The Program Manager 4 is a vital leader within Oracle's AI initiatives, responsible for managing and executing high-impact programs. This role demands exceptional organizational skills and a strategic mindset to align diverse teams and deliver innovative AI solutions. The successful candidate will play a key role in shaping Oracle's AI strategy, ensuring its ethical and sustainable integration across the organization.
Internal Responsibilities
- Independently lead large, ambiguous, or multi-phase AI and technology programs with significant organizational impact.
- Own end-to-end program outcomes across multiple projects, workstreams, teams, vendors, systems, or business units.
- Translate business and AI strategies into integrated roadmaps, milestones, deliverables, resource plans, and measurable outcomes.
- Make informed recommendations about program sequencing, priorities, scope, and investment based on business value, risk, delivery feasibility, and resource capacity.
- Maintain alignment between program execution, organizational priorities, approved funding, and expected business benefits.
- Support the progression of AI capabilities from discovery and experimentation through deployment, adoption, monitoring, and operational support.
Cross-Functional Leadership
- Lead through influence and expertise across product, engineering, data, business operations, security, privacy, legal, compliance, procurement, finance, and other participating functions.
- Align senior leaders and cross-functional stakeholders around shared objectives, responsibilities, timelines, decisions, and success measures.
- Facilitate decisions when stakeholders have competing priorities, requirements, constraints, or definitions of success.
- Clarify ownership and reinforce accountability for deliverables, dependencies, approvals, and follow-up actions.
- Serve as a trusted program advisor by presenting evidence-based recommendations, tradeoffs, risks, and decision options.
- Communicate complex AI, technical, and operational topics clearly to both technical and nontechnical audiences.
- Represent assigned programs in executive reviews, governance forums, steering committees, and strategic-planning discussions.
Program Planning and Execution
- Develop and maintain integrated program plans covering discovery, use-case evaluation, data readiness, solution development, testing, implementation, adoption, and operational transition.
- Coordinate activities and dependencies across multiple project teams and workstreams.
- Monitor progress against scope, schedule, budget, quality, risk, adoption, and business-outcome targets.
- Identify underperforming or delayed workstreams and lead corrective-action planning.
- Manage changes to program scope, timelines, priorities, resources, and requirements through an appropriate change-control process.
- Ensure key program decisions, assumptions, risks, dependencies, approvals, and commitments are documented and communicated.
- Anticipate delivery challenges and develop solutions before issues materially affect program outcomes.
Governance, Risk, and Responsible AI
- Establish fit-for-purpose governance structures, decision forums, escalation paths, reporting standards, and accountability mechanisms.
- Develop and maintain program risk registers, dependency maps, decision logs, action trackers, and change-control documentation.
- Identify and manage delivery, technology, data, privacy, security, compliance, adoption, vendor, and operational risks.
- Develop mitigation and contingency plans and escalate material concerns with clear recommendations.
- Partner with appropriate subject-matter experts to incorporate responsible AI, data-governance, privacy, security, legal, and regulatory considerations into program plans.
- Support realistic expectations regarding AI capabilities, limitations, appropriate use, and required human oversight.
- Ensure governance and control requirements are integrated into delivery activities rather than addressed only at the end of the program.
Financial, Resource, and Vendor Management
- Develop and manage program budgets, financial forecasts, staffing assumptions, and resource plans.
- Track spending against approved budgets and explain material variances.
- Identify capacity, capability, funding, or resource constraints and develop options for resolving them.
- Evaluate tradeoffs among cost, scope, timing, risk, quality, and expected benefits.
- Support procurement, contracting, vendor selection, and statement-of-work development when required.
- Monitor third-party commitments, dependencies, deliverables, and performance.
- Ensure program investments remain aligned with strategic priorities and anticipated business value.
Measurement and Business Outcomes
- Define clear program objectives, key performance indicators, adoption measures, and benefit-realization targets.
- Prepare concise executive dashboards, business reviews, status reports, decision materials, and recommendations.
- Evaluate whether delivered AI capabilities are producing intended operational, employee, customer, financial, or compliance outcomes.
- Monitor adoption, user readiness, process integration, and operational performance after implementation.
- Use data and stakeholder feedback to identify performance gaps and recommend corrective actions.
- Capture lessons learned and incorporate them into future planning, governance, and delivery practices.
Change Management and Operational Readiness
- Integrate stakeholder engagement, communications, training, adoption, and readiness activities into the overall program plan.
- Assess organizational readiness and identify barriers that could limit adoption or business value.
- Coordinate operational-readiness reviews before major launches, releases, or transitions.
- Ensure support models, ownership, documentation, monitoring, controls, and escalation procedures are established before implementation.
- Partner with business leaders to define new processes, responsibilities, and ways of working enabled by AI capabilities.
- Promote sustainable adoption by balancing innovation and delivery speed with operational readiness and responsible use.
Program-Management Excellence
- Create scalable program-management frameworks, governance mechanisms, templates, playbooks, and reporting standards.
