Ed. note: This article first appeared in ILTA’s Peer-to-Peer Magazine.
The promise of artificial intelligence in law feels almost boundless. Each week brings new announcements about firms adopting generative tools to automate drafting, summarize case law, or predict outcomes. But beneath the excitement lies a quieter, more stubborn problem, one that technology cannot solve alone.
Most firms’ data is not ready for AI.
Litigation teams in particular face the steepest challenge. Their work spans years, involves countless stakeholders, and touches everything from discovery to budgeting. That creates a mountain of unstructured information, including pleadings, correspondence, financial records, and court updates, often stored across dozens of systems that do not talk to one another.
Before AI can be transformative, firms must get serious about something far less glamorous: governance. That means understanding, structuring, and maintaining litigation data so it’s reliable, consistent, and ready to power intelligent tools. Without that foundation, AI is like building a skyscraper on sand.
The Reality of Litigation Data Today
If you ask a litigation coordinator where a document lives, the answer might depend on the day. Matter data lives in shared drives, emails, billing systems, case management tools, and sometimes even handwritten notes. Case teams rely on spreadsheets to track filings, manually copy data between systems, and recreate reports that never quite match.
This fragmentation carries a real cost. Attorneys spend time hunting for the correct version of information. Teams duplicate work or make decisions using outdated data. Forecasting legal spend becomes guesswork.
At a governance level, the issue is simple: poor data management leads to poor decision-making. Without a trusted source of truth, leaders cannot see where cases stand, how much exposure they face, or how workloads are distributed.
Even firms that have invested in practice management software often struggle to enforce consistent data entry. When each matter owner or paralegal uses their own naming conventions, automation breaks down. A tool cannot identify trends or flag anomalies if the trial date is recorded inconsistently across cases.
Why Data Governance Is the Foundation for AI
Generative AI has raised expectations and also exposed weaknesses. Every AI system, no matter how advanced, relies on the quality of its inputs. “Garbage in, garbage out” is more than a cliché; it is a technical truth. Structured, standardized data is what allows AI to function responsibly and accurately. If a firm wants to analyze case outcomes, predict litigation costs, or automate routine updates, it needs consistent, validated information. Without that, even the most innovative tool can produce unreliable or misleading results.
Many firms are discovering that AI readiness is more of a data hygiene problem than a technology challenge. The push toward automation is forcing leaders to confront long-standing issues around data ownership, accuracy, and accessibility. In some ways, AI is accelerating the maturity of governance. To deploy new tools safely, firms must first ask a few foundational questions:
Where does our data come from?
How clean and consistent is it?
Who is responsible for keeping it accurate?
Those are governance questions, not engineering ones.
What Evolving Governance Looks Like in Practice
Forward-thinking litigation teams are redefining governance beyond mere compliance. They see it as the backbone of operational efficiency and innovation, supporting how people actually work day to day. In practice, that shift comes down to a few core principles.
Centralization: Create one reliable source of truth for case data. That does not always mean a single system, but it does mean a single governing framework in which all data connects logically. When teams operate from a shared foundation, duplicate tracking disappears, reporting becomes faster, and decision-making becomes more transparent. Centralization also reduces risk by ensuring updates and disclosures are managed consistently across cases.
Standardization: Define consistent naming conventions, tags, and required data fields. The goal isn’t to add bureaucracy but to make information predictable and usable across matters. When data fields are structured the same way, whether for case type, jurisdiction, or stage, teams can run comparisons, automate updates, and surface insights that were previously buried. Standardization creates the conditions that enable automation to be trustworthy.
Access Control: Map permissions to roles and confidentiality needs. Governance isn’t just about visibility, but about the proper visibility. Clear access rules protect sensitive data, reduce accidental disclosures, and reinforce ethical walls without slowing collaboration. Well-designed access controls also make it easier to work with outside counsel, clients, and vendors in a controlled yet connected environment.
Accountability: Assign clear ownership for maintaining data quality. Someone (or a small committee) should be responsible for ensuring that data remains accurate and up to date. Governance does not succeed through software alone. It requires people who see themselves as stewards of the data. Defining accountability creates feedback loops, encourages consistency, and helps firms spot systemic issues before they become entrenched.
Many firms are finding that purpose-built matter or case management platforms can serve as the backbone of this governance framework. When data about case milestones, financials, documents, and outcomes lives in a single, structured environment, it becomes far easier to standardize naming conventions, apply permissions, and maintain accuracy over time. The goal is to create a governed system of record that supports collaboration and insight, rather than silos.
Modern governance is also scalable. There is no need to fix everything at once. Start with one practice group or data category, define what good looks like, and expand from there. Progress compounds as teams see tangible benefits like faster reporting, fewer discrepancies, and easier collaboration.
How Litigation Teams Can Start Improving Governance
For firms unsure where to begin, a simple self-audit can help clarify the path forward. Where does litigation data live today? How many versions of key documents exist? Which reports require manual re-entry or reconciliation? The goal is not to shame teams but to visualize complexity. Once you see the sprawl, improvement feels achievable.
From there, momentum builds through a few intentional steps. Start by assigning ownership. Governance rarely gains traction when framed as everyone’s job. Designate a data owner or a small governance committee to make decisions, track progress, and set standards that others can follow.
Next, simplify before layering in technology. Clean, consistent data in a basic system will outperform messy data in an advanced one. Resist the urge to fix disorganization by adding more tools.
For many teams, that simplification begins with consolidating scattered spreadsheets and trackers into a shared environment where data relationships are preserved automatically. Even a modest shift toward structured inputs, such as deadlines, budgets, and case stages, can reveal previously hidden gaps. The goal is to make good governance easy to practice within the normal flow of litigation work.
Finally, look for something measurable to pilot. A small reporting dashboard, a standardized intake form, or a short data-cleanup sprint can demonstrate the value of better governance almost immediately. Small wins build credibility and reinforce that governance is a daily habit, not a project to finish.
Governance Is Innovation
The firms that modernize governance will be the first to realize tangible benefits from AI, not because they adopt faster, but because they adopt smarter. In the long run, data governance is the foundation of innovation, not red tape. It enables efficiency, accountability, and insight. It allows legal teams to shift from reactive management to proactive strategy.
As the next wave of litigation technology evolves, the most valuable platforms won’t necessarily be those with the flashiest AI features. They will be the ones who help teams govern their data with clarity and confidence. Firms that build on that foundation today will be ready to harness whatever innovation comes next. True modernization starts with clarity. Knowing what data you have, where it lives, and how you can trust it. Once that foundation is in place, the potential of AI becomes not just possible, but sustainable.
Keao Caindec is the CEO and co-founder of Clarra, a fast-growing legal practice management platform redefining how firms and organizations manage matters. A veteran technology leader and entrepreneur, he has built and scaled companies that have transformed mature markets across legal, financial, and technology sectors. At Clarra, Caindec introduced a litigation-focused, docket-driven approach that has quickly gained traction among midsize plaintiffs’ firms, AmLaw 100 firms, and corporate legal teams. Before founding Clarra, he held leadership roles at Farallon Technology Group, Mocana, 365 Data Centers, OpSource, Yipes, and CyberCash, all of which achieved successful acquisitions. An active member of ILTA, ABA, and CLOC, Caindec frequently writes and speaks on legal technology and the business of law.