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Legal departments are accelerating their adoption of generative AI, but most are discovering the same truth: without the right data foundations, even the most advanced AI tools can’t deliver meaningful insights. ACC’s report, The Role of Generative AI in Proving Corporate Law Department Value, produced in partnership with Everlaw, has made this reality unmistakably clear.
While legal teams are eager to move beyond cost tracking and into value demonstration, a significant infrastructure gap continues to hold them back.
GenAI is ready — but legal data is not
Legal leaders overwhelmingly believe that generative AI can help them demonstrate value to the business. In fact, 96 percent say GenAI can or potentially can enhance the legal department’s ability to show its impact.
But AI doesn’t work in a vacuum. It relies on structured, centralized, high-quality data — something most legal teams lack today.
Top obstacles to metrics tracking include:
- Lack of time or resources: 57 percent
- Data scattered across systems: 50 percent
- Limited tools for automation: 43 percent
- Difficulty aligning metrics with business priorities: 29 percent
The issue isn’t a lack of strategic alignment. It’s the fragmented, manual, labor-intensive way data flows through the legal function.
The hidden cost of scattered systems
Most legal data lives in disconnected platforms — e-billing, matter management, CLM, document repositories, ticketing systems, email, etc. These systems rarely speak to each other, which prevents AI from generating the kinds of insights legal leaders increasingly need.
This fragmentation is why legal departments continue to track primarily cost metrics rather than performance or outcome metrics. Today:
- Outside counsel spend is tracked by 83 percent of teams,
- But only 28 percent track time to resolution,
- Only 12 percent track outside counsel performance,
- And only 9 percent track impact on business outcomes.
Without unified, structured data, GenAI can automate tasks, but it can’t answer the questions that matter most to CEOs, CFOs, and boards.
The challenge is not a lack of strategic direction; it’s the operational burden created by fragmented data.
The risk: AI adoption stalls before it creates value
GenAI adoption is rising quickly. But without foundational improvements to data infrastructure, legal teams will experience several predictable challenges:
1. Incomplete or inaccurate insights
AI models fed inconsistent or siloed data produce unreliable outputs, undermining trust and slowing adoption.
2. Limited ability to move beyond efficiency use cases
Most legal teams start with automation (e.g., drafting, summarizing, contract review), but plateau here because data readiness isn’t sufficient for advanced analytics.
3. Inability to deliver the business metrics executives expect
Executives want visibility into cycle time, risk trends, forecasting accuracy, and outside counsel ROI — metrics AI can produce, but only when legal data is connected and clean.
4. Resource drain on legal operations
Legal ops teams spend disproportionate time manually pulling data from multiple systems, leaving less time for strategic reporting, governance, and transformation.
The report notes this clearly: The challenge is not a lack of strategic direction; it’s the operational burden created by fragmented data.
The opportunity: Build a data-ready legal department
To unlock the full potential of GenAI, legal teams must focus on data readiness as a strategic priority, not an IT afterthought.
A modern legal data foundation requires:
- A centralized source of truth for spend, matters, and documents
- System integrations that reduce manual reporting
- Clear taxonomy and data governance standards
- Automated data cleanup and normalization practices
- Cross-functional alignment with IT, finance, and compliance
When these foundational elements are in place, AI can deliver higher-value insights, not just task automation.
What in-house counsel should do now
1. Treat data infrastructure as a strategic investment
GenAI is only as strong as the data underneath it. In-house counsel should champion funding for data integration, system modernization, and cross-platform connectivity.
2. Empower legal operations as the data and AI owners
Legal ops teams understand the day-to-day reality of legal data. Equip them with the tools, training, and authority to build and maintain a unified data environment.
3. Prioritize value-focused AI use cases
Start with AI projects that require better data, not just better automation. Examples include:
- Outside counsel performance scoring
- Trend analysis across litigation portfolios
- Negotiation-ready billing insights
- Predictive forecasting for matter budgets
These use cases produce the metrics CEOs and CFOs value most.
4. Build governance before scaling AI
A responsible AI framework — covering data security, prompt standards, auditability, and model selection — creates confidence and accelerates adoption.
AI success starts long before AI adoption
The ACC–Everlaw findings highlight what many legal leaders already sense: The future of legal value hinges on data integration, not just AI engines.
GenAI will transform how legal teams work, but only if organizations build the infrastructure to support it. Legal departments that invest now in clean, connected, governed data will be positioned to unlock strategic, high-value outcomes that elevate legal’s role across the enterprise.
Those that don’t may see AI adoption stall at the surface — stuck in automation, unable to deliver the insights that define modern legal leadership.
Disclaimer: The information in any resource in this website should not be construed as legal advice or as a legal opinion on specific facts, and should not be considered representing the views of its authors, its authors’ employers, its sponsors, and/or ACC. These resources are not intended as a definitive statement on the subject addressed. Rather, they are intended to serve as a tool providing practical guidance and references for the busy in-house practitioner and other readers.
This article was developed with the assistance of Generative AI.
