Chapter 7: Impact of AI on the Business and Practice of Law

Chapter Overview

In Chapter 6, we explored how prompt engineering empowers lawyers to guide AI models more effectively, ensuring that the output aligns with specific legal tasks and contexts. We contrasted naive prompts with more informed ones, examined the difference between single-shot and few-shot strategies, and introduced frameworks like RTF and RISEN to structure queries. We also learned how Retrieval-Augmented Generation (RAG) provides external grounding to reduce errors and keep responses up to date. By applying these techniques, legal professionals can craft more reliable, efficient AI interactions that enhance research, drafting, and client-facing services.

In this chapter, we'll learn how generative AI is transforming the legal profession, reshaping daily practice, business models, and professional roles. We will examine how AI streamlines tasks such as legal research, contract analysis, and litigation preparation, allowing lawyers to focus on higher-level strategic work. We will also explore how lawyers, law firms, and corporate legal departments must adapt by developing new skills and adopting emerging technologies.

By the end of this chapter, you should be able to:

  1. Analyze the impact of generative AI on daily legal practice by identifying key areas where AI enhances efficiency, such as legal research, contract analysis, and litigation support.

  2. Evaluate the shifting roles and responsibilities of legal professionals as AI automates routine tasks, and assess how this transformation influences hiring trends, skill requirements, and legal education.

  3. Propose strategic changes in law firm business models to reflect AI-driven efficiency gains, considering alternative billing structures, workforce optimization, and AI-augmented legal services.

  4. Synthesize insights from real-world case studies to develop strategies for leveraging AI while maintaining legal integrity and client trust.

Let’s begin by examining AI’s tangible influence on the day-to-day work of lawyers and legal teams.

Generative AI is automating a range of routine legal tasks that traditionally consumed significant lawyer time. Tasks that once required painstaking manual effort, such as legal research, document drafting, and contract review, are now significantly streamlined by AI. Here are a few concrete examples:

Example

Scenario: A mid-sized law firm lands a large due diligence project that involves reviewing over 5,000 commercial contracts. Traditionally, this might have required a small army of junior associates and paralegals, working long hours. Today, the firm uploads the contracts to an AI review platform, which flags unusual clauses and extracts key data in hours. The lawyers then analyze these flags and confirm that the platform has identified the relevant contractual obligations. Instead of spending weeks on routine tasks, the firm can devote its energy to the strategic aspects of the deal, like advising on negotiation points and risk management.

Reducing Lawyer Workload and Boosting Efficiency

Because AI can handle the drudge work of sorting, sifting, summarizing, and drafting, many lawyers are seeing a reduction in day-to-day workload. One study by Thomson Reuters (2024) found that AI-powered tools could save lawyers about four hours per week, translating into an increase in available billable time worth tens of thousands of dollars annually per lawyer.

Callout: Key Term – “AI as a Force Multiplier”
In military contexts, a “force multiplier” is something that dramatically increases the effectiveness of an individual or group. In the legal world, AI is a force multiplier because it lets a single attorney accomplish tasks that might otherwise require an entire team.

Practice Pointer:
If you’re working in a law office, consider experimenting with an AI-driven legal research tool to handle the first pass of case-law research. Always double-check the results, but this can be a major time-saver that also reduces the chance of missing relevant precedents.

Moravec’s Paradox (and “Moravec’s Irony”)

Moravec’s Paradox is named after Hans Moravec, a roboticist and AI researcher, who highlighted that tasks humans consider “difficult,” such as complex calculations, deep strategic thinking, or abstract legal analysis, can actually be easier for AI, while tasks we consider “easy,” like interacting intuitively with the physical world or applying contextual judgment, prove more challenging for machines. In the legal realm, drafting elaborate briefs or analyzing niche areas of law can be handled swiftly by AI, but the nuanced art of negotiation, reading clients’ emotional cues, or applying real-time moral judgment remains far harder to automate.

