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Enterprise AI Adoption Grows, Yet Earnings Impact Remains Limited, McKinsey Survey Finds
A new McKinsey & Company survey – the 10th edition of its annual State of AI series – shows that large enterprises are increasingly scaling artificial‑intelligence (AI) systems across their operations, but most firms still struggle to translate the productivity gains into earnings growth.
The survey, released in early 2026, surveyed more than 1,200 senior executives in companies that generate at least $1 billion in annual revenue. It collected data on how AI is being used, the benefits realized, and the financial impact on earnings before interest and taxes (EBIT).
Productivity and decision‑making gains
According to the survey, 80 percent of respondents reported that AI has improved individual productivity, while 50 percent said AI helps them make better decisions. These figures are similar to those from the previous year, indicating that the perception of AI’s operational benefits is steady.
Scaling across the enterprise
A key focus of the study is the shift from experimentation to enterprise‑wide scaling. In 2025, 38 percent of respondents said they were scaling AI across the organization; that figure rose to 44 percent in 2026. The increase is driven largely by larger firms: 54 percent of companies with $1 billion or more in revenue reported scaling AI across the enterprise, compared with about 33 percent of smaller firms. The share of large companies deploying AI agents in one or more functions grew from 27 percent in 2025 to 40 percent in 2026, while the rate for smaller organisations remained flat at 22 percent.
Financial impact
Despite the higher adoption rates, the survey found that only 37 percent of respondents attribute any EBIT impact to AI, a figure unchanged from the previous year. The proportion of respondents classified as AI high performers – those who report at least 5 percent of EBIT coming from AI and describe the impact as significant – stayed at about 6 percent.
These numbers suggest that while AI is improving efficiency and decision quality, most companies have not yet converted those gains into measurable earnings growth.
Cost constraints
About 20 percent of respondents said that AI‑related operating costs constrained their use of the technology. The survey does not detail the specific cost drivers, but the figure indicates that budgeting for AI infrastructure, data preparation, and talent remains a challenge.
Future investment outlook
Despite the cost and limited earnings impact, optimism about AI’s value is high. The survey reports that 60 percent of respondents plan to increase AI investments over the next year.
Implications for the AI ecosystem
The findings underscore a growing divide between firms that are still in the experimental phase and those that are scaling AI across multiple functions. The steady share of high performers suggests that only a small minority are successfully leveraging AI for significant financial returns.
For the broader AI industry, the data highlight the need for better integration of AI into business processes, stronger governance around data and model deployment, and clearer metrics that link AI initiatives to revenue and profitability.
McKinsey’s State of AI series continues to track these trends, providing a benchmark for enterprises and investors to gauge progress in the AI adoption journey.
— For further commentary or to suggest an article idea, contact Steph Brown at Stephanie.Brown@aicpa-cima.com.
The survey, released in early 2026, surveyed more than 1,200 senior executives in companies that generate at least $1 billion in annual revenue. It collected data on how AI is being used, the benefits realized, and the financial impact on earnings before interest and taxes (EBIT).
Productivity and decision‑making gains
According to the survey, 80 percent of respondents reported that AI has improved individual productivity, while 50 percent said AI helps them make better decisions. These figures are similar to those from the previous year, indicating that the perception of AI’s operational benefits is steady.
Scaling across the enterprise
A key focus of the study is the shift from experimentation to enterprise‑wide scaling. In 2025, 38 percent of respondents said they were scaling AI across the organization; that figure rose to 44 percent in 2026. The increase is driven largely by larger firms: 54 percent of companies with $1 billion or more in revenue reported scaling AI across the enterprise, compared with about 33 percent of smaller firms. The share of large companies deploying AI agents in one or more functions grew from 27 percent in 2025 to 40 percent in 2026, while the rate for smaller organisations remained flat at 22 percent.
Financial impact
Despite the higher adoption rates, the survey found that only 37 percent of respondents attribute any EBIT impact to AI, a figure unchanged from the previous year. The proportion of respondents classified as AI high performers – those who report at least 5 percent of EBIT coming from AI and describe the impact as significant – stayed at about 6 percent.
These numbers suggest that while AI is improving efficiency and decision quality, most companies have not yet converted those gains into measurable earnings growth.
Cost constraints
About 20 percent of respondents said that AI‑related operating costs constrained their use of the technology. The survey does not detail the specific cost drivers, but the figure indicates that budgeting for AI infrastructure, data preparation, and talent remains a challenge.
Future investment outlook
Despite the cost and limited earnings impact, optimism about AI’s value is high. The survey reports that 60 percent of respondents plan to increase AI investments over the next year.
Implications for the AI ecosystem
The findings underscore a growing divide between firms that are still in the experimental phase and those that are scaling AI across multiple functions. The steady share of high performers suggests that only a small minority are successfully leveraging AI for significant financial returns.
For the broader AI industry, the data highlight the need for better integration of AI into business processes, stronger governance around data and model deployment, and clearer metrics that link AI initiatives to revenue and profitability.
McKinsey’s State of AI series continues to track these trends, providing a benchmark for enterprises and investors to gauge progress in the AI adoption journey.
— For further commentary or to suggest an article idea, contact Steph Brown at Stephanie.Brown@aicpa-cima.com.
