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Will AI Replace Junior Lawyers on In-House Teams?

Jarryd Strydom
Jarryd Strydom

October 1, 2026 · 8 min read

Jarryd Strydom is the Co-Founder and Chief Operating Officer at Sandstone.

The question of AI replacing junior lawyers now comes up in nearly every conversation about legal technology, and for in-house legal teams the answer is no. Artificial intelligence is taking over a specific set of tasks that used to fill an early-career lawyer's week. That shift changes what general counsel should expect from the people at the start of their careers.

Why AI Will Not Replace Junior In-House Lawyers

Many legal leaders expect lawyers who use AI well to replace lawyers who don't, since the technology rewards the people who direct it. Early-career counsel are well placed to be those people, because much of the high-volume legal work AI now handles used to be theirs. The advantage is greatest in an AI-native legal department, a team that runs on AI as core infrastructure, with intake, drafting, review, and knowledge capture built around it from the start.

Their readiness depends partly on what they learn before they arrive, and legal education has been slow to catch up. On a past episode of Evolving with Jessica Nguyen, Catherine Romero, a former Perkins Coie lawyer now in an engineering role at Microsoft, noted that many law schools are not yet teaching the technology, and she was clear about what that means for new lawyers: "Entry-level positions are not gonna go away, but you need to know… the basics of the technology and how it works and how it doesn't work and when you need to review it." A new lawyer who cannot evaluate AI output has less to offer than one who can, which is why fluency with these tools is becoming part of the job whether or not law school covered it.

Related: Learn more about the evolution of the AI-native legal department.

How the In-House Role Differs From Law Firm Junior Associates

Most of the debate about AI and entry-level legal jobs centers on law firms. Junior associates at a firm have traditionally built their careers on doc review, due diligence, and first drafts, and firms have billed that time by the hour. When generative AI completes a first pass of document review in minutes, the billable hours attached to that work shrink, putting pressure on the model that has long funded associate training across the legal industry.

That pressure has opened a debate inside law firms about how associates will learn once the repetitive work that trained them disappears. Much of what a first-year associate absorbed came from reading large volumes of documents and seeing how partners marked up their drafts. As that volume shrinks, firms have to build the apprenticeship deliberately.

In-house legal teams face a different calculation, because they do not sell time. A legal department measures itself by how quickly and consistently it supports the business, so AI that removes low-value work frees capacity for the substantive work the company needs. For that reason, the effect on in-house hiring is gentler than at law firms: the role changes, but legal departments still need lawyers who understand the business.

What AI Can and Cannot Do for Junior Lawyers

AI handles discrete, repeatable tasks well, but it cannot replicate the contextual judgment and business intuition that make in-house counsel valuable to the company. Drawing that line task by task is the clearest way to see where early-career roles are heading.

Contract Review and First-Draft Redlines

AI contract review software can now produce a first-pass markup against a company's playbook and clause library in minutes. It also handles much of the routine contract drafting that used to take a new lawyer an afternoon. The lawyer's job moves from producing that markup to reviewing it and checking whether each suggested change reflects the company's actual position and the commercial context of the deal. That review still requires legal training, and it builds judgment when the reviewer has to defend each change.

Legal Research and Precedent Surfacing

For in-house legal teams, AI for legal research extends past case law into the company's own records: prior contracts, past positions with a given counterparty, and the outcomes of similar requests. When that precedent surfaces automatically, early-career counsel spend their time interpreting what it means for the matter in front of them instead of searching for it across shared drives and inboxes.

Intake Triage and Request Routing

Sorting incoming requests has long consumed a large share of a new lawyer's time, largely because those requests arrive scattered across email, chat, and hallway conversations. AI agents for legal work can now read a request as it arrives in Slack or email, gather relevant context, and route the matter to the right owner. With legal intake automation handling that first step, legal request management moves out of individual inboxes and into a shared system. Early-career lawyers then take on the escalations and the requests that need a real conversation, which is where they learn how the business works.

Strategic Advice and Business Judgment

AI cannot weigh a company's risk appetite, read the dynamics between stakeholders, or decide which commercial priority wins when two conflict. Those calls depend on knowing the people and the strategy behind a request, so they remain matters of professional judgment. Lawyers own them by recommending a course of action and deciding when to escalate.

Relationship Building and Stakeholder Trust

Business teams trust a legal partner who understands their goals and responds quickly. Early-career lawyers earn that trust by communicating clearly and collaborating across functions, and removing administrative work gives them more time to do it well.

Working relationships with the business also give in-house counsel information that often goes unrecorded, such as which executive owns a vendor relationship or why a deal timeline matters this quarter. That information turns a correct legal answer into useful advice.

Skills Junior Lawyers Need in an AI-Native Legal Department

As AI takes on more first-pass work, the skills that set new lawyers apart shift toward judgment and communication. Building them early is a career advantage, since the lawyers who develop them now will lead teams as AI adoption deepens across the legal profession.