A related twist, which I call Moravec’s Irony, is the tension between lawyers’ fear of AI-driven job displacement and their simultaneous desire for effortless automation. Many attorneys voice concerns that technology will eventually replace them, yet they also want the push “easy button” solution that instantly handles time-consuming tasks. This contradictory stance reveals how lawyers value their professional autonomy and expertise, while also wishing to offload tedious work to AI. Ultimately, it underscores the reality that, despite massive gains in efficiency, law still benefits from the uniquely human ability to interpret context, deliver empathy, and exercise holistic judgment. Lawyers who embrace technology as a collaborator rather than a threat can harness AI’s strengths without losing the professional skills that remain distinctively human.

Shifting Lawyer Roles and Responsibilities

As routine tasks become automated, the roles and responsibilities of lawyers are shifting in noticeable ways.

  1. Junior Lawyers. Tasks that used to occupy new associates, like cite-checking and initial drafting, may be done by AI. Junior lawyers, however, can benefit by focusing sooner on strategic work. Rather than spending hours reviewing documents for small details, they can concentrate on legal analysis, developing client relationships, and deeper case strategy.

  2. Senior Lawyers. Lawyers with more experience are becoming quality controllers and project managers, overseeing AI output rather than delegating to a large team. They exercise judgment to correct any AI-generated errors, adapt the drafts for specific client needs, and ensure that any final documents meet professional standards.

  3. Paralegals and Support Staff. The immediate effect of AI might reduce the overall headcount needed for tasks like e-discovery or basic document review. However, many professionals in these roles will find their job descriptions evolving: they might become “AI system operators,” helping to validate AI outputs or manage data for AI training.

  4. Impact on Hiring. Some experts predict that law firms will reduce the intake of junior associates or contract attorneys if AI can perform many of the same tasks. However, those who do enter the field will be expected to have “tech literacy” skills.

Callout: Key Term – “Human in the Loop”
In AI-enabled processes, the phrase “human in the loop” describes a workflow where human judgment remains essential. For instance, the AI may draft a brief, but a lawyer must review and finalize it before it goes to the client or the court.

Practice Pointer:
When drafting a document with the assistance of AI, always triple-check citations, quotes, and references. Recent incidents have demonstrated that AI systems can “hallucinate” or fabricate references. Maintaining accuracy remains the lawyer’s ethical responsibility.

With AI integrated into practice, lawyers increasingly need:

  1. Technological Fluency. While you don’t need to be a programmer, you should understand how to operate AI tools responsibly and how to interpret their outputs.

  2. Prompt Engineering. Lawyers must learn how to frame queries or “prompts” effectively so that AI systems provide the most relevant, helpful answers.

  3. Critical Evaluation. AI can quickly produce an answer, but it may be wrong or incomplete. Lawyers must develop refined judgment to spot potential inaccuracies, bias, or missing context.

  4. Soft Skills – Creativity, Empathy, Communication. As AI picks up many mechanical tasks, the attorney’s unique human traits, like creativity in argumentation, empathetic client communication, and moral reasoning, become even more important.

  5. Risk Management. Lawyers need to understand data security, confidentiality obligations, and ways to ensure AI does not compromise privileged information.

Example:
A commercial litigator might ask an AI tool to draft a motion. The AI quickly composes a thorough legal argument, but includes references to two cases that don’t actually exist. The lawyer, trained to be a “critical evaluator,” checks each citation carefully. Detecting the error, they correct the references, and finalize the motion. The final work product is high-quality and delivered at record speed, but this success depends on the lawyer’s diligence in verifying AI output.

Impact on the Business of Law

Beyond daily legal tasks, AI is driving broader changes in how legal services are delivered, priced, and valued.

Changing Law Firm Business Models

1. Hourly Billing Under Pressure.
AI makes many tasks faster, so the traditional “billable hour” model is under scrutiny. Clients push back on paying for time if the task can be done more efficiently by a machine. As a result, many firms anticipate a decline in hourly billing for routine work.

