Law Firms’ AI Strategies Need Development and Data Analytics

The Bottom Line

• Law firms that use AI to take care of routine work and free lawyers to focus on higher-value thinking will outperform their competitors.
  
• Clients want to see how a firm’s use of AI makes their work faster, more accurate, and more useful. Firms must show how their use of GenAI saves time and improves outputs.
  
• Testing AI tools in controlled real-world settings will help firm leaders figure out where these technologies add the most value and how to turn that into better pricing and stronger client relationships.

Whether generative artificial intelligence accelerates growth or erodes profit depends on the degree to which law firm leaders integrate two disciplines that have too often operated in silos: business development and legal spend data analytics.

An AI strategy that omits either discipline is incomplete, but a strategy that integrates both can spur a competitive advantage.

Client-Facing Value Proposition

Clients want outcomes—faster closings, clearer risk assessments, and data-driven predictions—not a running meter of billable hours. Deploying AI to handle low-value tasks while packaging and branding these capabilities with carefully crafted business development messages demonstrates firms’ wide-ranging service offerings.

By embedding GenAI into deliverables such as automated diligence reviews, predictive litigation analytics, smart-contract lifecycle management, and real-time regulatory trackers, firms can reframe engagements around solutions rather than labor input, which will make them more competitive in the long term.

Reclaiming Unwanted Work

Corporate legal departments have shown a strong trend toward insourcing work rather than outsourcing to law firms, with 66% of companies aiming to bring legal work in-house, according to 2024 surveys. Dissatisfaction with rising external counsel costs has contributed to this trend.

But nearly 45% of chief legal officers plan to increase outside counsel spending due to rising complexity and the volume of litigation and regulatory matters overwhelming in-house resources. When clients are reluctant to handle routine legal work internally due to resource or capacity constraints, the most strategic outside firms can use AI to efficiently reclaim this work.

Law firms can provide operational relief and predictability to overburdened in-house teams on routine matters while gaining access to broader streams of information that can be leveraged into better insights and stronger outcomes in complex matters. This enables clients to receive operational relief while firms expand both their role and their strategic value.

Defend, Extend, Create

Any credible growth plan begins with a precise picture of current revenue dynamics. Granular analysis—by practice area, phase and task, matter type, client segment, and geography—reveals which profit pools are most vulnerable to automation and which remain insulated or potentially more profitable.

Defend the high-margin, bespoke tasks and matters that rely on deep judgment and established relationships—bet-the-company litigation, complex cross-border M&A, and highly specialized regulatory counseling. These mandates remain largely immune to full automation but benefit from AI-enhanced speed.

Extend mid-margin tasks and matters—contract reviews, diligence, and routine compliance filings—in which GenAI can expand volume and reduce cost while maintaining relational stickiness. This represents the greatest opportunity for reclaiming work that clients have absorbed internally.

Create new offerings born of the AI era: model governance advisory, algorithmic bias audits, data privacy by design, and emerging regulatory compliance programs.

Cross-Practice Integration

Law firms may aspire to be more collaborative, but traditional models and compensation structures haven’t meaningfully incentivized it. AI now makes collaboration imperative. Efficiency alone won’t sustain revenue; the path forward requires expanding scope and integrating services around the client.

Cross-practice “go-to-market squads” must assemble integrated offerings—privacy, intellectual property, employment, and governance—around the client’s AI challenges and opportunities. Firms that align these practices into coordinated offerings will deliver holistic solutions and superior results.

Many firms win work through fragmented relationships throughout the client organization or through procurement channels that reduce legal services to commodities. When firms wrap varied legal work into more holistic, cost-effective, and outcome-focused models, the buyer relationship fundamentally shifts.

Instead of dealing with procurement managers, firm rainmakers increasingly engage with the C-suite and senior business executives who care about integrated risk management and long-term outcomes. This requires a different business development approach focused on demonstrating value across a client’s entire business rather than individual matters.

Storytelling and Leadership

Business development messages must be anchored in the language of value rather than technology, but they must also lead with education—helping clients understand what new models look like, how they work, and why they create better outcomes.

Clients expect firms to use the most cost-effective means of delivering legal services, whether its human input alone or tech-enabled legal advice without concern for the underlying code, algorithms, or language models. But clients instinctively know that tech-enabled legal services and results can be delivered better, faster, and more accurately and predictably.

Accordingly, thought leadership campaigns and content must translate the firm’s AI fluency into concrete business outcomes to win engagements against less tech-savvy competitors.

Economic Modeling

Most people who follow the legal industry believe that greater efficiency means fewer billable hours and reduced revenue for legal professionals. Lawyers billing by the hour stand to lose out unless the pricing for legal services evolves.

Part of that evolution is pure economics, meaning lawyers need to figure out creative ways to price their services in a way that doesn’t solely depend on input hours (i.e, time spent working on a client’s matter). An equally important part of the evolution relates to business development and client relations messages, which must pivot from effort to value.

From a financial perspective, the following economic models aren’t just supported by GenAI, but are fueled by it:

• Fixed or capped fees for standardized, AI-enabled processes give clients price certainty while allowing firms to retain a portion of the productivity surplus.
  
