Artificial Intelligence

• Own the HBS AI Champion Pilot Program and the Microsoft 365 Copilot Cowork Program rollout end-to-end.
• Deliver role-based enablement and career-development upskilling for Microsoft 365 Copilot, Microsoft Copilot Cowork, and Claude agents through training sessions, certifications, prompt clinics, peer coaching, and ongoing reinforcement.
• Recruit and support each Champion cohort in partnership with department leaders: produce the Champion Commitment Guide and Manager Fact Sheet, coach champions through 1:1 sessions and group office hours, and run structured peer-coaching sessions with their teammates.
• Drive cross-department engagement through use-case challenges and visible recognition.
• Track adoption analytics (daily active users, license utilization, prompt library usage, certification completions) and publish a monthly one-page dashboard against documented baselines.
• Capture candid pilot feedback from Cowork participants and synthesize it into actionable adoption, governance, and product insights for the Director of AI and the Executive Leadership.
• Curate and version-control role-based prompt libraries, custom Cowork skills, and agent templates; maintain a searchable internal knowledge base of resolved issues, prompt patterns, and best practices; embed Microsoft 365 Copilot learning paths and Copilot Cowork modules into the HBS internal training site.
• Act as the strategic bridge between business stakeholders and the technical AI delivery team by contributing business context, prompt design input, and adoption-readiness review on custom solution work, and pilot new Microsoft and Claude AI capabilities internally before they reach client engagements.
• Operate the AI Helpdesk tier-1 and tier-2 queue, contribute to the refinement of internal AI delivery methods and certification curricula, and apply the AI Use Policy in daily work while flagging exceptions to the Director of AI.

AI Enablement Specialist

• Own execution of the AI Review Framework
• Ensure governance processes are consistent, efficient, and audit-ready
• Execute AI governance activities through the AIS / Security Governance Team
• Perform AI risk classification (low, mid, high)
• Assess: Business impact, Customer and regulatory risk, Data sensitivity
• Prepare governance recommendations for CIO, CLO, and AIS Committee
• Own vendor AI governance execution, including: AI usage detection across vendor portfolio, FactSheet collection and review
• Ensure vendor AI is properly routed through the AIS Program review process
• Partner with Vendor Management on third-party risk alignment
• Coordinate twice-annual AI system validations
• Track: Model updates, Control gaps and remediation, Ensure FactSheet updates trigger re-reviews
• Work with member carriers to guide teams through AI governance requirements
• Ensure consistent application of policies and standards
• Support adoption of shared AI governance practices
• Maintain AI inventory, Governance documentation and decision logs
• Prepare materials for AIS Committee, Leadership reporting
• Partner with AI / Data Science teams, Legal and Compliance, Vendor Management, and coordinate with Cyber / Privacy teams as needed

Senior Manager, AI Risk & Governance

• Define and lead TMG’s enterprise AI strategy in partnership with the CITO, business executives, technology leaders, and risk governance partners.
• Establish the AI-First IT operating model, roadmap, governance approach, funding priorities, delivery rhythm, and measurable outcomes.
• Build a disciplined portfolio of AI initiatives that balances experimentation, speed, security, business value, operational readiness, and risk management.
• Translate emerging AI capabilities into practical, secure, scalable solutions that improve business outcomes, employee productivity, and technology delivery.
• Serve as a thought leader and practical operator who helps the organization understand where AI can create value, where it creates risk, and how to adopt it responsibly.
• Lead the strategy and delivery of foundational AI platform capabilities that support secure, scalable, and reusable AI-enabled applications.
• Define architecture patterns for AI-First applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.
• Guide platform capabilities such as model access, retrieval frameworks, vector databases, enterprise knowledge integration, prompt and response controls, observability, and governance guardrails.
• Introduce AI-assisted software engineering practices across the SDLC, including coding, testing, documentation, requirements analysis, code review, and engineering workflow automation.
• Partner with Applications, Engineering, Infrastructure, Operations, Architecture, Security, and Data teams to pilot, refine, and scale AI-enabled practices over time.
• Partner with underwriting, claims, operations, finance, customer service, and other business functions to identify and deliver high-value AI-enabled process improvements.
• Lead the development of AI capabilities such as decision support, workflow automation, document intelligence, knowledge assistance, summarization, triage, productivity tools, and service quality improvements.
• Help business teams move from AI ideas to practical use cases with clear outcomes, adoption plans, controls, and value measures.
• Lead enterprise enablement of AI productivity tools such as ChatGPT, Microsoft Copilot, and related assistants, including standards, training, adoption practices, and usage guardrails.
• Build reusable playbooks, enablement models, and communities of practice that raise AI fluency across IT and the broader organization.
• Work closely with the Senior Director, AI & Technology Risk Governance to ensure AI adoption is responsible, secure, compliant, and aligned with TMG’s risk appetite.

