Gabe Pereyra, President and Co‑founder of Harvey, explains how AI can transform legal work when it’s deployed as a secure, enterprise platform—not a standalone experiment. He shares lessons on moving from founder-led sales to scalable enterprise adoption, and why governance, workflow integration and user trust are the real levers for legal AI at scale.

Legal teams don’t need more tools — they need better leverage. That’s the core idea behind Harvey, an AI platform built for legal and professional services teams where the value comes from secure deployment, repeatable workflows, and adoption at scale.

In Notion’s First Block interview, Gabe Pereyra (President and Co‑founder of Harvey) shares how he and his co‑founder spotted a transformational opportunity in legal, and what it took to shift from early founder-led selling to a scalable enterprise motion.

This post pulls out the most useful themes for legal operations leaders, GC offices, and firms evaluating legal AI.

What Harvey is trying to solve

The legal sector is full of high-value expertise — and a lot of high-friction work around it. AI can help, but only if it’s implemented in a way that legal teams can trust.

Harvey’s positioning is deliberately enterprise-oriented: a platform for large firms and in-house teams, where the goal isn’t “AI drafts faster”, but “teams decide and deliver faster” with controls in place.

That’s why the discussion focuses less on flashy demos and more on the hard parts: procurement, security, governance, and adoption.

The founder story: why legal + AI is a powerful pairing

Gabe describes the opportunity as a mismatch between:

• legal work that is information-heavy, iterative, and document-driven

• workflows that often rely on manual review and repeated context transfer

The insight is straightforward: AI can help compress the “first 80%” of many legal tasks — but legal teams must retain control over judgement, risk, and accountability.

From founder-led sales to scalable enterprise adoption

One of the most useful parts of the conversation is the scaling lesson: in enterprise legal tech, early traction often comes from founders personally doing discovery, demos, and hands-on onboarding.

Scaling requires a different discipline:

1) Sell outcomes, not features

Legal teams don’t buy “a model”. They buy improvements in:

• time-to-first-draft

• matter turnaround time

• review consistency

• knowledge reuse across the team

2) Build trust with governance, not promises

In legal settings, “trust” is operational:

• who can access what

• what gets stored

• how outputs are reviewed

• how you audit decisions and usage

3) Make adoption part of the product

A platform only becomes valuable when usage is consistent across the team. That means:

• templates and workflows that match how matters run

• training that fits the roles (associates, PSLs, partners, ops)

• clear guidance on when AI is appropriate — and when it isn’t

Practical steps for scaling legal AI safely

If you’re responsible for legal AI adoption, here’s a sensible way to move from experimentation to impact.

Step 1: Pick one matter workflow to improve

Start with something repeatable:

• contract review and redlines

• due diligence summaries

• research memos

• playbook-driven drafting

Define 2–3 metrics (cycle time, rework, partner review time) so you can prove value.

Step 2: Set “human-in-the-loop” review points

Decide where humans must always approve:

• final advice

• client-facing outputs

• risk acceptance

• privilege and confidentiality decisions

Step 3: Agree your governance baseline

At minimum, define:

• data handling and retention

• role-based access controls

• audit and logging

• incident response expectations

This is where enterprise platforms differentiate from consumer tools.

Step 4: Train the team in repeatable patterns

Most legal AI value comes from consistent habits:

• how to structure prompts

• how to cite and verify sources

• how to draft in a way that’s easy to review

• how to keep outputs connected to the matter record

Step 5: Scale with templates, not tribal knowledge

If one associate has a great workflow, turn it into:

• a template

• a checklist

• a “house style” prompt pattern

That’s how adoption scales.

What leaders often get wrong

Legal AI programmes stall when they:

• roll out “AI access” without a workflow plan

• skip governance until after usage is widespread

• treat training as a one-off session

• measure only adoption, not outcomes

The lesson from Harvey’s scaling story is that enterprise adoption is a system: product, governance, enablement, and executive sponsorship move together.

How Generation Digital can help

If your legal team is moving from pilots into production AI, we can help you:

• identify the best workflow to start with

• design a measurable pilot with governance built in

• create role-based training and adoption playbooks

• connect AI work to your wider collaboration stack

Summary

Gabe Pereyra’s perspective is a useful reminder: legal AI succeeds when it’s treated as enterprise infrastructure, not a novelty tool. The biggest unlocks come from workflow integration, governance, and adoption patterns that make AI reliable and reviewable — so legal teams can move faster without increasing risk.

Next steps

• Choose one workflow to pilot in the next 30 days.

• Define governance guardrails before rollout.

• Train teams in repeatable patterns and review standards.

• Measure outcomes, then scale with templates.

6. FAQs

Q1: What is the main focus of Gabe Pereyra’s work at Harvey?
Building a secure, enterprise-ready legal AI platform and scaling it from early founder-led selling into repeatable adoption across large firms and in-house teams.

Q2: How does AI benefit the legal industry, according to Gabe’s approach?
AI can compress the time spent on the first-draft and synthesis stages of legal work, improve consistency, and help teams reuse knowledge—while keeping human judgement and risk ownership in place.

Q3: What challenges do legal firms face when scaling AI?
The main challenges are governance (security, access, auditability), workflow integration, training and adoption, and ensuring outputs are reviewable and accountable.

Q4: What’s the fastest safe starting point for legal AI?
Pilot one repeatable workflow (e.g., contract review or research memos), define review points, set a governance baseline, and measure outcomes before expanding.