> **来源：[研报客](https://pc.yanbaoke.cn)** # The Knowledge Manager's 2026 AI Roadmap The competitive advantage in 2026 is shifting from what tools law firms use to how well they leverage their own institutional knowledge. This guide helps Knowledge Managers lead their firms' AI transformation by focusing on private, secure use of firm data and building smarter systems that learn from every interaction. # Your Role Has Changed In 2026, Knowledge Management (KM) is about building firm intelligence. You're responsible for ensuring your firm's institutional memory – its documents, precedents, and tacit know-how – feeds private, compliant AI systems that continuously learn, instead of enriching public models that could assist competitors. In practical terms, this means curating both explicit knowledge (briefs, memos, contracts) and tacit knowledge (experience in lawyers' heads) into AI-ready formats. The bottom line: if knowledge is recorded but not easily accessible, it might as well not exist. Your mandate is to unlock knowledge across the firm. # What's keeping KM leaders up at night in 2026: - Ungoverned AI experiments spreading across practice groups - attorneys adopting tools on their own, risking a "shadow IT" situation - Disconnected systems that don't talk to each other - DMS, intranets, and AI platforms operating in silos, leading to version control issues and inaccessible insights. - No clear answer to "Who owns the data training these models?", uncertainty over whether the firm or vendors have rights to the outputs and learning derived from your data - Partners demanding AI results without a firmwide strategy – pressure to show quick wins without foundational policies or use cases - Risk of proprietary insights leaking into vendor-trained platforms - fear that uploading client work to third-party AI could inadvertently train tools that other firms use The Knowledge Manager role encompasses policy-maker, systems integrator, and change advocate. In short, you guard against risk while engineering the intelligence advantage for your firm. # Five Trends Reshaping Knowledge Work in 2026 # 1. Private Deployment Is Non-Negotiable Firms are abandoning shared AI platforms in favor of single-tenant or private systems trained exclusively on their own data. Confidentiality and control are paramount. For example, some firms have developed AI chatbots internally that run in a private cloud tenant, isolating firm data from public models. Why? A private AI ensures that sensitive client information isn't inadvertently used to train an AI that another firm could access. Security and data ownership now trump convenience. Law firm leaders recognize that a model trained on their carefully crafted work product reflects a unique competitive asset. In short, deploying AI privately, or with vendors who contractually guarantee isolation of your data, is now table stakes for serious AI adoption in law firms. # 2. Every Document Becomes Training Data Each brief, memo, and contract your firm produces is no longer just a one-off deliverable. It's training fuel for your AI. When properly captured and structured, every document contributes to collective intelligence, compounding the firm's know-how. For instance, a brief filed today can help AI suggest better arguments tomorrow on a similar matter. Forward-looking firms treat their document management system as a goldmine: past work product is systematically indexed and fed into AI models or retrieval engines so the system "learns" from prior successes. This turns your KM function into a continuous learning operation, where the goal is to maximize reuse of high-quality work. As noted by John Treddenick, discovery and investigations expert: "Training an AI model on decades of carefully crafted legal documents and hard-won insights presents an attractive vision of institutional knowledge preservation and leverage." In practice, this means establishing pipelines so that nothing good gets lost. Each new research memo or contract clause adds to your firm's evolving knowledge graph. # 3. Governance Is Your Strategic Advantage Far from being a bureaucratic hurdle, AI governance has become a competitive differentiator. Firms with robust governance frameworks can safely innovate faster. In 2026, you're shifting from gatekeeper to architect of safe experimentation; that means defining protocols for how data enters and leaves AI systems, who reviews AI outputs, and how to audit decisions. In fact, AI governance and risk controls are quickly becoming standard practice. Forward-thinking KM leaders work closely with privacy, compliance, and IT to enforce rules like: what prompts are permissible, how to validate AI-generated answers, and how to avoid conflicts of interest or data leakage. By getting ahead of this, you turn governance into a strategic asset; partners and clients gain confidence that your firm's AI use is disciplined and defensible. Notably, many law firms are still playing catch-up; as of late 2024, a large number had either very early-stage or no formal AI use policies in place. Establishing clear governance now positions your firm to compete from a place of trust and safety. # 4. AI