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Event round-up: AI Leader's Series: Small Language Models Explained

11 April 20253 min read
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Event round-up: AI Leader's Series: Small Language Models Explained

As part of our ongoing AI Leader’s Series, techUK hosted a panel of leading AI experts to explore the growing relevance of Small Language Models (SLMs). While Large Language Models (LLMs) continue to dominate the public AI conversation, SLMs are quickly emerging as a practical, accessible, and cost-efficient alternative that is enabling more organisations to deploy artificial intelligence at scale.

This virtual session brought together speakers from across the AI ecosystem to share insights on the current state of SLM adoption, where these models offer the greatest impact, and how organisations can leverage them effectively while managing associated risks.

Key topics included:

  • The opportunities of SLM adoption across sectors

  • Challenges, risks, and strategies for responsible adoption

  • Best practices for leveraging its capabilities in different sectors

Additionally, the panel examined the role of SLMs in driving forward some of the recomendations set out in the UK Government's AI Opportunities Action Plan.

Summary:

Speakers

  • Andrew Burgess, Co-Founder and CEO, Greenhouse AI

  • Dr. Juan Bernabé-Moreno, Director, IBM Research Europe

  • Grace Adamson, AI Product Marketer, Snowflake

  • Edward Kelly, UK and ROI Public Sector Lead, Databricks

  • Usman Ikhlaq, Moderator and Programme Manager – Artificial Intelligence, techUK 

 

Recording:

Summary:

Key themes and highlights:

1. What are Small Language Models?

  • The discussion began with an overview of SLMs, defined as compact models that are built for specific tasks or domains. These models are light, fast and require fewer computational resources, making them easier to fine-tune and more efficient to deploy on local servers, edge devices, or even personal hardware.

  • Key benefits include reduced infrastructure costs, increased data privacy, and improved performance for real-time and industry-specific applications. As AI adoption becomes more targeted and business-driven, SLMs offer a strategic advantage for organisations seeking control, adaptability, and value.

2. Opportunities and industry value

SLMs are opening new doors for innovation across sectors. Highlights from the discussion included:

  • Lower computational costs that enable adoption beyond large enterprises

  • Faster inference speeds suited to live chat, diagnostics, and automation

  • Greater accessibility for start-ups, SMEs, and public sector teams

  • Easier fine-tuning for use cases such as fraud detection, medical diagnosis, and legal automation

  • Growing relevance in agentic and compound AI systems

Speakers also discussed how SLMs can contribute to the UK’s broader innovation goals, including the ambitions outlined in the AI Opportunities Action Plan. Their potential to drive secure, sustainable, and sector-specific AI makes them a valuable tool for economic growth and public sector transformation.

Challenges and considerations

The panel also addressed the limitations and risks of SLMs. While these models offer many advantages, careful planning is needed to deploy them responsibly and at scale. Potential concerns raised included:

  • Limited generalisation and reasoning capabilities compared to LLMs

  • Higher risk of hallucination and bias when trained on narrow or synthetic datasets

  • Challenges in managing model sprawl across departments

  • The need for improved benchmarking, explainability, and governance tools

The conversation highlighted the importance of embedding trust and transparency into AI development from the outset, particularly as organisations explore models for high-stakes decision-making.

SLMs in practice: real-world use cases

The session included practical examples of SLM deployment in the UK and beyond:

  • Public sector organisations using AI to map and monitor environmental changes

  • Healthcare teams exploring diagnostic tools that run securely on local devices

  • Financial services adopting fine-tuned SLMs for fraud detection and compliance workflows

  • Enterprise support teams using lightweight AI agents for document summarisation and knowledge retrieval

  • Cybersecurity applications using on-device models to detect threats in real time

Each use case demonstrated the value of smaller, more focused models for real-world tasks that require speed, security, and efficiency.

Getting started with SLMs

The panellists offered a range of recommendations for organisations looking to explore Small Language Models:

  • Begin with a clearly defined use case aligned to business goals

  • Focus on data quality and governance as a foundation for success

  • Pilot with a larger model if needed, then scale down as performance is validated

  • Plan for explainability and monitoring from the start

  • Balance efficiency and performance when evaluating model size

Organisations were encouraged to think strategically about how SLMs fit within their broader AI ambitions and infrastructure, rather than viewing them as standalone tools.