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Job Description: ServiceNow Product Leader, AI and Automation

Job Title: ServiceNow Product Leader, AI and Automation

Position Summary

We seek a strategic ServiceNow Product Leader with 15+ years of experience across CSM, ITSM, domain separation, enterprise integrations, AI, and workflow automation.

The role owns the enterprise product vision, roadmap, governance, delivery, and value realization, with particular responsibility for the secure and responsible adoption of generative AI, intelligent automation, predictive intelligence, AI Search, Virtual Agent, and agent-assist capabilities.

Success will be measured through improved customer and employee experience, service quality, productivity, operational efficiency, adoption, and demonstrable return on investment.

Key Responsibilities

Product Strategy and Roadmap

Define and own the enterprise ServiceNow vision, strategy, roadmap, and backlog across CSM, ITSM, integrations, automation, and AI.
Align investment priorities with business outcomes, customer experience goals, enterprise standards, regulatory obligations, platform health, and technical debt.
Prioritize high-value AI use cases, including summarization, agent assistance, intelligent routing, knowledge recommendations, predictive analysis, virtual agents, self-service, resolution guidance, and workflow automation.
Develop business cases covering value, feasibility, risk, adoption needs, implementation complexity, licensing, and total cost of ownership.
Evaluate relevant ServiceNow capabilities and market innovations, incorporating proven opportunities into the roadmap.

AI Governance and Responsible Adoption

Establish governance for safe, transparent, ethical, and compliant AI adoption within ServiceNow.
Partner with security, privacy, legal, compliance, risk, and data teams to assess use cases and protect confidential, personal, and commercially sensitive information.
Define controls for data access, prompts, model use, approvals, human oversight, explainability, auditability, output validation, retention, and monitoring.
Maintain an AI use-case register and ensure higher-risk initiatives receive appropriate review and approval.
Assess accuracy, bias, hallucination, security, and unintended outcomes; govern third-party AI services and large language models.

CSM and ITSM Product Ownership

Lead the evolution of CSM and ITSM to deliver consistent, intelligent, and user-friendly service experiences.
Improve case, incident, request, problem, change, knowledge, and service-level management through embedded automation and AI.
Define appropriate automation and human oversight for customer-facing, agent-facing, and internal workflows.
Set standards for portals, workspaces, dashboards, knowledge bases, virtual agents, and self-service channels.
Drive measurable improvements in service quality, first-contact resolution, case deflection, resolution time, and productivity.

Stakeholder and Business Engagement

Engage executives, service owners, customers, agents, and technology teams to identify problems and translate them into product requirements, epics, use cases, and measurable outcomes.
Lead discovery workshops, design sessions, demonstrations, and adoption planning.
Set realistic expectations on AI capabilities, limitations, risks, costs, and timelines.
Communicate roadmap progress, value realization, adoption, risks, and decisions to senior stakeholders.
Promote responsible experimentation and evidence-based adoption.

Delivery and Adoption

Lead cross-functional teams of product managers, architects, developers, administrators, analysts, data specialists, testers, and implementation partners.
Define objectives, acceptance criteria, quality standards, release milestones, and operational readiness for AI-enabled capabilities.
Ensure solutions are tested with representative, secure, and approved data.
Drive training, communication, change management, adoption, feedback, and continuous improvement.
Govern releases, upgrades, model and prompt changes, configuration, support, monitoring, and escalation.

Performance and Value Measurement

Define and track KPIs for case deflection, first-contact resolution, handling and resolution time, agent productivity, recommendation accuracy, virtual-agent containment, knowledge effectiveness, customer satisfaction, adoption, automation success, cost reduction, and ROI.
Establish feedback mechanisms to identify inaccurate, unsafe, biased, or low-value outputs.
Periodically retire, redesign, or expand use cases based on performance, user feedback, value, and risk.

Required Qualifications

15+ years of ServiceNow experience, including enterprise product leadership and delivery.
Deep expertise in CSM, ITSM, product strategy, roadmaps, platform governance, enterprise integrations, APIs, and data flows.
Demonstrated delivery of automation, self-service, analytics, or AI-enabled capabilities.
Strong knowledge of generative and conversational AI, predictive analytics, intelligent automation, responsible AI, model risk, privacy, security, and regulatory considerations.
Experience with Now Assist, AI Search, Virtual Agent, Predictive Intelligence, or comparable technologies.
Proven ability to prioritize use cases by value, feasibility, risk, and user impact, and to manage backlogs, budgets, stakeholders, and dependencies.
Excellent executive communication, facilitation, negotiation, and leadership skills.
Bachelor’s or Master’s degree in Computer Science, Information Technology, Business Administration, or a related discipline.

Preferred Qualifications

Relevant ServiceNow, product management, agile, SAFe, Scrum, or PMP certification.
Experience with enterprise AI platforms, large language models, retrieval-augmented generation, AI search, conversational interfaces, and responsible AI frameworks.
Experience in regulated environments and familiarity with data governance, knowledge management, MLOps, model monitoring, AI lifecycle management, licensing, and vendor management.

Key Performance Indicators

Delivery of the approved ServiceNow and AI roadmap.
Increased adoption and improved customer, agent, and employee experience.
Reduced handling time, resolution time, manual effort, and operating cost.
Improved self-service, case deflection, knowledge effectiveness, platform stability, and data quality.
Demonstrable ROI with compliance to security, privacy, risk, and responsible AI standards.

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