• Develop and maintain the enterprise AI strategy, operating model, portfolio, and roadmap aligned with corporate objectives and financial goals.
• Identify, evaluate, and prioritize AI opportunities based on strategic alignment, business value, cost, complexity, data readiness, risk, and organizational readiness.
• Develop business cases, investment recommendations, and implementation roadmaps, including build, buy, configure, or partner options.
• Establish performance measures, baseline metrics, and value-realization frameworks to track AI adoption, ROI, productivity, cost savings, revenue impact, and other business outcomes.
• Provide executive leadership with visibility into AI investments, performance, realized benefits, opportunities, and risks.

AI Solution Development & Implementation

• Lead the evaluation, design, development, and deployment of AI solutions across enterprise productivity, function-specific applications, intelligent automation, advanced AI, and emerging agentic capabilities.
• Partner with business units and end users to identify opportunities, develop practical AI-enabled solutions, and improve processes and ways of working.
• Oversee AI pilots, proofs of concept, and enterprise implementations through the full lifecycle—from opportunity identification and feasibility through deployment, adoption, benefits realization, monitoring, and retirement.
• Coordinate with Cybersecurity, Human Resources and other stakeholders to ensure solutions are appropriately designed, implemented, adopted, and measured.
• Assess solution performance and recommend whether initiatives should be scaled, enhanced, consolidated, paused, or retired based on business value, adoption, cost, and risk.

AI Governance, Risk & Responsible Adoption

• Establish and maintain enterprise AI governance frameworks, policies, standards, controls, and risk-based processes to support responsible AI adoption.
• Ensure AI initiatives are appropriately assessed for cybersecurity, privacy, data, intellectual property, legal, ethical, third-party, model, and business continuity risks.
• Maintain an enterprise inventory of approved AI technologies, platforms, models, agents, and material use cases, with appropriate review, approval, testing, human oversight, monitoring, and escalation requirements.
• Ensure data quality, classification, access, security, information governance, and regulatory requirements are addressed before AI solutions are deployed.
• Monitor the evolving AI landscape, regulatory expectations, and associated risks, and update governance practices as required.

Enterprise AI Adoption & Change Management

• Lead enterprise AI literacy, adoption, and change-management initiatives across executives, leaders, employees, technical teams, and advanced AI users.
• Develop and deliver role- and function-specific training, guidance, and resources to promote effective and responsible use of AI.
• Serve as the enterprise resource for AI strategy, best practices, use-case guidance, and adoption support.
• Measure AI utilization, adoption, training effectiveness, employee feedback, and business outcomes to continuously improve AI enablement.
• Coordinate internal subject matter experts, technology teams, business stakeholders, and external partners to support successful AI adoption

Vendor, Technology & Partner Management

• Evaluate AI technologies, vendors, consultants, platforms, and implementation partners against business requirements, value, security, architecture, scalability, risk, and total cost of ownership.
• Manage AI technology and vendor relationships to ensure service quality, effective implementation, and value realization.
• Maintain oversight of the enterprise AI technology portfolio to identify duplicate capabilities, unnecessary licensing, overlapping solutions, and opportunities for consolidation.
• Monitor emerging AI technologies—including generative AI, large language models, predictive AI, intelligent automation, computer vision, and agentic AI—and assess their practical business applicability and potential value.

• Bachelor’s degree in Computer Science, Data Science, Information Technology, Engineering, Finance, Business Administration, or a related field; Master’s degree preferred.
• Minimum seven (7) years of progressive experience in AI, data analytics, digital transformation, automation, technology, or related fields, including significant experience leading enterprise AI or generative AI adoption.
• Proven experience leading complex, enterprise-wide AI or business transformation initiatives from strategy and business case development through implementation, adoption, scaling, and measurable value realization.
• Strong business and financial acumen, including experience with budgeting, forecasting, reporting, business case development, technology investment evaluation, performance measurement, and ROI.
• Strong knowledge of AI technologies, including generative AI, machine learning, predictive analytics, intelligent automation, large language models, AI agents, and emerging AI capabilities, with an understanding of data, cybersecurity, privacy, and AI governance.
• Strong strategic thinking, analytical, communication, stakeholder management, project management, and change management skills, with the ability to translate complex technology into practical business solutions and influence cross-functional decisions.
• Strong technical and business judgment to evaluate AI solutions, vendors, risks, data and technology dependencies, and investment decisions. Professional certifications in AI, analytics, project management, responsible AI, or AI governance are considered an asset.

PHYSICAL DEMANDS AND WORKING CONDITIONS

• Standard office setting - hybrid arrangement (4 days in-office)
• Fast paced office environment
• Travel expectations between facilities in Mississauga and North York