THE LEGAL AI ADOPTION & EVALUATION TOOLKIT
Make confident, evidence-based decisions about legal AI
The Legal AI Adoption & Evaluation Toolkit
Built through SuperTech WM’s collaborative, real-world Open Innovation lab, this practical toolkit helps legal professionals reduce uncertainty, evaluate AI consistently and make evidence-based adoption decisions that deliver real value.
It provides a practical, repeatable way to choose use cases, test AI tools against real legal work and decide what is right for your organisation.
A Toolkit Designed for:
Legal professionals and organisations
For teams exploring AI for the first time, reviewing existing tools or refining a wider technology strategy.
Legal clinics and smaller organisations
A practical evaluation approach for organisations that may not have extensive procurement, innovation or technical resources.
Technology providers
A clearer view of the evidence, governance, workflow fit and implementation support legal buyers need.
What is the toolkit?
The toolkit helps legal organisations evaluate, compare and adopt AI tools in a consistent, evidence-based way.
Developed through SuperTech WM’s LawTech Open Innovation Lab, it draws on structured testing by practising lawyers using realistic legal workflows and a common evaluation framework. It turns shared learning into practical resources you can use, whatever stage of your AI journey
The Legal AI Adoption & Evaluation Toolkit
A practical, repeatable way to choose use cases, test AI tools against real legal work and decide what is right for your organisation.
Why it matters?
AI adoption needs more than a convincing demo.
The real question is not simply whether an AI tool is good. It is whether it is good enough for a specific task, within your organisation, with the supervision you can realistically provide.
The toolkit helps you:
Test tools on your terms, using the work and use cases that matter to you
Compare products consistently, using a common, tool-agnostic framework
Identify risks early, including accuracy, hallucinations, traceability and supervision needs
Measure real value, including whether time savings survive prompting, review and rework
Strengthen AI readiness, by revealing gaps in skills, governance, workflows and infrastructure
Make decisions you can explain and defend, grounded in evidence rather than instinct or vendor claims
What you will get:
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