AI Benchmark Initiative - Use Case Templates
Introduction
The aim of these use case templates was to come up with a simple and effective way to help define and document use cases for legal technology products and services that have an element of GenAI. Within the Litig AI Benchmark Working Group, it quickly became clear there are many ways to do this, from creating very high-level use cases of one or two sentences through to mini business case documents that contain more details and may have a wider purpose. And then there is everything in between.
We are conscious the range of individuals and organisations who may pick this up is very wide. WHAT you want to do WHERE, with WHO, WHEN, WHY and HOW needs to work for you, your stakeholders and align with the objectives of the session(s) and your organisational needs.
Therefore, with flexibility and adaptability in mind we have set out below two example use case approaches that you may want to adopt and amend as required.
Use case definition – example approach 1 (high-level)
Some organisations, or teams within the same organisation, may be more mature than others in terms of knowing what use cases are, and how to create and apply them. This example is a good starting point.
Model/rubric wording:
[WHAT] – [WHO] in [WHERE] are [WHAT] by [HOW] to [WHY]
Model/rubric explanation:
[WHAT - high level categorisation of the type of AI use case, e.g. CREATE, SUMMARISE, COMPARE etc.] - [WHO - role] in [WHERE - team/department/office/jurisdiction] is/are [doing WHAT], [HOW/WHAT are they using to do it], and [WHY- the value proposition].
Examples of where the simple approach could be useful include:
engaging with clients and colleagues to show them what information might go into making up a use case, and letting them use it to consider and create some use cases they find interesting, and
preparing for or working with suppliers of Gen AI tools who are keen to hear what use cases may be interesting or relevant to your organisation.
Some further examples of use cases derived using the simple method are set out below:
1 - [SUMMARISE] - [Lawyers] in [London Commercial Contracts Team] are [extracting key data from contracts] by [using LLMs and prompting] to [accelerate delivery and enhance consistency of client due diligence reports].
2 - [AUTOMATE] - [Securities lawyers] in [Singapore] are [extracting information from trade term sheets] by [applying Named Entity Recognition models] to [automatically populate pricing supplements and accelerate trade times].
3 - [CREATE] - [M&BD team members] in [UK] are [creating relevant and targeted pitch responses and content for client RFPs] by [using LLMs and prompting] to [tailor the content, format, and language to meet client needs and improve the win rate for pitches].
Use case definition – example approach 2 (more detailed use cases)
This approach may be appropriate when you are considering the use case in more detail (or drafting a business case). It includes the opportunity to describe the value proposition, inputs and outputs, personas, and what good could look like. Again, how you use it and how much of it you use may be influenced by factors including your requirements, ways of working, governance, and strategy.
The template is set out below and an example of a completed template is also included at the end.
Example template for detailed GenAI use cases
What you want to do:
[very high-level statement]
Business outcome:
[brief description of what the business wants to get out of this use case]
Overview:
[more detail about what you want to do and how you will do it].
Data/knowledge sources:
[list of the relevant data and/or knowledge sources]
Value proposition:
Examples of anticipated benefits for this initiative include: [list of the anticipated benefits]
Proposed technology (if known):
[describe what technologies, elements, and/or methods may be involved] / [leave blank if unknown]
Use case personas:
Persona 1 – [role/job title]
Use: [what they will do for the purposes of this use case]
Objectives: [describe what they stand to gain]
Persona 2 – [role/job title]
Use: [what they will do for the purposes of this use case]
Objectives: [describe what they stand to gain]
Pilot user fears / expectations:
[Complete as part of pilot planning]
Next steps:
[Business decision, e.g., Proceed / Iterate / Pause / Stop etc.]
Example completed template for detailed GenAI use case
Example 1
What you want to do:
Create training materials using Gen AI.
Business outcome:
To use LLMs to help with preparing training materials and plans in the early careers team.
Overview:
We want to use LLMs to help the early careers team plan and deliver training and work experience. This may include tweaking already drafted content to suit a different audience, ideas and content generation, brainstorming or asking questions on a topic/content. We will use SMEs from the early careers team in the pilot.
Data/knowledge sources:
Draft notes or publicly available information on a topic, previous training plans or materials.
Value proposition:
Be quicker to plan training as preparation time will decrease.
