Head of AI Operationalization & Transformation

Siemens Healthineers

22h•$172,680 - $237,435•Hybrid

About The Position

As Head of AI Operationalization & Transformation in North America Customer Service, you turn selected AI-enabled ideas, prototypes, and technical capabilities into scalable, adopted, and measurable operational solutions. The role serves as the business-side bridge between AI innovation, process efficiency, IT, analytics, and operations. It ensures that AI solutions are connected to real service workflows, deployed with practical readiness criteria, adopted by users, and measured against realized business value. The role complements the existing Heads of Innovation, Process Compliance and Efficiency, Data Analytics, IT, and Business Excellence. The Head of AI Operationalization & Transformation reports to the Senior Manager, Customer Service Operational Excellence in North America.

Requirements

Bachelor’s degree in business, Engineering, Information Systems, Operations Management, Computer Science, Data/AI, or a related field with strong operational applicability; advanced degree preferred.
10+ years of experience in operational transformation, digital productization, process excellence, service operations, intelligent automation, or AI-enabled business transformation.
Demonstrated experience moving digital, automation, AI, or process transformation initiatives beyond pilot stage into scaled operational use across multiple business units, regions, or user groups.
Strong understanding of service operations, field service processes, dispatch, scheduling, case handling, customer support, process transparency, and operational performance management.
Experience redesigning workflows and operating models around digital or AI-enabled tools, ideally beyond proof-of-concept stage.
Proven ability to drive adoption, change management, user enablement, and sustained usage of new solutions in complex organizations.
Strong business product mindset, including use-case definition, requirement shaping, roadmap contribution, user feedback integration, and value tracking.
Knowledge of AI, automation, analytics, and digital platforms sufficient to challenge and collaborate with technical teams without duplicating AI engineering, analytics, or IT roles.
Familiarity with generative AI, workflow automation, service platforms, process mining, Power BI, Snowflake, Azure, ServiceNow, SAP, or similar enterprise systems is advantageous.
Strong stakeholder management and executive communication skills.
Ability to connect operational pain points with scalable AI-enabled solutions.
Healthcare, field service, or multinational enterprise experience is advantageous.
Willingness to travel up to 10%.

Nice To Haves

Advanced degree preferred.
Familiarity with generative AI, workflow automation, service platforms, process mining, Power BI, Snowflake, Azure, ServiceNow, SAP, or similar enterprise systems is advantageous.
Healthcare, field service, or multinational enterprise experience is advantageous.

Responsibilities

Translate AI Innovation into Operational Capability: Partner with the Head of Innovation to operationalize selected AI concepts, prototypes, and technical capabilities into scalable operational solutions without assuming ownership of model development, architecture, or technical implementation. Define the business problem, user need, workflow impact, and value hypothesis for AI-enabled initiatives before scale-up. Ensure AI solutions are connected to real Customer Service pain points, including scheduling, dispatch, service execution, knowledge retrieval, triage, documentation, and operational decision support. Assess whether a solution is ready to move from technical proof of concept into operational pilot, controlled rollout, or broader deployment. Create a structured intake and prioritization approach for AI-enabled process opportunities, aligned with business value, adoption feasibility, and workflow fit.
Redesign AI-Enabled Workflows: Analyze current-state service processes only where AI is being considered or deployed, and identify where AI can simplify work, improve decision quality, reduce friction, or increase transparency. Design future-state workflows that embed AI naturally into the daily work of field service, dispatch, scheduling, operations management, and support teams. Define clear roles, decision points, human-in-the-loop requirements, escalation paths, and process controls for AI-enabled workflows. Ensure AI tools improve the way work is performed rather than adding another layer of complexity on top of existing processes.
Drive AI Tool Adoption, User Readiness, and Sustained Usage: Own the AI adoption model for selected AI-enabled capabilities, including user readiness, communication approach, training needs, feedback loops, and sustained usage tracking. Build practical rollout plans for AI tools in partnership with process owners, field leaders, IT, analytics, and the Head of Innovation. Engage operational users to understand adoption barriers, usability challenges, workflow gaps, and trust issues related to AI recommendations or outputs. Create mechanisms to capture user feedback and route it to the appropriate owner: innovation/technical teams for solution changes, process teams for SOP/process changes, and analytics teams for data-quality or measurement needs. Ensure service teams are equipped with the knowledge, confidence, and support needed to use AI solutions effectively in daily operations.
Establish Deployment Readiness and Solution Quality Discipline: Define practical readiness criteria for AI-enabled solutions before operational rollout, focused on usability, reliability, workflow fit, user acceptance, exception handling, and measurable value. Partner with AI innovation, IT, analytics, cybersecurity, compliance, and operations teams to ensure solutions can be deployed responsibly and sustainably within the operational context. Define when human review, exception handling, escalation, or fallback procedures are required for AI-enabled workflows. Create lightweight quality gates that support speed, safety, and operational reliability without creating unnecessary bureaucracy.
Own Value Realization and Impact Tracking: Establish and maintain a formal benefits realization process for AI-enabled initiatives, including baseline definition, target setting and post-deployment validation. Define business outcomes and KPIs in partnership with existing KPI owners for AI-enabled operational improvements, focused on adoption, usage, workflow impact, decision quality, cycle-time contribution, productivity improvement, customer experience contribution, and realized business value. Partner with analytics teams to consume and interpret data without owning analytics production, dashboards, reporting, Snowflake, or data engineering platforms. Translate operational impact into executive-ready narratives, value cases, and scale-up recommendations. Compare intended value, actual adoption, and realized impact after deployment, and recommend whether AI-enabled capabilities should be scaled, redesigned, paused, or retired. Ensure benefits tracking aligns with existing innovation and process-efficiency metrics without duplicating ownership.
Act as the Bridge Between Process, AI, IT, and Operations: Serve as the business-side productization partner for selected AI-enabled Customer Service solutions. Translate operational needs into clear requirements for AI innovation, IT, analytics, and engineering teams. Translate technical capabilities into understandable operational process changes for service leaders and users. Facilitate alignment between innovation teams and process owners on workflow readiness, user adoption, operational value, and scale readiness. Ensure AI initiatives are integrated into broader Customer Service transformation priorities without becoming the central owner of all AI, process, analytics, or IT workstreams. Define and govern the transition of AI-enabled solutions from pilot and scale-up phases into sustainable operational ownership by the appropriate functional teams. Establish clear ownership, support models, KPI accountability, and handover criteria to ensure long-term adoption and value realization after implementation.
Monitor External AI and Field Service Trends: Continuously monitor external AI, automation, and field-service technology trends relevant to Customer Service workflows. Identify practical use cases from external benchmarks, industry developments, vendors, academic partners, and internal innovation hubs. Distinguish operationally viable solutions from general AI hype and translate external learning into practical recommendations for Customer Service. Coordinate with the Head of Innovation on broader AI trend scanning and with the Process Compliance and Efficiency function on operational performance benchmarking. Support partnerships with internal and external stakeholders to accelerate practical AI deployment.