URUS is seeking a strategic and innovative Artificial Intelligence (AI) Product Owner to lead our corporate artificial intelligence transformation. This role will be responsible for co-developing and executing our enterprise AI strategy, fostering an AI-first culture across the organization, identifying high-value use cases for generative AI and agentic systems, and establishing frameworks to measure AI effectiveness and business impact. The ideal candidate combines deep AI/ML expertise with business acumen, change management skills, and the ability to translate emerging AI capabilities into tangible business value.
Reporting to the SVP of Integrated Digital Solutions, the AI Product Owner plays a pivotal role in shaping the digital future of Urus and enabling the technological capabilities required to support sustainable growth and strategic execution.

Key Responsibilities

AI Strategy Development & Execution (30%)

• Develop a comprehensive corporate AI strategy aligned with business objectives, including 1-3 year roadmaps for AI adoption across all business functions
• Define AI governance frameworks including ethical AI principles, responsible AI guidelines, data privacy standards, and risk management protocols
• Establish AI investment priorities by evaluating emerging AI technologies (generative AI, agentic systems, computer vision, NLP, etc.) against business needs and ROI potential
• Create and maintain AI technology stack recommendations, including build vs. buy vs. partner decisions for AI platforms and tools
• Partner with executive leadership to secure funding, resources, and organizational commitment for AI initiatives
• Monitor AI industry trends and competitive landscape to ensure the organization remains at the forefront of AI innovation
• Develop AI maturity models and assess organizational readiness for AI adoption across different functions

AI-First Culture & Change Management (25%)

• Lead organizational change to build an AI-first mindset across all departments and levels of the organization
• Design and deliver AI education programs including executive briefings, department-specific training, and hands-on workshops
• Establish AI Centers of Excellence or communities of practice to share knowledge, best practices, and success stories
• Create internal AI evangelism programs including champions networks, lunch-and-learns, and innovation challenges
• Develop change management strategies to address resistance, fear, and misconceptions about AI adoption
• Build cross-functional AI literacy by creating accessible resources, playbooks, and documentation for non-technical stakeholders
• Foster an experimentation culture by establishing safe-to-fail environments and rapid prototyping processes
• Partner with People and L&D to integrate AI skills into talent development and recruitment strategies

Use Case Identification & Prioritization (25%)

• Conduct discovery sessions with business unit leaders to identify pain points, inefficiencies, and opportunities where AI can create value
• Evaluate and prioritize AI use cases using frameworks that balance business impact, technical feasibility, and strategic alignment
• Develop business cases for AI initiatives including cost-benefit analysis, resource requirements, timelines, and success metrics
• Identify opportunities for generative AI applications including content creation, code generation, customer service automation, and knowledge management
• Explore agentic AI applications where autonomous systems can handle complex multi-step workflows, decision-making, and task orchestration
• Map AI opportunities across the value chain from supply chain optimization to customer experience enhancement
• Maintain an AI opportunity backlog with clear prioritization criteria and regular review processes
• Conduct competitive analysis to identify AI capabilities that could provide strategic differentiation

Product Management & Delivery (15%)

• Own the AI product roadmap including features, releases, and dependencies across multiple AI initiatives
• Define product requirements for AI solutions in collaboration with technical teams, business stakeholders, and end users
• Manage AI product lifecycle from ideation through MVP, pilot, scaling, and optimization
• Coordinate with data science, R&D, engineering, and Digital/IT teams to ensure successful implementation of AI solutions
• Oversee vendor relationships for third-party AI platforms, tools, and services
• Ensure AI solutions meet quality standards including accuracy, reliability, fairness, transparency, and security
• Manage AI project portfolios balancing quick wins with transformational initiatives

Measurement & Value Realization (5%)

• Establish AI performance frameworks with clear KPIs for each AI initiative linked to business outcomes
• Define metrics for AI effectiveness including accuracy metrics, operational efficiency gains, cost savings, revenue impact, and user adoption
• Create dashboards and reporting mechanisms to track AI performance, ROI, and business value across the organization
• Conduct post-implementation reviews to capture lessons learned and refine future AI initiatives
• Measure AI-first culture adoption through surveys, usage analytics, and behavioral indicators
• Calculate total cost of ownership (TCO) for AI initiatives including infrastructure, licensing, talent, and maintenance costs
• Develop value attribution models to accurately connect AI capabilities to business outcomes
• Establish continuous improvement processes based on performance data and user feedback
• Report on AI progress to executive leadership with clear business impact narratives

Required Qualifications

Education

• Bachelor's degree in Computer Science, Data Science, Business, Engineering, or related field
• MBA, Master's in Data Science, AI, or related advanced degree strongly preferred

Experience

• 5+ years of progressive experience in product management, AI/ML implementation, or digital transformation roles
• 3+ years of hands-on experience with AI/ML technologies including generative AI (GPT, Claude, Gemini, etc.) and agentic systems
• Proven track record of developing and executing AI strategies in enterprise environments
• Demonstrated success leading matrixed, cross-functional teams and managing complex technology initiatives
• Experience building AI products from concept through production deployment
• Experience with change management and organizational transformation in technology adoption

Technical Knowledge

• Deep understanding of AI/ML concepts including supervised/unsupervised learning, natural language processing, computer vision, and reinforcement learning
• Expertise in generative AI including large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and fine-tuning approaches
• Knowledge of agentic AI systems including autonomous agents, multi-agent orchestration, tool use, and reasoning capabilities
• Familiarity with AI platforms and tools such as OpenAI, Anthropic Claude, Azure AI, Google Vertex AI, AWS Bedrock, and open-source frameworks
• Understanding of MLOps practices including model deployment, monitoring, versioning, and governance
• Understanding of data architecture and engineering principles that enable AI applications
• Awareness of AI ethics, bias, and fairness considerations in model development and deployment
• Knowledge of AI security and privacy requirements including data protection and adversarial attack mitigation

Business & Leadership Skills

• Strategic thinking with ability to connect AI capabilities to business value and competitive advantage
• Strong business acumen with experience developing ROI models and business cases for technology investments
• Exceptional communication skills with ability to explain complex AI concepts to non-technical audiences
• Stakeholder management expertise including experience influencing senior executives and board members
• Change leadership capabilities with demonstrated ability to drive cultural transformation
• Data-driven decision making with strong analytical and problem-solving skills
• Product management expertise including agile methodologies, user story development, and prioritization frameworks
• Project and portfolio management skills with ability to manage multiple concurrent initiatives

Preferred Qualifications

• Certification in AI/ML such as Google Cloud ML Engineer, AWS ML Specialty, or similar credentials
• Product management certifications such as Certified Scrum Product Owner (CSPO) or Pragmatic Institute
• Knowledge of agricultural or bovine industry applications would be an asset
• Experience with AI governance frameworks and responsible AI practices
• Experience with AI prompt engineering and building effective prompts for various use cases
• Understanding of AI model evaluation techniques and quality assurance processes
• Experience with AI-powered automation tools like Microsoft Power Platform, UiPath, or similar