Lives in Workflows The real value of AI in legal practice comes from embedding intelligence directly into legal workflows, not just using standalone chatbots for Q&A. By 2026, successful firms have threaded AI into matter management, document drafting, and review processes. This means your drafting software, deal platforms, and research tools all have AI "assistants" built-in, working behind the scenes. For example, instead of lawyers toggling to a separate chatbot, an AI agent automatically suggests clause modifications as they draft a contract, or summarizes relevant precedents as they open a new matter. Industry trends show that legal teams are combining mature automation frameworks with AI capabilities to create smarter workflows. Rather than isolated experiments, AI becomes workflow-native: triaging new matters, gathering background documents, preparing first drafts – all integrated with your existing systems. The motto for 2026: If Al isn't in the flow of work, it isn't delivering real value. # 5. Synthesis Replaces Search 2026 marks the end of the keyword-hunting era. Lawyers expect AI to not just find documents, but to understand context and deliver actionable insight. In practice, this means a shift from traditional search results to synthesized answers. Conversational AI interfaces are turning complex queries into complete answers or even finished work products, rather than a list of hits. A lawyer might ask an AI assistant a multi-step question (e.g., "Find our best brief on X issue and draft a motion tailored to Judge Y using our preferred arguments") and receive a coherent, compiled result drawing from multiple knowledge sources. This "answer over search" paradigm is a game-changer for KM. Instead of spending hours sifting through DMS search results, attorneys get distilled knowledge, with sources attached for verification. Contextual synthesis is the new normal, and firms must ensure their AI has rich enough data (and well-maintained knowledge graphs) to deliver it. In short, if your KM systems still just do keyword search, users will consider them obsolete when compared to next-gen AI assistants. # Your 2026 Priorities To harness these trends, Knowledge Managers should focus on five priority areas in 2026: 1. Build Your Governance Framework: Establish clear protocols for data use, AI output review, and audit logging now. Don't wait for IT or outside counsel to dictate the rules - KM should lead with a policy that addresses confidentiality, ethics, and risk management. (See the AI Governance Policy Template below for a head start.) 2. Integrate Core Systems: Connect your document management system (DMS) and other knowledge repositories directly to your AI environment. Stop duplicating documents or exporting data to external tools whenever possible. A unified knowledge platform (matter management, DMS, research, etc.) is crucial – it ensures AI has direct, up-to-date access to firm-approved content and reduces "rogue" data copies. 3. Capture Institutional Knowledge: Turn every research memo and outcome into reusable intelligence. Encourage lawyers to contribute to centralized collections or knowledge graphs that map relationships between cases, arguments, judges, clauses, and outcomes. The goal is to retain final documents and the context around them: who worked on it, what it was used for, what result it achieved. This helps new matters immediately benefit from past learnings and highlights who has relevant experience (tacit knowledge) for a given issue. 4. Measure What Matters: Define metrics that actually reflect KM impact in the AI era. Old metrics like "number of documents in the brief bank" don't capture value. Instead, track things like reusability rates (how often work product gets re-used by others), knowledge coverage (what percentage of practice areas or matter types have a curated AI-ready dataset), and AI adoption metrics (e.g. percentage of lawyers actively using approved AI tools each month). These show whether your firm's intelligence is compounding or sitting idle. 5. Align Your Stakeholders: Bring partners, IT, security, and firm leadership onto the same page regarding your AI roadmap. Partners care about revenue, client satisfaction, and risk; IT cares about security and support load; leadership cares about competitive positioning. You'll need to translate the AI strategy for each perspective and address their concerns (see How to Present AI Strategy to Partners below). An aligned coalition prevents bottlenecks – for instance, partners won't push unvetted tools if they understand the governance plan, and IT won't lock things down if they're involved from the start. <table><tr><td>Challenge</td><td>The firm had amassed a 15-year brief and precedent library, but utilization was under 12%. Associates complained they “couldn’t find anything useful”; the keyword search tool was outdated, and documents lacked context or tags. Valuable work product was being under-used, and lawyers often reinvented the wheel.