How to Prepare Information Architecture to Leverage AI Effectively

BY ROBBIE PHILBRICK, RACHEL WILKA & MICHAEL CALLIER

Introduction

Legal organizations face a defining challenge: preparing their information systems for AI integration. While AI tools have the potential to transform legal practice, success depends on an organization’s readiness to adopt them. Such readiness demands more than acquiring new technology—it requires a fundamental paradigm shift in how organizations capture, process, and leverage their information assets (i.e., your information architecture).

This article aims to provide legal professionals with a framework to navigate conversations with technical stakeholders, whether internal IT teams or external legal technology vendors. By arming attorneys with targeted reference points and conceptual tools, we seek to bridge the communication gaps that often undermine technology projects, enabling a more straightforward and productive dialogue around AI adoption.

The key to getting the most from AI tools lies in a robust information architecture. Information architecture allows organizations to be adaptable knowledge ecosystems—rather than static structures—with a practiced understanding of how to balance technological capability with professional judgment.

Using a biological systems metaphor, we illustrate how legal organization information architecture converts raw data into actionable information. This article also examines the critical role of human oversight in maintaining healthy information ecosystems, particularly when deploying AI tools that require experienced human judgment to work well. Finally, the article provides practical assessment tools for evaluating information-management maturity and concrete steps for improvement. Through this structured approach, legal teams can chart a clear path toward effective AI implementation.

A Living Systems Framework for Information Architecture

To mature information management capabilities in preparation for AI, it is important to understand information architecture. Information architecture includes the IT systems, information flows, and digital content that make up an enterprise’s digital ecosystem. In many ways, information architecture mirrors how biological systems operate. Just as living systems transform raw materials into nutrients with specialized metabolic pathways, mature legal organizations develop structured information architectures that convert discrete data into usable intelligence.

The below framework presents a fresh perspective on information architecture, highlighting three interconnected components that are crucial for AI readiness:

1.  Core Data Elements

Like a cell’s nucleus directing essential functions, your organization’s core data elements—e.g., standardized client identifiers, matter classifications, and document types—help convert raw data into actionable information. 

In practice, data elements fuel enterprise tools such as document management systems, practice management platforms, and ethical screening software. Thoughtfully structured core data elements enhance daily operations by helping legal teams easily find precedent documents, share information resources, and navigate complex matters across practices. Even basic elements, like standardized client identifiers, yield immediate benefits in billing accuracy and service quality. Establishing robust core data elements not only strengthens foundational information architecture but also lays a solid groundwork for future AI capabilities.

2. Information Pathways

Just as cells rely on specialized transport channels, organizations depend on information pathways to transfer core data elements between internal databases and across third-party systems efficiently. Without these pathways in place, the manual information shepherding required will introduce delays, increase error risks, and divert attention from higher value tasks. 

Modern contract lifecycle management software exemplifies these pathways. Consider a high-stakes client agreement moving through your organization. Well-designed pathways transfer critical executed contract data to enterprise storage locations, route signature data through authorization verification processes, convey matter updates to matter management systems, and direct status notifications to collaboration tools responsible for coordinating stakeholder action. Successful pathways turn disconnected tools into integrated systems, allowing legal professionals to focus on legal work. 

3. Operational Endpoints

Like a muscle relying on inbound nerve cell signals to contract, operational endpoints use information pathways to call on a cascade of core data elements and related instructions when engaged by stakeholders. 

Operational endpoints include web portals, dashboards, practice tools, and computer operating systems. Ideally, operational endpoints express data in a way that encourages meaningful professional interactions such as dashboard and reports. However, as the saying goes, “garbage in, garbage out,” meaning that even the flashiest of dashboards and portals will fail if the data elements and pathways supporting them fall short.

Applying the Living Systems Framework

Legal professionals understand the impact of inefficient document retrieval on productivity. Here, we apply the living systems framework to understand how document retrieval could transform into a strategic asset. 

When it comes to the role of core data elements in enhancing search functionality, improving an organization’s metadata is essential. Metadata is the “digital fingerprint” of your documents and files, including details like file type, who created or modified it, when it was created or modified, version history, comments and edits, and where it’s stored. In practice, this means establishing a standardized master metadata model with matter classification codes that account for different types of attorneys, practices, industries, and sub-specialties within each. With these structured data elements in place, attorneys can locate relevant documents more efficiently and spend less time searching in frustration.

For information pathways, the improvement focuses on automating metadata extraction, tagging, and upload processes. No attorney wants to spend valuable time manually tagging volumes of documents, so these processes must operate automatically in the background whenever possible. This requires implementing mandatory data capture protocols that require specific client identifiers, practice codes, and relationship links when creating new documents or files. It also means developing metadata extraction rules that capture file properties and transform them into structured information. This extraction, transformation, and uploading happens across systems through Application Programming Interfaces (APIs)—the technological connections that enable different software systems to communicate and share information securely. With effective pathways in place, data elements flow smoothly to provide improved searchability.

