Enterprise AI platforms are at the forefront of digital transformation, enabling businesses to innovate, compete, and operate efficiently. These platforms empower organizations to streamline processes, enhance decision-making, and accelerate their AI adoption journeys by unifying data governance, model training, deployment, and monitoring in a single ecosystem. Gen AI further amplifies this by enabling dynamic automation, content generation, and personalized customer interactions.

In this report, we analyze enterprise AI platforms, including their architecture, core components, and technology advances. The report highlights key trends shaping these platforms’ adoption, including the rise of Small Language Models (SLMs) tailored to specific business needs, the growing importance of ethical AI governance, and the integration of real-time decision-making capabilities. It also examines enterprise challenges in scaling AI adoption. This research offers valuable insights for businesses harnessing AI platforms for sustainable growth and competitive differentiation.

Scope

• Industry: cross-industry
• Geography: global

Contents

In this report, we examine:

• Introduction to enterprise AI platforms: overview of architecture, capabilities, and definitions
• Technology advances: insights into gen AI, agentic AI, and governance tools shaping platform capabilities
• Market trends and drivers: the role of SLMs, real-time analytics, and scalable AI solutions in adoption
• Ecosystem evaluation: leading players across big tech, data management heritage, native AI, and specialized AI providers
• Challenges and strategic solutions: addressing integration, data complexity, and cost management

Membership(s)

Artificial Intelligence (AI)

Sourcing and Vendor Management