Senior TALL Manager

Job description

Job Description Job Title: AI Lead - Capability Development Function: Learning & Development (L&D) Reports To: Head of Learning & Development / Chief Growth Officer (dotted line to AI Business Unit Leadership) Location: [Insert Location] | Hybrid/Remote as applicable Job Summary The AI Lead - Capability Development owns the end-to-end strategy, design, and execution of the organization's AI upskilling and workforce transformation agenda. This role sits at the intersection of Learning & Development, AI Business/Alliance teams, and Delivery organizations -translating partner ecosystem commitments (e.g., OpenAI, Anthropic, and other AI/LLM providers) into a scalable, measurable capability-building engine. The role is accountable for building the talent pipeline via certification pipeline (e.g., partner-led programs and internal capability tracks), lateral & campus upskilling/cross-skilling- driving cross-functional stakeholder alignment, designing curriculum with business relevance, demonstrating ROI on L&D investment, and managing a team and portfolio of concurrent capability-building initiatives. This is a strategic-cum-execution role -the person will need to operate as a thought partner to leadership while remaining hands-on with curriculum design, program delivery, and reporting. Key Responsibilities 1. AI Strategy & Workforce Transformation - Define and own the organization's AI capability-building roadmap, aligned to enterprise AI strategy, partner commitments, and evolving market/client demand for AI-skilled talent. - Translate leadership commitments (e.g., "100 certifications," partner-tier requirements, FDE pod readiness) into structured, time-bound workforce transformation programs. - Continuously scan the external AI talent and certification landscape to keep the internal roadmap current and competitive. - Segment the workforce by role archetype and define differentiated AI fluency and specialization pathways for each. - Drive change management and adoption strategy to embed AI capability as a core organizational competency, not a one-time training event. 2. Stakeholder Management - Serve as the primary L&D interface to senior stakeholders across Business Units, Growth/Partnerships, Delivery Leadership, HRBPs, and external partner teams - Manage expectations and commitments made by leadership (e.g., certification numbers, timelines) by translating them into deliverable execution plans, and proactively flag risks/blockers. - Build and maintain governance rhythm (steering committees, monthly/quarterly reviews) with Business Unit heads, Practice Leads, and Executive Sponsors to report progress, risks, and course corrections. - Act as a trusted advisor to leadership on capability gaps, talent readiness, and workforce risk related to AI adoption. - Manage external partner relationships ( license utilization tracking, escalations on training platform issues). 3. Business Collaboration & Curriculum Design - Partner with Business Unit and Practice Leaders to identify role-specific capability needs - Co-design curriculum architecture spanning foundational AI literacy, tool-specific certification, and applied/project-based learning. - Blend external certification pathways (partner-provided) with internally built modules addressing organization-specific tools, use cases, and client delivery contexts. - Ensure curriculum is continuously validated against real project/delivery needs via SME input and delivery leadership feedback loops - not designed in a vacuum. - Own the learning experience design: cohort structuring, learning journeys, blended formats (self-paced, instructor-led, hands-on labs, capstone projects), and platform/LMS integration. 4. ROI & Business Case Development - Define and track measurable outcomes for all capability-building investments - completion rates, certification pass rates, badge issuance, deployment readiness, and downstream business impact (e.g., billable utilization of certified talent, win-rate impact on AI-related deals, client satisfaction on AI delivered engagements). - Build the business case and cost-benefit model for capability investments, including license costs, platform fees, SME/trainer time, and opportunity cost of learning hours. - Establish a measurement framework (Kirkpatrick or equivalent) to assess learning effectiveness at reaction, learning, behavior, and business-impact levels. - Present ROI dashboards and impact narratives to leadership, linking capability development spend directly to revenue enablement, delivery quality, and partner-tier progression (e.g., OpenAI Select Advanced Elite tier requirements). - Recommend course corrections, reallocation, or scale-up of programs based on data-driven insight. 5. Subject Matter Expertise (SME) - Maintain deep, current knowledge of the AI/LLM ecosystem - foundation model providers, partner certification frameworks, agentic AI, FDE/Forward Deployed practices, prompt engineering, and enterprise AI deployment patterns. - Act as an internal SME and point of escalation for curriculum content accuracy, partner program requirements, and emerging AI skill taxonomies. - Represent the organization in partner enablement calls, curriculum advisory sessions, and industry forums to stay ahead of program changes (e.g., shifts in OpenAI's certification structure, new Anthropic Academy offerings). - Mentor and upskill internal trainers/facilitators to ensure consistent, high-quality delivery of technical content. 6. Project Management - Own end-to-end program management for all capability-building initiatives - planning, resourcing, timelines, risk management, and stakeholder communication. - Manage concurrent workstreams (e.g., multiple partner cohorts running in parallel, each with differen