AI Business Partner Senior Principal

Role Purpose 

The Senior Principal AI Business Partner (AIBP) is a senior technical and product leader who shapes AI investment, portfolio prioritization, and delivery standards across business units. This role combines executive problem framing with enterprise solution design—setting direction for complex, multi-stakeholder AI programs while remaining credible in architecture and early-stage delivery. 

Senior Principal AIBPs operate at portfolio and cross-BU scope: they align SBU sponsors, technology leaders, and AI IO delivery teams on where to invest, how to structure engagements, and how to scale what works. They mentor Principal AIBPs and contribute reusable patterns to the enterprise AI operating model. 

AI-native practice is expected at a leadership level—spec-driven development, agentic tooling, and the judgment to know when to engage hands-on versus when to orchestrate teams and forums. 

Key Responsibilities 

Enterprise Problem Framing & Portfolio Prioritization 

• Partner with SBU executive sponsors and functional leadership across domains to identify fundamental business problems with enterprise-scale AI potential. 

• Frame AI opportunities with financial, strategic, and technical rigor to enable confident investment decisions at portfolio and SteerCo forums. 

• Stack-rank and socialize AI initiatives across the portfolio based on value potential, feasibility, execution risk, and strategic alignment. 

• Provide Gate 1–ready recommendations and executive briefings that support sponsor decision-making. 

Enterprise AI Solution Design & Standards 

• Engage CIOs and technology leaders as a credible AI solution architect—contributing to architectural decisions, trade-off analysis, and solution design for the most complex initiatives. 

• Define and evolve reusable AI patterns, engagement models, and reference architectures adopted across AIBPs and delivery teams. 

• Shape enterprise build, buy, and adapt decisions with accountability for scale, security, and responsible AI. 

Product & Specification Leadership at Scale 

• Set the bar for execution-ready use case definition, specs, and acceptance criteria across programs you sponsor or oversee. 

• Review and coach Principal AIBPs and initiative teams on product thinking, user value, viability, and iteration paths. 

• Ensure solutions align with enterprise AI frameworks—not only local strategic fit. 

Complex Program & Early-Stage Leadership 

• Lead or oversee the most complex POCs, pilots, and early production efforts—engaging hands-on when risk or novelty requires it, and orchestrating cross-functional teams when scale requires it. 

• Normalize AI-first development practices across business units; model spec-driven and agentic workflows for the AIBP cohort. 

• Guide business units through the full AI lifecycle—from discovery and qualification through value realization and transition to run-state. 

Portfolio Leadership, Mentorship & Cross-Functional Influence 

• Co-lead portfolio management with PMO and AI Enablement—owning prioritization views, value realization tracking, and escalation paths for at-risk initiatives. 

• Mentor Principal AIBPs; contribute to hiring bar, onboarding, and capability development for the AIBP function. 

• Communicate progress, risk, and impact to C-suite and technology executive stakeholders. 

• Champion adoption of enterprise AI frameworks, governance, and best practices across business and technology teams. 

Qualifications & Experience 

Required 

• Demonstrated technical foundation with at least two of the following: 

• Formal education in engineering, computer science, data science, or related field 

• Professional training in AI/ML 

• Hands-on experience building AI or data-intensive systems 

• Proven experience working in an AI-first way, actively using modern agentic development tools. 

• Senior product management, product engineering, or technical program leadership experience—from executive narrative to portfolio outcomes and shipped solutions at scale. Fundamentally understands what it means to build great products.

• Experience partnering with engineering and data science leaders as a technical peer and thought partner. 

• Financial and analytical acumen to assess portfolio-level value, trade-offs, and execution risk. 

• Executive presence with credibility at C-suite, CIO, and engineering leadership levels. 

• Typically 12+ years of relevant experience, including leading AI or digital initiatives in complex enterprise environments. 

• Demonstrated experience mentoring senior ICs or leading a cohort of technical product leaders. 

Preferred 

• Experience spanning multiple business units, domains, or enterprise portfolio governance. 

• Familiarity with LLM application design, enterprise AI architecture patterns, and responsible AI principles at scale. 

• Track record of taking multiple AI initiatives from POC to production and transitioning ownership to business operations. 

• Experience contributing to operating models, playbooks, or COE standards adopted beyond a single team. 

Core Competencies 

• AI-Native Technical Leadership: Deep practitioner understanding plus judgment on when and how to apply AI at enterprise scale. 

• Product Engineering at Scale: Sets standards for specs, prototypes, and iterations across programs. 

• Executive Problem Framing: Identifies enterprise-worthy problems and structures AI opportunities for investment forums. 

• Enterprise Solution Architecture: Engages technology executives as a peer on the hardest architectural decisions. 

• Portfolio Judgment: Balances value, feasibility, risk, and capacity across a multi-initiative portfolio. 

• Force Multiplier: Elevates other AIBPs and delivery teams through mentorship, patterns, and governance clarity. 

• Trusted Executive Advisor: Earns trust through depth, delivery, integrity, and consistent follow-through at senior forums. 

What Success Looks Like 

• Executive sponsors and technology leaders view the Senior Principal AIBP as a strategic advisor and credible architect—not a coordinator. 

• The enterprise AI portfolio is prioritized, visible, and progressing with clear ownership and value tracking. 

• Complex POCs and pilots reach production with measurable business impact and clean transition paths. 

• Principal AIBPs and engineering teams benefit from clearer patterns, faster decisions, and accelerated delivery. 

• Enterprise AI frameworks and AI-first practices are adopted consistently across business units.