Womble Bond Dickinson Builds In-House AI Unit to Industrialize Routine Legal Work

Womble Bond Dickinson has stood up a dedicated internal AI development unit, Womble AI Labs, positioning the 1,300-lawyer transatlantic firm to build proprietary automation tools rather than rely solely on third-party legal tech vendors. The lab operates inside the firm's IT function and reports to Chief AI and Technology Officer Chad Ergun — a structural choice that signals intent to treat AI development as infrastructure, not experimentation.

The launch arrives as Am Law 200 firms face intensifying pressure to demonstrate measurable efficiency gains to cost-conscious clients. Mid-market and large firms have broadly adopted off-the-shelf tools from vendors such as Harvey, CoCounsel, and Thomson Reuters, but a growing cohort is now concluding that bespoke internal capability offers sharper competitive differentiation. Womble's move fits a pattern seen at firms including Allen & Overy and Mishcon de Reya, which have built or invested in proprietary AI platforms rather than ceding that layer of the stack to outside providers. For a firm celebrating its 150th anniversary, the timing is also a deliberate rebranding signal — legacy tenure paired with technology ambition.

According to the firm, AI Labs will concentrate on structured, repeatable legal and administrative workflows: regulatory monitoring, document generation and packaging, contract review and lifecycle management, and early-stage case assessments. Those categories are not accidental. They represent the highest-volume, lowest-discretion tasks in most law firm operations — the work most amenable to automation and the work clients most frequently scrutinize for billing efficiency. The unit operates alongside two existing programs: an AI Champions Initiative, a voluntary internal adoption network that has attracted more than 200 legal and business professionals across every department since its early 2026 launch, and an AI Training Program developed in partnership with Berkeley Law Executive Education. Chair and CEO Merrick Benn framed the initiative publicly around client value and operational evolution, language that tracks closely with how general counsel currently evaluate outside counsel relationships.

Industry Implications

Firms that build internal AI development capacity gain a structural advantage: the ability to iterate on proprietary workflows without waiting on vendor release cycles or sharing competitive intelligence with outside platforms. That calculus is pushing more firms toward hybrid strategies — licensing foundation models while building application layers in-house. For legal tech vendors selling horizontally to law firms, the emergence of internal labs at clients of Womble's scale is a meaningful demand-side shift; firms building their own document generation and contract review tooling are less likely to renew or expand licenses for point solutions in those same categories. Enterprise legal operations teams watching this development should note that firms with dedicated AI units will increasingly be able to offer differentiated, workflow-specific products — not just faster versions of traditional legal services.

DreamLegal Perspective

Legal technology vendors selling into Am Law 200 and UK Top 50 firms should treat internal lab announcements like this one as an early indicator of procurement consolidation risk. When a firm hires a Chief AI and Technology Officer and formalizes a build function around high-volume structured tasks — document generation, contract lifecycle, regulatory monitoring — it is signaling category-level substitution, not supplementation. Vendors in those categories should accelerate integration plays, deepen data partnerships, and reframe their value proposition around what internal teams cannot efficiently replicate: breadth of training data, cross-firm benchmarking, and continuous model improvement. Legal operations professionals evaluating outside counsel should simultaneously view labs like Womble's as leverage for fee negotiation on commodity-adjacent matters, where automation should structurally reduce hours.