AI Agent Operational Lift for Burns & Levinson Llp in Boston, Massachusetts

Why AI matters at this scale

Burns & Levinson LLP, a Boston-based full-service law firm with 201-500 employees, operates in a sector where the unit of value—the billable hour—is under existential pressure. Mid-sized firms face a double squeeze: from global mega-firms investing heavily in AI and from alternative legal service providers (ALSPs) offering tech-driven, fixed-fee solutions. For a firm of this size, AI is not a luxury; it is a strategic imperative to protect margins, attract talent, and meet client demands for efficiency and predictability.

With practices spanning corporate, intellectual property, litigation, and real estate, the firm generates massive volumes of unstructured text—contracts, emails, briefs, and discovery documents. This is precisely the type of data where large language models (LLMs) excel. By adopting AI, Burns & Levinson can reduce non-billable hours, accelerate matter turnaround, and unlock new revenue models based on value rather than time.

1. Contract Intelligence as a Revenue Engine

The highest-impact AI opportunity lies in contract review and drafting. Corporate and real estate practices can deploy generative AI to review incoming contracts against firm playbooks, flag deviations, and suggest preferred clauses in minutes—work that currently takes associates hours. This allows the firm to offer fixed-fee contract packages, a growing client demand. ROI is direct: reallocate associate time to higher-value work, reduce write-offs, and win new business by offering faster, more predictable service. A typical mid-sized firm can save 2,000-4,000 attorney hours annually in contract review alone.

2. E-Discovery and Litigation Support Transformation

Litigation is a document-intensive practice. Technology-assisted review (TAR) using machine learning has been court-approved for over a decade, but generative AI takes it further. AI can summarize key documents, identify privileged material, and even draft initial discovery responses. For Burns & Levinson, this means handling larger cases with the same team, reducing client costs, and improving margins on fixed-fee or capped engagements. The risk of missing a critical document also decreases, lowering malpractice exposure.

3. Institutional Knowledge Unlocked

A 60-year-old firm possesses a treasure trove of briefs, memos, and transactional documents. An internal AI-powered knowledge management system allows lawyers to query past work product in natural language—"Find me a successful summary judgment motion in a software patent case from the last five years." This dramatically reduces research time and ensures the firm leverages its collective experience. It also smooths onboarding for new associates and captures knowledge from retiring partners.

Deployment Risks Specific to This Size Band

Mid-sized firms face unique AI adoption risks. Unlike Big Law, they lack dedicated innovation teams and large IT budgets, making vendor selection critical. The primary risk is data security: using public AI tools with client data can breach confidentiality and attorney-client privilege. Burns & Levinson must deploy private, walled-off instances of LLMs within its Azure or AWS environment. A second risk is ethical compliance; lawyers must supervise AI outputs and cannot blindly rely on them. Establishing an AI ethics committee and clear usage policies is essential. Finally, cultural resistance from partners who built careers on the billable hour model can stall adoption. Change management, starting with a pilot in a tech-savvy practice group and showcasing time-saved metrics, is the proven path to buy-in.