AI Insights & Governance Analyst

• →Track and quantify business value delivered by AI use cases—cost savings, productivity gains, revenue impact—across Vertiv's global processes (SIOP, NPDI, OTO, OTC) and functions.
• →Build and maintain centralized dashboards covering: AI use case portfolio, enterprise AI tool adoption, and user engagement/usage trends/proficiency levels.
• →Monitor and report KPIs for the Enterprise AI Tool, including adoption, usage frequency, response quality, and business outcomes.
• →Analyze enterprise usage of internal and external AI tools and surface optimization opportunities.
• →Define and track AI governance metrics: compliance, approved-model usage, and adherence to enterprise AI policies.
• →Partner with business, IT, and AI teams to standardize value-measurement frameworks and reporting structures.
• →Produce executive-level scorecards, summaries, and insights for the ELT/leadership audience.
• →Support intake and prioritization of new AI use cases with analytics on expected value and impact.
• →Identify gaps in adoption, training, or tooling and recommend data-driven improvements.

• Bachelor’s degree in business Analytics, Information Systems, Data Analytics, or a related field.
• 2–4 years in business analytics, reporting, or data analysis roles, including hands-on work within AI/GenAI initiatives.
• Solid working knowledge of the AI/GenAI landscape, tools, and vendors—able to speak credibly with both business and technical stakeholders without requiring a deep engineering background.
• Strong analytical skills with proven experience in data visualization and reporting tools (Power BI, QuickSight, or equivalent).
• Ability to translate business metrics into structured reporting and insight.
• Experience working across cross-functional teams (business, IT, product).
• Strong communication skills for executive-facing reporting.

• Direct experience supporting AI/GenAI programs or enterprise AI initiatives.
• Familiarity with the AI lifecycle (use case intake, deployment, governance, monitoring).
• Knowledge of enterprise AI platforms and ecosystems (e.g., AWS AI services, internal AI platforms).
• Experience defining KPIs and performance-measurement frameworks.
• Exposure to data platforms (e.g., Snowflake or similar).

• Business analytics and KPI development
• Data visualization and dashboarding
• AI adoption, usage, and proficiency analysis
• Stakeholder communication and executive reporting
• Governance tracking and compliance awareness 

• Accuracy and completeness of AI value tracking across use cases
• Adoption, engagement, and proficiency metrics across Enterprise AI Tools
• Quality and usability of dashboards and executive reports
• Improvement in visibility of AI portfolio performance
• Contribution to governance maturity and compliance reporting

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