Small Language Models (SLMs) for Enterprise

Small Language Models (SLMs) are emerging as one of the most strategically important technologies shaping the future of enterprise AI. While the early generative AI wave was dominated by large, general-purpose foundation models, enterprises are now shifting toward smaller, more controllable, and more cost-efficient models that can be fine-tuned on proprietary data and deployed flexibly across cloud, on-premise, and edge environments. This transition marks a new phase of enterprise AI adoption—one defined not by experimentation but by operational integration. As a result, SLMs are powering an expanding set of business-critical use cases, from customer service automation and content generation to internal knowledge management, workflow optimisation, and domain-specific decision support.

This report analyses the economics, technical advantages, and deployment patterns underpinning the rise of SLMs, drawing on market forecasts, competitive dynamics, and regulatory trends across the United States and Europe. We highlight how SLMs enable enterprises to combine performance with governance, offering greater transparency, controllability, and security than large general models. With projected market growth exceeding 25% CAGR through 2030, SLMs are positioned to become foundational to enterprise operations. Yet realising value at scale requires thoughtful strategy: aligning model capabilities with business needs, managing lifecycle and risk governance, and adopting multi-vendor or hybrid approaches that avoid dependency and enhance resilience.

For executives, SLMs represent both a competitive opportunity and an emerging necessity. Companies that successfully leverage these models will benefit from faster innovation cycles, improved productivity, and differentiated customer experiences. Those that delay adoption risk falling behind in an economy increasingly defined by AI-enhanced agility. This report provides a comprehensive, strategic framework for understanding and capitalising on SLM adoption over the remainder of the decade. Show more