Be able to produce more content.
Be able to better tailor / produce more content tailored for specific audiences.
Proposed technology (if known):
We will use LLMs and Gen AI to create and summarise content, plus [TOOL/SUPPLIER] to transform this to presentations.
Use case personas:
Persona 1 – Subject Matter Expert
Use: Creation / editing of training documents.
Objectives: Spend less time on content creation/editing (so can do other things/more content) without hampering quality of output.
Persona 2 – Learning and Development team member:
Use: Reporting and understanding usage.
Objectives: Have clear and readily accessible usage data and other analytics.
Pilot user fears / expectations:
To be completed in pilot planning, e.g., content/tone of output, over-reliance on output
Next steps:
Business decision, e.g., Proceed — identify suitable training topics and draft materials for the pilot, select SME users, and set evaluation criteria (e.g. time saved, output quality, user satisfaction).
Example 2 (Private Practice)
What you want to do:
Use GenAI to support faster and more consistent contract drafting through a clause bank assistant.
Business outcome:
To improve drafting efficiency and quality across transactional teams, enabling lawyers to produce first drafts and mark-ups more quickly and consistently.
Overview:
We want to pilot a GenAI-powered assistant that retrieves appropriate clauses from an internal clause bank and suggests edits based on client templates, sector, and commercial context. The tool would support fee-earners in generating initial drafts and reviewing mark-ups, particularly on high-volume contract types like NDAs, supplier T&Cs, and software licences.
Data/knowledge sources:
Internal clause banks / precedents
Matter-specific templates and mark-ups
Fallback positions and standard comments from playbooks
Knowledge team guidance notes
Value proposition:
Reduce drafting time for common agreements
Promote consistency across client-facing documents
Enhance junior fee-earner confidence in clause selection
Free up senior lawyer time for more strategic review work
Proposed technology (if known):
GenAI platform with clause retrieval and comparison capabilities (e.g., [TOOL/SUPPLIER] or [TOOL/SUPPLIER] integration)
Use case personas:
Persona 1 – Associate (1–3 PQE)
Use: Drafting and revising contracts using clause suggestions.
Objectives: Work more efficiently and independently, improve quality of drafting.
Persona 2 – Senior Associate / Partner
Use: Reviewing draft outputs and aligning with client preference.
Objectives: Reduce time spent on mark-ups and ensure consistency across teams.
Pilot user fears / expectations:
To be completed in pilot planning, e.g., accuracy of outputs, trust in clause suggestions
Next steps:
Business decision – e.g. Iterate - select pilot teams (e.g. Commercial, Tech/IP), collate standard clauses, define evaluation criteria.
Example 3 (In-House)
What you want to do:
Deploy GenAI as a legal Q&A assistant to support business stakeholders with contract interpretation queries.
Business outcome:
To reduce legal bottlenecks by empowering business teams to answer low-risk contract interpretation queries independently.
Overview:
We aim to pilot a GenAI assistant that can provide plain English explanations of specific contract clauses or defined terms. The assistant will be trained on a controlled dataset of frequently asked questions, internal policy documents, and standard contracts. It will be positioned as a self-service tool for commercial and procurement teams.
Data/knowledge sources:
Internal contract templates (e.g., NDAs, MSAs, SOWs)
FAQs maintained by legal
Internal commercial policy documents
Legal team playbooks
Value proposition:
Reduced legal team time spent on repetitive queries
Faster response times for commercial teams
Improved consistency in contract understanding
Increased confidence in low-risk decision-making
Proposed technology (if known):
GenAI chatbot interface integrated with internal [TOOL/SUPPLIER] or [TOOL/SUPPLIER]
Use case personas:
Persona 1 – Business Stakeholder (e.g., Sales / Procurement)
Use: Asking questions about specific contract clauses or obligations.
Objectives: Faster answers without needing to wait for legal input.
Persona 2 – Legal Counsel
Use: Maintaining oversight of the assistant’s scope and accuracy.
Objectives: Reduce time spent on low-value work while managing legal risk.
Pilot user fears / expectations:
To be completed in pilot planning, e.g., concerns around legal accuracy, misuse beyond low-risk questions
Next steps:
Business decision – e.g. Proceed to scoping data sources and define sandbox pilot boundaries