• Own the end-to-end colleague experience that spans AI Marketplace tools, Loom, MemX, and Forge49, the product no single engineering pillar owns, defining how a colleague’s work moves across platforms as one coherent journey.
  
• Overseeing engineering pillars’ roadmaps. Adoption evidence sets what Platform Engineering teams build next. The pillars own how and how well; this team owns what matters most and for whom. The CDAO arbitrates when demand and feasibility collide.
  
• Maintain product-minded ownership of the three-platform experience, validated through continuous engagement with colleagues across R&D, Commercial, PGS, and enabling functions.
  

• Own the adoption model for the three platforms, including role-cohort rollout on a regular cadence, so capability reaches the colleagues who carry the heaviest load first.
  
• Operate a central enablement hub that owns the rollout method, AI literacy, and practitioner training, focused on how colleagues think and work with these tools, not only how to click through them.
  
• Orchestrate a federated champions network. Champions are embedded in the divisions on a hub-and-spoke model, dotted-line and division-funded, not centralized headcount. The hub sets the standard and the cadence; the divisions supply the reach.
  
• Own the feedback path from the field back into the roadmap, ensuring adoption friction is surfaced as a prioritization signal rather than absorbed silently.
  

• Own colleague journey mapping and friction analysis across the three platforms, the qualitative discipline that makes “voice of the divisions” a practice rather than a slogan.
  
• Investigate why colleagues route around the governed path, and translate those findings into product and roadmap direction for the engineering pillars.
  
• Serve as the qualitative complement to adoption measurement: measurement shows what is happening, design research explains why.
  

• Own the AI marketplace and catalog scoped to AI-native reusable assets and the three strategic platforms, the reuse-before-build front door that surfaces duplication before a second team builds the same thing.
  
• Curate the prompt and skill-pattern library, the approved patterns and reusable workflows colleagues call instead of a blank prompt box. This is content and practice curation; the registry and runtime infrastructure are owned by the engineering pillars.
  
• Coordinate with the Head of Portfolio and Operations on reuse intelligence. Enterprise-wide reuse across all software demand sits with Portfolio and Operations; this catalog is the AI-native, three-platform view that feeds it.
  

• Own adoption measurement for the three platforms as the source of truth, governed-path versus shadow-path usage, reuse rate, leakage, and hours recovered. This is the steering signal that drives prioritization, not a downstream report card.
  
• Own value realization for AI Marketplace tools, Loom, MemX, and Forge49, and report it up to Portfolio and Operations on their shared value standard. Portfolio and Operations owns the value-measurement methodology and the enterprise scorecard; this team applies that standard to the three platforms and AI Marketplace tools and reports conformant results. Enterprise aggregation across Tableau, Snowflake, and all other investments sits with Portfolio and Operations.
  
• Feed the three-platform adoption and value metrics into the AI value scorecard as its source for those platforms, so there is one instrumentation pipe and one owner rather than competing dashboards.
  

• Heads of AI & Agentic and Data & BI Platform Engineering, the daily relationship. This role supplies the binding demand signal that shapes their roadmaps; they supply feasibility, cost, and sequencing reality. A productive peer tension, arbitrated by the CDAO.
  
• Head of Portfolio and Operations, the enterprise boundary. Demand intake, run-the-business demand, and portfolio-level value aggregation live there; this role hands off at both ends of the loop and reports three-platform value up on their shared standard.
  
• Head of Trusted AI, this role surfaces trust as an adoption barrier when colleagues will not use a system they do not trust, and feeds it to Trusted AI, which owns the responsible-use standard. This role never owns governance policy, the accelerator and the brake stay separate.
  
• Division leaders across R&D, Commercial, PGS, and enabling functions, the stakeholder relationships through which adoption actually propagates and through which the champions network is federated.
  

• Lead, develop, and grow a small senior core across four functions, Cross-Platform Product Management, Experience & Design Research, AI Marketplace & Catalog, and Adoption Measurement, and orchestrate a federated champions network beyond it.
  
• Serve as a senior voice in the CoE leadership team, contributing to enterprise AI strategy and operating-model decisions as the authority on the colleague experience and adoption.
  
• Build a team culture that treats adoption as a product outcome earned on merit, not imposed by mandate, the conviction that the governed path wins because it is genuinely the better path.
  
• Champion a practitioner community across the federated functions, propagating adoption through embedded champions rather than only through central programs.
  

• Master's degree in Computer Science, Information Systems, Engineering, or a related discipline.  Significant relevant experience with a Bachelor's degree may be considered in lieu of a Master's degree.
  
• 15+ years of progressive experience in enterprise product management, digital adoption, or platform go-to-market, with a minimum of 10 years in a senior leadership role.
  
• Proven track record of driving adoption of enterprise platforms at scale in a large, complex, federated organization, measured by realized usage and value, not launch activity.
  
• Demonstrated ability to own a cross-platform product experience spanning multiple engineering teams that this role does not directly manage, through influence and a credible prioritization mandate.
  
• Strong command of adoption and value measurement, defining metrics, instrumenting them, and translating them into an executive narrative connected to enterprise outcomes.
  
• Exceptional cross-functional credibility: able to carry equal authority with engineering leaders, division heads, and senior business stakeholders.
  

• Experience standing up a champions or practitioner community on a federated, hub-and-spoke model.
  
• Background in design research or service design, or a track record of embedding it in a product organization.
  
• Familiarity with agentic AI and modern AI-native colleague tools, and the workflow change they require.
  
• Experience in a regulated industry where adoption must respect governance and validation boundaries
  

Relocation assistance may be available based on business needs and/or eligibility.

Candidates must be authorized to be employed in the U.S. by any employer.

U.S. work visa sponsorship (such as TN, O-1, H-1B, etc.) is not available for this role now or in the future.