AI and Automation Lead

Role Purpose The role is responsible for leading the company's AI and automation capability. The initial emphasis will be on Generative AI and practical business applications, while the role retains a broader mandate for future AI capabilities. The role combines strategic leadership with hands-on delivery to identify opportunities, shape priorities, build internal capability, establish effective ways of working, and ensure solutions are adopted, governed, measured, and improved. The role focuses on identifying high-value AI opportunities, aligning AI initiatives with business priorities, and ensuring the organization adopts AI in a responsible, scalable, and outcome-driven way, while enabling innovation and operational efficiency across the organization. Key Accountabilities • • Define and maintain the AI and automation strategy and roadmap, aligned with business priorities, expected value, risk, and available capacity. • Work with IT Business Partners, the IT Portfolio Manager, business stakeholders, and technology teams to identify, assess, prioritize, and govern AI and automation opportunities based on ownership, value, feasibility, risk, adoption, and available capacity. • Lead delivery of practical solutions from discovery through design, build, release, operation, improvement, and retirement. • Create standards and governance for responsible, secure, maintainable, and cost-aware AI and automation solutions. • Build internal capabilities, methods, reusable components, documentation, and knowledge transfer, coordinating external partners where needed. • Improve AI literacy and adoption by developing practical guidance and helping teams use AI solutions effectively, responsibly, ethically, and transparently, with appropriate human judgment and accountability. • Ensure solutions are secure, scalable, maintainable, cost-aware, and integrated with business workflows, data, cybersecurity, and technology standards. • Lead, support, and develop team members and partners, setting clear expectations and ensuring accountability, knowledge transfer, and sustainable ownership. • Define testing, evaluation, monitoring, feedback, incident, and retirement practices, while tracking adoption, quality, risk, cost, and business impact. • Assess business, privacy, security, and human risks, challenge low-value or poorly defined requests, and make clear recommendations. Qualifications, Experience, Knowledge & Skills • • Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence or related discipline, including relevant certifications • Typically 8+ years in AI, data science, automation, software engineering, data, or related technology roles. • Proven hands-on experience designing, building, integrating, deploying, and operating enterprise AI and automation solutions, including modern generative AI capabilities. • Strong understanding of AI, machine learning, generative AI, advanced analytics, and automation, with practical experience using Azure, cloud-based AI services, APIs, data protection, access control, and technology integration. • Experience establishing and operating AI governance, responsible-use controls, evaluation, testing, production monitoring, support, cost management, and platform administration. • Experience leading technical teams, building internal capability, and managing external delivery partners in an environment where processes and capabilities are still developing. • Ability to translate business problems into practical AI-solutions with clear adoption plans and measurable outcomes. • Hands-on leader combining technical direction, delivery, governance, adoption, and people leadership. • Proactive and accountable, with the persistence and judgment to keep work moving. • Pragmatic and business-focused, choosing the right solution and challenging low-value requests. • Clear, collaborative communicator across business, technical, and executive audiences. • Adaptable and outcome-driven, with a focus on building capability and measurable value. Show more Show less

Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence or related discipline, including relevant certifications. Typically 8+ years in AI, data science, automation, software engineering, data, or related technology roles. Proven hands-on experience designing, building, integrating, deploying, and operating enterprise AI and automation solutions, including modern generative AI capabilities. Strong understanding of AI, machine learning, generative AI, advanced analytics, and automation, with practical experience using Azure, cloud-based AI services, APIs, data protection, access control, and technology integration. Experience establishing and operating AI governance, responsible-use controls, evaluation, testing, production monitoring, support, cost management, and platform administration. Experience leading technical teams, building internal capability, and managing external delivery partners in an environment where processes and capabilities are still developing. Ability to translate business problems into practical AI-solutions with clear adoption plans and measurable outcomes. Hands-on leader combining technical direction, delivery, governance, adoption, and people leadership. Proactive and accountable, with the persistence and judgment to keep work moving. Pragmatic and business-focused, choosing the right solution and challenging low-value requests. Clear, collaborative communicator across business, technical, and executive audiences. Adaptable and outcome-driven, with a focus on building capability and measurable value.

Define and maintain the AI and automation strategy and roadmap, aligned with business priorities, expected value, risk, and available capacity. Work with IT Business Partners, the IT Portfolio Manager, business stakeholders, and technology teams to identify, assess, prioritize, and govern AI and automation opportunities based on ownership, value, feasibility, risk, adoption, and available capacity. Lead delivery of practical solutions from discovery through design, build, release, operation, improvement, and retirement. Create standards and governance for responsible, secure, maintainable, and cost-aware AI and automation solutions. Build internal capabilities, methods, reusable components, documentation, and knowledge transfer, coordinating external partners where needed. Improve AI literacy and adoption by developing practical guidance and helping teams use AI solutions effectively, responsibly, ethically, and transparently, with appropriate human judgment and accountability. Ensure solutions are secure, scalable, maintainable, cost-aware, and integrated with business workflows, data, cybersecurity, and technology standards. Lead, support, and develop team members and partners, setting clear expectations and ensuring accountability, knowledge transfer, and sustainable ownership. Define testing, evaluation, monitoring, feedback, incident, and retirement practices, while tracking adoption, quality, risk, cost, and business impact. Assess business, privacy, security, and human risks, challenge low-value or poorly defined requests, and make clear recommendations.