Please enjoy this blog post co-authored by Colin Cahill, Practice Director, Microsoft Solutions, InOutSource; Priscila Martins, Director of Information Governance and Special Projects, Conn Kavanaugh Rosenthal Peisch & Ford LLP; and William Grady, Director of Technology and Business Operations, Conn Kavanaugh Rosenthal Peisch & Ford LLP.
Artificial intelligence is no longer a discrete technology initiative. It is rapidly becoming the operating model for modern legal services. Law firms are deploying AI tools for legal research, document review, drafting, contract analysis, knowledge management, and business analytics. As generative AI, large language models (LLMs), and emerging agentic systems become more embedded in legal work, the question is not whether to adopt AI, but rather how to ensure those systems can operate on data that are trusted, governed, secure, and relevant.
For law firms, preparing for AI should not be simply a technology decision, but rather an information governance decision. AI depends on the firm’s ability to identify, classify, secure, retrieve, and responsibly use its information assets. Before a firm can expect AI to deliver reliable legal, operational, or business insights, it must ensure that the information feeding those systems is accurate, secure, well-governed, and connected to the appropriate business context.
Much of the conversation focuses on which AI product to buy. While product selection is important, the more fundamental question is whether an organization’s underlying data is ready for AI. The quality of an AI system’s output is directly tied to the quality, structure, security, and reliability of the information it draws on.
Achieving that level of readiness requires the alignment of data governance, data platforms, change management, and grounding AI in trusted organizational data. Successful AI adoption also depends on effective change management, including user education, clear governance policies, executive sponsorship, and ongoing reinforcement of new workflows. These layers must work together so that AI systems can access the right information, within the right permissions, and in the right context. Without this foundation, legal practices may expose themselves to unnecessary risks, including data leakage, non-compliance, reputational damage, poor user adoption, and operational inefficiencies.
This matters even more in legal practice than in most industries. Inaccurate AI output is not merely an operational inconvenience. Incorrect legal analysis, overlooked precedent, improper disclosure of confidential information, or reliance on a superseded document can expose a firm to malpractice claims, ethics violations, client dissatisfaction, and reputational harm. For lawyers, preparing high-quality data is not just the best practice; it is a condition of responsible AI adoption (Kolochenko, 2024).
The Cleanup Imperative
For most firms, the first obstacle is the data itself. Decades of growth have produced enormous collections of documents, emails, matter records, and billing data spread across different systems, stored under inconsistent naming conventions, and rarely classified in any meaningful way. Experienced lawyers navigate this clutter through institutional knowledge. AI cannot. To a model, a duplicate file, an outdated precedent, or a conflicting matter description is indistinguishable from current, authoritative information and any of them can surface in an answer.
This is why data cleanup must come before deployment. The most widely discussed failure of generative AI is hallucination: output that looks plausible but is wrong or unsupported. Although hallucinations can never be eliminated entirely, their frequency drops sharply when a system relies on organized, verified, de-duplicated information rather than incomplete or contradictory data. Just as important, cleanup prevents a subtler problem: an AI tool confidently citing a superseded or abandoned document. Removing duplicates, retiring superseded precedent documents, reconciling conflicting metadata, and purging content that should have been deleted under the firm’s retention schedule all reduce the risk that stale information ends up in a client deliverable. Better inputs produce better outputs, and in legal work that difference is measured in professional exposure.
Governance, Taxonomy, and Metadata
Cleanup is not a one-time project; it requires ongoing governance. Information governance is the set of policies, standards, and accountability structures that determine how information is created, classified, secured, retained, and retired across its lifecycle. Governance enables a firm to answer the questions AI makes increasingly important: What data exists? Where does it live? Who owns it? Who can access it? How long should it be kept?
Consistent classification is what makes those answers durable. Applying a standard taxonomy is essential to creating reliable metadata that both people and machines can interpret. Well-structured metadata improves search, reporting, and AI accuracy by replacing inconsistent naming with predictable, contextual labels. A firm need not adopt every taxonomy or standard at once, but it should commit to following a taxonomy so that newly created data stays organized rather than adding to the backlog. Beyond improving AI, consistent metadata also supports matter reporting, pricing analysis, knowledge management, and operational decision-making across the practice.
From Data Readiness to Generative and Agentic AI
As legal practices improve their data governance and information management practices, those investments become the foundation for increasingly sophisticated AI architectures. Modern AI increasingly operates across three interconnected architectural patterns, which together support the development of advanced, increasingly autonomous AI systems.