- Identify opportunities to simplify delivery, strengthen controls, improve decision-making, and reduce unnecessary administrative work.
- Promote consistent project- and program-management practices across participating teams.
- Mentor project managers, program managers, and workstream leaders while remaining an individual contributor.
- Contribute expertise to portfolio planning, prioritization, investment decisions, and organizational capability-building initiatives.
- Model sound judgment, accountability, transparency, collaboration, and continuous improvement.
Required Qualifications
- Bachelor’s degree in business administration, program management, information technology, computer science, engineering, data analytics, or a related discipline, or equivalent relevant professional experience.
- Eight or more years of progressively responsible experience in program management, project management, technology delivery, operations, business transformation, or a related field.
- Demonstrated experience independently owning complex, enterprise-level programs with multiple projects, workstreams, stakeholders, systems, or dependencies.
- Experience leading technology, AI, data, automation, analytics, cloud, or emerging-technology programs.
- Proven ability to operate effectively with limited day-to-day direction and make sound decisions in ambiguous or rapidly changing environments.
- Demonstrated ability to influence senior leaders and cross-functional teams without formal authority.
- Experience translating strategic objectives into structured roadmaps, governance mechanisms, delivery plans, and measurable outcomes.
- Experience identifying and resolving complex cross-functional risks, dependencies, resource constraints, and delivery challenges.
- Strong executive communication, presentation, facilitation, negotiation, and stakeholder-management skills.
- Experience managing program scope, schedules, budgets, resources, vendors, risks, issues, and performance measures.
- Ability to communicate technical and AI-related concepts clearly to both technical and nontechnical audiences.
- Experience preparing executive dashboards, program reviews, recommendations, and decision materials.
- Strong analytical, organizational, problem-solving, and decision-making capabilities.
- Proficiency with standard project-management, collaboration, spreadsheet, reporting, and presentation tools.
Preferred Qualifications
- Master’s degree in business administration, management, information systems, data science, technology, or a related field.
- Project Management Professional, Program Management Professional, Agile, Scrum, Lean Six Sigma, or change-management certification.
- Experience managing AI or machine-learning initiatives through discovery, evaluation, deployment, adoption, monitoring, and operational support.
- Experience incorporating privacy, security, model-risk, compliance, data-governance, or responsible AI requirements into program delivery.
- Experience leading enterprise transformation, technology implementation, process-improvement, or operational-change programs.
- Experience working in a regulated, highly matrixed, or geographically distributed organization.
- Familiarity with portfolio management, product operating models, benefits realization, and executive-level governance.
- Experience managing external technology, consulting, implementation, or strategic vendor relationships.
External Responsibilities
- Independently lead large, ambiguous, or multi-phase AI and technology programs with significant organizational impact.
- Own end-to-end program outcomes across multiple projects, workstreams, teams, vendors, systems, or business units.
- Translate business and AI strategies into integrated roadmaps, milestones, deliverables, resource plans, and measurable outcomes.
- Make informed recommendations about program sequencing, priorities, scope, and investment based on business value, risk, delivery feasibility, and resource capacity.
- Maintain alignment between program execution, organizational priorities, approved funding, and expected business benefits.
- Support the progression of AI capabilities from discovery and experimentation through deployment, adoption, monitoring, and operational support.
Cross-Functional Leadership
- Lead through influence and expertise across product, engineering, data, business operations, security, privacy, legal, compliance, procurement, finance, and other participating functions.
- Align senior leaders and cross-functional stakeholders around shared objectives, responsibilities, timelines, decisions, and success measures.
- Facilitate decisions when stakeholders have competing priorities, requirements, constraints, or definitions of success.
- Clarify ownership and reinforce accountability for deliverables, dependencies, approvals, and follow-up actions.
- Serve as a trusted program advisor by presenting evidence-based recommendations, tradeoffs, risks, and decision options.
- Communicate complex AI, technical, and operational topics clearly to both technical and nontechnical audiences.
- Represent assigned programs in executive reviews, governance forums, steering committees, and strategic-planning discussions.
Program Planning and Execution
- Develop and maintain integrated program plans covering discovery, use-case evaluation, data readiness, solution development, testing, implementation, adoption, and operational transition.
- Coordinate activities and dependencies across multiple project teams and workstreams.
- Monitor progress against scope, schedule, budget, quality, risk, adoption, and business-outcome targets.
- Identify underperforming or delayed workstreams and lead corrective-action planning.
- Manage changes to program scope, timelines, priorities, resources, and requirements through an appropriate change-control process.
- Ensure key program decisions, assumptions, risks, dependencies, approvals, and commitments are documented and communicated.
- Anticipate delivery challenges and develop solutions before issues materially affect program outcomes.
Governance, Risk, and Responsible AI
- Establish fit-for-purpose governance structures, decision forums, escalation paths, reporting standards, and accountability mechanisms.
- Develop and maintain program risk registers, dependency maps, decision logs, action trackers, and change-control documentation.
