Search the keyword “AI” on the Kirkland job page and you get over ninety job openings. Search on Latham, Covington, or King & Spalding and you’ll find many more.
This data shows a new layer of roles forming around lawyers. That story doesn’t get much attention, and it matters if you’re going to make this transition well. Whether your firm can cover these roles is what will separate firms over the next few years.
While large firms can hire full-time employees for each role, smaller firms can cover the same ground by assigning an existing attorney or bringing in part-time support. Either way, these are roles that turn an AI license into real value.
I read dozens of job descriptions and found seven functions in high demand. One is a sleeper category: Knowledge Management. It’s an existing back-office role that has now become a competitive advantage. It determines how good your AI is, because the better your playbooks and precedents, the better the output. Here’s each of the seven.
Legal Engineering or Applied AI
This person breaks down legal processes into steps that AI can execute repeatedly and that produces output up to the firm’s quality standards. Skills required are prompt engineering, experience with legal AI platforms, evaluation discipline, and legal practice experience.
Recurring responsibilities:
- Decompose legal work into AI-executable pieces: “decomposing complex transactional and litigation workflows into discrete, measurable tasks with defined inputs, outputs, decision points, and quality criteria suitable for AI execution” (Latham)
- Capture what senior lawyers know: “codifying the tacit knowledge of senior practitioners, including fallback positions, market terms, negotiation flexibility, and drafting conventions, in machine‑readable form” (Latham)
- Translate practice knowledge into reusable AI assets: “translate White Collar practice knowledge – investigation methodologies, interview approaches, enforcement patterns, and reporting conventions – into reusable prompt patterns, agent workflows, and AI tooling” (Covington)
- Hold AI output to professional standards: “define and apply quality controls, evaluation criteria, and review checkpoints to ensure AI-enabled work product is accurate, defensible, and consistent with Covington standards” (Covington)
Examples are Kirkland AI Innovation Advisor; Covington Applied AI Manager (one per practice group) and Director of Applied AI; Latham Legal Engineering Attorney and AI Services Attorney; King & Spalding Agentic Workflow Specialist
Adoption, training, and change management
This position gets attorneys to actually use the tools and quantifiably measures their impact so the firm can make data-driven decisions. Skills required are teaching, communication, familiarity with AI concepts, experience with technology enablement.
Recurring responsibilities:
- Sustain enablement beyond the launch: “drive firm-wide adoption of applied AI through training, demos, office hours, internal communications, and sustained enablement” (Covington)
- Build champion networks inside practice groups: “establish and support a network of AI champions and super users within each practice group, assisting in activities such as prompt management and promotion for reuse” (King & Spalding)
- Keep a running record of what works: “proactively share learnings and emerging patterns across the team” (Kirkland); “cultivating a robust pipeline of litigation-focused technology needs, workflow challenges, innovation opportunities, and potential solutions” (Latham)
- Measure adoption and act on it: “gather user feedback, monitor adoption metrics, and drive initiatives to maximize engagement and value realization” (King & Spalding)
Examples are Latham Senior Manager of Legal Innovation; King & Spalding Legal GenAI Platform Specialist, the Covington Director of Applied AI, and the Kirkland AI Innovation Advisor
Knowledge Management rebuilt for AI
This role converts institutional knowledge into AI-ready assets. It’s an indication that the firm believes its know-how and playbooks are its competitive advantage.
Recurring responsibilities:
- Convert institutional knowledge into AI fuel: “establishing multi-year roadmaps for converting transactional precedents, contract templates, and practice playbooks into structured, AI-optimized knowledge repositories” (Latham)
- Maintain the working documents: “clause libraries, negotiation playbooks, market terms databases, brief banks, motion templates, litigation strategy guides, and practice-specific guidance materials optimized for utilization as AI context” (Latham)
- Govern the lifecycle of knowledge assets: “establish governance frameworks for the lifecycle of knowledge assets – including creation, contribution, review, maintenance, retirement, access control, and responsible use” (Covington)
- Reposition KM as infrastructure: “transforming KM from a passive repository function into strategic infrastructure for legal service delivery, attorney leverage, and firmwide innovation” (Covington)
Examples are Latham Associate Director of AI - Knowledge Transformation; Covington Director of Knowledge Management and Practice Technology
AI Governance and Risk
This person ensures AI usage is compliant with internal policies, ethics rules, and regulatory requirements.
Recurring responsibilities:
- Own the governance framework: “leading the development and execution of the firm’s AI governance framework, ensuring compliance with evolving legal and ethical standards, including AI safety and the responsible use of AI” (Latham)
- Track the regulatory landscape: candidates must have “a comprehensive knowledge of relevant AI and privacy legislation, including the EU AI Act and GDPR” (Latham)
- Gate what goes live: “define and operate a workflow lifecycle with required quality gates, including explicit approval criteria prior to publishing (tested prompts, consistent outputs, clear/non-confidential naming, scoped access, and tagging)” (King & Spalding)
- Answer clients directly: “engaging with firm clients as necessary to answer questions related to the responsible use of Generative AI and the firm’s AI governance framework” (Latham)
Examples are Latham Associate Director of AI Governance; governance duties also embedded in the King & Spalding Agentic Workflow Specialist, Covington Director of Applied AI
Platform and Product Owner
This person owns the tools. They’re responsible for the vendor relationships, product roadmap, pilots, and return on investment (ROI). Skills include product management, enterprise implementation experience, and change management.
Recurring responsibilities:
- Integrate it with the tools lawyers already use: “oversee the deployment and integration of GenAI solutions into existing legal workflows, including K&S Canvas, iManage, legal research platforms, and eDiscovery services” (King & Spalding)
- Run disciplined pilots with measurable results: “leading pilots, proof-of-concept initiatives, feature testing, controlled releases, and practice group use case evaluations, including defining success criteria, gathering feedback, assessing outcomes, and supporting go/no-go decisions” (Latham)
- Manage the vendor: “maintaining active relationships with third-party AI application vendors, monitoring roadmap developments, product releases, known issues, feature changes, performance trends, and platform limitations” (Latham)
- Evaluate and implement: “hands on experience leading enterprise software implementation from evaluation through deployment, including both strategic planning and day-to-day execution” (Kirkland); “complete deep dives on practice group use case requests, developing and stress testing workflows” and “advising on build-versus-buy decisions” (Covington)
- Tie the tool to money: “define and track key performance indicators (KPIs) such as AI-assisted matter volume, revenue, profitability, and user satisfaction” and “analyze platform impact on alternative fee arrangements, write-offs, and matter efficiency” (King & Spalding)
Examples are King & Spalding Legal GenAI Platform (Harvey) Specialist; Latham Innovation Attorney - Product Owner
Client-facing AI strategy
This is a business development role to own the firm’s client-facing AI narrative and value proposition. They can answer the client’s questions about data privacy and governance while helping to sell AI capability as a differentiation.
Recurring responsibilities:
- Own the firm’s AI story: “owning the development, refinement, and governance of the firm’s client‑facing technology narrative, value proposition, and market messaging” (Latham)
- Coach the partners: “serving as a senior advisor to partners, practice leaders, Business Development, client teams, and firm leadership on positioning the firm’s AI and innovation capabilities in strategic client conversations” (Latham)
- Deliver AI on client matters: “partnering with practice teams to deliver AI-assisted legal services directly to clients” (Kirkland AI Innovation Advisor); serve “as a trusted advisor embedded within case teams” (Kirkland Review & AI Services Principal)
Examples are Latham Associate Director of AI & Innovation - Client Services; client-facing elements in the Kirkland AI Innovation Advisor and Kirkland Legal Practice Technology Principal of Review & AI Services
AI Engineering
These are technical roles for the few large firms building their own AI products. Small firms can safely ignore this.
Recurring responsibilities:
- Build custom AI systems in-house: “maintain RAG pipelines using vector databases and knowledge graph solutions. Develop AI agents that automate multi-step workflows” (Kirkland)
- Ship real software: “the design and development of full-stack Machine Learning (ML) and Generative AI (GenAI) applications and workflows tailored to optimize existing legal and business processes” (Latham)
- Run the hardware and cloud underneath: lead “on‑premise Graphics Processing Unit (GPU) clusters, Microsoft Azure AI and Machine Learning (ML) services, and shared AI platform components” (Kirkland)
Examples are Kirkland Innovation AI Developer, AI Infrastructure Director, and AI Infrastructure Senior Engineer I; Latham Supervisor of AI Software Engineering