The AI Use Spectrum#
TL;DR
- The people getting the most AI value right now are the ones the firm has least visibility into. The partner prepping depositions with Claude and the associate who vibe-coded a contract script aren’t filing technology requests. Levels 1–3 are where real adoption happens, largely ungoverned.
- AI use spans five levels — mismatching level to task wastes money in both directions. A six-month vendor evaluation for a task an associate could solve in an afternoon is over-engineering. A fragile undocumented script becoming the firm’s de facto contract pipeline is under-engineering. Both happen constantly.
- Vibe coding delivers fast early wins and fragile results. AI-generated code has 1.7x more major issues than human-written code. A script that works on 50 standard contracts will silently fail when contract 12 of the next deal uses a cross-reference instead of a dollar figure.
- Knowledge management is the bridge from individual prompting to institutional workflows. Connecting a firm’s playbooks and clause libraries to AI tools is what moves teams from Level 1 to Level 2 or 3 — Freshfields increased AI usage 500% in six weeks by doing exactly this.
- When someone says “AI-native,” ask at which levels they’re actually operating. Crosby, Lawhive, and Garfield AI represent three structurally different answers — and none of them compete for the same work BigLaw handles.
A litigation partner at a midsize firm told me last month that she uses Claude to prep for depositions. She pastes in transcripts, gets summaries, asks follow-up questions. She doesn’t tell anyone. It’s not a firm initiative. It’s not on an approved vendor list. She just found it useful and kept going.
Three floors up, the firm’s innovation team is spending $400,000 to evaluate enterprise AI platforms for contract review. They’ve been at it for six months. They haven’t deployed anything yet.
Neither approach is wrong, but they require completely different frameworks to evaluate — and most firms treat them as the same conversation. AI use falls along a spectrum, and understanding where a given use case sits determines everything: what to buy, what to build, what risks to manage, and what to ignore.
The Five Levels#
Level 1: Personal Enhancement#
A lawyer opens ChatGPT, Claude, or Gemini and asks it to do something. Summarize this deposition excerpt. Rewrite this email to sound less aggressive. Explain what “anti-dilution ratchet” means in plain English. Draft a first pass of interrogatories.
This is the most common form of AI use in law today, and it’s almost entirely invisible to firm management. Thomson Reuters’ 2025 Future of Professionals Report found that individual professionals were adopting AI tools faster than their organizations. The gap hasn’t closed.
At this level, the AI is a personal productivity multiplier. The lawyer provides the input, evaluates the output, and decides what to use. There’s no system integration, no retrieval pipeline, no firm data involved — just a human and a chat window. The cost is $0-20/month for a consumer subscription.
The trade-off is real but manageable: output quality depends entirely on the individual’s prompting skill, there’s no institutional knowledge in the loop, and the firm has no visibility into what’s being processed. If the partner pasting deposition transcripts into a consumer chat product hasn’t read the provider’s data retention terms, that’s a privilege and confidentiality question the firm doesn’t even know to ask. ABA Formal Opinion 512 (July 2024) requires lawyers using AI to understand how the technology handles confidential information — a requirement that’s hard to satisfy when the firm doesn’t know the technology is being used.
Level 2: Workflow Automation#
A legal ops manager connects an AI model to a specific, repeatable process. New client intake emails get classified and routed automatically. Standard NDAs get a first-pass review against the firm’s playbook. Invoices get coded to the right matter.
The difference from Level 1 isn’t sophistication — it’s repeatability. The AI isn’t answering a one-off question; it’s performing the same task hundreds of times with consistent instructions. The human wrote the prompt once, tested it, and let it run.
Tools like Zapier, Make, and Microsoft Power Automate let non-engineers wire AI models into existing workflows without writing code. A firm that routes 200 intake emails per week through an LLM classifier instead of a paralegal isn’t building software — it’s automating a task. The prompt is the product.
