Why Legal Should Be at the Center of Your Organization’s AI Strategy

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Every department feels mounting pressure to adopt AI and Legal is no exception. Pilots launch quickly, early demos look promising, and teams often get swept up in the appeal of the shiny new tool. But the results rarely match the hype. A 2025 Boston Consulting Group study of more than 280 finance executives found the median ROI from AI initiatives was just 10 percent, far short of the 20 percent or more many companies were aiming for. 

Often, the challenge isn’t the technology itself. It’s the assumption that AI is a simple “tool rollout” rather than a deep organizational rewiring. Implementing AI changes far more than software — it impacts workflows, accountability, incentives, data flows, decision rights, and risk exposure. It also requires legal professionals to adopt a new way of working, which naturally creates hesitation. In many ways, this mirrors the early days of search engines in the early 2000s: it took time and repetition for users to become comfortable searching, validating, and trusting the vast array of articles, opinions, and outright fiction that the search engines returned. But as search technology improved, and the quality and relevance of results increased, users grew more comfortable with this new way of searching and saw meaningful productivity gains as information became more accessible and reliable.  

The misunderstanding that AI can be treated as just another tool rollout rather than a shift in how work gets done shows up most clearly in how companies involve Legal. In many organizations, Legal is brought into discussions late in the work cycle: to review and redline a contract, tighten a clause, approve a policy, or flag a risk. But that sequencing is backward and often misses the full potential of AI use for Legal.  

AI success depends on building systems that are explainable, transparent, governed, and compliant with the full stack of constraints an organization operates under, including data-privacy regulations, industry standards, contractual commitments, and internal governance policies. These are all domains Legal already lives and breathes. 

The past year marked a turning point, as in-house legal departments moved from planning to real-world implementation. In the US, GenAI adoption more than doubled, rising from 23 percent of professionals in 2024 to 52 percent in 2025. This surge was driven largely by organizations removing restrictions on AI, as the share of professionals pointing to company policies banning its use fell sharply from 29 percent to 9 percent. Despite this progress, the most significant barrier to broader adoption continues to be concerns about the reliability and trustworthiness of GenAI.

That’s no coincidence. The very qualities that make AI (generally) reliable and scalable, including clear reasoning, verifiable standards, documented decision trails, cross-functional collaboration, disciplined risk management, all are Legal’s native language. Of course, any professional advice still requires a human in the loop and citations should be verified to catch potential hallucinations in legal documents. 

That’s why Legal’s role doesn’t just expand in the AI era; it becomes foundational to AI’s long-term success. The organizations that scale AI effectively don’t treat Legal as a gatekeeper. They treat it as the function that helps define the decision architecture within which AI must operate. Organizations such as ACC provide essential legal guidance and resources, and privacy-focused organizations like IAPP offer certifications in AI Governance. These are helpful to legal, privacy, and compliance professionals as they build this decision architecture. 

This article explains why Legal (often in partnership with its Privacy and Security counterparts) is uniquely positioned to lead an organization’s AI strategy — and offers concrete playbooks for in-house counsel to operationalize that leadership. 

The structure AI needs already exists in legal 

Legal work follows patterns that are unusually consistent across matters, legal professionals, and industries. A contract review in healthcare will typically look different from one in SaaS, but the underlying sequence is similar:  

Going further, AI can be used to explain a contract in “business language” tailored to each stakeholder. For example, AI is particularly effective at tasks such as: 

This same patterned logic often appears in incident response, employment matters, procurement, discovery, regulatory reporting, investigations, and policy interpretation. In fact, whether they realize it or not, lawyers have long been trained to generate useful prompts through the IRAC formula. Translated into AI prompting:  

These workflows are designed to be stable and repeatable because inconsistency creates exposure, undermines credibility, and invites challenge. 

That repeatability makes legal work especially conducive to AI. When AI observes hundreds of similar decisions handled in consistent ways, such as patterns of issue-spotting, interpreting, escalating, and documenting, it learns what "good" looks like. If Legal reliably flags uncapped indemnities, identifies missing DPAs, or treats certain security addenda terms as non-negotiable, those behaviors form a blueprint AI can follow. AI can also be used to transform your existing contracts into a structured playbook or preferred set of terms, and benchmark your contract templates against industry standards, competitor norms, or internal preferred criteria. 

Consider a legal team that has negotiated 300 sales contracts over three years. Each negotiation follows the same general framework: liability caps typically should not exceed contract value, data processing addenda must include specific provisions such as standard contractual clauses, and termination and refund clauses should be fair and unambiguous. When AI reviews the 301st contract, it doesn't need to guess what matters. The precedent is clear and documented.  

Other departments rarely offer this level of structure. Marketing campaigns hinge on creative judgment. Product planning sessions depend on shifting priorities. Design critiques rely on aesthetic preference. While other functions follow patterns, those patterns shift far more frequently with context and changing objectives. Legal's predictability gives AI a more stable, disciplined scaffolding than perhaps any other function. This is particularly true in certain “structured document” use cases: 

Legal decisions are verifiable 

Most corporate decisions can’t be evaluated against a clear standard. There's no statute that determines whether a product roadmap was "good" or whether a design review was "effective." 

Legal decisions are different. They trace back to source authority: contract language, statutes, regulatory rules, case law, or internal policy. An attorney's decision to classify data as personal, mark a clause as non-negotiable, or escalate a potential data incident or breach can often be evaluated against specific, objective criteria. 

