What McDermott Does

McDermott Will & Emery is a prominent international law firm with over 1,000 lawyers. Founded in 1934, it provides comprehensive legal services across key practice areas including corporate law, litigation, tax, intellectual property, and healthcare. Operating at a large enterprise scale (1,001-5,000 employees), the firm advises sophisticated clients—from Fortune 500 companies to private equity funds—on complex transactions, regulatory matters, and disputes. Its business model relies on deep expertise, client relationships, and the efficient management of vast amounts of structured and unstructured data contained in contracts, case files, and research materials.

Why AI Matters at This Scale

For a firm of McDermott's size and stature, AI is not a futuristic concept but a pressing competitive necessity. The legal industry is undergoing a profound shift where clients increasingly demand greater value, predictability, and speed. Manual processes for document review, due diligence, and legal research are no longer scalable or cost-effective for servicing a global clientele. AI presents a lever to enhance the productivity of highly compensated legal professionals, improve service delivery, and unlock insights from the firm's immense repository of past work product. Firms that successfully integrate AI will gain significant advantages in winning mandates, managing matter profitability, and attracting top talent eager to work with cutting-edge tools.

Concrete AI Opportunities with ROI Framing

1. AI-Powered Contract & Due Diligence Analysis: Implementing Natural Language Processing (NLP) platforms can automate the extraction and comparison of key clauses across thousands of documents during mergers or financings. The ROI is direct: reducing manual review time by 50-70% translates to lower client costs, faster deal closure, and the ability to reallocate associate hours to higher-value strategic advice, directly boosting realization rates and client satisfaction.

2. Generative AI for Legal Research & Drafting: Deploying secure, firm-tuned large language models can assist lawyers in drafting memos, summarizing case law, and generating first-pass contract language. The ROI manifests in accelerated research cycles, reduced reliance on expensive external databases for preliminary queries, and enhanced consistency in work product, allowing junior lawyers to develop skills more rapidly.

3. Intelligent E-Discovery and Knowledge Management: Using machine learning for predictive coding in litigation discovery cuts the cost of outsourced document review by millions on large cases. Internally, AI can tag and connect insights from completed matters to a searchable knowledge base. The ROI includes hard cost savings on review vendors, winning litigation budgets, and capturing institutional expertise to avoid reinventing the wheel, improving win rates and efficiency.

Deployment Risks Specific to This Size Band

Deploying AI in a large, partnership-structured law firm introduces unique challenges. Change Management is significant, as convincing hundreds of independent partner-owners to adopt new workflows requires demonstrating clear, individual economic benefit. Data Silos & Integration are major hurdles, as matter data is often locked in separate systems; a unified data strategy is a prerequisite. Ethical & Liability Exposure is paramount; any AI tool must be meticulously supervised to avoid breaches of confidentiality, conflicts of interest, or the unauthorized practice of law. Vendor Selection & Cost at scale requires substantial investment, and choosing the wrong platform or being locked into a proprietary system could lead to significant sunk costs without the promised transformation.