Enterprise AI after the hype curve

In 2025, enterprise AI focused more on understanding the constraints of existing models rather than achieving breakthroughs in capabilities, as top-tier models showed strong performance but limited differentiation in real-world applications. Organizations recognized that public benchmarks were not sufficient for decision-making, leading them to create proprietary test sets tailored to their specific workflows. The transition from singular reasoning models to AI systems capable of handling complex, multi-step tasks became evident, emphasizing the importance of tools, context retrieval, and step validation. Despite widespread exploration, AI adoption remained primarily internal, with RAG pipelines being favored due to their reliability. The year also saw the rise of Special or Small Language Models (SLMs) for specific tasks like policy enforcement and content moderation, highlighting the need for efficiency and consistent performance. Open-source AI began to diverge, with commercial interests influencing model availability. As orchestration became crucial for managing multi-agent systems, data quality and availability emerged as key factors for AI success, though the choice of models for specific tasks remained a challenge. The year's developments underscored the importance of treating AI as a system rooted in data and internal evaluation to drive progress in a rapidly evolving landscape.