Sr. Director AI-Driven Product Development

Position Summary

Our organization is transforming how software gets built.

We're moving beyond ad-hoc AI tool adoption and into a fully operationalized AI-Driven Development Lifecycle (AI-DLC), a continuous model that replaces the traditional SDLC.

The AI Enablement Lead owns that transformation.

This role governs how AI is adopted and used: practice, tooling, training, and value tracking.

This role reports to the CTO and will work closely with the Software Development Leadership.

Objectives


* Own AI initiative intake and prioritization.

Build a repeatable process for triaging every pilot or build request against business value, data readiness, and team capacity.


* Standardize tooling and practice.

Evaluate and select agentic coding tools within the architecture the Software Technical Lead defines.

Build internal playbooks covering when and how to use agents, prompting and context patterns, and review standards for AI-generated code.

Prevent fragmented, team-by-team tool sprawl.


* Run a Product Operating Model (preferred method) transformation through pilot teams.

Frame initiative intake and prioritization around a small number of pilot teams pursuing strategy, discovery, and delivery changes together.


* Extend tooling standards to product discovery, not just engineering.

Product managers and designers using AI for rapid prototyping, synthesizing customer insight, and testing feasibility assumptions is in scope for the same playbooks currently written for engineers.


* Defines data availability requirements.

Coordinates with Hardware/Firmware leads on data availability requirements for strategic roadmap of AI capabilities; not to build firmware, but to make sure the product data contract is co-designed rather than software discovering the gap after the fact.


* Scout and promote reuse.

Actively look for overlapping AI solutions and patterns across teams.

Treat duplication as a standing risk to manage, not an occasional cleanup exercise.


* Build and run a champion network.

Recruit and coordinate AI champions embedded in each team to drive peer-led adoption, including training curriculum, and track completion and competency skills for engineering and product roles.


* Define and report value-realization metrics.

Track cycle time, PR throughput, defect rate, developer satisfaction, and cost/efficiency impact.

Report results to the CTO framed as business value, not adoption counts.


* Track governance capacity as a metric.

Monitor review- and security-gate throughput.

Under AI-DLC, this is the bottleneck that replaces code-writing speed, and it needs to be measured like one.


* Partner across the organization.

Align adoption pacing with the Director of Software Development's sprint capacity and delivery commitments.

Collaborate with the EAD team and Operations team on tools and best practices of AI adoption and program acceleration.

Coordinate with security and governance owners on data handling, code...