AI Enablement & Adoption Manager
Vertiv · Mandaluyong City
AI Enablement & Adoption
The role is responsible for identifying, piloting, and embedding practical AI solutions into existing business processes. The role focuses on hands-on delivery, early wins, and building foundational AI capability across the organization with cross-functional collaboration.
Key Responsibilities:
AI Use-Case Delivery
- Identify high-impact, low-complexity AI use cases across the various functions in the organization
- Work with IT and relevant business functions to identify and enable required AI tools / technologies for application
- Design, prototype, and deploy AI-enabled tools and workflows
- Integrate AI into existing systems and processes
Adoption & Enablement
- Embed AI into daily workflows
- Deliver practical training to drive adoption
- Act as an internal advisor on effective AI usage
Foundations & Guardrails
- Establish lightweight AI usage guidelines (data handling, accuracy, risk awareness)
- Document playbooks and reusable patterns
- Identify risks and limitations early
Stakeholder Collaboration
- Partner with Sales Operations, Business Transformation, IT, and other teams
- Escalate blockers and dependencies
- Contribute to longer-term AI roadmap development
Requirements:
- At least 10 years of total work experience of which 3 years are in the area of AI deployment, enablement, and adoption (with proven track record on it); rest of work experience on related or adjacent fields like automation, analytics, digital transformation, etc.)
- Strong business acumen
- Structured problem solving
- Change management and adoption
- Project and program management
- Practical experience with generative AI tools (e.g., large language models, AI copilots)
- Strong prompt engineering and workflow design skills
- Experience using no-code / low-code automation platforms
- Ability to integrate AI tools with existing systems (CRM, reporting, ticketing)
- Working knowledge of APIs and basic scripting (e.g., Python or similar)
- Understanding of data concepts such as data quality, privacy, and access controls
- Ability to evaluate AI tools and vendors based on business fit and risk