</td></tr><tr><td>KM Response</td><td>KM undertook a complete knowledge architecture overhaul. They implemented an AI-powered search and knowledge graph system that maps relationships between cases, filings, clauses, judges, and outcomes. Instead of isolated PDFs, each precedent is now enriched with metadata (jurisdiction, legal topics, which clients/matters it was used in, which attorney has expertise, etc.). The AI can infer connections; for example, it can surface “similar fact pattern” cases or suggest, “Lawyer X in our firm has argued before Judge Y three times with these outcomes.” This context-driven approach (synthesis over search) drastically improved findability. KM also launched training sessions to show associates how to query the new system (e.g., asking natural language questions to retrieve summarized answers, not just docs).</td></tr><tr><td>Outcome</td><td>Within a year, precedent reuse jumped to 47% of new matters (from 12%). Associates now find relevant examples in minutes instead of hours, because the AI delivers a concise summary with links to the best on-point documents. Partners have begun requesting custom intelligence briefings (e.g., a summary of all our firm’s prior matters on a niche issue). These are generated in hours by leveraging the firm’s institutional memory, something that was impossible before. The firm has seen faster turnaround on work product and more consistency in quality, powered entirely by knowledge it already possessed but wasn’t leveraging.</td></tr><tr><td>Challenge</td><td>Competing against much larger firms (AmLaw 100) in complex IP litigation, this boutique lacked the budget for expensive enterprise AI suites. The concern was that bigger firms' investments in AI would give them an insurmountable edge in efficiency and insight.</td></tr><tr><td>KM Response</td><td>Rather than buying a generic AI tool, the firm took a focused approach: they deployed a lightweight private AI model fine-tuned exclusively on two decades of their own IP case filings, motions, and deposition transcripts. This niche training meant the AI had deep knowledge of a specific practice area where the firm's experience was richest. KM worked with the litigation team to build custom AI-assisted workflows for drafting motions and preparing deposition outlines. For instance, a new associate could input a complaint and the AI would highlight similar past cases the firm handled and suggest a draft outline for the response, complete with references to arguments that had worked before.</td></tr><tr><td>Outcome</td><td>By leveraging focused institutional knowledge, new associates onboarded 40% faster, since the AI could brief them on the firm's relevant experience on day one. The partners could produce case strategies in hours instead of days, because the AI quickly surfaced winning tactics from similar cases in the firm's history. In the past year, the boutique punched above its weight – it won two major cases against significantly larger opponents. The deciding factor, according to the partners, was the speed and depth of insight the team could bring, courtesy of their internally trained AI. In essence, they turned their size (focused experience) into an advantage, while larger firms were still wrangling off-the-shelf tools to figure out basic answers.</td></tr></table> # How to Present AI Strategy to Partners When addressing firm leadership (partners and executives), remember that they primarily care about revenue, risk, and reputation. Frame your AI roadmap in those terms. You'll get more buy-in by linking AI to competitive wins, client service, and risk mitigation. Here's a framework for presenting your AI strategy to decision-makers: # The Partner Presentation Framework Open with Business Impact Set the tone by highlighting business outcomes, not technology. For example: $\times$ "We need to implement a knowledge graph architecture with vector embeddings..." (too technical and abstract) VS "Three of our competitors are now delivering research memos in half the time, with higher quality. Here's how we can catch up – and then pull ahead." (focuses on competitive gap and firm's plan) Lead with how AI will help the firm make money, save money, or avoid losing money. Use concrete competitor or client examples whenever possible. # Frame Risk Concretely Partners respond when risks are real and specific. Lay out the risks of doing nothing or doing Al wrong, for instance: "Right now, our client work could be training our competitors' AI systems." (If attorneys use tools like ChatGPT with client data, that data may go into public models.) "We have no audit trail for Al-generated content, which creates malpractice exposure if an Al-made error slips through." "Our associates are using consumer AI tools with confidential client info because we haven't provided a secure alternative. This is a major confidentiality risk". By pinpointing these risks, you create urgency and justify the investment in a governed, firmwide solution. # Present Options with Clear Trade-offs Don't corner leadership into one path; show you've evaluated multiple approaches. A simple table can clarify choices: <table><tr><td>Approach</td><td>Investment</td><td>Timeline</td><td>Risk</td><td>Outcome</td></tr><tr><td>Status Quo</td><td>$0</td><td>None</td><td>High – data leaks, inefficiency</td><td>Falling