Overall, over 80 percent of companies report that their generative AI applications meet or exceed expectations, with nearly 60 percent noting business improvements as a result.

Investment in talent has also increased, with a 30 percent rise in personnel dedicated to generative AI, averaging around 160 employees involved in AI-related projects.

This surge in adoption is accompanied by a doubling of investment in generative AI, with average budgets now around $10 million, largely funded through standard budget allocations.

The number of companies prioritizing AI has grown from 9% to 15%, with nearly half now having clear implementation roadmaps, reflecting deeper adoption strategies.

However, challenges persist in scaling generative AI, including talent shortages, security concerns, output quality, and the need for leadership support.

Key barriers to adoption include organizational readiness and data security, with 75 percent of companies struggling to acquire the necessary talent.

Discontent with generative AI varies, as pilot users face issues with process redesign and leadership support, while those in production struggle with vendor quality and output.

Concerns about data security and privacy have intensified, particularly among early adopters, although confidence in output accuracy is gradually improving.

The number of generative AI use cases in software development and production has doubled, with a notable increase in IT adoption.

Experts emphasize the importance of training and empowering teams in AI tools and change management to build confidence and promote widespread adoption.

Among companies that have fully scaled their generative AI solutions, about 90% report meeting or exceeding their goals, indicating strong performance in established use cases.

Generative AI adoption has surged dramatically, with 95 percent of US companies now utilizing it, reflecting a 12 percentage point increase over the past year.