Director, Workplace AI Portfolio Lead

About this role

The Workplace AI Portfolio Lead owns the enterprise portfolio strategy, prioritization, and value realization for AI-enabled workplace experiences and workflow automation. This role partners with the Head of Digital Workplace AI to help define the Workplace AI vision and translates it into a focused set of sequenced initiatives, ensuring engineering capacity is aligned to the highest impact outcomes. The scope spans platforms like M365 Copilot, ChatGPT Enterprise, Copilot Studio agents, and workflow automation across Microsoft 365 and the Power Platform, with a focus on scalable employee experiences, reusable patterns, and measurable business impact. 
 
This leader is accountable for portfolio clarity and outcomes. They set the “what and why,” run the portfolio operating model, and ensure impact can be measured and communicated. They partner closely with Workplace AI Engineering, Workplace AI Operations, Adoption and Change Management, and Release and Transformation, as well as AI leaders across the firm. They help shape the Workplace AI North Star, including the target employee experience, priority scenarios, and the sequencing required to scale responsibly. 

Key Responsibilities 

Portfolio Strategy, Intake, and Prioritization 

• Own a single enterprise portfolio and backlog across Workplace AI initiatives (including M365 Copilot, ChatGPT Enterprise, Copilot Studio agents, and automation), consolidating intake to reduce duplication and improve transparency. 

• Establish a standardized prioritization rubric that drives repeatable trade-offs across impact, scalability, reuse potential, readiness, risk, and effort. 

• Own and run portfolio decision forums. Lead quarterly planning and maintain a resourced roadmap (Now, Next, Later) aligned to actual engineering capacity and key dependencies. 

• Operate a clear “stop doing” and re-sequencing mechanism to protect capacity for priority initiatives and avoid perpetual pilots. 

Engineering Capacity Alignment and Execution Readiness  

• Build and maintain a capacity model that allocates effort across priority initiatives, reusable platform capabilities, reliability and technical debt, and time-boxed experimentation. 

• Partner with Workplace AI Engineering leadership to reduce parallel work, sequence dependencies, and improve throughput and time to impact. 

• Define stage gates from idea through scaled release (discovery, pilot, production, post-deployment) with clear readiness and exit criteria. 

• Ensure delivery plans account for constraints such as data readiness, integrations and connectors, identity and permissioning, security reviews, and release readiness. 

Vision and Product Strategy 

• Co-develop and maintain the Workplace AI vision and multi-horizon strategy (Now, Next, Later), in partnership with the Head of Digital Workplace AI and Digital Workplace AI Exco. 

• Translate the vision into a small set of enterprise scenario bets, with clear principles, guardrails, and sequencing that reflect real capacity and dependencies. 

• Own the portfolio narrative for Workplace AI, ensuring leadership and stakeholders understand what we are building, why it matters, and how it connects across tools. 

Experience Strategy and Scenario Focus 

• Translate the Workplace AI vision into an executable scenario blueprint and capability map that clarifies what will be standardized, reused, and bespoke. 

• Maintain a prioritized scenario library that defines target users, workflows, and expected outcomes for high-frequency moments that matter. 

• Drive coherence across Copilot, chat, agents, and automation so employees have a consistent path from question to action across channels. 

Value Realization, Measurement, and Executive Reporting 

• Define outcome metrics for priority scenarios (time saved, cycle time reduction, deflection, quality, user satisfaction, etc.) and ensure they are measurable and attributable. 

• Establish a m