behind competitors</td></tr><tr><td>Shared Platform (e.g. Harvey)</td><td>Mid to High (e.g. $100-$20K/year)</td><td>Immediate</td><td>Medium – moderate control</td><td>Quick start, but not unique (others use same tools)</td></tr><tr><td>Private System or Instance (e.g. Alexi)</td><td>High ($20K+/year)</td><td>2-6 weeks (sometimes more)</td><td>Low – full control, higher upfront effort</td><td>Compounding advantage – builds unique firm intelligence</td></tr></table> Explain the trade-offs: e.g., a shared/public platform might be cheaper short-term but could dilute competitive advantage and pose data risks, whereas a private system requires investment but yields a unique, defensible edge over time. # Address "Why Not Just Use ChatGPT?" Inevitably, someone will ask why the firm shouldn't just use free generic AI tools. Be ready with a clear, confident answer in plain language. For example: "ChatGPT is trained on the entire internet. It's equally accessible to every firm. It won't know what arguments we have successfully made before Judge Rodriguez, or which contract clauses our clients prefer. That institutional knowledge is our competitive advantage. But it's only an advantage if we protect it and leverage it in our own AI systems, instead of giving it away to a public tool." This kind of response underlines the unique value of the firm's data and the need for a private approach, without getting too technical. # End with a Clear Ask Conclude your presentation with a specific request. For instance: "I'm requesting approval for a 3-month pilot of [AI solution] with the Litigation practice group, with a budget of $X. We'll measure key metrics (e.g., research time reduced, user adoption, error rates) and report results to inform a firmwide rollout decision." Make sure your ask includes a timeline, budget, scope, and success criteria. This gives partners a clear decision to make and confidence that it's a controlled, measurable initiative. By framing your strategy in terms of business impact, risk mitigation, and clear options, you speak the language of firm leadership. This greatly improves your chances of getting the green light (and resources) to execute your AI roadmap. # Template: AI Governance Policy Starter Every firm needs an AI use policy in 2026. Below is a starter template you can adapt for your organization. This covers approved tools, prohibited uses, required practices, data classifications, and vendor requirements. It's a foundation to ensure safe and effective AI use. # [FIRM NAME] - AI Use Policy Approved AI Tools [Primary Firm AI System]: A private, firm-approved AI environment for research, drafting, and analysis (single-tenant or on-premises). Other AI Tools: All other AI or chatbot tools require written approval from the Knowledge Management team before use on firm matters. # Prohibited Uses (strictly not allowed): - Uploading or pasting client documents into public or shared AI platforms (e.g., public ChatGPT, Google Bard, Claude) without approval. - Relying on AI-generated content without attorney review and verification. - Sharing firm work product (briefs, templates, research memos) with any external or vendor AI systems that are not approved by the firm. - Using AI tools that do not maintain an audit trail of queries and outputs (we require traceability for any AI-assisted work). # Required Practices (for all approved AI use): - Verify Accuracy: Attorneys must carefully review and validate all AI-generated material. The lawyer is responsible for the content's accuracy and must never "copy-paste and send" without checking. - Maintain Attribution: If AI is used in drafting or research, note it in the work (e.g., a brief's footnote or internal notation) so the firm has an audit trail of AI involvement. This transparency is important for quality control and potential disclosure obligations. - Protect Confidentiality: Only use firm-approved, private AI systems for any client-related work. Do not input confidential or sensitive data into any tool that isn't explicitly cleared for that purpose (ensures compliance with ethical duties). - Report Issues: Immediately report to KM or IT any malfunction, suspicious behavior, or inaccuracies discovered in AI outputs, especially those that could pose risk (e.g., a hallucinated case citation or a security concern). <table><tr><td>Data Type</td><td>Approved AI Use</td><td>Risk</td></tr><tr><td>Public information (e.g., published laws, forms)</td><td>May use any approved AI tool (including external research tools)</td><td>No special restrictions (still follow client-specific sensitivity if applicable)</td></tr><tr><td>Internal firm knowledge (work product, templates not client-specific)</td><td>Private firm AI only</td><td>Do not use on external/public AI platforms</td></tr><tr><td>Client confidential data (case/matter documents, client communications)</td><td>Private firm AI only</td><td>Use requires matter-specific permission if needed; ensure ethical wall compliance for restricted matters</td></tr><tr><td>Privileged communications (attorney-client privileged material)</td><td>Private AI with manual review</td><td>Only with Partner approval and after stripping metadata; never use external AI. Human review output before any further use.