AVP, Delivery Practices

Job Description

The AVP, Delivery Practices Lead is responsible for shaping how AI-enabled tools, methods, and ways of working are incorporated into the software development lifecycle across the Transformation & Delivery Office (TDO). This role leads the design, implementation, and adoption of modern delivery practices that reflect how AI will change the way teams plan, analyze, design, build, test, document, and support technology solutions over time.

This leader will help define the future state of delivery practices for the TDO, with a focus on improving speed, quality, consistency, and productivity while maintaining appropriate discipline, controls, and business alignment. The role partners closely with business architecture, business analysis, program delivery, quality assurance, technology, and governance teams to ensure AI-enabled delivery practices are practical, scalable, and effectively adopted across the organization.

The title and career band/level for this position are flexible based on the candidate's experience.

AI-Enabled Strategy

• Define the future-state software development lifecycle for the TDO, with a focus on how AI will reshape planning, requirements development, solution design, testing, delivery execution, documentation, and support.
• Identify opportunities to improve speed, quality, and efficiency through responsible use of AI-enabled tools and practices.
• Develop a roadmap for introducing AI-enabled SDLC capabilities over time, balancing innovation with practicality, adoption readiness, and delivery needs.
• Partner with senior leaders to align AI-enabled delivery practices to broader transformation goals, modernization priorities, and business outcomes.

Delivery Practices & Standards

• Establish standards, playbooks, and guidance for how AI should be incorporated into delivery practices across the TDO.
• Define practical ways of working using AI across the lifecycle, including business analysis, requirements, design, documentation, testing support, and other delivery activities.
• Ensure AI-enabled practices complement existing agile, waterfall, and hybrid delivery models.
• Promote consistent methods that improve repeatability, transparency, and execution quality across teams.

Tooling & Workflow Enablement

• Evaluate and help guide the adoption of AI-enabled tools that support delivery teams across the SDLC.
• Partner with technology and governance teams to define how AI tools should be used within approved workflows, controls, and delivery environments.
• Support common patterns for integrating AI into team routines, templates, documentation practices, and execution processes.
• Help ensure teams understand where AI adds value, where human judgment remains essential, and how outputs should be reviewed and validated.

Adoption & Capability Building

• Lead the adoption of AI-enabled delivery practices through training, coaching, communications, and practical enablement.
• Help teams incorporate AI into day-to-day work in ways that are effective, efficient, and aligned to delivery expectations.
• Develop adoption plans that support changes in behavior, team routines, skills, and operating models as AI becomes more embedded in delivery work.
• Create a repeatable approach for scaling successful practices from pilot teams to broader use.

Testing, Quality & Production Readiness

• Support AI-enabled methods that strengthen testing effectiveness, quality discipline, and pre-production readiness.
• Partner with quality assurance, business analysis, and delivery teams to improve test scenarios and cases, validation activities, and issue analysis.
• Help define where AI can improve quality, accelerate testing, and increase confidence before solutions move into production.
• Ensure AI-enabled practices support quality outcomes without compromising rigor, traceability, or business confidence.

Governance, Risk & Responsible Use

• Partner with governance, technology, risk, and other stakeholders to ensure AI-enabled delivery practices are introduced with appropriate controls and oversight.
• Help define standards for responsible use, review, validation, documentation, and accountability when AI is used in delivery activities.
• Identify risks associated with AI-enabled SDLC practices and help develop mitigation approaches that support safe and effective adoption.
• Ensure teams understand expectations for disciplined use of AI in a complex and regulated business environment.

Continuous Improvement & Innovation

• Monitor emerging practices and identify how the TDO should evolve its delivery model as AI capabilities mature.
• Run pilots, gather lessons learned, and refine standards based on practical experience and measurable outcomes.
• Establish feedback loops with delivery teams to understand where AI is improving outcomes and where additional changes are needed.
• Contribute to continuous improvement of the TDO operating model by helping the organization adapt to the future of technology delivery.

Stakeholder Engagement

• Partner closely with leaders across…