Business Acumen and Cross-Functional Communication

Lawyers early in their careers need to translate legal advice into terms a sales leader or product manager can act on. Doing that requires understanding revenue targets, launch timelines, and the pressure behind each request. AI can handle the drafting, while reading an organization's dynamics, or telling when a stakeholder needs reassurance more than analysis, still falls to the lawyer.

AI Tool Proficiency and Prompt Engineering

Legal departments increasingly expect new in-house hires to be comfortable with legal AI tools. Most of these tools run on large language models (LLMs), so knowing how to prompt a model and recognize when it is working outside its strengths is a practical skill. Lawyers who test new workflows and share what works become the colleagues others rely on as their teams adopt these tools.

Judgment and Risk Escalation

AI can flag an unusual indemnity clause or a missing data protection term, but deciding whether the issue is material takes legal judgment. New lawyers need to know when to approve, when to push back, and when to escalate, and that critical thinking develops as they own real matters. Mentorship from senior counsel counts for more than it did when junior work was mostly volume, because the reasoning behind each call now has to be taught directly instead of picked up through repetition.

Institutional Knowledge Capture and Documentation

Every negotiated position, approved exception, and resolved dispute is knowledge the next lawyer can use. Early-career counsel who record the legal reasoning behind a decision, along with the outcome, add directly to the team's knowledge base and the playbooks AI draws on, making those systems more accurate over time.

How Institutional Knowledge Gives Junior Lawyers an Edge

In-house legal teams hold years of contracts, playbooks, negotiation history, and decisions about how the company handles risk. Most of it sits scattered across drives, inboxes, and senior lawyers' memories. AI makes that knowledge usable at the moment of work, surfacing a counterparty's history or the company's fallback position as soon as a new request arrives. Lawyers who understand that body of knowledge and add to it become hard to replace, because they connect what the system knows to what the business needs. For the department, the same knowledge is an advantage that compounds, since each resolved matter makes the next one faster and more consistent.

How Junior Lawyers Should Supervise and Verify AI Output

LLMs can produce fluent, confident text that is wrong, and hallucination is one of the most serious risks in using them for legal work. Human oversight is therefore part of the workflow, and early-career lawyers are often the first to review AI work product for accuracy, tone, and strategic fit. A consistent process makes that review faster and catches the errors that matter most:

  • Check every citation and source for hallucination. Confirm that cases, statutes, and clauses exist and say what the output claims, since a hallucinated citation reads exactly like a real one.
  • Validate against the playbook. Compare suggested redlines with the company's approved positions and fallbacks, and flag any deviation for a decision.
  • Confirm the business context. Make sure the output reflects the deal's value, the relationship with the counterparty, and any commitments the business has already made.
  • Protect confidentiality. Use only approved tools for privileged material, trade secrets, and intellectual property, and keep sensitive details out of general-purpose models.
  • Record every correction. Logging corrections, including any hallucinations caught, helps the team refine prompts and playbooks, reducing the chance of the same error next time.

What the Future Holds for Junior Lawyers on AI-Native Legal Teams

Early-career lawyers on AI-native teams will do higher-value work sooner, owning the kind of matters lawyers once waited years to handle. AI expands what a first- or second-year in-house lawyer can contribute, and the business benefits from faster legal services grounded in the company's own precedent. General counsel adopting legal AI need to structure the team so new lawyers learn judgment while AI handles volume, and that structure depends on systems that capture context and knowledge as the work happens.

Hiring and training are already shifting. Many general counsel filling entry-level roles now weigh critical thinking, comfort with ambiguity, and the ability to explain a legal position to a non-lawyer as heavily as academic credentials, since AI cannot supply those qualities. Structured review of AI output will replace some of the repetition that used to teach new lawyers the basics. Senior counsel, in turn, will spend more time explaining why the team takes the positions it does.

Sandstone is built to support that kind of team. It takes in requests from the tools the business already uses and attaches counterparty history, prior positions, and relevant contracts automatically. AI agents then handle first-pass work, so lawyers at every level can focus on judgment. Sandstone connects every person, company, and document behind legal work, so agents and lawyers start from full context and playbooks improve with each matter — that's what Sandstone means by Legal Relationship Management.

Learn how Sandstone enables in-house legal departments with AI.

FAQs About AI and Junior In-House Lawyers

Will AI eliminate entry-level legal jobs entirely?

No. AI moves entry-level work toward review and judgment, and legal departments still need early-career lawyers to do that work and to become the next generation of senior counsel.

How long before AI significantly disrupts in-house legal hiring?

The shift is already underway across the legal profession, though its pace depends on how quickly each organization adopts AI. Teams investing in AI infrastructure now will see the fastest change in how they hire and what they expect from new lawyers.

What is the difference between AI replacing lawyers and AI augmenting lawyers?

Replacement means AI eliminates the role, while augmentation means AI handles specific tasks so lawyers can focus on judgment, strategy, and relationships. Augmentation describes what is happening on in-house teams.

Works Cited