MPL Legal Tech Advisors: The Legal AI Brief

Thursday, 15th January 2026 - 19th Edition

This Week's Theme

Many firms are reaching for AI tools when the underlying issue is basic operational friction, such as slow intake, manual handoffs, deadlines living in inboxes, and drafts scattered across email and personal folders.

The result is predictable. Firms buy “judgment” tools to solve problems that need consistency and rules.

Insight of the Week

In practice, AI adoption shifts effort into supervision, validation, and exception handling. Outputs must be reviewed, discrepancies resolved, and decisions justified after the fact.

That work accumulates gradually and is rarely visible in planning discussions. This pattern tends to appear when tasks that could be governed by predefined rules are instead handled by systems that interpret ambiguity.

The category of technology used determines whether outcomes remain predictable or require ongoing judgment.

As AI use expands, the volume of oversight grows. Responsibility for that oversight often remains fragmented across teams and difficult to measure, which is why expected efficiency gains frequently diverge from reality.

Legal AI in Action

🎬 When Tone Changes AI Output

How persuasion affects AI outputs

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🎬 Rebuilding Legal Ops Step by Step

Principles before systems

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This Week's Big Risk Signal

When automation and AI are treated as interchangeable, workflows begin to behave differently than expected.

Tasks that were previously governed by fixed rules start producing variable outcomes.

Data flows change, review requirements expand, and responsibility becomes harder to locate.

These changes are rarely visible during normal operation. They surface when an output is questioned, a decision is challenged, or an incident needs to be examined.

At that point, firms often discover they cannot reconstruct how data moved through the system, which rules applied at each step, or where human judgment entered the process.

That loss of traceability is not a theoretical concern. Legal cybersecurity and governance research increasingly points to reconstruction and explainability as the critical failure point when AI-influenced workflows come under scrutiny.

What Breaks First in Practice

Across multiple conversations I had this week with law firms, the same issues surfaced early.

1. Document systems were never structured for machines to understand. Naming, versioning, and retention rely on habit rather than design, which becomes visible once AI is introduced.

2. Legal teams struggle to trace failures. When outputs are wrong or misleading, firms cannot tell whether the cause sits in data quality, retrieval logic, access controls, or model behavior.

3. Ownership is fragmented. Different teams control documents, security, and tools, but no one can describe the full path a document takes through an AI-assisted workflow.

Looking Ahead

🎙 This Saturday at 2pm CET!

This week’s guest on Rok’s Legal AI Conversations is Elgar Weijtmans, Head of Technology at HVG Law. We talk about what legal AI vendors actually add on top of the same foundation models, why “context” and retrieval quality determine output more than branding, and how vector search can look relevant while still being legally wrong. We also get into where data flows across vendors and which three questions leadership should ask vendors before any rollout.

All legal AI vendors use the same AI models!

Each edition of Legal AI Brief brings practical lessons from firms using AI safely.