Enterprise AI Transformation Lead

Responsibilities

Enterprise AI Operating Model and Governance

• Own and operate Everlaw’s federated AI framework, including recurring operating cadences, department AI owners, accountability, and leadership reporting.
• Mature 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, acceptable use, autonomy limits, and agent guardrails.

Enterprise AI Stack and Evaluation

• Build hands-on expertise in enterprise AI platforms, including their capabilities, limitations, integrations, and roadmaps.
• Research and shortlist AI tools and capabilities that could materially improve company operations.
• Lead evidence-based evaluations using rubrics, baseline tasks, scorecards, and business-value criteria.
• Build and test tools directly, then recommend whether to extend existing platforms, buy new capabilities, or build internally.
• Maintain a coherent shared AI stack and advise departments on specialized tools.
• Coordinate procurement, implementation, rollout, and change management with IT, Procurement, Security, Legal, and other partners.

Adoption and Enablement

• Build and maintain enablement playbooks and template libraries for intake, proof of concept, change management, and training.
• Guide teams through workflow redesign and build agents or templates alongside them.
• Partner with Learning & Development on practical AI fluency, tool and model selection, effective AI collaboration, and cost-conscious use.
• Lead the AI champion community of practice and incorporate adoption feedback into playbooks and guidance.

Measurement and Portfolio Management

• Own the measurement system for AI adoption, including baselines, metric targets, and measured results.
• Track workflow improvement, quality, risk reduction, and business value rather than activity metrics alone.
• Partner with department AI leaders to define success measures and validate value claims.
• Run portfolio reviews and determine which initiatives should scale, consolidate, pause, or stop.

Spend, Vendors, and Partners

• Maintain a portfolio-level view of AI spending across licenses, usage-based consumption, pilots, consultants, and implementation services.
• Own cost-versus-value analysis, annual planning, and investment recommendations for shared AI platforms.
• Define consumption guardrails such as alerts, thresholds, approval flows, and caps.
• Manage strategic AI vendor relationships and inform negotiations and renewal recommendations.
• Build and maintain a vetted external partner bench for workflow redesign, agent design, configuration, integration, and enablement.

Requirements

• Hands-on builder-operator with practical fluency in enterprise AI tooling, strong business judgment, and the ability to run a company-wide program.
• Ability to turn ambiguity into roadmaps, decision rights, operating rhythms, and measurable outcomes.
• Experience building and testing agents, configuring connectors, prototyping end-to-end workflows, and troubleshooting tool limitations.
• Understanding of how models, permissions, data, and integrations affect practical AI deployment.
• Ability to identify, redesign, and operationalize workflows where AI creates meaningful value.
• Sound judgment about when to build, buy, extend, scale, pause, or retire capabilities.
• Ability to mobilize teams across the company without relying on direct authority.

Compensation and Benefits

• Compensation and benefits details were not provided in the available posting content.