Product Owner COE

Job Description Product Owner (AI CoE)
Position Details
Position Title: Product Owner AI Centre of Excellence (CoE)
Reporting To: Head AI CoE
Location: Any
Industry: Telecom & Cloud Services
Qualifications

1. Bachelors or masters in computer science, Data Science, Economics, Business Management or related fields
2. 6+ years in data, analytics, or AI leadership roles, including at least 3+ years in Gen AI, AI/ML solution delivery, analytics-led consulting, or change management
3. Proven track record in delivering Gen AI, AI/ML solutions at-scale and data driven decision making at-scale
4. Deep understanding of AI governance, MLOps, and responsible AI practices
5. Strong leadership and stakeholder management skills
6. Excellent communication and change-management capabilities
7. Product Owner certification (e.g., CSPO, SAFe POPM) preferred
8. Familiarity with telecom BSS/OSS or cloud platforms is a plus

Job Summary
Responsible for shaping AI-driven business solutions, establishing ROI and IRR for initiatives, and prioritizing product requirements to maximize business impact. Act as the voice of the customer and business within the AI CoE, coordinating with cross-functional teams, and ensuring that Gen AI, AI/ML products deliver tangible business value. The Product Owner plays a crucial role in shaping the roadmap and execution of AI/analytics initiatives in telecom and cloud domains.
Key Responsibilities

1. Own the product vision, roadmap, and backlog for assigned Gen AI, AI/ML or analytics products
2. Gather and refine requirements from business stakeholders, domain SMEs, and users
3. Collaborate with data scientists, engineers, and UI/UX teams to develop high-quality deliverables. Vendor and partner management as required
4. Prioritize features and user stories based on business impact, value, and dependencies
5. Conduct sprint planning, backlog grooming, and user acceptance testing (UAT)
6. Drive continuous feedback loops with users to refine and enhance the product
7. Ensure alignment with AI CoE governance, data privacy, model explainability, and operationalization standards. Compliance with AI regulations
8. Prepare product demos, training, and documentation for effective rollout and adoption
9. Track KPIs such as accuracy, adoption, ROI, risk and user satisfaction

Objectives

1. Accelerate AI-driven transformation and innovation
2. Maximize ROI from AI investments through strategic alignment and execution
3. Promote widespread AI adoption across business units
4. Ensure responsible, explainable, and secure AI usage

Key Result Areas (KRAs)

1. Measurable improvement in AI maturity across functions
2. Impact of AI use cases and number of use cases deployed
3. Time-to-market for AI solutions
4. AI adoption rate and cross-functional engagement. Business unit NPS
5. Accuracy, reliability, and relevance of deployed models. Model deployment cycle time, cost savings achieved
6. Compliance with AI governance and ethical standards
7. Training hours and AI upskilling metrics across the organization

Expected Outcomes

1. Tangible business advantage through applied AI innovation
2. Scalable and reusable AI solutions across the enterprise
3. Improved decision-making and operational efficiency
4. Strong AI governance and minimized risk exposure

Key Competencies

• Product Thinking, Problem solving & Business Value Orientation
• Technical Curiosity & Applied AI Knowledge
• Agile Delivery & Stakeholder Management
• Cross-functional Collaboration
• Data Storytelling & Influential Communication
• Change Management & Adoption Leadership
• Regulatory Awareness & Ethical AI Practices
• Data Driven Decision Making