Senior TALL Manager

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 different
  license validity windows, e.g., 30-day expiry constraints) without slippage.
• Set up and maintain governance artifacts: project plans, RAID logs, status dashboards, and milestone
  tracking visible to leadership.
• Coordinate cross-functional dependencies (IT/LMS teams, partner admins, finance for licensing spend,
  HR for workforce data) to ensure smooth program execution.
• Manage vendor/partner relationships operationally - license provisioning, admin access, cohort
  scheduling, and issue escalation.

7. Team Management

• Build, lead, and develop a team of L&D professionals, instructional designers, and/or program
  coordinators supporting the AI capability agenda.
• Set clear goals, growth plans, and performance expectations for the team; conduct regular 1:1s,
  feedback, and career development conversations.
• Foster a culture of curiosity, continuous learning, and ownership within the team - modeling the same
  AI fluency the team is enabling across the organization.
• Manage capacity planning and workload distribution across concurrent programs and cohorts.
• Identify and develop future L&D/AI capability leaders through delegation and stretch assignments.
  Required Qualifications
• 8-14 years of overall experience, with at least 3-5 years in Learning & Development, Talent
  Transformation, or AI/Technology Enablement roles.
• Proven experience designing and scaling large cohort-based certification or upskilling programs (100+
  learners), ideally involving external technology partners (cloud, AI/LLM providers, or SaaS platforms).
• Strong understanding of the AI/LLM partner ecosystem - familiarity with OpenAI, Anthropic, data, cloud,
  or equivalent partner network structures, certification frameworks, and enablement models is highly
  preferred.
• Demonstrated experience building ROI/business case frameworks for L&D or transformation
  investments.
• Strong stakeholder management experience with senior leadership (VP/CxO level) and cross
  functional teams.
• Experience with instructional design methodologies and modern learning platforms/LMS.
• People management experience - has led teams of 3+ direct or indirect reports.
  Preferred Skills & Attributes
• Executive presence with the ability to influence without direct authority.
• Analytical mindset - comfortable building and defending data-driven ROI narratives.
• High personal curiosity about AI - actively uses AI tools and stays current with the fast-evolving
  landscape.
• Structured project management discipline combined with comfort operating in ambiguity (partner
  programs, certification structures, and timelines shift frequently in this space).
• Strong written and verbal communication skills for both technical and non-technical audiences.
• Ability to balance strategic thinking with hands-on execution - this role is not purely strategic or purely
  operational.

Required Skills