Practical Guide to Using AI in Legal Teams

Step 1: Identify Legal AI Opportunities

Start by mapping everyday legal tasks and finding repetitive, low-risk activities suitable for AI assistance. This ensures focused use cases and measurable results.

1. Document repetitive processes such as contract markup or basic reviews.
2. Select 1–3 use cases like contract analysis, research notes, or policy templates that yield clear time savings.
3. Define success metrics—time saved per review, correction rate, and user satisfaction scores.

Step 2: Define Requirements and Select Tools

Create an AI requirements brief outlining functional, compliance, and security needs. Compare tools through demos to identify the best fit for your workflows.

1. List must-have criteria: contract comparison, semantic search, GDPR compliance, language support for Danish/EU law, and integration with Office tools.
2. Shortlist 2–3 solutions and run demo sessions using identical document sets.
3. Evaluate precision, user experience, and compliance outcomes before deciding.

Step 3: Establish Governance and Risk Controls

Define a legal AI code of conduct before expanding usage. This framework ensures transparency, compliance, and accountability in every AI-assisted workflow.

1. Outline AI’s permissible use cases and areas requiring human review.
2. Specify responsible roles for approvals and compliance checks.
3. Enforce a Human-in-the-Loop Policy for lawyer validation before external output.
4. Maintain audit trails and confirm data management agreements with vendors.

Step 4: Test and Integrate with a Pilot

Run a short Proof of Concept (POC) to measure AI’s impact. Use structured datasets and clear metrics for time saved and accuracy achieved.

1. Set up a 2–4 week pilot analyzing 50–100 sample contracts.
2. Track performance using time saved, model accuracy, and user satisfaction.

import openai

openai.api_key = "YOUR_API_KEY"

system_msg = (
   "You are a Danish legal assistant. "
   "Do not provide legal conclusions. "
   "Identify risk clauses, unusual terms, and limitations of liability."
)

contract_text = open("contract.txt", "r", encoding="utf-8").read()

response = openai.ChatCompletion.create(
   model="gpt-4.1",
   messages=[
       {"role": "system", "content": system_msg},
       {"role": "user", "content": f"Analyze this contract:\n{contract_text}"}
   ],
   temperature=0.2,
)

print(response["choices"][0]["message"]["content"])

For deeper integration, use document management or contract lifecycle platforms to embed AI review directly within existing workflows.

Step 5: Train Your Legal Team

Training is essential for smooth AI adoption. A structured 60-day program helps build confidence and ensures consistent results across teams.

1. Weeks 1–2: Introduce AI basics, internal policies, and hands-on demos.
2. Weeks 3–4: Conduct workshops with standardized prompt templates.
3. Weeks 5–8: Run open “AI office hours” for collaborative learning.

Develop prompt templates for contracts or memos and include disclaimers like “AI-generated draft – must be reviewed by a lawyer.”

Nominate AI Champions within your team to sustain engagement and communicate measurable improvements.

Step 6: Measure and Improve Performance

Regular reviews help ensure ongoing AI value. Combine quantitative and qualitative metrics to capture a full picture of performance.

• Quantitative KPIs: Time per contract, revision count, turnaround speed.
• Qualitative KPIs: User and client satisfaction (NPS, perceived quality).
• Cycle: Monthly performance checks and quarterly compliance updates.

Common Issues & Solutions

Key Takeaways

• AI implementation in legal teams is a strategic shift—start small, scale carefully.
• Ensure human oversight before any external output is delivered.
• Establish robust governance and compliance frameworks early.
• Focus on continual improvement and transparent client communication.
• Explore workflow automation in Workflows for long-term scalability.