</td></tr></table> Rationale: The stricter the confidentiality, the more controlled the AI environment must be (up to forbidding AI use entirely for certain highly sensitive data). # Vendor Requirements - For any AI software or service the firm uses, the vendor must contractually agree to: - Single-Tenant Deployment: Our data is isolated in a dedicated environment (no co-mingling with other customers' data). - No Training on Our Data: The vendor will not use our firm's data or interactions to train their general models or for any purpose outside serving our account. - Audit Logs: The system provides detailed logs of who accessed AI, what queries were made, and when – to support audits or investigations. - Data Ownership & Deletion: The firm retains ownership of its data and any AI-generated outputs. Vendor must delete our data upon request and ensure no residual copies remain in their systems after agreed retention periods. This policy should be reviewed regularly (at least annually) and updated as AI technology and regulations evolve. It should also align with any ABA or local bar guidance on AI ethics (e.g., ABA Formal Opinion 512 on use of generative AI tools addresses confidentiality and competence duties). # Template: Stakeholder Alignment Worksheet Use this worksheet to map out stakeholder perspectives and ensure everyone's priorities are addressed in your AI initiative. Filling this out can prepare you for committee meetings or business case discussions. # Current State Assessment - AI Tools in Use: (List all AI or automation tools currently being used in the firm – officially or unofficially.) - Top 3 Pain Points Driving AI Interest: 1) , 2) , 3) (E.g., "Clients demanding faster turnaround," "Associates spending too long on research," "Fear of falling behind competitors.") # Stakeholder Priorities - Partners care about: Revenue growth, competitive advantage, meeting client demands, and risk management (avoiding malpractice, protecting client confidentiality). - IT cares about: Security and data protection, system integration with existing tech stack, vendor reliability/support, and the maintenance burden of any new tool. - KM (you) cares about: Data governance and quality, capturing institutional value, user adoption of KM systems, and measurable impact on efficiency and quality. - (Note: Also consider other stakeholders like Compliance, HR (training needs), and Clients if client-facing.) # Success Definition - In 12 months, how will we know the AI initiative is successful? Fill in a specific success statement: e.g., "We reduce average research time by $50\%$ on pilot matters," or "At least $70\%$ of attorneys are using the AI tool weekly." Success means: # Decision Framework - Recommend: (Who will research options and recommend a course of action? E.g., KM Director + Innovation Committee) - Approve: (Who has sign-off authority? E.g., Firm Managing Partner, Executive Committee) - Execute: (Who will implement and manage the project? E.g., KM Team, IT Project Manager, Vendor Partner) # Budget Range (for initial phase or pilot) • Minimum: $ (bare-bones option that addresses core need) - Preferred: $\$$ (optimal investment for desired solution and impact) • Maximum: $ (upper limit for full-feature solution or contingency) # Timeline - Decision by: (target date for go/no-go from approvers) - Pilot launch: ______ (when the pilot project would start) - Pilot review: ______ (when to evaluate pilot results) - Firm-wide rollout: (target start date if scaling up, if applicable) By completing this worksheet, you'll have a clearer picture of who needs to be involved, what each cares about, and how to communicate the value of your AI roadmap in terms that matter to each stakeholder. # How Alexi Supports Your Strategy If your firm is exploring AI and knowledge management improvements, Alexi can be a strong ally. Alexi's platform is designed with many of the 2026 priorities in mind: # Single-Tenant Architecture Alexi deploys in a dedicated private cloud environment just for your firm. Your data stays isolated and under your control – no co-mingling with other firms' data, addressing the confidentiality mandate from day one. # DMS Integration Alexi offers secure connectors to popular document management systems (iManage, NetDocuments, SharePoint). This means you can leverage your entire brief bank or contract repository without exporting documents or breaking your existing security model. The integration ensures that knowledge flows seamlessly from where it's stored into the AI's reasoning engine. # Contextual Reasoning Alexi links related cases, filings, issues, and even which attorneys worked on them. This powers contextual answers (synthesis over search). For example, Alexi can surface how a specific argument fared in front of a certain judge, because it understands relationships in your data. # Built-In Governance Features Our platform was built for legal compliance, including audit logs, encryption, and granular access controls. Every query and AI-generated answer can be logged and attributed. Admins can set ethical wall rules and