AI Solutions & Delivery Manager

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The International Institute for Management Development (IMD) has been pioneering
leadership development for nearly 80 years. Founded by business for business, we are an
independent university institute with Swiss roots and global reach. Operating from
Lausanne with strategic hubs in Singapore, Shenzhen, and Cape Town, IMD works with
20,000+ executives from 120+ countries annually. Our 145,000+ alumni form a powerful
global network. Consistently ranked among the world s top business schools, IMD bridges
cutting-edge research with real-world application to help leaders solve problems, scale
solutions, and drive impact. Real Learning for Real Impact.

To reinforce our team, we are looking for a

Leads the design and delivery of AI-enabled solutions across IMD; today generative and agentic AI, tomorrow whatever comes next. The role treats AI as an engine that amplifies IMD s human expertise and institutional impact, never as a replacement for it. It translates business opportunities into secure, scalable, cost-effective, and ethically governed solutions that strengthen productivity, decision-making, and service quality.
Working across IMD s business units and with IT, data, security, legal, and external partners, the AI Solutions & Delivery Manager leads IMD s AI Advanced team, drives the agentic AI transformation, and builds the institutional capability to adapt as the technology landscape evolves.

• Partner deeply with business units to understand their intricacies, from strategic objectives to cross-team interdependencies, identifying where AI can enable innovation, creativity, and productivity.
• Identify, shape, and deliver high-value AI use cases across business units: machine learning, assistants, automation flows, agentic solutions, and emerging paradigms as they mature.
• Apply product management discipline: define clear problem statements, success metrics, roadmaps, and prioritization frameworks so AI initiatives are run and measured as products, not one-off projects
• Apply systems thinking, see how a use case ripples across processes, teams, data, and existing tools before committing to build it, and design for the whole system rather than the isolated task
• Apply design and critical thinking: use design thinking and UX / customer-centricity to ensure solutions are intuitive, genuinely adopted, and solve the user s real problem
• Translate business needs into solution designs, adoption plans, success measures, and operating models, keeping people at the center of every workflow.
• Design agentic workflows where AI supports tasks, triggers actions, and coordinates across systems, always with meaningful human oversight and clear accountability.
• Assess feasibility, value, cost, data readiness, and risk before moving AI ideas into delivery.
• Scan the horizon continuously, translating fast-moving AI developments into practical, durable opportunities for IMD.

• Lead the delivery lifecycle end to end: intake, prioritization, design, development, testing, release, support, and continuous improvement, in line with IMD s PMO practices.
• Architect solutions deliberately with IT, architecture, data, security, and vendors, so they are scalable, integrated, maintainable, secure, and built to evolve.
• Define reusable technical patterns: enterprise integrations, data sources, APIs, identity, permissions, monitoring, and support models.
• Own the cost dimension of AI: forecast, monitor, and optimize consumption and licensing so value demonstrably exceeds spend.
• Make deliberate build-vs-buy calls for each opportunity, weighing vendor and platform solutions (e.g. Microsoft 365 Copilot, Azure AI Foundry) against custom build, and own the resulting AI tool and vendor portfolio, including licensing and contracts.
• Prune and consolidate proactively: retire pilots that didn't deliver, merge overlapping tools and duplicated capabilities, and simplify IMD s AI landscape to cut cost and complexity — treating this as a core, ongoing accountability…