AI Is Reshaping the Legal Function

In most organizations, legal is still built around manual workflows, email chains, and document review that depends on individual expertise.

That model is under pressure.

As AI accelerates decision-making across finance, operations, and revenue teams, legal processes that remain manual become a bottleneck. Research shows that fewer than half of legal leaders prioritize identifying high-value GenAI use cases, and only a third feel confident in their ability to do so.

At the same time, a new generation of agentic AI is emerging. These systems do not just generate content. They take action.

If legal departments delay planning, they risk falling behind in two waves of AI adoption instead of one.

For legal leaders, this is not a technology story. It is an operational risk story.

What Is Changing in Legal AI

The current AI landscape for legal can be grouped into three broad approaches:

• Public GenAI tools used for drafting or research
• Industry-tailored GenAI platforms trained on legal data
• System-integrated GenAI embedded into legal workflows

Now a fourth layer is forming: agentic AI.

Agentic AI systems can:

• Triage intake requests
• Execute multi-step research plans
• Redline contracts using playbooks
• Route work autonomously
• Escalate only when human review is required

This is not theoretical. Vendors are already integrating these capabilities across research, contract lifecycle management, litigation support, and matter intake.

Strategic planning assumptions suggest that:

• Legal technology budgets will double in the coming years to support GenAI and agentic AI
• Half of contract reviews may shift to self-service systems
• A majority of legal departments will use AI-driven intake systems that resolve routine requests without human involvement

The direction is clear.

Routine legal work is moving toward automation. Human legal talent is shifting toward high-value judgment.

Where AI Is Being Applied Today

AI adoption in legal is expanding across three major domains.

Legal Foundations

Core capabilities include:

• Legal research and drafting
• Matter management
• Enterprise search
• Intake and triage automation
• Case and knowledge management

Platforms such as Thomson Reuters CoCounsel, LexisNexis Lexis+ AI, and Brightflag embed AI into daily legal workflows.

The operational impact is simple: faster routing, cleaner data capture, and fewer manual bottlenecks.

Transactions and Contract Management

Capabilities include:

• Clause extraction
• Risk scoring
• Automated redlining
• Contract summarization
• AI-supported negotiations

Examples include Evisort, Ontra, and Pramata.

For mid-market companies, contract velocity directly impacts revenue recognition, vendor onboarding, and compliance exposure.

AI does not replace legal oversight. It compresses the first pass review cycle.

Litigation and E-Discovery

AI is being applied to:

• Evidence summarization
• Deposition analysis
• Motion drafting
• Large-scale document review

Vendors such as Everlaw and Relativity are embedding GenAI into discovery and review workflows.

For organizations facing regulatory scrutiny or litigation exposure, this can materially reduce review time and outside counsel costs.

The Mid-Market Reality

Most mid-market organizations face a different challenge than large enterprises.

You likely have:

• A small in-house legal team
• Heavy reliance on outside counsel
• Fragmented document repositories
• Manual intake via email
• Limited IT resources dedicated to legal

At the same time, you face:

• Increased compliance obligations
• Cybersecurity and data protection scrutiny
• Contract complexity across vendors and customers
• Pressure from insurers and boards

AI can improve legal efficiency.

But without a structured plan, it can also introduce:

• Data leakage risks
• Uncontrolled licensing costs
• Shadow AI usage
• Governance gaps
• Overconfidence in unverified outputs

The risk is not only adopting too slowly.

It is adopting without guardrails.

The Common Failure Pattern

We are seeing three patterns in mid-market organizations:

• Experimentation without governance
  Teams test public AI tools with sensitive content and no oversight.
• Tool sprawl without integration
  Legal adopts point solutions that do not connect to enterprise systems.
• Budget approval without ROI clarity
  Leaders approve licenses without defined metrics for intake resolution rates, contract cycle time, or outside counsel reduction.

AI in legal is not a software decision.

It is an operating model decision.

A Better Approach: Strategy Before Tools

Before evaluating vendors, leadership should clarify:

• Which legal workflows are highest volume and lowest risk
• Where human review truly adds value
• What data must remain private or on-prem
• What measurable outcomes matter

Examples of measurable outcomes include:

• Percentage of intake resolved without lawyer intervention
• Reduction in contract review cycle time
• Decrease in outside counsel spend
• Time reclaimed for strategic advisory work

Only after defining outcomes should vendor evaluation begin.

The research includes practical questions to ask AI vendors across:

• Output accuracy and hallucination mitigation
• Data privacy and IP protection
• Autonomy thresholds for agents
• Governance and auditability
• Cost transparency and scaling terms

These are not technical questions.

They are risk management questions.

The IT and Security Layer Cannot Be Ignored

AI platforms will:

• Integrate with document repositories
• Access contract databases
• Store prompt data
• Interact with sensitive personal information

That means legal AI is also a cybersecurity decision.

This is where cross-functional alignment becomes critical. Legal, IT, security, and finance must evaluate:

• Deployment model: public cloud, private cloud, hybrid
• Data retention policies
• Encryption and access controls
• Integration architecture
• Usage and cost monitoring

For mid-market organizations, this is often where progress stalls. Legal identifies value. IT raises risk concerns. No unified plan exists.

This gap is operational, not philosophical.

What Leaders Should Do Next

If you are a CEO, CFO, COO, or CIO in a mid-market organization, consider the following steps:

• Map your top 10 recurring legal requests by volume and time burden.
• Identify which of those could be partially automated with low regulatory risk.
• Define two to three success metrics before engaging vendors.
• Require legal and IT to co-own AI vendor evaluation.
• Establish governance standards before licenses are purchased.

This does not require a full digital transformation.

It requires clarity and structure.

AI in the legal marketplace is moving quickly. Vendors are expanding into agentic capabilities. Categories are converging. Budgets are rising.

For mid-market organizations, the opportunity is real.

So is the risk of fragmentation, cost creep, and governance gaps.

The goal is not to automate everything.

The goal is to remove friction from routine work so your legal function can focus on protecting the business, accelerating decisions, and advising leadership with speed and confidence.

If you would like a structured conversation around AI readiness across legal, IT, and security, that discussion should start with operating model alignment, not product demos.

That is where meaningful progress begins.