role-based permissions. This helps you implement the governance framework you design, rather than having to enforce policy manually after the fact. # Workflow Integration Beyond Q&A, Alexi allows you to embed AI into your firm's unique workflows. You can use our ready-built workflows in our Workflow Library, or create firm-specific automations (with no-code or low-code tools) that, for instance, generate a first draft of a brief from a template, populate it with clauses from your precedent library, and route it for approval – all following your standard processes. In other words, Alexi can act as the AI layer in your existing workflow, not just a standalone chatbot. In sum, Alexi is aligned with the vision that the smartest legal AI is one trained on your work. It provides the infrastructure and tools to ensure that every brief, email, and insight in your firm actually makes your next output better, all while protecting your confidentiality and meeting compliance needs. # Your 2026 Action Plan Finally, here is a high-level action plan checklist to guide your next steps. These focus areas align with everything discussed above. <table><tr><td>Focus Area</td><td>What Success Looks Like</td><td>First Step</td></tr><tr><td>Governance</td><td>Clear ownership and accountability for AI outputs - no confusion on who's responsible</td><td>Draft a firmwide AI use policy and circulate for feedback among key partners</td></tr><tr><td>Data Architecture</td><td>A connected “knowledge ecosystem” - all key data sources feed into AI, minimal silos</td><td>Map where all critical knowledge resides (DMS, SharePoint, practice databases) and identify integration points</td></tr><tr><td>Security</td><td>Private, encrypted AI environment; zero incidents of unauthorized data exposure</td><td>Evaluate single-tenant AI solutions or confirm your vendor's data isolation and security certifications</td></tr><tr><td>Adoption</td><td>Lawyers using AI in daily workflows naturally, not forced; e.g., X% of matters leveraging AI assistance</td><td>Launch one high-value pilot use case (e.g., AI-assisted brief drafting in Litigation) and champion success stories</td></tr><tr><td>Measurement</td><td>Quantifiable intelligence growth – e.g., increase in reuse rate, faster cycle times, fewer hours on low-level tasks</td><td>Define baseline metrics now (e.g., current research time, current precedent reuse rate) to later measure improvement</td></tr></table> By focusing on these areas, you ensure a balanced approach: not just technology, but also policy, culture, and process. Adjust the specific steps based on your firm's context, but maintain a mix of governance, tech integration, security, user adoption, and metrics for a holistic program. # Further Reading & Resources To deepen your understanding and help build your case, consider these resources: # Industry Research & Surveys - Thomson Reuters "2025 Report on the State of the US Legal Market": Insights on how law firms are evolving with AI and changing client demands. - ILTA 2025 Technology Survey: Benchmarking data. # Ethics & Governance - ABA Formal Opinion 512 (July 2024): Guidance on Generative AI use by lawyers. - State Bar Ethics Opinions: Check if your jurisdiction (e.g., California, New York City Bar) has issued GenAI advisories. - IAPP (International Association of Privacy Professionals) video – Built to scale: Privacy and AI risk frameworks: Best practices for managing privacy and security risks in AI deployments. # Knowledge Management Practices - KMWorld Magazine: Regular articles on knowledge management trends (including AI's impact on KM) - LLRX.com (Law Library Resource Xchange): Articles by law librarians and KM professionals. # Technical Implementation - NIST AI Risk Management Framework: A comprehensive framework for managing risks in AI deployment (not legal-specific but very useful for structuring governance). - SOC 2 Compliance for AI Vendors: As you evaluate vendors, ensure they have SOC 2 (or similar) security certification. Reading up on SOC 2 criteria will help you ask the right questions about data handling. # Professional Communities - ILTA KM Community: A community within ILTA specifically for KM. - LinkedIn Groups (Legal KM Professionals): Join active groups or follow thought leaders in legal KM and legal tech. # The Bottom Line The law firms that succeed in 2026 won't be the ones who simply buy the most AI tools. They'll be the ones who train systems on their own work and use them pervasively. As a Knowledge Manager, you are at the vanguard of this transformation. Every brief, email, and insight your firm generates can and should strengthen your competitive position going forward. By establishing the right governance, integrating your knowledge sources, and championing AI adoption, you ensure that happens – privately, securely, and cumulatively over time. Alexi is here to support that mission, helping you turn your firm's collective knowledge into a lasting advantage. Ready to build your AI roadmap and take your firm's knowledge strategy to the next level? Reach out to us to schedule a consultation and let's ensure your firm leads the way into the future of legal Al.