That verifiability makes Legal an ideal starting point for AI adoption. When an AI system proposes a redline, a reviewer can check it against fallback positions. When it flags an incident, the reviewer can compare it to contractual thresholds or reporting timelines. Legal often provides a rare "right or wrong" framework in an enterprise environment where most decisions are subjective or preference-driven. 

And because legal outcomes often face external validation (Would this clause survive litigation? Does this language meet regulatory expectations?), AI outputs can be assessed not only internally but against real-world constraints. Few other functions offer that level of clarity. 

Legal is a data-rich environment for AI 

Beyond contract review and traditional legal use cases, AI can already analyze large volumes of inputs (e.g., emails, chat messages, contract database fields, and related metadata) to produce quantifiable metrics for legal operations and spend reporting. For transaction and contracting functions, providing reliable data on the following metrics demonstrates operational rigor: 

Executive view (monthly): 

Operations view (weekly): 

The result is a domain where performance can be measured, audited, corrected, and improved with confidence, using AI both to perform core tasks, and to run the data analytics. Legal becomes a domain where organizations can clearly assess whether AI is performing as intended and build the systems needed to scale it across the enterprise. 

The economic case: Legal Delivers Some of the Highest ROI 

Legal work sits at a rare intersection in the enterprise: it is highly structured, highly repeatable, and highly expensive. Even routine matters often demand specialized judgment, detailed review, and rigorous documentation. A single commercial contract can take hours if reviewed “the old way” (i.e., manually). A regulatory inquiry can absorb weeks or even longer. Discovery can run into millions. Even a sudden spike in NDAs and DPAs at quarter end can drain capacity and stretch contracting teams. None of this is inefficiency. Rather, it’s the rigor the work requires. 

That combination of repeatable workflows with high cost and high stakes makes Legal one of the most effective leverage points for AI. 

AI excels where processes follow recognizable patterns. Legal generates those patterns every day. As a result, even modest automation unlocks meaningful returns. Automating portions of routine contract review frees attorney and paralegal hours immediately. Any questionnaire-type task is ripe for AI automation: privacy and security questionnaires, vendor onboarding checklists, requests for information (RFIs) and requests for proposals (RFPs), compliance and licensing responses, and many others. Long questionnaires can be completed in minutes using AI, with clear citations to source documents and supporting text, which greatly speeds up the response process. Of course, a human should always review such AI-generated responses, but using AI to do the heavy lifting can greatly improve the efficiency of the human reviewer, and help reduce the cognitive load and fatigue involved in filling out the responses. 

AI-assisted triage reduces the volume sent to outside counsel and can often classify contracts by complexity and business risk. First-pass contract analysis shortens turnaround times without sacrificing accuracy. The net effect: legal professionals spend more time on the judgment-heavy work of negotiation, strategy, and interpretation and less time on repetitive manual tasks. 

The impact on outside counsel spend is even more pronounced. When firms bill for every redline, memo, and review cycle, AI-generated first drafts and structured clause analysis can materially reduce costs, often offsetting the cost of the AI tools themselves. Discovery datasets that once required hundreds or even thousands of attorney hours can now be triaged in a fraction of that time, both reducing costs and accelerating turnaround. Regulatory deadlines that previously triggered emergency outside-counsel hours can be met with automated analysis and, in many cases, draft response generation. 

AI also excels at tracking changes to online terms (from a webpage) and legislative drafts or enacted legislation. AI agents can be configured to repeatedly check any connected or public data source, flag changes for human review, and quantify the impact of those updates. Traditionally, this work required manual comparison on a recurring schedule — a task that is both repetitive and taxing. The math speaks for itself: reducing a routine contract review from 90 minutes to 20 minutes saves more than 1,000 hours a year for a team handling 100 contracts per month. At a blended BigLaw outside-counsel rate of often $1,000–$1,500 per hour, that single use case delivers $1.5–$2 million in savings.   

These aren’t hypothetical outcomes. Recent real-world deployments show the scale of results legal teams can achieve:  

All of these gains, however, depend on the quality of the information the AI is retrieving. Legal workflows require the system to surface the correct clauses, policies, negotiation history, and governing documents with precision. If the retrieval layer pulls incomplete, outdated, or irrelevant materials, the downstream analysis will reflect those gaps — the familiar “garbage in, garbage out” problem. High-quality, context-relevant search ensures the AI operates on the same authoritative sources attorneys rely on, which is essential for producing outputs that are accurate, defensible, and aligned with established legal standards. 

Conclusion: Why legal is poised to lead enterprise AI  

AI is reshaping how work gets done, but its impact depends entirely on how well organizations design the systems around it. Legal is uniquely positioned to lead that effort. Its work is structured, repeatable, auditable, and grounded in standards — exactly the environment where AI performs reliably and where its outputs can be measured and improved with confidence. Legal already operates at the intersection of risk, governance, precedent, and decision architecture, making it an ideal starting point for building AI systems that are transparent, explainable, and compliant. 

When Legal moves from being a late-stage reviewer to a strategic partner in AI adoption, organizations benefit twice: Legal teams gain powerful tools that reduce low-value work and improve cycle times, and the broader enterprise gains a disciplined framework for deploying AI safely and at scale, incorporating privacy and governance by design. AI doesn’t supplant the role of Legal. It elevates it — reducing low-value work, improving cycle times, and enabling attorneys to focus on the decisions that drive key business outcomes. And because Legal’s work is traceable, auditable, and grounded in standards, it offers one of the safest, clearest pathways for deploying AI at scale. 

If enterprises want AI that is reliable, governed, and aligned to real-world constraints, Legal isn’t just a stakeholder. It’s the function that should lead the way.