2. Alternative Fee Arrangements (AFAs).
Instead of billing by the hour, some firms offer flat fees, subscription-based services, or performance-based pricing. If a contract review can be completed in minutes by AI, charging a predictable flat fee might be more appealing to clients, and also profitable for the firm, thanks to efficiency gains.

3. Internal Staffing Changes.
Law firms have historically relied on a pyramid structure with many junior associates doing repetitive work. AI could flatten this pyramid. Fewer junior associates may be needed for tasks like discovery or contract review. Meanwhile, mid- and senior-level attorneys may shift to more complex strategic tasks.

4. Productized Legal Services.
Some firms develop in-house AI tools and sell them to clients as subscription products. For example, a law firm might create an automated contract management solution that monitors a client’s vendor agreements and flags any compliance issues.

Practice Pointer:
If you’re exploring law firms for employment or partnership, inquire about their approach to AI. Ask how the firm prices AI-augmented services and what opportunities exist for attorneys to learn and work with new technology.

Cost Savings and Revenue Implications

AI adoption often means substantial cost savings for law firms and clients alike:

For instance, if a task that used to require 10 billable hours can be done in 2 hours with AI, the firm might charge a flat rate reflecting high-quality output rather than the old 10-hour rate. The client pays less overall, but the firm remains profitable because it requires far fewer attorney hours to complete the work.

Case in Point: SmartEsq
A startup called SmartEsq, founded by former BigLaw partners, is using AI to streamline private equity fund formation. Traditionally, creating a $1 billion fund could cost millions in legal fees and consume thousands of lawyer hours. By applying AI to many core workflows, SmartEsq aims to cut that workload by 80% and reduce costs by 75%. This kind of disruption shows how AI can reshape entire segments of legal practice, forcing traditional firms to adapt (ArtificialLawyer, 2025).

Jevons’ Paradox: More Efficiency, More Demand

Jevons’ Paradox suggests that when technology makes a process more efficient (and thus cheaper), the total use of that process can actually go up, not down. In other words, increasing efficiency sometimes stimulates new demand that can exceed any immediate cost savings.

Illustrative Example
Consider the ATM story in banking. When ATMs first appeared, many assumed bank teller jobs would vanish; ATMs could handle routine transactions, thus reducing the need for human staff. However, because ATMs made it cheaper to open and operate branches, banks expanded their branch networks. With more branches came more opportunities for face-to-face customer service, and in turn, teller employment actually increased in many regions.

How It Might Apply to Law

In this light, while many fear AI will reduce legal headcount, Jevons’ Paradox offers a counterpoint: if AI boosts productivity and lowers costs, the net result may be increased overall demand for legal services, potentially leading to more specialized roles, new market segments, and thus more employment within the legal sector.

Evolving Client Expectations

Clients, both corporate and individual, are increasingly tech-savvy and expect their lawyers to use the most efficient methods available. This “first wave of change” driven by client demand includes:

  1. Greater Speed and Responsiveness. Corporations with high-stakes deals want documents reviewed and drafted quickly. AI can deliver near-instant results, placing pressure on firms to maintain faster turnaround times.

  2. Cost Control and Predictability. Clients prefer more predictable billing arrangements like flat fees, especially for standard tasks. AI enables that by reducing the unknown labor component.

  3. Transparency About AI Use. Some clients specifically request that their law firms leverage AI to lower costs. Others may want to know precisely how AI is used and whether it affects confidentiality or data privacy.

  4. Integration with Corporate Systems. In-house legal departments may have AI tools for intake or compliance, and they expect outside counsel to collaborate seamlessly. This creates a push for standardized platforms and formats.

Practice Pointer:
When working with clients, outline how AI will be used on their matters, especially if it will speed up processes or reduce costs. This kind of proactive communication can build trust and demonstrate that you are using cutting-edge technology responsibly.