This is where AI starts saving measurable time. It’s also where errors start compounding. A bad classification on one email is a mistake. A bad classification rule running on 200 emails a week for three months is a systemic failure that no one catches until something goes wrong. Level 1 has a human reviewing every output. Level 2 often doesn’t. That gap creates a supervision problem: Model Rule 5.1 holds supervisory lawyers responsible for the work done under their authority, and an unsupervised AI pipeline classifying client communications is work done under someone’s authority — whether they realize it or not.
Level 3: Ad Hoc Tools#
An associate who knows a little Python — or more likely, knows how to describe what she wants to Claude Code, Cursor, or Replit — builds a small application for a specific problem. A script that extracts indemnification caps from a stack of 50 purchase agreements. A dashboard that tracks opposing counsel’s motion practice across three related cases. A tool that compares two contract versions and produces a redline summary.
This is vibe coding: describing what you want in natural language and letting an AI generate the software. The term, coined by Andrej Karpathy in early 2025, captures a real shift. Building functional software no longer requires knowing how to write it. A quarter of Y Combinator’s Winter 2025 startups had codebases written almost entirely by AI.
Instead of waiting six months for the innovation team to evaluate vendors, an associate builds what she needs in an afternoon. The tool does exactly what her workflow requires. It costs nothing beyond the AI subscription she already has.
It also has no tests, no error handling, no documentation, and no one who can fix it when it breaks. A grey literature review of 101 practitioner sources found a consistent pattern: vibe coders experience rapid early success, then hit a wall when the generated code encounters inputs it wasn’t built for. Analysis of AI-generated pull requests found 1.7x more major issues than human-written code, and 94.4% of LLM agents tested were vulnerable to prompt injection.
In a legal context, those failure modes aren’t abstract. An associate builds a script to extract indemnification caps from 50 purchase agreements for a deal. It works — every contract in the set uses a “not to exceed $[amount]” pattern the model handles cleanly. Six months later, another associate reuses the script on a different deal. Contract 12 in the new set caps indemnification through a cross-reference to a defined term on a different page. The script doesn’t follow the reference. It reports “no cap identified.” The deal team relies on that output, and no one catches the $5 million error until the client asks why the indemnification analysis is missing a key term. Nobody remembers how the script works. Nobody can audit what it did.
There’s a deeper problem beyond fragile code. LLM-powered tools are nondeterministic — run the same document twice, get slightly different output. A traditionally engineered parser runs the same way every time, on commodity hardware, for fractions of a cent. An LLM-powered parser sends the full document to an API, pays per token, and produces results that vary between runs. For classification, that’s usually harmless. For extracting a dollar figure a deal team will rely on, it’s a problem. Law runs on consistency, and a tool that produces slightly different extractions each time is solving one problem while creating another.
The ethical dimension sharpens at this level. Model Rule 1.1 (competence) requires lawyers to understand the tools they use. An associate who deploys a vibe-coded tool she can’t debug, on client data she can’t trace, is relying on a system she doesn’t understand to produce work product she’s responsible for.
Level 4: Internal Applications#
The firm’s technology team takes a Level 3 concept and turns it into something durable. They build a contract analysis application with proper authentication, error handling, logging, and a user interface that doesn’t require a command line. It connects to the firm’s document management system, uses the firm’s playbook as a retrieval source, and routes outputs to the right practice group.
This is software development — not vibe coding, not prompt engineering, but actual engineering. Platforms like Harvey and DeepJudge sell infrastructure for building these applications: retrieval pipelines, agent frameworks, and compliance tooling that sit between the foundation model and the firm’s data.
Level 4 requires dedicated engineering resources. A firm building internal applications needs at least one developer who understands LLM architecture, plus ongoing maintenance as underlying models change. (When Anthropic ships a new Claude version, prompts that worked on the old version may not work on the new one.) The payoff is a tool tailored to the firm’s specific document types, workflows, and quality standards — something no off-the-shelf product replicates exactly.
