Ben Chiriboga recently shared something that caught my attention: "Over 50+ lawyers registered for our reframe.lawyer masterclass. This week, we covered the roles available today at the intersection of law, business, and AI." He added, "Our next cohort starts Feb. 20th at 10am EST" and closed with a line that deserves to be repeated: "Practice is not the end, it's the beginning."

That framing is exactly right. For many lawyers, practice feels like the destination: you graduate, pass the bar, join a firm or an in-house team, and you have arrived. Ben’s point flips the mental model. Practice is not a finish line. It is a platform, and right now that platform connects to a fast-growing set of roles where legal judgment, business fluency, and AI literacy overlap.

In that spirit, here is an expanded map of what those roles look like, why they exist, and how to start moving toward them without pretending you need to become an engineer overnight.

Practice is the beginning, not the end

Ben’s line works because it names a common anxiety: "If I step off the traditional track, am I leaving the profession?" In reality, the center of gravity in legal work is shifting.

Businesses want faster legal cycles and clearer risk decisions.
AI tools are changing how research, drafting, and review get done.
Regulators are paying closer attention to automated decision systems.
Clients and internal stakeholders expect lawyers to be pragmatic operators, not just technical experts.

So the question is less "Should I leave practice?" and more "Which version of legal value do I want to deliver next?"

"Practice is not the end, it's the beginning." - Ben Chiriboga

The new map: roles at the intersection of law, business, and AI

When Ben says they covered "roles available today" at this intersection, it signals something important: these are not hypothetical job titles. Many of them already exist across legal tech vendors, startups, large companies, consulting firms, and increasingly within law firms themselves.

Below are categories of roles that show up repeatedly, plus what they actually do day to day.

1) Legal operations and process design (with AI in the mix)

Legal ops has been around, but AI is raising the ceiling. These roles focus on how legal work gets delivered.

Typical responsibilities:

Mapping workflows (intake, triage, contracting, compliance reviews)
Implementing tools for contract lifecycle management and matter management
Setting playbooks and decision trees so routine questions get answered faster
Measuring cycle time, cost, and quality (yes, legal metrics)

Where AI comes in:

Automating first-pass triage and routing
Using assisted drafting for standard templates
Summarizing obligations and extracting key terms at scale

2) AI governance, risk, and compliance

Companies adopting AI need policies that survive real-world pressure: product deadlines, sales commitments, and regulatory uncertainty.

Typical responsibilities:

Designing AI use policies (internal tools and customer-facing features)
Reviewing model risk and vendor risk
Coordinating with security, privacy, product, and data teams
Preparing for audits and regulator inquiries

The legal skill that matters most here is not memorizing statutes. It is translating messy reality into a defensible governance approach.

3) Privacy, data protection, and security-adjacent roles

AI runs on data, and that pulls privacy and security closer to the product surface.

Typical responsibilities:

Drafting and negotiating data processing terms
Building guidance on data retention, minimization, and access
Supporting incident response and investigations
Evaluating whether AI features create new categories of personal data risk

Even if you are not a privacy specialist today, this is an area where lawyers can upskill quickly because the work rewards structured reasoning and strong writing.

4) Product counsel and legal product partnership

The best product counsel do more than say "yes" or "no." They help teams ship.

Typical responsibilities:

Advising product on feature risk, claims, disclosures, and user experience
Designing safe defaults and guardrails (not just disclaimers)
Supporting roadmap decisions with risk-based options
Working with AI teams on data sources, evaluation, and user transparency

In AI products, the legal partner who can speak in scenarios, tradeoffs, and measurable controls becomes a multiplier.

5) Legal knowledge, training, and enablement

As tools change, internal enablement becomes a major lever.

Typical responsibilities:

Creating playbooks for drafting, review, and negotiation
Training teams on tool usage and policy boundaries
Curating precedent and maintaining template libraries
Running pilot programs for new AI tools and measuring outcomes

This is where many lawyers can start: by becoming the person who operationalizes "how we do the work" in a modern way.

6) Customer-facing roles at legal tech and AI companies

There is growing demand for lawyers in roles that sit near customers and revenue.

Common titles:

Solutions consultant
Legal engineer
Implementation specialist
Customer success manager (legal)
Sales enablement (legal and compliance)

Typical responsibilities:

Translating customer legal workflows into product configurations
Supporting procurement, security questionnaires, and contracting
Helping customers achieve outcomes (faster review, better compliance)

If you can combine calm stakeholder management with crisp explanations, you can thrive here.

7) Contracting for AI vendors and procurement support

AI adoption creates contracting complexity: data use rights, model training restrictions, IP, audit, and liability all become sharper.

Typical responsibilities:

Building standardized addenda for AI tools
Negotiating customer and vendor terms around data and model usage
Aligning legal positions with business risk tolerance
Creating fallback clauses and escalation rules

This work rewards lawyers who can reduce ambiguity without slowing the business to a crawl.

What lawyers already have that transfers well

One reason Ben’s post resonated is that it gives lawyers permission to see their current work as preparation, not a cul-de-sac.

Here are the practice-developed skills that map directly to modern law-business-AI roles:

Issue spotting under uncertainty: AI work is full of gray areas. Lawyers are trained for that.
Writing with precision: policies, contracts, product guidance, and disclosures all depend on clear language.
Stakeholder management: cross-functional teams need someone who can align competing incentives.
Risk framing: not just "is it allowed," but "what is the risk, what is the control, what is the acceptable threshold."
Professional judgment and ethics: AI changes the tools, not the need for trust.

A practical 30-day plan to explore the intersection

If Ben’s masterclass prompted you to look up and ask "What roles are out there for me?" here is a concrete way to start.

Week 1: Pick a problem, not a title

Choose one real workflow you understand well, such as contract review, compliance intake, discovery triage, or policy drafting. Write down:

Who the stakeholders are
What inputs and outputs look like
Where time and errors accumulate

Week 2: Build AI literacy that is job-relevant

You do not need to become technical, but you do need to be conversant.

Learn the difference between a general-purpose model and a domain tool.
Understand common failure modes: hallucinations, bias, data leakage, prompt injection.
Read one credible AI governance framework and summarize it in your own words.

Week 3: Create a small portfolio artifact

Make something you can show or discuss:

A one-page AI use policy for a legal team
A model clause set for AI vendor contracting (data use, audit, liability)
A workflow map showing where AI can assist and where human review is required

Week 4: Talk to people in the roles

Informational interviews beat speculation. Ask:

What are the top 3 problems your team is solving?
What skills are rare and valuable?
What would you hire a former practicing lawyer to do immediately?

What to look for in programs like Ben’s

Ben mentioned the reframe.lawyer masterclass and a new cohort date. Programs can be helpful if they do three things:

They show real roles with real hiring signals (titles, teams, outcomes).
They teach practical artifacts, not just trends (policies, playbooks, contract positions).
They connect you to a network that can validate and accelerate your pivot.

The goal is not to collect buzzwords. It is to develop credible, repeatable value in a changing market.

Closing thoughts

Ben Chiriboga’s short post carries an important message: lawyers are not being replaced, but the definition of legal contribution is expanding. The winners will be the ones who treat practice as a base layer and then build business context and AI fluency on top of it.

If you have been feeling that your legal training should open more doors than the traditional ladder suggests, Ben’s framing is a useful reset. Practice was never the end. It was the beginning.

This blog post expands on a viral LinkedIn post by Ben Chiriboga. View the original LinkedIn post →