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Most companies still pilot AI programs despite widespread adoption

McKinsey's November 2025 report reveals 88% of organizations use AI regularly, but only one-third have scaled implementations beyond experimental phases.

Luis Rijo

• 9 min read
Most companies still pilot AI programs despite widespread adoption

Three years after generative AI tools triggered widespread enterprise adoption, organizations face a persistent scaling problem. McKinsey released its latest Global Survey on artificial intelligence on November 9, 2025, revealing that while 88 percent of respondents report regular AI use in at least one business function, most implementations remain stuck in pilot or experimental phases.

The survey, conducted between June 25 and July 29, 2025, gathered responses from 1,993 participants across 105 nations representing diverse industries, company sizes, and functional specialties. According to the research, only approximately one-third of respondents report their companies have begun scaling AI programs across the enterprise.

This scaling challenge arrives despite dramatic increases in adoption rates. The 88 percent reporting regular AI use represents a significant jump from 78 percent in McKinsey's 2024 survey. More than two-thirds of respondents now say their organizations use AI in multiple business functions, with half reporting use in three or more functions.

AI agents emerge but remain confined

Organizations are beginning to explore AI agents—systems based on foundation models capable of acting autonomously, planning and executing multiple workflow steps. Twenty-three percent of respondents report their organizations are scaling an agentic AI system somewhere in their enterprises, while an additional 39 percent say they have begun experimenting with AI agents.

However, actual deployment remains limited. Most organizations scaling agents report doing so in only one or two functions. In any individual business function, no more than 10 percent of respondents say their organizations are scaling AI agents.

AI agent adoption has accelerated throughout 2025, with major advertising platforms introducing autonomous campaign management capabilities. Amazon announced its Ads Agent on November 11, 2025, enabling natural language campaign creation across its DSP and Marketing Cloud platforms. Google released AI agent frameworks in November 2025, establishing production-grade standards for autonomous systems.

Agent use is most commonly reported in IT and knowledge management, where use cases such as service-desk management and deep research have quickly developed. By industry, AI agents are most widely reported in technology, media and telecommunications, and healthcare sectors.

Industry analysis from July 2025 identified agentic AI as the most significant emerging trend for marketing organizations, with McKinsey projecting the market could reach approximately $1 trillion by 2035-2040.

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Enterprise impact remains limited despite qualitative gains

While 39 percent of respondents attribute some level of EBIT impact to AI, most report less than 5 percent of their organization's EBIT is attributable to AI use. This limited financial impact contrasts sharply with other reported benefits.

A majority of respondents say their organizations' AI use has improved innovation, while nearly half report improvement in customer satisfaction and competitive differentiation. Many respondents report seeing cost benefits from individual AI use cases, especially in software engineering, manufacturing, and IT.

Revenue increases resulting from AI use are most commonly reported in use cases within marketing and sales, strategy and corporate finance, and product and service development. This pattern has remained consistent across multiple years of McKinsey's survey research.

The gap between widespread adoption and limited financial impact reflects broader implementation challenges. Research from Gartner in June 2025 predicted that over 40 percent of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, and inadequate risk controls.

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High performers distinguish themselves through ambition

Respondents who attribute EBIT impact of 5 percent or more to AI use and report their organization has seen "significant" value from AI—defined as AI high performers, representing about 6 percent of respondents—demonstrate distinct characteristics from their peers.

High performers are more than three times likely than others to say their organization intends to use AI to bring about transformative change to their businesses. These organizations are nearly three times as likely to report fundamentally redesigning individual workflows, which shows one of the strongest contributions to achieving meaningful business impact of all factors tested.

While most respondents report efficiency gains as an objective of their organizations' AI use, high performers are more likely than others to say their organizations have also set growth or innovation as objectives. Eighty percent of respondents say their companies set efficiency as an objective, but organizations seeing the most value often set multiple objectives including growth and innovation.

High performers regularly use AI in more business functions than their peers. They are much more likely than others to report use in marketing and sales, strategy and corporate finance, and product and service development. In most business functions, AI high performers are at least three times more likely than their peers to report scaling their use of agents.

The advertising industry has experienced this pattern throughout 2025. IAB Europe research from September 2025 found that 85 percent of companies already deploy AI-based tools for marketing purposes, with targeting and content generation leading adoption at 64 percent and 61 percent respectively.

Leadership commitment separates successful implementations

AI high performers demonstrate significantly higher levels of senior leadership engagement. These respondents are three times more likely than their peers to strongly agree that senior leaders at their organizations demonstrate ownership of and commitment to AI initiatives. High performers report that senior leaders actively engage in driving AI adoption, including role modeling the use of AI.

High performers are more likely to employ a range of practices to realize value from AI use. They are more likely than others to say their organizations have defined processes to determine how and when model outputs need human validation to ensure accuracy—another top factor distinguishing high performers.

The management practices align with McKinsey's broader Rewired research based on more than 200 at-scale AI transformations. These practices span six dimensions essential to capturing value from AI: strategy, talent, operating model, technology, data, and adoption and scaling. All tested management practices correlate positively with value attributable to AI.

Having an agile product delivery organization shows strong correlation with achieving value. Establishing robust talent strategies and implementing technology and data infrastructure similarly show meaningful contributions to AI success. Practices such as embedding AI into business processes and tracking KPIs for AI solutions further contribute to achieving significant value.

Platform automation continues expanding as demonstrated by Google's announcement removing language targeting settings from Google Ads search campaigns by 2025, relying on artificial intelligence rather than manual advertiser selections.