Generative AI
Generative AI systems use large language models (LLMs) to generate text, summarize information, draft documents, answer questions, and perform other knowledge-based tasks. Their outputs are influenced by both the model's training data and any information provided during a prompt. Without curated and governed organizational data, these systems are more prone to outdated reasoning, inconsistent outcomes, and hallucinations.
Retrieval-Augmented Generation (RAG)
Increasingly, AI relies on Retrieval-Augmented Generation (RAG) architectures that connect LLMs to an organization's trusted data sources at the time of a request. Rather than relying solely on information learned during training, RAG retrieves relevant documents and data to provide more accurate, current, and context-specific responses. These systems require high-quality metadata, consistent taxonomy, well-organized content, and policy-driven access controls to retrieve the right information while maintaining appropriate security and confidentiality.
Agentic AI
Emerging AI systems are moving beyond answering questions to planning and executing multi-step workflows with limited human intervention. Agentic AI can coordinate multiple tools, retrieve information from various systems, make conditional decisions, and complete business workflows on behalf of users. Because these systems operate across multiple data sources and applications, they amplify both the value of well-governed data and the risks associated with poor data quality, weak governance, or inappropriate access permissions.
Microsoft Purview as the AI Governance Control Plane
Many law firms already license Microsoft 365, positioning Microsoft Purview as the control plane for improving AI readiness. Purview helps organizations discover, classify, and protect sensitive information across Exchange, Teams, SharePoint, OneDrive, Azure, Fabric, and other repositories. It also supports data classification through sensitivity labels that identify the confidentiality of information and apply consistent protections based on organizational policies. By helping firms understand what information they possess and how it should be handled, Purview provides greater visibility into the data that AI systems may eventually access.
Before implementing Purview, many organizations benefit from a readiness assessment that evaluates whether governance, security controls, identity and access management, classification processes, and logging are mature enough to support deployment. The assessment should also determine whether the organization aligns with frameworks such as NIST Cybersecurity, SOC 2, and ISO/IEC 27001. Closing those gaps first reduces implementation risk and builds a stronger foundation for responsible AI.
Purview’s value extends well beyond data organization. Sensitivity labels, data loss prevention rules, retention controls, and access restrictions work together so that confidential and privileged material is reachable only by authorized users. As a firm expands its use of AI, these controls reduce the risk that a model retrieves inappropriate content or exposes sensitive information. No platform replaces a genuine governance program, but Purview provides practical capabilities for data discovery, classification, policy enforcement, and lifecycle management.
Security, Confidentiality, and Compliance
These capabilities matter because of what law firms hold: privileged communications, trade secrets, personal data, and litigation strategy. Any AI deployment must preserve those protections, and data classification is the mechanism that makes it possible. Proper classification is especially important for legal practices managing ethical walls, lateral-hire restrictions, and client-specific confidentiality obligations. Structured security labels ensure AI tools return only what a given user is already entitled to see, reducing the risk of inadvertent disclosure and ethics violations (Kolochenko, 2024).
The same discipline helps protect attorney-client privilege. As firms weigh AI tools, a recurring concern is whether confidential information could be exposed to an external system or absorbed into a shared training model. Accurate tagging, governance policies, and access controls let a firm decide which information any given tool may use, and under what conditions (Kolochenko, 2024; Couture, 2025). Classification also underpins regulatory compliance. Privacy regulations, including state, national, and international laws such as GDPR and CCPA, govern how personal information is collected, stored, and shared. Structured data helps a firm locate sensitive information, apply the right controls, and demonstrate compliance when asked.
AI Success Begins with Data
Data quality and AI capability have become inseparable. A firm that buys AI tools without first cleaning and governing its data may find the technology amplifies its worst information rather than its best. The legal practices that will benefit most from AI will treat AI readiness as an ongoing discipline by cleaning up legacy repositories, retiring outdated content, following a consistent taxonomy, and enforcing classification and access through tools they likely already own.
Successful AI adoption does not begin with a prompt; it begins with the data foundation and governance strategy that determines what AI can safely act upon.
Attending ILTACON? Don't miss this related session, Legal Taxonomies and the Modern DMS (Session #4531), on Monday, August 24, from 4:00–5:00 PM CDT.
References
#PracticeManagement
#PracticeManagementandPracticeSupport
#ArtificialIntelligence
#GenerativeAI
#DataManagement
#Adoption
#200Level