Companies are actively implementing GenAI, most are doing so without a well-defined roadmap suggesting a largely exploratory approach. But a consistent part of the players have launched bold initiatives, positioning themselves as early movers and potential future disruptors.
The most cited obstacles are technological complexity and internal skills gaps, underscoring the need for investments in infrastructure and talent.
Operational efficiency stands out as the most
commonly expected and observed benefit, followed by cost reduction and enhanced customer personalization.
The dominant development model is "more buy than build", reflecting a preference for external solutions due to complexity, skill gaps and investments needed.
GenAI refers to algorithms capable of creating new content—text, images, code, data—by learning patterns from vast datasets. Unlike traditional AI systems focused on classification or prediction, GenAI is creative, adaptive and context-aware.
It is rapidly transforming industries by enabling new capabilities such as content generation, automation, and decision support.
As part of our deep dive into the state of GenAI adoption, we recognized the importance of direct industry insights to complement our research and ensure our findings are grounded in real-world experiences. Therefore, we conducted a survey across a panel of top leaders from our customer base, ranging from early adopters to more mature users of GenAI, spanning diverse industries and regions.
The purpose of this survey was to:
