Enterprise AI Transformation Lead

At Everlaw, our mission is to promote justice by illuminating truth. We're changing how legal teams at law firms, corporations, government agencies, and nonprofits find the information they need to achieve their truth-finding goals. We believe technology should be a force multiplier for human intellect, and we see AI as an important part of how Everlawyers can work more effectively and thoughtfully.

Everlaw is building a deliberate, company-wide approach to AI—not simply to help people work faster, but to transform how we operate: redesigning workflows, improving the speed and quality of decisions, and creating new ways for teams to deliver meaningful outcomes.

Reporting to the VP of IT & Security, you’ll own Everlaw’s horizontal Corporate AI program end to end across governance, shared tooling strategy, adoption, measurement, and cost and vendor management. You’ll drive AI transformation by mobilizing department AI owners and champions and partnering closely with Learning & Development, Legal, and IT & Security.

This is a deeply hands-on role. You’ll get deep into enterprise AI platforms, agents, connectors, and workflows—evaluating tools, building and testing practical solutions, and translating what works into reusable capabilities that scale. You’ll move promising ideas from exploration to adoption, partner with Legal and Security to establish the guardrails needed to scale them responsibly, and measure whether they are changing how work actually gets done.

This is not an AI research or production engineering role, nor is it a traditional program-management or consulting role. It is a builder-operator role for someone who can work deeply in the systems while running the program with rigor—connecting systems, people, and execution so AI becomes a durable part of how Everlaw operates.

Keep Everlaw's federated AI efforts coherent, secure, and aligned to business priorities.

• Own and operate Everlaw’s federated AI framework: run its monthly and quarterly cadence, convene department AI owners, maintain clear accountability across the program, and report progress and risks to the leadership. 
• Mature the operating model as Everlaw learns—including its principles, decision rights, ownership boundaries, and RACI across Enterprise AI, IT, Security, Legal, and department AI owners.
• Partner with Legal and Security on data-use governance, including data classification, acceptable use, autonomy limits, and agent guardrails—enabling low-risk work to move quickly while applying appropriate scrutiny to higher-risk uses. 

Be Everlaw’s resident expert on its enterprise AI platforms and the market around them, shaping what enters the stack, what scales, and what exits.

• Build deep, hands-on expertise in Everlaw’s enterprise AI platforms. Use them regularly, understand their strengths, limitations, integrations, and roadmaps, and determine which capabilities are worth piloting, promoting, or standardizing.
• Stay ahead of the AI landscape by proactively researching and shortlisting tools and capabilities that could materially improve how Everlaw works—while filtering out noise and deciding what merits formal evaluation.
• Lead structured, evidence-based evaluations using rubrics, baseline tasks, scorecards, and business-value criteria. Build and test with the tools directly rather than relying on vendor demonstrations, and own recommendations on whether to extend existing platforms, buy new capabilities, or build internally.
• Maintain a coherent shared stack by preventing duplicative or fragmented adoption where an approved platform already meets the need. Advise departments on specialized tools while respecting their ownership of domain-specific applications.
• Coordinate procurement, implementation, rollout, and launch change management with IT, Procurement, Security, Legal, and other partners that own the relevant approval and delivery mechanics.

Turn the tools Everlaw already owns into changed workflows and measurable use — most companies use a fraction of what they pay for; you close that gap. 

• Build and maintain the central enablement playbook and template library—including intake, POC, change-management, and training patterns—so teams can move quickly without reinventing the basics.
• Provide hands-on guidance for high-value use cases: work directly with teams to redesign workflows and build agents or templates alongside them where targeted support is more effective than a formal engagement.
• Partner with Learning & Development to build practical AI fluency across Everlaw, including tool and model selection, effective AI collaboration, and cost-conscious use.
• Lead the AI champion community of practice, working with department AI owners and champions to surface adoption barriers, training needs, and reusable patterns—and feed what is learned back into playbooks and guidance.

Own the methodology that shows whether the Enterprise AI program is creating real value — and the discipline to act on what it shows.

• Own and mature the measurement system behind Everlaw's AI adoption framework — standards for pre-AI baselines, metric targets, and measured results — moving from adoption and activity metrics toward outcomes tied to workflow improvement, quality, risk reduction, and business value.
• Partner with department AI leaders to define success measures for function-specific initiatives, and validate value claims as initiatives graduate through the funnel, so reported impact holds up.
• Run regular portfolio reviews and manage the path from experiment to sustained adoption, with clear criteria for what should scale, consolidate, pause, or stop.

Ensure Everlaw’s AI investments remain sustainable and supported by the right partners.

• Maintain a portfolio-level view of AI spend across licenses, usage-based consumption, pilots, consultants, and implementation services.
• Own the cost-versus-value lens, annual planning, and investment recommendations for shared AI platforms, partnering with Finance, IT, Procurement, and business owners.
• Define practical consumption guardrails—including alerts, thresholds, approval flows, and caps—and adjust them as usage patterns evolve.
• Manage strategic AI vendor relationships, using roadmap insight, product feedback, adoption, and value data to inform negotiations and renewal recommendations.
• Build and maintain a vetted external partner bench for workflow redesign, agent design, configuration, integration, and enablement.

You are a hands-on builder-operator with deep practical fluency with enterprise AI tooling, strong business judgment, and the discipline to run a company-wide program. You can turn ambiguity into an operating program, work deeply with tools and workflows, and mobilize teams across the company without relying on direct authority.

You are likely to succeed in this role if you:

• Build structure from ambiguity, translating broad goals into clear roadmaps, decision rights, operating rhythms, and measurable outcomes.
• Get deep into the tools. You are a hands-on power user who builds and tests agents, configures connectors, prototypes end-to-end workflows, and troubleshoots where tools break down. You understand how models, permissions, data, and integrations shape what is practical—then turn that firsthand expertise into clear guidance, reusable patterns, and better platform decisions, without needing to write production code.
• Understand how teams work and can identify, redesign, and operationalize workflows where AI creates meaningful value.
• Exercise sound judgment about when to build, buy, extend, scale, pause, or retire capabilities.
• Exercise sound judgment about when to build, buy, extend, scale, pause, or retire capabilities.
• Influence across executives, department leaders, IT, Security, Legal, Finance, Procurement, and Learning & Development.
• Communicate complex technical, risk, cost, and business-value tradeoffs clearly.
• Bring 10+ years of experience in digital transformation, AI and automation, internal platforms, product operations, or corporate systems, including 3+ years leading complex cross-functional programs.