (WHAIR) initiative. Puntoni noted that generative AI adoption is progressing at an eye-opening pace.
“I don’t think there’s any company that now says, ‘Generative AI isn’t for us,’” he said.
The third annual WHAIR study, conducted with GBK Collective, underscores this acceleration, Puntoni told me. A survey of 800 senior leaders in finance, IT, HR, and other functions at U.S. companies with more than $50 million in annual revenue found that 88% expect to increase generative AI investment in the next year, and 62% expect budgets to rise by more than 10% within two to five years.
This marks a sharp reversal from 2023, when concerns around data leakage, regulatory liability, and consumer protection — especially in heavily regulated industries — led many companies to ban generative AI outright, Puntoni explained.
Today, most enterprises are moving ahead and figuring out optimal implementation with guardrails, he said.
“I think it’s going to take a decade to really find out how to use this technology, but it’s improving so rapidly,” he added.
Usage patterns show the shift. In 2023, only 37% of senior leaders used generative AI weekly. Now, 82% do, and 46% report daily use, according to the report. Because generative AI is a general-purpose technology, Puntoni and his colleagues expect usage to reach near-universal levels.
“Half of senior leaders in a large sample of corporate America are saying that they’re using a tool every day; that is really quite incredible,” he said.
Measuring progress
Leaders appear optimistic about returns. Nearly three-quarters of respondents said their companies track ROI through metrics such as profitability, productivity, and throughput, according to the report. Four out of five expect positive returns within two to three years, with top executives more optimistic than mid-level managers.
Still, progress varies by company size. Larger enterprises are seeing slower results as they manage complex integrations, while midsized and smaller firms report quicker progress. Tech, banking, and professional services firms are among the sectors making strides.
The ROI reports rely on self-assessments rather than hard evidence, Puntoni said. Many organizations are still refining how they measure success, often focusing on intermediate metrics.
“We should look at this data as more like a vibe of how they feel about it than hard evidence for what’s happening inside these companies,” he added.
MIT’s August report found that, based on its dataset, most firms struggle to generate immediate ROI from generative AI — from a profitability perspective — with back-office automation delivering the biggest impact.
However, both the MIT and WHAIR reports highlight a persistent barrier: workforce skill gaps.
Wharton’s survey shows that 43% of leaders warn of “skill atrophy,” underscoring the need for better AI training programs. As generative AI matures in the enterprise, organizational readiness is paramount — leadership alignment, workforce skills, governance, and change management, not just technical capacity, according to the report.
Enterprise AI is already a major focus on Wall Street, and investors are watching closely as big tech companies and their customers scale adoption.
As we head into 2026, a clearer question is emerging: not whether generative AI will create value, but how companies can build the skills, systems, and governance to capture it.
Written by Sheryl Estrada for Fortune as “Why generative AI went from risk to business imperative at U.S. companies” and republished with permission.
