Generative AI adoption is gaining momentum across horizontal functions such as IT security, HR, marketing, and legal, with technology-centric functions such as IT and security leading in enterprises’ gen AI spend. Key use cases include automated code generation, enhanced cybersecurity, and customized learning and development. Industry-specific applications vary, with finance leveraging gen AI for fraud detection and personalized financial advice, while retail focuses on personalized marketing and supply chain management.

CIOs face challenges such as a lack of clear success metrics, a rapidly evolving technology landscape, budget constraints, and talent shortages. Key risks in gen AI adoption involve data security, privacy, interpretability, ownership, responsibility, bias, and ethical concerns. In response, organizations are implementing their own guardrails while regulators develop legal frameworks. The future of AI is marked by core technological shifts: AI compute moving to the edge, the rise of small language models (SLMs) due to resource efficiency challenges, and the need for model orchestration.

In this report, we explore generative AI adoption trends across horizontal business functions and industries. We examine the top challenges CIOs face with gen AI adoption and key risks in its implementation. Additionally, we discuss future outlook trends, including shifts in core technologies and gen AI’s impact on productivity and adoption trends.

Scope

All industries and geographies

Contents

In this report, we examine:

• Horizontal function-specific use cases and adoption trends
• Industry-specific use cases and adoption trends
• Top challenges that CIOs face with gen AI adoption
• Key risks in adopting gen AI
• Corporate guardrails for gen AI
• Future outlook

Membership(s)

Artificial Intelligence (AI)

Vendor and Sourcing Management