• Subscription or retainer models convert episodic matters into recurring revenue streams—continuous contract-risk monitoring, quarterly algorithmic bias assessments, or evergreen regulatory updates.
  
• Outcome-based or success fees align incentives for disputes, enforcement actions, or complex transactions whose AI-accelerated timelines allow firms to shoulder more risk for greater upside.

Packaging New Offerings

AI-enabled services need clear scoping and guardrails. This allows business development teams to transform abstract AI capabilities into tangible value propositions that address specific client pain points while establishing new revenue streams. For instance:

A mergers and acquisitions certainty suite would have diligence, risk modeling, and closing acceleration packaged as a comprehensive offering.

An AI bias audit defense would include monitoring, documentation, and dispute readiness delivered as an ongoing service.

A regulatory foresight service would have horizon scanning, tailored playbooks, and quarterly briefings structured as a subscription model.

When capitalizing on AI-driven outputs and business intelligence, proprietary dashboards can translate complex legal analyses into executive-friendly recommendations that elevate a law firm from commodity service provider to strategic partner. Early adopters that showcase case studies quantifying client cost savings or risk reduction are powerful marketing assets and forge reputations difficult for competitors to match.

Robust cost modeling—encompassing attorney time, software licensing, prompt engineering resources, cybersecurity, and data storage— will ensure that fee structures protect and expand profit margins. Attorney compensation systems must also evolve. Rewarding only billed time discourages innovation, but tying bonuses to profit margin expansion, revenue, and client satisfaction accelerates adoption.

Strategy to Reality

Both innovation and integration happen where lawyers work: the practice group. Each team should inventory repeatable tasks, map them to client pain points, and test alternative pricing through pilot projects.

For example, a corporate group might deploy an AI-driven M&A diligence engine on mid-market deals under a fixed-fee model, measure turnaround time and error reduction, and iterate pricing based on historic versus current billing data. Meanwhile, the litigation practice may pilot predictive analytics to triage matters and offer value-based fees on cases flagged as high-probability wins.

The resulting proof points become marketing narratives for business development teams and benchmarks for firmwide rollouts.

Because this represents a fundamental shift from the legacy model, strategic piloting is essential. Partnering with a smaller, motivated client interested in minimizing legal spend and optimizing outcomes is one effective approach.

With stakeholder buy-in, the firm can shape the model for that client—defining scope, fee structure, and success metrics—then use the proof points to create compelling stories for broader market deployment. These pilot relationships provide the case studies and quantifiable outcomes necessary to convince larger, more risk-averse clients to embrace the new approach.

Competitive Advantage

AI introduces complex questions around data security, privilege, intellectual property ownership, and algorithmic bias. Rather than viewing these as obstacles, leading firms convert compliance rigor into marketable features.

Demonstrable controls—segregated data environments, documented quality-assurance protocols, and AI-specific ethical guidelines—become differentiators that reassure risk-averse clients and support premium pricing.

These same policies serve as the foundation for advisory offerings to clients establishing their own AI governance frameworks, turning internal discipline into external revenue. As AI narrows the gap in technical output, trust, credibility, and human judgment only grow in importance. The differentiator isn’t who has the most advanced tool, but who clients trust to interpret results, make judgment calls, and advise strategically.

Effective Execution

Technology without cultural adoption is shelfware. Firm-wide AI literacy programs—covering prompt engineering, critical evaluation of machine outputs, and ethical constraints—equip lawyers to wield new tools with confidence. Emerging roles such as AI auditors and technology-liaison attorneys ensure quality control and bridge domain expertise with technical depth.

Measurement systems must evolve alongside culture. Key performance indicators shift from hours billed to metrics such as matter-level margin improvement, product revenue growth, client NPS for AI-enabled services, and competitive win rates on technology-intensive pitches. Metrics must align with compensation structures that reward innovation, collaboration, and client value creation.

Looking Ahead

The legal industry will not be spared the commoditizing force of AI—but commoditization isn’t destiny. Firms that act decisively can defend their highest-value work, extend mid-margin services through efficiency and scale, and create new advisory domains unimaginable just a few years ago. The catalyst is an integrated approach where business development pinpoints and wins the right opportunities while economic modeling captures the full value of AI-driven efficiency.

This transformation requires firms to master both disciplines simultaneously: identifying and reclaiming work clients prefer to outsource, building cross-practice integration that serves the whole client rather than fragmented matters, and developing educational narratives that guide clients through fundamental changes in legal service delivery.

Those that master both disciplines will transform the GenAI threat of billable-hour compression into an engine of growth; those that miss the connection will struggle to escape a race to the bottom they didn’t foresee but unwittingly fueled.

The window for strategic action remains open, but it will not remain so indefinitely. The time for decisive action is now.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.

Author Information

Lana Manganiello is the founder of Practice Growth Partner.

Joe Tiano is the founder and president of Legal Decoder Inc. and an adjunct professor of law at Arizona State University Law School.

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