Alternative Fee Structures and Pricing Models

Because AI transforms the time required for many tasks, law firms are experimenting with new ways to charge for their services:

  1. Flat Fees for routine services like contract drafting or reviews.
  2. Subscription Models where a client pays a monthly or yearly rate for ongoing access to AI-powered legal support.
  3. Success Fees or performance-based pricing, common in litigation, but now also emerging in transactional matters where speed or outcome can be measured.
  4. Unbundled Services in which clients pay only for specific AI-supported tasks, such as an AI-generated research memo, and then decide if they need additional human-driven legal support.

Case Study: Wilson Sonsini’s Fixed-Fee AI-Powered Contract Offering
Wilson Sonsini, a major tech-focused law firm, launched an AI-assisted commercial contract review service through its Neuron platform. They charge a set fee rather than billing by the hour. This approach allows startups and mid-market clients to budget easily while still getting expert lawyer review of AI-generated suggestions (Wilson Sonsini, 2024). As a result, Wilson Sonsini can handle many more contracts, maintain profitability, and make their clients happy.

Recent Statistics and Case Studies

Adoption of Generative AI in Law – Key Statistics

Over the past two years, the legal sector has seen a massive surge in generative AI adoption:

Despite this enthusiasm, some lawyers remain cautious. A significant minority worry about AI’s potential mistakes or bias. Still, the prevailing trend is toward rapid, widespread integration.

Callout: Key Term – “Hallucination”
In AI, a “hallucination” refers to an output that seems plausible but is factually wrong or fabricated. This can include made-up case citations or invented clauses. Always verify AI-driven content!

Case Studies: AI in Practice for Efficiency and Growth

1. Allen & Overy’s “Harvey” Deployment

Allen & Overy integrated a GPT-based AI assistant called “Harvey” into its workflow, making it available to thousands of lawyers across multiple jurisdictions. Harvey assists with tasks such as quick legal research and first-draft document generation. Lawyers still review everything, but the speed and efficiency gains have been significant. The firm provided training on best practices and instituted policies requiring that all AI output be validated, especially in litigation contexts (A&O Shearman, 2024).

In 2023, PwC gave 4,000 of its legal professionals access to an AI platform (also named “Harvey”). This tool is used for contract analysis, due diligence, and regulatory compliance. By automating the repetitive portions of a merger review, PwC’s attorneys can handle more clients simultaneously and focus on risk assessment and negotiation strategies (PwC, 2023).

3. Wilson Sonsini’s Neuron Platform

Wilson Sonsini’s fixed-fee AI service for commercial contracts uses an AI system trained on the firm’s standard clauses and negotiating positions. The AI flags deviations and suggests language. Lawyers finalize the work product, ensuring it meets client needs. This model represents a new revenue stream, charging a predictable price for sophisticated document review that used to be billed hourly (Wilson Sonsini, 2024).

Regulatory and Ethical Considerations Shaping AI Adoption

As exciting as AI’s capabilities are, lawyers must remain vigilant about potential ethical and regulatory pitfalls. Key considerations include:

  1. Duty of Technological Competence.

    • The American Bar Association (ABA) states lawyers must understand the “capabilities and limitations” of AI tools they use (ABA, 2023). This implies ongoing education, not just a one-time tutorial.

  2. Confidentiality and Data Security.

    • Attorneys must ensure client data is protected. Public AI platforms could store or reuse uploaded information. Many firms now use private instances of AI systems to mitigate these risks.

  3. Accuracy and “Hallucinations.”

    • Submitting AI-generated content that includes fabricated citations can violate the duty of candor to the court. Lawyers must verify the output of AI before filing anything in court.

  4. Supervision (“Human in the Loop”).

    • AI is treated akin to a nonlawyer assistant. Lawyers remain responsible for reviewing and signing off on AI outputs, ensuring the final work meets ethical standards.