VP, AI – IT and Transformation

• Serve as EnerSys’ executive leader and single point of accountability for enterprise AI strategy, execution, and outcomes.
• Translate enterprise and business strategies into a clear, multi‑year AI vision and roadmap aligned with growth, productivity, quality, safety, and risk objectives.
• Advise the CIO, ELT, and Board on AI trends, risks, opportunities, and competitive implications.
• Represent EnerSys externally with strategic AI partners, technology providers, and industry forums.
• Design, implement, and continuously evolve the enterprise AI operating model, including governance forums, funding and intake processes, prioritization methods, and decision rights.
• Chair and run the Executive AI Steering Committee, ensuring disciplined decision making, clear accountability, and execution follow through; set agendas, align key stakeholders, and communicate decisions and actions clearly.
• Establish enterprise standards for responsible AI, data usage, security, privacy, and risk management in partnership with Legal, Cybersecurity, IT Architecture, HR, and Compliance.
• Own AI investment governance, ensuring alignment with capital planning, transformation funding, and enterprise portfolio priorities.
• Own the global AI use case portfolio and prioritization framework, balancing short term value delivery with long term strategic capability building.
• Ensure AI initiatives progress efficiently from ideation to pilot to scaled production—eliminating “pilot only” stagnation.
• Define, track, and report enterprise AI value metrics, including operational efficiency, cost reduction, quality improvement, risk mitigation, revenue enablement, and customer impact.
• Regularly recalibrate the AI roadmap based on realized outcomes, business performance, and evolving priorities.
• Orchestrate cross-functional delivery across IT, data, digital platforms, business units, and external partners—creating shared plans, clear ownership, and fast decisions.
• Resolve enterprise level dependencies and escalations impacting delivery, adoption, or value realization by facilitating trade-off discussions, aligning decision makers, and driving timely, documented outcomes.
• Ensure AI initiatives are integrated with core enterprise platforms (ERP, data environment, digital products) rather than delivered as isolated solutions.
• Build scalable execution playbooks, delivery standards, and reusable components to accelerate adoption across regions and functions.
• Partner with HR and Communications to lead enterprise-wide change management, AI literacy, and workforce enablement.
• Sponsor AI capability building, including training programs, citizen developer models, communities of practice, and leadership education.
• Drive cultural adoption by positioning AI as a core business capability embedded in how work gets done—not as a technology experiment.
• Support workforce transformation planning, including role evolution, ethical considerations, and future skills development.
• Build, lead, and develop a high performing enterprise AI organization with clear roles, succession, and career paths.
• Set performance expectations, accountability, and development plans for direct and indirect reports.
• Foster a strong culture of accountability, collaboration, and execution discipline across the AI ecosystem.

VP Enterprise AI Enablement

• Design and implement agentic AI architectures, including multi-agent workflows, tool-calling systems, memory and state management, and orchestration logic for complex multi-step tasks.
• Develop and evaluate retrieval-augmented generation (RAG) pipelines, with attention to retrieval strategy, document chunking and indexing, embedding model selection, re-ranking, and end-to-end evaluation.
• Contribute to LLM evaluation and validation frameworks — defining test coverage, constructing evaluation datasets, assessing output reliability, and identifying failure modes through structured testing and adversarial analysis.
• Conduct prompt engineering and instruction design for domain-specific tasks, and support experimentation with parameter-efficient fine-tuning approaches where applicable.
• Perform model benchmarking and comparative analysis, including evaluation of commercial and open-source LLMs for specific task types, latency and cost tradeoffs, and domain adaptation requirements.
• Support integration of LLM and agentic components with broader system architectures, including data pipelines, APIs, and domain-specific tooling.
• Contribute to data preparation and preprocessing workflows for structured and unstructured industrial datasets, including cleaning, transformation, and schema design.
• Engage in the full engineering rigor expected of production AI/ML systems — including unit and integration testing, model verification and validation, experiment evaluation, simulation workflows, data and output visualization, and technical documentation and reporting — as continuous activities throughout the project lifecycle.

See all jobs on JobTailor

AI Enablement Specialist

Senior Manager, AI Risk & Governance

VP, AI – IT and Transformation

VP Enterprise AI Enablement

Applied AI – Graduate Intern