The build-vs.-buy calculus: if the task is high-volume, narrow, and poorly served by existing vendors, building makes sense. If you’re replicating a well-served commercial product, you’re spending engineering salary to save on license fees.
Level 5: Enterprise Platform#
The firm deploys a commercial legal AI platform across practice groups: CoCounsel for research, Kira for due diligence, Everlaw for e-discovery, Spellbook for contract review. These are full products with managed infrastructure, compliance certifications, vendor support, training programs, and integration with the firm’s existing systems.
At Level 5, the firm is a buyer, not a builder. The value proposition is everything the firm doesn’t have to do: prompt engineering, model evaluation, retrieval pipeline design, security audits, ongoing testing. The vendor has already solved these problems and amortized the cost across hundreds of customers. That’s the 60-200x markup from raw model cost to product price — and for most firms, it’s worth it.
The risk at Level 5 is vendor dependency. If your contract review workflow runs on a single vendor’s platform and that vendor changes its model, reprices its API, or gets acquired, your workflow changes whether you want it to or not. Enterprise buyers should be asking: What foundation model does this run on? Where are my client’s documents processed? What happens when the model updates? These platforms also face growing pressure from below: as Levels 3 and 4 make it easier for firms to build narrow tools in-house, vendors that can’t justify their markup over raw model costs — the SaaSpocalypse that erased roughly $2 trillion from software valuations in early 2026 — will lose to internal builds and AI-native competitors.
The Spectrum in Practice#
Most firms don’t sit neatly at one level. They operate across several simultaneously, often without realizing it.
The litigation partner prepping for depositions at Level 1 doesn’t know — and doesn’t care — that the firm is evaluating enterprise platforms at Level 5. The associate building extraction scripts at Level 3 isn’t waiting for the innovation team to finish its six-month evaluation. These uses coexist, and the firm is better off acknowledging them than pretending only the sanctioned ones exist.
The practical question for any legal team is: which level does this task belong at?
A one-off deposition summary? Level 1. Classifying intake emails that arrive 200 times a week? Level 2. Extracting key terms from 50 contracts for a single deal? Level 3. Standardizing contract analysis across the corporate practice group? Level 4 or 5, depending on whether the firm has the engineering talent to build or should buy.
Mismatching the level to the task wastes money and time in both directions. Running a six-month vendor evaluation for a task an associate could solve in an afternoon with a chat window is over-engineering. Letting an associate’s fragile, undocumented script become the firm’s de facto contract analysis pipeline is under-engineering. Both happen constantly.
The Knowledge Management Bridge#
The most common firm-level strategy right now is using knowledge management to push lawyers from Level 1 (individual chat windows) toward Level 2 and 3 (repeatable workflows with institutional knowledge in the loop). The idea: if you can capture the firm’s playbooks, clause libraries, and practice-group standards in a structured way, you can wire that knowledge into AI workflows that produce consistent outputs instead of one-off answers that vary by whoever wrote the prompt.
This isn’t new. Law firms have been trying to systematize knowledge management for 10-15 years — Harvard’s Center on the Legal Profession notes that about a third of firms have some form of practice methodologies in place, often under the banner of legal project management. The results have been marginal. Lawyers don’t fill out knowledge management systems for the same reason they don’t fill out time sheets promptly: the benefit is collective, the cost is individual, and the deadline is always something else.
AI changes the value proposition. A clause library sitting in a SharePoint folder is inert — useful only if someone remembers it exists and searches for it. The same clause library connected to an AI workflow is active: the system pulls the firm’s preferred indemnification language when an associate asks it to review a contract, flags deviations from the playbook, and suggests fallback positions from the firm’s own negotiation history. The knowledge management layer becomes the difference between a generic LLM output and one grounded in how this firm actually practices.
