The Principal Program Manager is responsible for executing complex enterprise generative AI implementation programs with discipline, urgency, and measurable results.

This individual will orchestrate multiple integrated workstreams, ensuring that technical implementation, business objectives, governance, organizational readiness, and user adoption remain aligned throughout the program. The Principal Program Manager will establish the operating structure required to move programs forward, proactively address risks and dependencies, and ensure that new capabilities become embedded within the client’s organization.

This is not a traditional project coordination or status-reporting position. The Principal Program Manager must be capable of leading large-scale enterprise software implementations, driving accountability across teams, navigating complex technical and organizational environments, and communicating effectively with senior executives.

The successful candidate will combine strong program-management discipline with an understanding of generative AI, enterprise technology, data, security, governance, and organizational change.

Key Responsibilities

Strategic Implementation Leadership

• Lead large-scale enterprise generative AI implementation programs from planning through deployment, adoption, and ongoing optimization.
• Translate business objectives and technical requirements into an integrated implementation strategy, program roadmap, and measurable delivery plan.
• Develop comprehensive adoption plans addressing organizational change, communications, training, skill development, process redesign, and user readiness.
• Establish governance models, decision-making structures, success metrics, and scalable roadmaps for embedding generative AI capabilities throughout client organizations.
• Ensure implementation decisions remain aligned with business outcomes, technical feasibility, security requirements, governance standards, and responsible AI practices.
• Partner with business, technology, data, security, legal, compliance, and change-management leaders to resolve issues and maintain alignment.
• Help clients move from experimentation and isolated use cases to scalable, repeatable, and governed enterprise AI capabilities.

Program Management Excellence

• Lead cross-functional teams of consultants, engineers, architects, data professionals, change-management leaders, and subject matter experts.
• Manage multiple concurrent workstreams while maintaining visibility into integration points, critical dependencies, milestones, risks, and decisions.
• Establish the program structure, execution methodology, documentation standards, controls, and stage gates required for predictable delivery.
• Develop and maintain integrated program plans covering scope, schedule, resources, dependencies, milestones, risks, decisions, and business outcomes.
• Drive accountability across teams by assigning clear ownership, monitoring commitments, escalating obstacles, and ensuring timely resolution.
• Create and operationalize risk-mitigation strategies addressing technical, organizational, data, security, governance, adoption, and delivery challenges.
• Ensure required documentation, approvals, quality controls, and governance checkpoints are completed throughout the implementation.
• Monitor program health and proactively adjust plans based on emerging business needs, technical constraints, implementation findings, and adoption results.
• Identify cross-program conflicts and influence peripheral teams to prioritize work required for successful program delivery.
• Maintain strong delivery discipline while adapting implementation approaches as client needs and technical capabilities evolve.

Governance and Executive Communication

• Establish and facilitate cross-functional and cross-program operating cadences.
• Lead program syncs, workstream reviews, governance meetings, steering committee sessions, and executive business reviews.
• Provide clear visibility into program progress, outcomes, risks, dependencies, decisions, and required leadership actions.
• Create executive-level reporting that connects implementation progress to business value and adoption.
• Facilitate decisions and tradeoff discussions involving scope, timing, risk, resources, technical complexity, and organizational readiness.
• Ensure business and technical stakeholders remain aligned on priorities, responsibilities, decisions, and success criteria.
• Communicate upcoming changes and explain their potential impact on users, business processes, technical teams, and customers.
• Define and report metrics covering delivery performance, implementation quality, risk, adoption, operational readiness, and business outcomes.

Adoption and Organizational Change

• Ensure generative AI capabilities are operationalized and adopted rather than simply deployed.
• Partner with change-management and business leaders to identify impacted roles, processes, skills, and operating models.
• Develop adoption strategies that address stakeholder engagement, communications, training, enablement, feedback, and reinforcement.
• Track adoption and usage metrics, identify barriers, and implement corrective actions.
• Establish feedback loops that allow implementation and adoption strategies to evolve based on user needs and program results.
• Support the development of sustainable governance and operating models that remain effective after implementation.

Required Qualifications

• Ten or more years of progressive program or project management experience.
• At least five years of experience managing complex enterprise software, technology, platform, or digital transformation implementations.
• Demonstrated experience leading large, multi-workstream programs involving business, technology, data, security, governance, and organizational change.
• Strong understanding of artificial intelligence and machine-learning technologies, particularly generative AI applications in enterprise environments.
• Experience managing the implementation, integration, adoption, or scaling of AI-enabled products or capabilities.
• Ability to navigate complex technical architectures, system integrations, data ecosystems, and implementation dependencies.
• Understanding of enterprise considerations involving data quality, security, privacy, governance, performance, responsible AI, and production readiness.
• Experience developing and executing organizational change-management and adoption plans.
• Experience establishing program governance, success metrics, reporting, delivery methodologies, documentation standards, and stage gates.
• Experience leading programs using Scrum@Scale, SAFe, Disciplined Agile, LeSS, or a comparable scaled delivery framework.
• Demonstrated ability to manage concurrent workstreams and proactively resolve cross-team dependencies.
• Experience facilitating steering committees, governance reviews, executive business reviews, and cross-functional program meetings.
• Strong executive presence and the ability to communicate technical progress, risks, decisions, and tradeoffs to non-technical leaders.
• Demonstrated ability to influence teams and stakeholders without direct authority.
• Strong sense of accountability, urgency, organization, and ownership.
• Excellent written, verbal, facilitation, and stakeholder-management skills.

Preferred Qualifications

• Experience leading enterprise generative AI or agentic AI implementations.
• Experience taking an AI capability from proof of concept through production deployment and enterprise adoption.
• Consulting or professional services experience working directly with large enterprise clients.
• Experience within Adobe, digital experience, creative production, content supply chain, MarTech, or enterprise workflow environments.
• Experience coordinating globally distributed teams and delivery partners.
• Experience managing vendor, platform, and systems-integration dependencies.
• Knowledge of AI governance, responsible AI, model evaluation, human-in-the-loop workflows, and production AI operations.
• Relevant program-management or Agile certifications.

What Success Looks Like

Success in this role means complex implementation programs move forward with urgency, discipline, and clear accountability. Workstreams remain aligned, dependencies and risks are addressed before they affect delivery, executives have reliable visibility into progress, and implementation decisions support measurable business outcomes.

Most importantly, generative AI capabilities must become a sustainable part of how the client operates. Success is measured not only by deployment, but also by adoption, operational readiness, governance, and lasting business value.