At the operational endpoints, it is important to maintain data quality through both automated and human quality control measures. Standardized data intake processes ensure consistent capture of essential identifiers and relationship data from the outset of any new matter. Automated systems can then create clear audit trails of that metadata to support any manual intervention necessary to address data errors. When search capabilities are built on verified, high-quality information, attorneys can confidently rely on search results for time-sensitive client matters.

The Human Element

Human oversight should be intentionally woven into any organization’s information architecture strategy. 

Even at early stages of maturity, organizations can implement critical oversight touchpoints: practice leaders validating taxonomies, knowledge managers verifying metadata extractions, and operations teams monitoring billing accuracy. Subject matter experts integrating these touchpoints into their regular workflows provide important periodic quality control reviews of the automated outputs.

As technological capabilities mature, these oversight mechanisms should mature in parallel. For example, expert reviewers can evaluate automated quality control exceptions, practice leaders can guide the evolution of metadata classification frameworks, and designated specialists can assess escalated issues. 

Establishing human resources to address downstream problems associated with AI performance, such as model drift—where AI system performance degrades as underlying relationships evolve—is crucial. Such human intervention becomes critical when legal practice definitions change over time (e.g., evolving interpretations of contractual provisions), or when document characteristics shift away from the original model’s training parameters (e.g., new industry terminology or matter types). While automated monitoring can flag potential issues, professional judgment remains essential for evaluating impact and guiding appropriate adjustments to maintain work product quality.

Without structured quality control feedback channels to catch variations early, organizations risk delivering inaccurate guidance, missing critical deadlines, or damaging counterparty relationships. Accordingly, ongoing professional oversight is essential for creating resilient information systems capable of sustaining AI-enabled legal practice.

Each step in developing your information architecture will prepare your organization to succeed with generative AI.

The Information Architecture Maturity Model 

Understanding your organization’s current state of maturity is critical for success. To help assess maturity, we have provided a three-stage framework with anecdotal benchmarks.

1. The first stage of maturity is the Foundational Stage. In this stage, organizations have a handle on core data elements such as basic metadata for documents and counterparties. They also have some established information pathways between relevant systems (i.e., integrations) and deployed operational endpoints like basic document management systems, matter tracking, and reporting. While providing basic structure, the operational endpoints operate as disconnected islands, requiring manual information transfer between platforms. This duplication introduces inefficiencies and practical challenges such as uncertain document versioning, difficulty tracking information access, and confusion about which document represents the authoritative client record.
2. At the Intermediate Stage, organizations have modern information-driven operations that leverage information architecture to transform disconnected systems into a coordinated ecosystem. Document management, billing, and matter platforms now communicate seamlessly, largely eliminating redundant data entry and creating a single source of truth. When opening matters, systems automatically surface relevant precedents and institutional knowledge. Document updates flow naturally into client or counterparty records without manual intervention. An organization in this intermediate stage has established consistent document protocols that are actually followed, making information retrieval intuitive rather than frustrating. The benefits are tangible: attorneys spend less time searching for documents, client and counterparty communications reflect comprehensive matter knowledge, and risk monitoring happens proactively rather than reactively.
3. Finally, there is the Advanced Stage where legal organizations have extensive protocols for continuous validation of data quality and accuracy. Technology automates routine data processing and incorporates legal professional judgment to ensure a user-friendly experience. Advanced legal organizations usually have a practical governance structure—typically a committee blending practice leaders and technical staff—that makes informed technology investments to ensure decisions serve strategic priorities and manage risks appropriately.

Conclusion: From Architecture to Action

While AI offers transformative potential, successful use requires organizational commitment beyond technology acquisition. Legal organizations must adapt their information architecture to get the most from AI tools. This requires an investment in establishing strong core data elements, maintaining reliable information pathways, and orchestrating the two through operational endpoints. Legal professional inputs must be interwoven with the architecture. 

Recent research from the Washington State Bar Association highlights a critical inflection point: With 75 percent of legal professionals yet to engage with generative AI,¹1 Memo to WSBA Board of Governors, “Interim Update from the WSBA Legal Technology Task Force to the Board of Governors,” Dec. 13, 2024. the profession finds itself at a pivotal crossroads. Each step in developing your information architecture will prepare your organization to succeed with generative AI. Simply put, the golden approach to AI enablement lies in information architecture maturity and appropriately embedding human judgment into the loop. 

NOTE

1. Memo to WSBA Board of Governors, “Interim Update from the WSBA Legal Technology Task Force to the Board of Governors,” Dec. 13, 2024.