In practice, this looks like practice groups building what amount to prompt-and-retrieval packages: a contract playbook (preferred positions, fallback language, deal-breaker terms), a clause bank, a set of templates, and a curated prompt library — all feeding into an AI tool like Harvey, Spellbook, or even a firm-specific RAG pipeline. Freshfields recently announced a multi-year collaboration with Anthropic to build exactly this: firm-wide AI workflows connected to the firm’s institutional knowledge, deployed across all 33 offices and every practice group. Within six weeks, usage increased 500%.
The approach works best when firms treat it as a transition strategy rather than a destination. A practice group that builds a contract review playbook and connects it to an AI tool has moved from Level 1 (individual associates prompting from scratch) to Level 2 (repeatable workflow with institutional knowledge). If that playbook gets built into a proper application with error handling and quality control, it reaches Level 3 or 4. The knowledge management layer is the bridge — it’s what turns ad hoc AI use into something the firm can govern, improve, and scale.
The hard part isn’t the technology. It’s the same problem knowledge management has always had: getting lawyers to contribute. The firms seeing results are the ones that build capture into the workflow itself — when a lawyer corrects an AI’s contract markup, the correction updates the playbook automatically, so the next review starts from a better baseline. The knowledge management system improves as a byproduct of doing the work, rather than requiring a separate act of documentation.
AI-Native Law Firms#
“AI-native” has become one of those phrases that means whatever the speaker needs it to mean. A solo practitioner who uses Claude for every task calls herself AI-native. A firm that bought Harvey licenses for one practice group calls itself AI-native. A BigLaw chair who announced an “AI-first strategy” at a conference calls the whole firm AI-native. The term gets applied to everything from a lawyer with a ChatGPT subscription to a fully autonomous service that handles cases without human involvement.
The same ambiguity plagues “AI-native legal department.” Does it mean the GC replaced outside counsel with AI agents? That the department uses AI at every stage of the contract lifecycle? That they have an approved prompt library? That someone automated the intake form? When every adoption level from a chat window to a fully engineered platform gets described with the same label, the label stops communicating anything useful.
The spectrum helps here. When someone says “AI-native,” the question to ask is: at which levels of the spectrum are they actually operating? A firm using AI at Level 1 across the board — every lawyer has a subscription, no institutional workflows — isn’t AI-native in any meaningful sense. It’s AI-available. A firm operating at Levels 2 through 4 — repeatable workflows, institutional knowledge in the pipeline, purpose-built applications — is doing something structurally different. A firm where AI handles the default workflow and humans intervene by exception has reorganized around the technology, not just adopted it.
The firms that warrant the label share three characteristics: AI handles the default workflow (humans intervene by exception, not by rule), pricing is fixed or outcome-based rather than hourly, and the technology stack is proprietary rather than purchased. By that definition, very few firms qualify — but the ones that do are worth watching, not because they’re about to replace BigLaw, but because they show what each level of the spectrum looks like without legacy infrastructure in the way. Y Combinator’s 2025 Request for Startups challenged founders to build exactly this, and several took them up on it.
Where they sit on the spectrum depends on how they’re built.
Level 4 model: custom software + lawyers. Crosby is a registered law firm that built its own AI system for contract review. Clients submit NDAs, MSAs, and DPAs via Slack or email; AI agents do the initial analysis and drafting; Crosby’s in-house lawyers handle the judgment calls and quality control. Fixed pricing per document, not per hour. The firm has raised $85 million including a $60 million Series B, reviewed over 13,000 contracts, and reports median turnaround under an hour. Crosby sits at Level 4 on the spectrum: it built proprietary internal applications, staffed a legal team to operate them, and carries malpractice insurance. The AI does the volume work; the lawyers do the work that matters.
Level 5 model: AI platform as the firm. Lawhive built what it calls an AI operating system for consumer law — family law, landlord disputes, property transactions — and runs a network of roughly 500 lawyers through it across the UK and US. The platform automates intake, document drafting, research, and case management. Lawyers working through Lawhive reportedly earn 2.8x what they’d make at a traditional practice because they handle far greater case volume. Lawhive originally tried to sell its software to existing firms. The firms wouldn’t buy it — in part because spending less time on cases made it harder to justify their fees. So Lawhive became a law firm itself. It raised $60 million in Series B funding in February 2026, with $35 million in annual revenue growing sevenfold year-over-year.
