Enterprise AI Transformation Lead

About the role

Everlaw drives how legal teams discover and use truth-focused information, using technology to multiply human capability. This role is centered on being a hands-on builder-operator deeply working within enterprise AI platforms, agents, connectors, and workflows to shape how technology is used in practice. You will own the design and execution of the AI transformation roadmap for the legal and security technology stack, ensuring that tools are integrated into a coherent shared stack rather than fragmented point solutions. A core part of this position involves researching the AI landscape, maintaining awareness of tools and capabilities, and directing formal evaluation to filter noise and identify clear value. You will guide departments on specialized tools while respecting domain ownership, translating broad goals into tangible decision rights, operating rhythms, and measurable outcomes. Success in this role requires strong judgment on when to build, buy, extend, scale, pause, or retire capabilities across the enterprise. You will act as a bridge between technical teams and business stakeholders, influencing executives, Legal, Security, Finance, Procurement, and Learning & Development to ensure alignment with strategic priorities.

Key facts

What you'll do

Build hands-on expertise in Everlaw's enterprise AI platforms, understanding strengths, limitations, integrations, and roadmaps to guide piloting, promotion, or standardization.

Research the AI landscape and maintain awareness of tools and capabilities, filtering noise and directing formal evaluation where value is clear.

Prevent fragmented adoption by maintaining a coherent shared stack and advising departments on specialized tools while respecting domain ownership.

Translate broad business goals into clear roadmaps, decision rights, operating rhythms, and measurable outcomes for AI initiatives.

Exercise strong judgment on when to build, buy, extend, scale, pause, or retire capabilities to align with enterprise priorities.

Influence executives, department leaders, IT, Security, Legal, Finance, Procurement, and Learning & Development to drive consistent adoption practices.

Explain complex technical, risk, cost, and business-value tradeoffs in clear language to stakeholders with diverse backgrounds.

Evaluate enterprise AI tooling through hands-on use, testing, and assessment to support informed adoption decisions.

Champion standards for AI platforms, agents, connectors, and workflows to enable scalability and reduce redundancy.

Partner with department AI owners and champions to ensure solutions meet operational needs and compliance standards.

Support the design of operating models that define roles, responsibilities, and decision frameworks for AI initiatives.

Contribute to Learning & Development efforts by sharing best practices, lessons learned, and playbooks for AI usage.

Monitor emerging capabilities in the AI space and assess potential integration into the enterprise stack where appropriate.

Collaborate with Security and Legal to ensure risk, governance, and regulatory considerations are embedded in AI transformation efforts.

Requirements

10+ years of experience in digital transformation, AI and automation, internal platforms, product operations, or corporate systems.

3+ years leading complex cross-functional programs involving multiple stakeholders and dependencies.

Hands-on fluency with enterprise AI tooling and platforms, including regular use, evaluation, and testing.

Demonstrated ability to translate broad goals into clear roadmaps, decision rights, operating rhythms, and measurable outcomes.

Strong judgment on when to build, buy, extend, scale, pause, or retire capabilities.

Experience influencing executives, department leaders, IT, Security, Legal, Finance, Procurement, and Learning & Development.

Clear communication skills for explaining complex technical, risk, cost, and business-value tradeoffs.

Bachelor's degree or equivalent practical background.

Comfort working in an at-will environment where responsibilities may evolve with business needs.

Ability to manage multiple priorities in a fast-paced enterprise technology landscape.

Commitment to maintaining confidentiality and handling sensitive information with integrity.

Readiness to engage with tools and workflows that may require rapid adaptation to new platforms and processes.

Practical notes

an at-will position; benefits details are available Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

About the company

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