You are juggling a crowded inbox, a contract queue that never quite ends, and a board that wants AI yesterday. You are also the adult in the room calibrating risk, budget, and commercial reality - often with fewer hands than you need. If that sounds familiar, this guide is for you. It shows how an in-house legal team can deploy generative AI in 90 days with the right guardrails, the right use cases, and metrics the CFO will respect.

First - what should Legal actually use AI for?

Start where AI removes repeatable friction and gives back time to the business:

- Intake and triage - auto classify requests, route to the right template or playbook, and capture basic facts up front
- NDA self service - let approved users generate standard NDAs from guardrailed templates, with Legal oversight for exceptions
- Clause help and playbooks - draft and compare boilerplate, surface fallbacks, and flag deviations for human review
- Search and memory - answer "have we signed X before" by querying past agreements

Demand is rising and AI adoption is accelerating across legal ops, so focusing on these quick wins puts you with the grain of your peers. The latest CLOC State of the Industry Report notes rising demand and fast AI uptake across 186 departments.

Baseline your maturity

Spend a day benchmarking where you are across intake, templates, knowledge, metrics, and tech. The Association of Corporate Counsel has a lightweight yardstick - its Legal Operations Maturity Model 2.0 - to set a realistic start line and pick gaps to close first.

Know your regulatory backdrop

If you operate in or sell to the EU, the AI Act is phasing in. The European Commission has issued guidelines for providers of general purpose AI models and confirms that these obligations apply from 2 August 2025. Track whether your use cases touch high risk categories and plan disclosures and testing accordingly.

In the UK, the ICO's guidance on AI and data protection sets clear expectations on transparency, fairness, purpose limitation, and data minimisation. Build your rollout so you can evidence that thinking. When in doubt, run or refresh a DPIA for each use case.

Cyber security needs equal weight. The UK NCSC's guidelines for secure AI system development and the joint launch notice from CISA are clear on secure by design principles. The NCSC has also assessed the impact of AI on the cyber threat to 2027. Partner closely with Security from day one.

The 90 day plan

Days 1-15 - set the guardrails and pick two use cases

- Form a micro squad - Legal, IT Security, Privacy, and one business stakeholder
- Write a simple AI acceptable use policy that bans feeding personal or confidential client data into unmanaged tools, sets approval paths for new use cases, and mandates human review - align it to the ICO's AI guidance
- Run a DPIA for each candidate use case and record risk mitigations
- Pick two use cases with clear value - for example NDA self service and clause playbooks - and define what "good" looks like in business terms, not just legal terms

Days 16-45 - pilot with controls

- Vendor diligence - data residency, access controls, encryption, model provenance, red team approach, retention, and audit logs
- Secure by design - apply NCSC principles to prompts, outputs, storage, and integration points. Keep sensitive data out of prompts unless you control the environment
- Build prompt and review playbooks - show the team when to use AI and when to stop
- Train the pilot group - short, scenario led micro sessions beat long webinars. Record office hours questions to refine policy

Days 46-75 - measure and tune

- Metrics that matter - cycle time, first time right rate, exception rate, internal client satisfaction, and hours returned to the business
- Compare to a pre pilot baseline and to ACC maturity targets so you can show movement, not just anecdotes
- Close the loop - capture edge cases and update playbooks and templates weekly

Days 76-90 - scale what works

- Roll NDA self service to a second business unit and publish a two page "how to"
- Integrate with your CLM where possible so approved language flows through. Adoption and consolidation in CLM are accelerating as genAI matures - see the Financial Times on CLM and genAI and a recent FT piece on build vs buy for legal AI tooling that highlights time savings from off the shelf tools (example)
- Institutionalise the controls - add AI checks to your contract checklist, add an AI section to your vendor due diligence, and create a quarterly AI risk review with Security and Privacy

A sample AI acceptable use checklist for Legal

- Do not input personal data, client confidential information, or trade secrets into unmanaged public tools
- Use only approved tools listed in our register
- Human in the loop is mandatory for any AI generated legal content
- Record material reliance on AI in matter notes
- For any decision that affects a person, prepare an explanation suitable for a non lawyer audience and keep it with the file
- Log prompts that contain non public information and store them in your matter workspace, not in the tool
- Report suspected leakage or unsafe outputs to Security immediately

Common pitfalls and how to swerve them

- Garbage in - garbage out - poor templates and messy clauses amplify errors. Simplify before you automate
- Shadow AI - if you do not provide safe options, colleagues will find unsafe ones. Publish the policy and the approved tools
- Hallucinations - never let AI authorise, sell, or sign. Treat outputs as a smart junior that needs review
- Skills erosion - protect core training for juniors by pairing AI with annotated examples and deliberate rotations. Your future mid level bench depends on it

Tell the CFO the story they expect

Translate wins into numbers. Faster cycles free sales capacity and reduce value erosion. Independent benchmarking finds average contract value erosion around 8.6 percent and shows how better contracting improves outcomes - see WorldCC and Deloitte's ROI of Contracting Excellence and Deloitte's summary of findings on contract lifecycle ROI. If you run CLM plus narrow genAI well, you should see materially shorter cycles and lower cost to serve.

The headline to remember

Small, safe, and specific beats grand and vague. Pick two use cases, add real guardrails, measure like a hawk, and scale deliberately. You will reduce noise for the team, return time to the business, and keep regulators and Security onside.