AVP, Delivery Practices

Chubb•Readington Township, NJ

3d•$125,000 - $175,000

About The Position

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.

Requirements

Experience shaping how AI-enabled tools, methods, and ways of working are incorporated into the software development lifecycle.
Experience leading the design, implementation, and adoption of modern delivery practices.
Experience defining future state of delivery practices with a focus on improving speed, quality, consistency, and productivity.
Experience partnering with business architecture, business analysis, program delivery, quality assurance, technology, and governance teams.
Experience establishing standards, playbooks, and guidance for AI incorporation into delivery practices.
Experience defining practical ways of working using AI across the lifecycle (business analysis, requirements, design, documentation, testing support, etc.).
Experience ensuring AI-enabled practices complement existing agile, waterfall, and hybrid delivery models.
Experience promoting consistent methods that improve repeatability, transparency, and execution quality.
Experience evaluating and guiding the adoption of AI-enabled tools that support delivery teams.
Experience partnering with technology and governance teams to define AI tool usage within approved workflows, controls, and delivery environments.
Experience supporting common patterns for integrating AI into team routines, templates, documentation practices, and execution processes.
Experience ensuring teams understand where AI adds value, where human judgment is essential, and how outputs should be reviewed and validated.
Experience leading the adoption of AI-enabled delivery practices through training, coaching, communications, and practical enablement.
Experience helping teams incorporate AI into day-to-day work effectively and efficiently.
Experience developing adoption plans that support changes in behavior, team routines, skills, and operating models.
Experience creating repeatable approaches for scaling successful practices.
Experience supporting AI-enabled methods that strengthen testing effectiveness, quality discipline, and pre-production readiness.
Experience partnering with QA, business analysis, and delivery teams to improve test scenarios, validation activities, and issue analysis.
Experience defining where AI can improve quality, accelerate testing, and increase confidence.
Experience ensuring AI-enabled practices support quality outcomes without compromising rigor, traceability, or business confidence.
Experience partnering with governance, technology, risk, and other stakeholders to ensure AI-enabled delivery practices have appropriate controls and oversight.
Experience defining standards for responsible use, review, validation, documentation, and accountability for AI in delivery activities.
Experience identifying risks associated with AI-enabled SDLC practices and developing mitigation approaches.
Experience ensuring teams understand expectations for disciplined AI use in a complex and regulated business environment.
Experience monitoring emerging practices and identifying how delivery models should evolve with AI capabilities.
Experience running pilots, gathering lessons learned, and refining standards.
Experience establishing feedback loops with delivery teams.
Experience contributing to continuous improvement of operating models.
Experience partnering closely with leaders across planning, governance, business architecture, solutions, quality assurance, and technology.
Experience communicating clearly with stakeholders on the value, implications, and practical application of AI within the SDLC.
Experience serving as a trusted advisor on how AI will change delivery work.
Experience supporting leadership in decision-making regarding AI-enabled delivery transformation.

Responsibilities

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.
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.
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.
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.
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.
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.
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.
Partner closely with leaders across planning, governance, business architecture, solutions, quality assurance, and technology to align AI-enabled practices to enterprise needs.
Communicate clearly with stakeholders on the value, implications, and practical application of AI within the SDLC.
Serve as a trusted advisor on how AI will change delivery work and what the organization should do to prepare.
Support leadership in making thoughtful decisions about priorities, sequencing, investments, and readiness related to AI-enabled delivery transformation.