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.

1. 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).

1. 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.

1. 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.

1. 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.

1. 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.

1. 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).

- Robust 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

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