Fully autonomous model: AI delivers the service. Garfield AI became the first AI-driven firm authorized by the UK’s Solicitors Regulation Authority in May 2025. It handles small business debt recovery for claims under £10,000 — starting at £2 for a demand letter. The AI guides clients through the entire small claims process; named solicitors maintain oversight, and the system requires client approval before each step. The SRA has since authorized a second AI firm, LawFairy, for immigration work. Both operate in narrow, standardized practice areas where the legal reasoning is constrained enough for current AI to handle reliably.
The pattern across all three: AI-native firms target high-volume, standardizable work where the traditional model’s overhead — partner review of routine contracts, billable-hour pricing for uncontested filings — creates a price gap wide enough to build a business in. They aren’t competing with firms that handle complex M&A or bet-the-company litigation. They’re competing for the work that partners already consider low-margin and associates consider tedious. That’s a large share of the legal market by volume, even if it’s a small share by revenue.
Whether AI-native firms will scale beyond these niches depends on whether the technology can handle work that requires more judgment. For now, the answer is no — but the niches themselves are enormous. Lawhive estimates the US consumer legal market at $200 billion in annual revenue, with up to $1 trillion in unmet need. These firms don’t threaten legal SaaS vendors — they threaten the law firms those vendors sell to, by competing for the same work at lower cost with a fundamentally different operating model.
The Uncomfortable Part#
The AI use spectrum isn’t a maturity model. Level 5 isn’t better than Level 1. But the framework exposes something most firms haven’t reckoned with: the people getting the most value from AI right now are the ones the firm has the least visibility into.
The litigation partner prepping depositions with Claude isn’t filing a technology request. The associate who vibe-coded a contract extraction script isn’t submitting it to IT for review. These are the people actually using AI on client work, today, and the firm’s governance framework — if it has one — almost certainly doesn’t account for them. The formal AI strategy, the six-month vendor evaluation, the approved tool list — all of that addresses Levels 4 and 5. Levels 1 through 3 are where the real adoption is happening, largely ungoverned.
That’s not a problem to solve with a policy memo. It’s a problem to solve by being honest about what’s already going on, and building governance that starts from reality rather than from an org chart. The firms that get this right won’t be the ones with the most sophisticated AI platform. They’ll be the ones that figured out which level each task actually belongs at — and stopped pretending the spectrum doesn’t exist.
Further Reading#
- 10 AI Law Firms to Watch in 2026. Lupl’s survey of AI-native legal service providers.
- SRA Approves First AI-Driven Law Firm. The UK Solicitors Regulation Authority’s announcement on Garfield AI.
- UK’s SRA Takes Unprecedented Approach in Authorising AI-Enabled Firms. International Bar Association analysis of the regulatory trend.
- Lawhive Raises $60 Million in Series B Funding. Fortune’s profile of the AI-native consumer law firm.
- AI for Legal Knowledge Management: Build a Precedent + Prompt System. Clio’s practical guide to building KM-powered AI workflows.
- The Impact of AI on Law Firms’ Business Models. Harvard Law’s Center on the Legal Profession on practice methodologies and AI.
- Vibe Coding in Practice: Motivations, Challenges, and a Future Outlook. Grey literature review of 518 practitioner accounts on vibe coding trade-offs.
- ABA Formal Opinion 512. The ABA’s 2024 guidance on lawyers’ duties when using AI.
This post is part of the AI Adoption Strategy series on LegalRealist AI. It is intended for informational and educational purposes only and does not constitute legal advice. AI capabilities, pricing, and product features described here reflect publicly available information as of the publication date and are subject to rapid change. Laws governing AI use vary by jurisdiction.