Accountable Acceleration: Gen AI Fast-Tracks Into the Enterprise

This document presents the third annual edition of a study conducted by Wharton Human-AI Research and GBK Collective examining the evolution of generative artificial intelligence in U.S. enterprises between 2023 and 2025. Based on surveys of approximately 800 business leaders from organizations with over 1,000 employees and $50 million in revenue, the report offers a unique perspective on generative AI's transition from initial experimentation to integration into daily operations.

Adoption evolution: The study documents how generative AI use has gone from being a novelty (37% weekly use in 2023) to becoming part of work routine (82% weekly and 46% daily in 2025). Familiarity and expertise have increased significantly, especially in functions like IT, Legal, and Finance, although notable differences persist between industries and departments.

Results measurement: A key finding is that accountability now dominates the landscape. 72% of organizations formally measure return on investment (ROI), and three out of four report positive results. The most successful use cases focus on routine productivity tasks: data analysis, document summarization, writing and editing. Investments remain robust, with 88% anticipating budget increases in the next 12 months, and approximately one-third of technology budgets going to internal research and development.

Differences by size and industry: Larger companies (Tier 1, with revenues exceeding $2 billion) are closing the adoption gap with smaller organizations, although the latter report greater agility. By sectors, technology/telecommunications, financial services, and professional services lead adoption and report better returns, while manufacturing and retail lag behind.

Human factor as constraint: The report identifies that the main obstacle is no longer technology, but people and processes. Although 89% of leaders consider that AI enhances employees' skills (versus replacing them), 43% express concern about loss of proficiency. Investment in training has decreased (-8pp), and confidence that training alone will generate fluency has fallen significantly (-14pp). Simultaneously, 49% of organizations report difficulties hiring talent with advanced AI skills.

Leadership and governance: Strategic responsibility has consolidated in the C-suite, with 67% of organizations where executives lead adoption (+16pp versus 2024). 60% of companies now have Chief AI Officer (CAIO) roles, although more than half are responsibilities added to existing roles. Data security policies (64%) and employee training programs (61%) are increasing, reflecting maturation in risk management.

Employment impact: Expectations about the future of employment are mixed. Senior leaders predict that junior roles will be most affected (both positively and negatively), with 17% anticipating fewer intern hires but 49% expecting more. This reflects uncertainty about how AI will transform workforce structure.

Study conclusion: The report suggests that 2026 could be an inflection point, where "accountable acceleration" becomes "performance at scale." To achieve this, organizations must align three pillars: talent (hiring and training), training (effective programs and adequate resources), and trust (clear policies and change management). The message is clear: the tools are available and work, but it's people who determine the pace and success of transformation.

This report is essential for executives, human resources managers, technology directors, and any leader seeking to understand not only AI adoption trends but also best practices for converting everyday use into sustainable business value.

Key points

• Daily use normalized: 82% use generative AI weekly and 46% daily, marking the shift from experimentation to routine operation.
• ROI measured and positive: 72% formally measure return on investment, and three out of four organizations report positive results.
• Human capital as bottleneck: People, not technology, limit progress: advanced skills are lacking (49% have difficulty hiring talent) and training is insufficient.
• Consolidated use cases: The most successful applications are productivity tasks: data analysis, document summarization, writing and editing.
• Sustained investment: 88% anticipate budget increases in the next 12 months, with one-third of technology budget on internal R&D.
• Gap between sectors: Technology, banking, and professional services lead with ROI above 80%, while manufacturing and retail lag behind.
• Concern about skill loss: 89% see AI as capability enhancement, but 43% fear decline in human competencies.
• Leadership consolidated in C-suite: 67% have executives leading adoption (+16pp vs. 2024), and 60% have a Chief AI Officer.
• Training investment decreasing: Confidence in training as main pathway drops 14pp, while training investment falls 8pp.
• Governance increasing: 64% have data security policies and 61% have training programs on responsible use.
• Uncertain labor impact: 17% anticipate fewer junior hires, but 49% expect more, reflecting uncertainty about employment future.