As the year draws to a close, the legal profession is awash in retrospectives: the growing rate of solo and small AI adoption (now around 70%), the way AI is level the playing field between large and small businesses (including on the hallucination front where big companies and solos are equal offenders) and the familiar observation noting the moving from chat-based tools to agent systems. Much of this commentary is accurate, but it is also repetitive and boring. Rather than regurgitating what most lawyers already know or could easily learn from AI, I will take a different approach and examine five distinct business models that provide new opportunities for solo attorneys and small firms in the AI era.
Let’s take a step back. According to Wikipedia, A business model describes how a business organization creates, delivers, and captures value. For decades, law firm business models have been downright boring, based largely on selling time in billable increments. This has changed to some extent in recent years with flat rates and law subscription models like that of Mathew Kerbis. The subscription lawyerbut these approaches – as well as six new economic models of law which I had imagined years ago – never really gained popularity.
In the age of AI, all this could change. And in fact, some would argue that this needs to change, because with the reduction in the time required by AI for legal tasks, billable revenue is set to decline. But AI can also power new business models or revive traditional models. Below are five law firm business models fit for the AI era that can help individual law firms and small law firms diversify their offerings.
1. Artisanal law: the “good old law” reinvented
The first model may seem counterintuitive in the age of AI: Artisanal Legal. It is the deliberate adoption of high-quality, tailored legal services grounded in human judgment, strategy and insight – augmented, but not replaced, by AI.
In this model, AI operates largely behind the scenes. It takes care of research, drafting and problem detection, allowing the lawyer to focus on what clients value most: interpretation, advocacy and trust. The mark of a lawyer is not about speed or scale, but about expertise. Consider appellate advocacy, regulatory advice, complex negotiations, and niche advisory practices where results depend on experience rather than volume.
As AI commoditizes routine legal outcomes, the perceived value of deep expertise and personalized advice will increase, not decrease. Clients facing high-stakes issues will look for lawyers who can explain Why a strategy works, not just producing a document. Small-scale law firms will likely charge higher fees, emphasize reputation and credentials, and remain relatively small by design.
2. Human-AI document review: the dott.legal model
On the opposite end of the spectrum is Human-AI document review, exemplified by platforms such as dot.legal. This model addresses a persistent reality: Even though AI is exceptionally effective at sorting, grouping, and flagging documents, clients and courts still want humans to be accountable. Under this model, clients present to a lawyer with an AI-generated document, and the lawyer steps in to validate and certify the results.
This model requires efficiency and knowledge of the subject. Dott.legal is priced at $199 for a document or demand letter – which doesn’t seem feasible for more than four or five pages. And if the document is a real dump, the revisions will be more complex. AI cannot solve the problem because the model promises a review by a lawyer. Still at a higher price and with reservations about document size, this model may have legs.
3. AI-powered contract lawyer services (shared access)
A third category of AI-enabled legal services adapts the traditional contract lawyer model to the economics of modern legal technology. As the cost of advanced AI search and discovery tools continues to rise – often $1,000 per month or more – many sole traders and small businesses cannot justify maintaining subscriptions for occasional use. What they can justify pays for the production of these tools when a business requires it.
Under this model, a lawyer invests upfront in premium AI-enhanced platforms, such as comprehensive Westlaw products, AI research tools, or business discovery systems, and offers the benefits of these tools to other lawyers on a contract basis. The service is not sold as software access, but as a work product supervised by an attorney, preserving respect for ethics and professional responsibility.
For example, a lawyer with a full Westlaw AI suite could provide day-to-day or project-based services such as 50-state investigations, multi-jurisdictional research memoranda, problem detection analyzes or first draft briefs, complete with supporting authorities and research leads. The hiring firm receives a defensible, high-quality work product without incurring ongoing technology costs, while the supplying attorney monetizes both expertise and infrastructure.
The same model applies to discovery and litigation assistance. An attorney with access to an advanced discovery platform can manage document review, privilege analysis, and issue tagging for other firms, using AI to expedite review while maintaining human oversight. Rather than each company purchasing and mastering complex discovery software, discrete litigation functions are outsourced to a specialist who already has the tools and systems in place.
This shared access approach turns expensive AI platforms into revenue-generating assets and creates a new class of supercharged contract legal services – faster, more scalable, and accessible to businesses that don’t need to own advanced legal technology full-time.
4. Next-generation legal practices capturing knowledge from older lawyers
One of the most underappreciated opportunities in the AI era is the capture and reuse of institutional legal knowledge, particularly that of senior and retired attorneys. Decades of expertise – how to deal with regulators, negotiate industry-specific contracts or handle recurring litigation – often disappear when a lawyer retires.
In this model, law firms would acquire not a practice of law, but the knowledge of seasoned lawyers that could be encoded in AI systems: curated document libraries, annotated precedents, decision trees, and training datasets. These systems are then used to train young lawyers, support client-facing work or even generate new sources of income.
The firm becomes not only a legal service provider, but also a knowledge manager. This model is particularly powerful in niche, sector-specific or regional regulatory practices, where tacit knowledge matters more than published law.
It also offers a compelling succession strategy. Rather than selling a portfolio of cases, older lawyers can help build sustainable systems that preserve their expertise while reducing their reliance on their personal availability.
5. AI-Forward Law Firm Offshoots
Another emerging model is the creation of AI-based offshoots within or alongside traditional law firms. These are not full-service companies, but specialized entities focused on AI-enabled services: compliance audits, internal investigation support, contract analysis, discovery management, or regulatory oversight.
The structure of the branches is important. By separating these services from the main practice, attorneys gain flexibility in pricing, staffing, and technology adoption without disrupting existing billing models. These entities may employ technologists, project managers, and non-lawyer specialists alongside attorneys.
Clients benefit from clarity: they know they are purchasing a process-driven, technology-enabled service rather than bespoke legal advice. Businesses benefit from diversification and innovation without existential risk to the main firm.
Over time, some branches may become standalone businesses – or even surpass their parent companies in revenue.
The AI era does not portend a unique future for the practice of law or a mass disappearance of lawyers. Instead, it opens up several viable pathways, each with different tradeoffs in scale, revenue, identity, and impact. Lawyers who focus solely on AI tools without thinking about the underlying business model risk missing a larger opportunity: rethinking how legal value is created and delivered.
Carolyn Elefant is one of the country’s most recognized advocates for independent lawyers and small firms. She founded MyShingle.com in 2002, the longest-running blog for independent practitioners, where she has published thousands of articles, resources and guides on starting, running and growing independent law firms. She is the author of Solo by Choice, widely considered the definitive handbook for starting and maintaining a law firm, and has spoken at countless bar events and legal conferences on technology, innovation and regulatory reform impacting solos and small businesses. Elefant also develops practical tools such as AI learning to help small firms adopt AI and she consistently champions reforms aimed at leveling the playing field for independent attorneys. Along with this work, she leads the Carolyn Elefant Law Firm, a national energy and regulatory practice that handles selective, complex and high-stakes matters.