Rethinking legal productivity in the age of AI

Legal teams have always been judged on output. How many contracts can you process in a month? How quickly can you turn around an NDA?

But output isn’t impact. And in 2025, with AI tools doing the heavy lifting once left to junior lawyers, there’s a bigger question to answer: what does legal productivity actually mean?

That’s what we explored in our latest Juro webinar, hosted by Michael Haynes, General Counsel, Juro, with our fantastic panel: Daniela Dwyer, Head of Legal Operations, Bill, and Beatrice Bonaita, Legal Counsel, Shieldpay.

Here’s what we learned:

The problem with old metrics

Traditional productivity metrics, such as contracts per lawyer, time-to-signature, number of redlines, etc, were never designed to reflect legal’s real contribution. Sure, they show activity levels, but do they show any value?

Well, as our panel discussed, these old metrics might be keeping legal boxed in and hindering innovation. Lawyers are smart and strategic lawyers, not admin machines.

So, with AI now able to draft, review, and even apply playbook rules, sticking with volume-based measurement misses the point entirely, but what should we be tracking instead?

What modern teams are tracking

So if not volume, what should you track?

Forward-thinking teams are building metrics that align with the business, not just the back office. Common examples include:

• Speed to revenue: How quickly revenue-generating contracts move from draft to signature.
• Playbook adoption: The percentage of deals closed without legal input because playbooks did the work.
• Satisfaction scores: From both the business and the legal team - because happy lawyers are productive lawyers.
  Time allocation: Breaking down where legal spends its time (R&D vs. routine tasks), so you can spot automation opportunities.

Some teams are even experimenting with quality scores: using AI to check contracts against playbooks and measure how consistently terms align with policy.

The shift is clear: it’s less about “how much did we do?” and more about “how much did we help the business achieve?”

Automation > manual tracking

The challenge isn’t just deciding what to track - it’s how.

We heard from multiple leaders who tried manual tracking (think: dropdowns in spreadsheets) only to watch adoption collapse after a few weeks. Lawyers forget, admin slip-ups happen, and the data can get messy.

The better approach? Automation.

• CLM systems that track contract progress automatically.
• Ticketing tools that log requests without manual input.
• AI dashboards that surface trends and insights from existing data.

The golden rule: fewer manual touchpoints = better data, more consistency.

Making metrics stick

Metrics don’t mean much if they sit in a silo. The best legal teams make reporting a habit:

• Regular updates to leadership - not just “we closed X contracts” but “we accelerated $Y in revenue by cutting turnaround times.”
• Resourcing decisions - proving automation reduces the need for headcount, or justifying investment in new tools.
• Strategy shifts - reallocating work when time-tracking shows legal drowning in low-value requests.

In one example shared, a team used time allocation data to prove 40% of their workload was low-value admin. That data justified investment in an AI-enabled CLM — freeing lawyers to focus on strategic projects.

The AI effect

AI isn’t just speeding things up. It’s creating a new category of metrics.

Instead of tracking how many contracts legal touches, teams can now measure:

• AI-assisted quality checks: How often contracts pass first-time with no edits.
• Performance against playbooks: Benchmarking lawyers (and AI agents) against policy rules.
  Workflow efficiency: Beyond drafting - approvals, signatures, escalations.

This shifts legal from reactive to proactive. You’re not just measuring output, you’re using data to train lawyers, refine playbooks, and prove ROI to leadership.

And as one GC pointed out, CEOs aren’t asking “how many contracts did legal review?” anymore. They’re asking: “What can we automate to reduce spend?”

If you’re not tracking AI-enabled impact, you risk being left behind.

For legal teams, that means:

1. Rethink what you measure - make metrics business-aligned, not volume-driven.
2. Automate tracking - trust tools, not spreadsheets.
3. Frame results in business terms - hours saved = faster revenue recognition.
4. Experiment with AI metrics - quality, consistency, proactive training.

The tools have changed. The way you measure value should too.

Want to dive deeper? Reach out to the Juro team to explore how AI can help your legal function scale impact, not admin.