Investment levels correlate with scaling success

High-performing organizations invest more in AI capabilities. More than one-third of high performers say their organizations commit more than 20 percent of their digital budgets to AI technologies. These resources help them scale AI technologies across the business: About three-quarters of high performers say their organizations are scaling or have scaled AI, compared with one-third of other organizations.

Larger companies—both in terms of revenues and number of employees—are more likely to have reached the scaling phase. Nearly half of respondents from companies with more than $5 billion in revenue have reached the scaling phase, compared with 29 percent of those with less than $100 million in revenues.

This size advantage reflects the scale required to invest in AI advancement. Small business research from October 2025found that most companies surveyed operate with monthly marketing budgets under $1,000, limiting their ability to invest in sophisticated AI infrastructure despite 60 percent reporting some level of AI adoption.

Workforce impact expectations diverge

Organizations report differing perspectives on how AI might affect their workforce size in the year ahead. Looking at functions where organizations use AI, a plurality of respondents observed little to no change in the number of employees due to AI use in the past year.

However, expectations for the coming year show more variation. Across business functions, a median of 17 percent of respondents report declines in workforce size in the past year as a result of AI use, but a median of 30 percent expect a decrease in the next year.

At the enterprise level, 32 percent of respondents predict an overall workforce reduction of 3 percent or more in the year ahead, while 13 percent predict an increase of that magnitude. A plurality expect little or no effect on their organizations' total number of employees. Respondents at larger organizations are more likely than those at smaller ones to expect an enterprise-wide AI-related reduction in workforce size.

Most respondents note their organizations hired for AI-related roles over the past year. Software engineers and data engineers are the most in demand, though talent needs differ by company size overall.

Marketing technology platforms face security vulnerabilities as AI adoption accelerates. DoubleVerify disclosed findings on September 25, 2025, regarding a substantial escalation in fraudulent mobile applications employing artificial intelligence technologies to perpetrate ad fraud.

Risk mitigation efforts increase as consequences materialize

Over the past six years, McKinsey's research has consistently found that few AI-related risks are mitigated by most organizations. In the latest findings, the share of respondents reporting mitigation efforts for risks such as personal and individual privacy, explainability, organizational reputation, and regulatory compliance has grown since 2022.

Respondents reported acting to manage an average of two AI-related risks in 2022, compared with four risks today. Overall, 51 percent of respondents from organizations using AI say their organizations have seen at least one instance of a negative consequence, with nearly one-third reporting consequences stemming from AI inaccuracy.

Respondents from AI high performers, who report deploying twice as many AI use cases as others, are more likely than others to report negative consequences—particularly related to intellectual property infringement and regulatory compliance. High performers also try to protect against a larger number of risks.

Regulatory frameworks continue evolving to address AI deployment challenges. The Dutch Data Protection Authority launched a comprehensive consultation in May 2025 outlining how data protection laws should apply to generative artificial intelligence development and deployment, with the consultation open until June 27, 2025.

Industry standards development accelerated throughout 2025. IAB Tech Lab announced its 2025 product roadmap on January 29, focusing on technical standards across digital advertising sectors, with plans to deliver 31 new specifications addressing privacy regulations, data handling, and streaming media.

The research suggests that most organizations are still navigating the transition from experimentation to scaled deployment. While they may be capturing value in some parts of the organization, they're not yet realizing enterprise-wide financial impact. The experience of highest-performing companies suggests treating AI as a catalyst to transform organizations, redesigning workflows and accelerating innovation, rather than pursuing incremental efficiency gains alone.

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Summary

Who: McKinsey & Company conducted research affecting organizations across all industries, with particular relevance for marketing professionals, technology executives, and enterprise decision-makers implementing AI systems. AI high performers—representing approximately 6 percent of respondents who attribute more than 5 percent EBIT impact to AI—demonstrate distinct implementation patterns from their peers.

What: McKinsey's Global Survey on artificial intelligence reveals that while 88 percent of organizations report regular AI use in at least one business function, only one-third have progressed beyond pilot or experimental phases to enterprise-wide scaling. The research identifies significant gaps between adoption rates and financial impact, with only 39 percent attributing any EBIT impact to AI use. Organizations are beginning to experiment with AI agents, with 62 percent at least testing agentic systems, though widespread scaling remains limited.

When: The survey was conducted between June 25 and July 29, 2025, with findings released on November 9, 2025. The research represents the latest iteration of McKinsey's ongoing analysis of AI adoption patterns, following previous surveys in 2024, 2023, and earlier years that document the progression from initial generative AI introduction through current implementation challenges.

Where: The research encompasses 105 nations representing diverse geographic regions, industries, and company sizes. Thirty-eight percent of respondents work for organizations with more than $1 billion in annual revenues. Data is weighted by each respondent's nation's contribution to global GDP to adjust for response rate differences. The findings apply particularly to marketing and advertising sectors where AI adoption shows strong momentum but faces similar scaling challenges.

Why: The research addresses critical questions about AI's actual business impact versus adoption hype, revealing that most organizations have not yet embedded AI deeply enough into workflows and processes to realize material enterprise-level benefits. High performers distinguish themselves by setting transformative rather than purely efficiency-focused objectives, fundamentally redesigning workflows, securing senior leadership commitment, and investing substantially more resources—often more than 20 percent of digital budgets—in AI capabilities. The persistent gap between widespread adoption and limited financial impact reflects implementation complexity, technical challenges, and the difficulty of moving from successful pilots to scaled production systems.