Manager, AI Operations

Overview

The Manager, AI Operations establishes and leads the business‑side operating function required to turn enterprise AI capabilities into measurable, adopted, and sustainable ways of working. This role bridges AI Innovation, IT, PMO, commercial/product stakeholders, and business operations by defining the value case for AI work, translating scaled technology into operational implementation plans, and building the change, training, readiness, hypercare, and sustainment practices that enable teams to use AI effectively.

• AI Operations Strategy & Operating Model
  • Establish the business‑side AI Operations function, including its service model, intake criteria, implementation standards, readiness criteria, launch playbooks, and success measures.
  • Define how AI solutions transition from innovation and IT scaling into business ownership, including roles, responsibilities, decision rights, handoff requirements, support models, and sustainment expectations.
  • Build repeatable frameworks for AI operational readiness, including process impacts, workflow changes, role impacts, controls, adoption risks, training needs, communications needs, data considerations, and support requirements.
  • Develop the organizational skillsets required to implement AI at scale, including AI change management, adoption analytics, prompt/tool usage standards, human‑in‑the‑loop operations, business process redesign, and responsible AI ways of working.
  • Lead and coach direct reports responsible for implementation planning, adoption support, KPI tracking, training coordination, stakeholder readiness, and operational sustainment.
  • Anticipate future AI operating needs as adoption expands and proactively build scalable tools, processes, templates, and capabilities that can be reused across brands, functions, and teams.
• Business Implementation
  • Own the business implementation plan for AI capabilities after IT scaling, including launch planning, stakeholder readiness, business cutover, operational communications, training approach, support model, and hypercare execution.
  • Translate AI capabilities into practical business usage by defining who will use the tool, when it will be used, what decisions or workflows it supports, what behaviors need to change, and how performance will be measured.
  • Partner with business operations, functional leaders, learning/training teams, IT, and AI Innovation to develop enablement content, job aids, user guidance, FAQs, readiness checklists, office hours, and adoption support materials.
  • Lead change‑impact assessments and stakeholder adoption plans for AI initiatives, including readiness risks, resistance points, workforce implications, governance requirements, and operational dependencies.
  • Manage hypercare after launch, including issue triage, user feedback loops, adoption monitoring, escalation paths, enhancement requests, and transition to steady‑state support.
  • Ensure AI implementations are usable, supportable, and embedded into day‑to‑day business processes rather than remaining isolated tools or one‑time pilots.
• Product Sustainment & Continuous Improvement
  • Define sustainment requirements for AI‑enabled tools and processes, including ownership, documentation, support channels, refresh cadence, usage monitoring, escalation paths, and enhancement governance.
  • Build feedback mechanisms to understand user experience, adoption barriers, process friction, content gaps, quality issues, and opportunities for automation or workflow redesign.
  • Partner with IT and AI Innovation to ensure business feedback, operational defects, usage patterns, and enhancement opportunities are incorporated into product backlogs and lifecycle planning.
  • Establish operational controls for responsible use, including user guidance, approval paths, data handling expectations, auditability needs, human review points, and alignment with enterprise AI governance.
  • Maintain implementation assets and knowledge repositories so teams can reuse proven approaches, avoid duplicated effort, and scale AI adoption more consistently.
  • Drive continuous improvement of the AI Operations function by assessing what worked, what did not, and how future deployments…