What Is AI in Legal Operations?

Artificial Intelligence (AI) in legal operations refers to the use of machine learning, natural language processing, and predictive analytics to automate, streamline, and enhance legal workflows. This includes applications such as contract review, legal spend analysis, eDiscovery, matter intake, document classification, and regulatory compliance monitoring. For legal ops teams, AI represents both an opportunity to increase capacity and a responsibility to manage risk, accuracy, and ethical implications.

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‍Beyond the Buzz: What AI Really Means for Legal Teams

In 2023, a multinational company’s legal department implemented an AI-powered contract review tool to accelerate third-party paper processing. Within six months, the team reduced contract review time by 40% and flagged over 300 high-risk clauses that previously went unnoticed. However, they also learned that unmonitored AI outputs occasionally recommended clause deletions that violated company policy.

The takeaway? AI can elevate legal operations—but only when implemented with oversight, strategy, and ongoing evaluation.

Legal operations professionals are ideally positioned to integrate AI solutions into core legal systems while managing expectations, ethical use, and measurable outcomes.

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Common Challenges

1 - Treating AI as a Plug-and-Play Solution

The challenge: Legal teams often expect AI tools to work immediately, without investing in training data, configuration, or integration with internal workflows.

Why this creates risk: AI tools are only as good as the data and governance behind them. Without customization or calibration, outputs can be inaccurate, irrelevant, or even noncompliant.

The business impact: According to Thomson Reuters, 56% of legal departments report that initial AI implementations underperformed due to poor planning or unrealistic expectations (Thomson Reuters, 2023).

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2 - Failing to Monitor or Validate AI Output

The challenge: Some teams over-rely on AI outputs without implementing quality checks or human review.

Why this creates risk: Errors in contract analysis, legal research, or spend categorization can lead to regulatory issues, incorrect filings, or missed obligations.

The business impact: In a 2022 ALM report, 42% of legal leaders expressed concern about “invisible errors” caused by over-automation and lack of audit trails.

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3 - Ignoring Ethical and Data Privacy Implications

The challenge: AI tools may process sensitive legal data without adequate attention to client confidentiality, data storage, or jurisdictional limitations.

Why this creates risk: Legal data often contains privileged, regulated, or jurisdiction-bound content. Mishandling that data through third-party AI platforms can lead to data breaches or regulatory fines.

The business impact: Gartner predicts that by 2026, 60% of organizations using third-party AI in legal workflows will face increased scrutiny around data privacy compliance (Gartner, 2023).

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Best Practices for Using AI in Legal Operations

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1. Start with a Clear Use Case and ROI Model

Before selecting an AI tool, define the problem you are solving. Whether it’s reducing contract review time, improving spend analytics, or streamlining intake, start with a specific workflow and measurable KPIs. Prioritize high-volume, repeatable tasks that benefit most from automation.

Legal operations can lead ROI modeling and track impact against time savings, cost reduction, or throughput improvements.

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2. Choose AI Tools That Integrate with Core Legal Systems

Look for AI solutions that integrate with your CLM, eBilling, or document management systems. Avoid data silos. AI should enhance—not replace—existing legal workflows. Integration enables continuous learning, reduces duplication, and makes outputs actionable.

Vendors like Kira, Luminance, and Evisort offer AI tools that plug directly into existing legal tech stacks with robust API support.

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3. Implement Human-in-the-Loop Review

Maintain human oversight over all critical AI outputs. For example, use AI to flag risky clauses in contracts, but rely on legal reviewers to approve or modify final terms. In eDiscovery, allow AI to sort documents but require legal review before production.

This approach improves confidence in the system and helps train the AI model over time.

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4. Establish Governance for Ethical and Compliant Use

Legal ops should create clear policies for:

• Data governance and privacy compliance
• AI audit logs and traceability
• Responsible use and transparency in decision-making
• Vendor risk assessments and access controls

Work with IT, compliance, and privacy teams to ensure AI tools align with internal standards and external regulations.

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5. Track, Measure, and Evolve the Implementation

Once deployed, regularly review the AI’s performance. Track accuracy rates, efficiency gains, and user adoption. Solicit feedback from legal staff to identify training gaps or areas for expansion. Set quarterly checkpoints to adjust or scale the use of AI.

Legal operations can lead performance reviews and incorporate AI metrics into broader legal operations reporting.

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Conclusion: AI Is a Tool—Legal Operations Makes It Strategic

Artificial Intelligence will not replace legal professionals, but legal professionals who use AI effectively will outperform those who do not. The difference lies in how thoughtfully AI is integrated, governed, and measured.

At Team Jenni, we help legal departments move from AI hype to practical, responsible implementation. Whether deploying contract review automation, legal analytics, or smart intake tools, we bring structure, oversight, and strategy to every stage of adoption.

Because when AI is managed well, legal teams don’t just work faster—they work smarter.

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