- Identify and manage delivery, technology, data, privacy, security, compliance, adoption, vendor, and operational risks.
- Develop mitigation and contingency plans and escalate material concerns with clear recommendations.
- Partner with appropriate subject-matter experts to incorporate responsible AI, data-governance, privacy, security, legal, and regulatory considerations into program plans.
- Support realistic expectations regarding AI capabilities, limitations, appropriate use, and required human oversight.
- Ensure governance and control requirements are integrated into delivery activities rather than addressed only at the end of the program.
Financial, Resource, and Vendor Management
- Develop and manage program budgets, financial forecasts, staffing assumptions, and resource plans.
- Track spending against approved budgets and explain material variances.
- Identify capacity, capability, funding, or resource constraints and develop options for resolving them.
- Evaluate tradeoffs among cost, scope, timing, risk, quality, and expected benefits.
- Support procurement, contracting, vendor selection, and statement-of-work development when required.
- Monitor third-party commitments, dependencies, deliverables, and performance.
- Ensure program investments remain aligned with strategic priorities and anticipated business value.
Measurement and Business Outcomes
- Define clear program objectives, key performance indicators, adoption measures, and benefit-realization targets.
- Prepare concise executive dashboards, business reviews, status reports, decision materials, and recommendations.
- Evaluate whether delivered AI capabilities are producing intended operational, employee, customer, financial, or compliance outcomes.
- Monitor adoption, user readiness, process integration, and operational performance after implementation.
- Use data and stakeholder feedback to identify performance gaps and recommend corrective actions.
- Capture lessons learned and incorporate them into future planning, governance, and delivery practices.
Change Management and Operational Readiness
- Integrate stakeholder engagement, communications, training, adoption, and readiness activities into the overall program plan.
- Assess organizational readiness and identify barriers that could limit adoption or business value.
- Coordinate operational-readiness reviews before major launches, releases, or transitions.
- Ensure support models, ownership, documentation, monitoring, controls, and escalation procedures are established before implementation.
- Partner with business leaders to define new processes, responsibilities, and ways of working enabled by AI capabilities.
- Promote sustainable adoption by balancing innovation and delivery speed with operational readiness and responsible use.
Program-Management Excellence
- Create scalable program-management frameworks, governance mechanisms, templates, playbooks, and reporting standards.
- Identify opportunities to simplify delivery, strengthen controls, improve decision-making, and reduce unnecessary administrative work.
- Promote consistent project- and program-management practices across participating teams.
- Mentor project managers, program managers, and workstream leaders while remaining an individual contributor.
- Contribute expertise to portfolio planning, prioritization, investment decisions, and organizational capability-building initiatives.
- Model sound judgment, accountability, transparency, collaboration, and continuous improvement.
Required Qualifications
- Bachelor’s degree in business administration, program management, information technology, computer science, engineering, data analytics, or a related discipline, or equivalent relevant professional experience.
- Eight or more years of progressively responsible experience in program management, project management, technology delivery, operations, business transformation, or a related field.
- Demonstrated experience independently owning complex, enterprise-level programs with multiple projects, workstreams, stakeholders, systems, or dependencies.
- Experience leading technology, AI, data, automation, analytics, cloud, or emerging-technology programs.
- Proven ability to operate effectively with limited day-to-day direction and make sound decisions in ambiguous or rapidly changing environments.
- Demonstrated ability to influence senior leaders and cross-functional teams without formal authority.
- Experience translating strategic objectives into structured roadmaps, governance mechanisms, delivery plans, and measurable outcomes.
- Experience identifying and resolving complex cross-functional risks, dependencies, resource constraints, and delivery challenges.
- Strong executive communication, presentation, facilitation, negotiation, and stakeholder-management skills.
- Experience managing program scope, schedules, budgets, resources, vendors, risks, issues, and performance measures.
- Ability to communicate technical and AI-related concepts clearly to both technical and nontechnical audiences.
- Experience preparing executive dashboards, program reviews, recommendations, and decision materials.
- Strong analytical, organizational, problem-solving, and decision-making capabilities.
- Proficiency with standard project-management, collaboration, spreadsheet, reporting, and presentation tools.
Preferred Qualifications
- Master’s degree in business administration, management, information systems, data science, technology, or a related field.
- Project Management Professional, Program Management Professional, Agile, Scrum, Lean Six Sigma, or change-management certification.
- Experience managing AI or machine-learning initiatives through discovery, evaluation, deployment, adoption, monitoring, and operational support.
- Experience incorporating privacy, security, model-risk, compliance, data-governance, or responsible AI requirements into program delivery.
- Experience leading enterprise transformation, technology implementation, process-improvement, or operational-change programs.
- Experience working in a regulated, highly matrixed, or geographically distributed organization.
- Familiarity with portfolio management, product operating models, benefits realization, and executive-level governance.
- Experience managing external technology, consulting, implementation, or strategic vendor relationships.