  5. Unauthorized Practice of Law (UPL).

    • AI itself cannot be the legal service provider. The attorney or law firm must be the responsible entity. Offering AI-only legal advice to clients could cross into UPL territory in many jurisdictions.

  6. Regulatory Environment.

    • Some judges and courts now require disclosure if filings are AI-assisted. Lawyers should stay abreast of emerging rules in their jurisdictions.

Example:
A lawyer in New York used ChatGPT to write a brief but failed to check the cases cited. The AI fabricated non-existent precedents, leading to sanctions when the judge discovered the error. This underscores the non-negotiable requirement that lawyers must independently validate AI outputs.

Larger Economic Impact of Generative AI

Beyond the legal sector, AI is reshaping the global economy. According to an Anthropic report (Anthropic, 2025):

This broader economic context suggests that AI is not yet displacing entire professions but is reshaping many job roles from the inside out: law is no exception. For lawyers, this reinforces the reality that while routine tasks can be automated, the profession’s core strategic and interpersonal elements will likely remain vital for the foreseeable future.

Political Context

In early 2025, AI regulation has become a hot political topic:

For lawyers, these shifting regulatory sands mean that the legal frameworks around AI could change quickly. The best practice is to stay informed about emerging legislation, court decisions, and ethical guidelines. Law firms with international operations, in particular, must navigate different regulatory climates.

Tomorrow’s Lawyers: What Do They Do?

Richard Susskind, a renowned authority on the future of legal services, delineates 15 emerging roles for legal professionals in his seminal works, Tomorrow's Lawyers. These roles reflect the evolving landscape of the legal industry, driven by technological advancements and changing client demands.

1. Legal Design Thinker
Focuses on applying design principles to legal services, aiming to make them more user-centric and accessible. This role involves reimagining legal processes and documents to enhance clarity and client engagement.

2. Legal Knowledge Engineer
Specializes in structuring and modeling complex legal knowledge for use in automated systems. These professionals develop frameworks that allow legal information to be processed by AI and other technologies.

3. Legal No-Coder
Utilizes no-code or low-code platforms to create legal applications and automate processes without traditional programming. This role enables the rapid deployment of tech solutions within legal contexts.

4. Legal Technologist
Integrates technology into legal practice to improve efficiency and service delivery. Legal technologists assess, implement, and manage tools such as AI, blockchain, and legal research databases.

5. Legal Hybrid
Combines legal expertise with another professional domain, such as technology, project management, or data science. This interdisciplinary approach allows for more comprehensive solutions to complex legal issues.

6. Legal Process Analyst
Examines and optimizes legal workflows and processes. By analyzing existing procedures, they identify inefficiencies and implement improvements to enhance productivity.

7. Legal Project Manager
Applies project management methodologies to legal cases and transactions. This role ensures that legal projects are completed on time, within scope, and on budget.

8. Legal Data Scientist
Analyzes large datasets to extract insights relevant to legal matters. Legal data scientists use statistical tools and machine learning to inform case strategies, predict outcomes, and identify trends.

9. Legal Data Visualizer
Transforms complex legal data into visual formats like charts and infographics. This role aids in the comprehension of intricate information by clients and legal teams.

10. Research and Development (R&D) Worker
Focuses on innovating and developing new legal products, services, and technologies. R&D workers experiment with emerging tech to create cutting-edge legal solutions.

11. Digital Security Guard
Ensures the protection of sensitive legal information against cyber threats. This role involves implementing security protocols and monitoring systems to safeguard data.

12. Online Dispute Resolution (ODR) Practitioner
Specializes in resolving disputes through online platforms. ODR practitioners facilitate negotiations and mediations in virtual environments, offering alternative avenues to traditional court proceedings.

13. Moderator
Oversees and manages online legal forums, communities, or platforms. Moderators ensure discussions remain productive, informative, and adhere to established guidelines.

14. Legal Management Consultant
Advises law firms and legal departments on business strategies, operational improvements, and technology adoption. This role focuses on enhancing the overall performance and competitiveness of legal service providers.

