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
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Andrew Burgess, Co-Founder and CEO, Greenhouse AI
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Dr. Juan Bernabé-Moreno, Director, IBM Research Europe
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Grace Adamson, AI Product Marketer, Snowflake
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Edward Kelly, UK and ROI Public Sector Lead, Databricks
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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:
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Lower computational costs that enable adoption beyond large enterprises
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Faster inference speeds suited to live chat, diagnostics, and automation
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Greater accessibility for start-ups, SMEs, and public sector teams
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Easier fine-tuning for use cases such as fraud detection, medical diagnosis, and legal automation
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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:
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Limited generalisation and reasoning capabilities compared to LLMs
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Higher risk of hallucination and bias when trained on narrow or synthetic datasets
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Challenges in managing model sprawl across departments
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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:
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Public sector organisations using AI to map and monitor environmental changes
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Healthcare teams exploring diagnostic tools that run securely on local devices
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Financial services adopting fine-tuned SLMs for fraud detection and compliance workflows
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Enterprise support teams using lightweight AI agents for document summarisation and knowledge retrieval
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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:
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Begin with a clearly defined use case aligned to business goals
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Focus on data quality and governance as a foundation for success
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Pilot with a larger model if needed, then scale down as performance is validated
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Plan for explainability and monitoring from the start
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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.
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Contact the team
Kir Nuthi
Kir Nuthi
Kir Nuthi is the Head of AI and Data at techUK.
She holds over seven years of Government Affairs and Tech Policy experience in the US and UK. Kir previously headed up the regulatory portfolio at a UK advocacy group for tech startups and held various public affairs in US tech policy. All involved policy research and campaigns on competition, artificial intelligence, access to data, and pro-innovation regulation.
Kir has an MSc in International Public Policy from University College London and a BA in both Political Science (International Relations) and Economics from the University of California San Diego.
Outside of techUK, you are likely to find her attempting studies at art galleries, attempting an elusive headstand at yoga, mending and binding books, or chasing her dog Maya around South London's many parks.
- Email:
- [email protected]
Usman Ikhlaq
Usman Ikhlaq
Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.
He leads techUK’s AI Adoption programme, supporting members of all sizes and sectors in adopting AI at scale. His work involves identifying barriers to adoption, exploring solutions, and helping to unlock AI’s transformative potential, particularly its benefits for people, the economy, society, and the planet. He is also committed to advancing the UK’s AI sector and ensuring the UK remains a global leader in AI by working closely with techUK members, the UK Government, regulators, and devolved and local authorities.
Since joining techUK, Usman has delivered a regular drumbeat of activity to engage members and advance techUK's AI programme. This has included two campaign weeks, the creation of the AI Adoption Hub (now the AI Hub), the AI Leader's Event Series, the Putting AI into Action webinar series and the Industrial AI sprint campaign.
Before joining techUK, Usman worked as a policy, regulatory and government/public affairs professional in the advertising sector. He has also worked in sales, marketing, and FinTech.
Usman holds an MSc from the London School of Economics and Political Science (LSE), a GDL and LLB from BPP Law School, and a BA from Queen Mary University of London.
When he isn’t working, Usman enjoys spending time with his family and friends. He also has a keen interest in running, reading and travelling.
Sue Daley OBE
Sue Daley OBE
Sue leads techUK's Technology and Innovation work. This includes work programmes on AI, Cloud, Data, Quantum, Semiconductors, Digital ID and Digital ethics as well as emerging and transformative technologies and innovation policy. In 2025, Sue was honoured with an Order of the British Empire (OBE) for services to the Technology Industry in the New Year Honours List. She has also been recognised as one of the most influential people in UK tech by Computer Weekly's UKtech50 Longlist and was inducted into the Computer Weekly Most Influential Women in UK Tech Hall of Fame.
A key influencer in driving forward the tech agenda in the UK, in December 2025 Sue was appointed to the UK Government’s Women in Tech Taskforce by the Technology Secretary of State. She also sits on the UK Government’s Smart Data Council, Satellite Applications Catapult Advisory Group, Bank of England’s AI Consortium and BSI’s Digital Strategic Advisory Group. Previously, Sue was a member of the Independent Future of Compute Review and co-chaired the National Data Strategy Forum. As well as being recognised in the UK's Big Data 100 and the Global Top 100 Data Visionaries in 2020, Sue has been shortlisted for the Milton Keynes Women Leaders Awards and has been a judge for the Loebner Prize in AI, the UK Tech 50 and annual UK Cloud Awards. She is a regular industry speaker on issues including AI ethics, data protection and cyber security.
Prior to joining techUK in January 2015, Sue was responsible for Symantec's Government Relations in the UK and Ireland. Before that, Sue was senior policy advisor at the Confederation of British Industry (CBI). Sue has an BA degree on History and American Studies from Leeds University and a Master’s Degree in International Relations and Diplomacy from the University of Birmingham. Sue is a keen sportswoman and in 2016 achieved a lifelong ambition to swim the English Channel.
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Authors
Usman Ikhlaq
Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.