Eversheds Sutherland

Eversheds Sutherland faced a structural tension common to large law firms: M&A diligence workloads are dominated by high-volume document review that consumes significant associate hours while clients increasingly expect faster deal execution and more flexible pricing. AI had been introduced only through isolated pilot programs — useful for proof-of-concept but insufficient to change delivery economics at scale. The billable-hour model created internal resistance to efficiency gains, and the firm risked losing competitive ground as clients began treating AI-enabled diligence speed and pricing transparency as baseline expectations rather than differentiators.

Eversheds Sutherland formally transitioned AI from pilot programs into firmwide infrastructure embedded across core M&A diligence workflows. Using large language models and generative AI capabilities, the firm deployed risk prioritization, document triage, and value-focused diligence analytics at practice scale rather than in isolated matters. The shift was deliberate: rather than layering AI tools on top of existing workflows, the firm restructured diligence delivery around AI-assisted outputs — moving the unit of work from document volume reviewed to deal risk and value identified. This required investment in governance, process design, and attorney adoption across the M&A practice, not just tool procurement.

Diligence workflows now operate at firmwide scale, enabling Eversheds Sutherland to compete directly against AI-native entrants like Stella Legal and platform-led approaches like Emma Legal's $1B+ deal volume. Key outcomes include:

• Improved time-to-close by compressing diligence cycles through automated document triage and risk prioritization
• Pricing flexibility for clients, shifting away from volume-based billing toward outcome-oriented models
• Repositioned client conversations — diligence is now framed around risk and value identification rather than pages reviewed
• Firmwide consistency in AI-assisted diligence quality, replacing ad-hoc pilot variability with standardized infrastructure

• Moving AI from pilot to production firmwide requires deliberate process redesign and governance investment — tool adoption alone is insufficient.
• Reframing diligence from document volume to risk-and-value identification changes both internal economics and the client value proposition simultaneously.
• Client expectation management is now a primary operational risk: AI-enabled speed must not be perceived as reducing the bespoke analytical judgment clients pay for.
• Firms that delay firmwide embedding cede ground to AI-native advisory models designed around AI economics from inception.
• Post-close obligation tracking and integration monitoring represent the next frontier for firms that have already embedded AI in diligence workflows.