15. Legal Risk Manager
Identifies, assesses, and mitigates potential legal risks within organizations. Legal risk managers develop strategies to minimize exposure and ensure compliance with relevant laws and regulations.

These roles signify a paradigm shift in the legal profession, emphasizing the integration of technology, interdisciplinary collaboration, and innovative service delivery models. For law students and emerging legal professionals, Susskind advocates for the cultivation of diverse skill sets, technological proficiency, and a proactive approach to embracing these new opportunities.

Advice for Law Students

Drawing from Susskind and other experts, here are some strategies for students preparing to enter an AI-transformed legal profession:

  1. Embrace Technology.

    • Familiarize yourself with popular AI research tools, e-discovery platforms, and document automation software. Basic tech fluency is increasingly mandatory.

  2. Build Interdisciplinary Skills.

    • Consider electives or certificates in business, computer science, or data analytics. Understanding these fields will expand your career options.

  3. Pursue Lifelong Learning.

    • Technology evolves quickly, so keep up with new releases, attend webinars, and join legal tech communities.

  4. Explore Alternative Paths.

    • Be open to roles like legal tech developer, legal data scientist, or process analyst. These specialized roles can be highly sought after by forward-thinking firms.

  5. Assess Future-Readiness.

    • When interviewing with potential employers, ask about their AI strategy. If a firm seems averse to technology, consider whether that aligns with your professional goals.

  6. Adopt an Entrepreneurial Mindset.

    • Think creatively about how to deliver legal services more efficiently. You might launch a startup or become an “intrapreneur” within a larger firm, driving innovative solutions.

  7. Focus on Client-Centric Services.

    • Clients care most about outcomes and value. Use AI tools to address client pain points—like cost, accessibility, or speed.

  8. Engage with Professional Communities.

    • Join legal tech forums, attend conferences, and collaborate with classmates. Networking can reveal emerging job opportunities and keep you on the cutting edge of the profession.

Callout: Key Term – “T-Shape Lawyers”
A “T-shaped professional” has deep knowledge in one area (legal expertise) plus broad skills in complementary areas (technology, design, communication). Law schools increasingly encourage students to become T-shaped lawyers.

Chapter Recap

The chapters leading up to this point laid the groundwork for understanding how AI technologies work and how they are becoming integral to legal practice. In this chapter, we investigated the specific ways generative AI is transforming the nature of legal work, from daily tasks to the structures and systems that underpin the broader industry. Below are the key takeaways:

Together, these insights highlight how generative AI is shaping a more efficient, client-centric, and technologically integrated future for legal services.

Final Thoughts

Generative AI is here, and it’s rapidly changing how attorneys deliver services and grow their practices. By recognizing which tasks are suited to AI and which still require the human touch, today’s lawyers and law students can position themselves as leaders in this new era. Technological competence is quickly becoming as fundamental to legal practice as knowing how to write a persuasive brief.

At the same time, ethical safeguards and regulatory frameworks must continue to evolve to ensure the responsible use of AI. Lawyers remain gatekeepers of quality and fairness, responsible for verifying AI’s outputs and protecting client interests.

Overall, generative AI should be seen as an opportunity: it can make legal services more efficient, accessible, and cost-effective, and it can free lawyers to do higher-level work that AI simply cannot handle. Embracing this mindset, while vigilantly managing the risks, is the hallmark of a modern, forward-thinking legal professional.

What’s Next?

In Chapter 8, we’ll bring to light some of the most pressing issues that arise when legal advice and technology intersect. We’ll consider concrete scenarios—for instance, whether uploading client documents into a public AI tool could violate privilege, or how law firms should respond if a model inadvertently “fabricates” citations. We’ll also discuss the unauthorized practice of law as it relates to AI systems providing direct legal advice, examine new guidelines aimed at preventing discriminatory outcomes, and explore strategies to ensure robust data protection.

References