Learn More: MSc in Strategic AI and Innovation through the Applied AI Graduate Pathway

The MSc in Strategic AI and Innovation is the second stage of a stackable graduate-level learning pathway. Admission requires successful completion of the Graduate Certificate in Applied AI Fundamentals.
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This MSc is designed for professionals who want to lead strategic decisions and organizational responses in a world shaped by AI, complex systems, and rapid change. It is intended for those who need to understand how AI intersects with people, organizations, and broader systems, without needing to become AI engineers.
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It is especially relevant for:

Systems Thinking Skills

Analyze how climate, technological, and socioeconomic disruptions cascade across interconnected systems, identifying risk interdependencies, feedback loops, and emergent vulnerabilities across domains.
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Strategic Analysis Skills

Evaluate competing strategic options for adaptation and mitigation, assessing tradeoffs, stakeholder considerations, and implementation constraints to develop defensible organizational responses.

Implementation and Execution Skills

Design and lead organizational responses to disruption, including stakeholder engagement, blended financing arrangements, and monitoring systems that demonstrate measurable outcomes.

Data and AI Application Skills

Work with real datasets and AI-driven tools to visualize disruptive forces, model scenarios, and evaluate how different responses perform across complex, fast-changing contexts.
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AI Governance Skills

Examine the ethical, operational, and organizational implications of AI adoption, developing frameworks for responsible AI use across diverse institutional and community contexts.

Communication Skills

Translate complex AI-driven analysis into clear, evidence-based recommendations for both technical and non-technical audiences, including executive boards, regulators, funders, and community stakeholders.

Course 6 • 7 Weeks

This course equips strategic leaders to harness business intelligence and analytics for organizational resilience in the face of climate, technological, and socioeconomic disruption. Students learn to translate data insights into strategic business value, build analytics capabilities across organizations, and create cultures of evidence-based decision-making. The course builds a strategic analytics foundation aligned with data-based decision-making, business objectives, stakeholder engagement, and the governance frameworks necessary for responsible data use in complex, uncertain environments.

Course 7 • 7 Weeks

This course prepares leaders to harness artificial intelligence and machine learning strategically to enhance organizational decision-making in volatile, uncertain, complex, and ambiguous environments. The course builds an artificial intelligence foundation where learners explore how to evaluate, select, and govern AI solutions; integrate predictive and prescriptive analytics into strategic processes; and manage the organizational, ethical, and competitive implications of AI adoption. Emphasis is placed on understanding AI capabilities and limitations, building human-AI decision partnerships, and creating strategic advantage through intelligent systems while managing associated risks.
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Course 8 • 7 Weeks

This course develops strategic design skills for creating organizational adaptation and mitigation strategies in response to climate change, technological disruption, and socioeconomic shifts. Students learn to conduct comprehensive risk and opportunity assessments using AI-powered analytics, design flexible and resilient business models, engage stakeholders in co-creating solutions, and develop adaptation pathways that balance short-term pressures with long-term sustainability. Building on the analytical foundations from the previous 2 courses, this course integrates artificial intelligence into an ecosystem approach that leverages AI throughout all phases: designing strategic responses, scenario planning and stress testing, innovating adaptive solutions, and evaluating implementation pathways to build organizational antifragility. Students master frameworks for translating complex data insights into actionable strategies while navigating uncertainty and stakeholder dynamics.
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Course 9 • 7 Weeks

This course focuses on the practical leadership and management skills required to implement adaptation and mitigation systems within complex organizational environments. Students learn to manage large-scale change initiatives, build cross-functional implementation teams, navigate organizational politics, monitor progress against strategic objectives, and course-correct when implementation challenges arise. Building on the strategic design frameworks from the previous course, students apply AI-driven tools for project execution, real-time performance monitoring, and adaptive risk management throughout the implementation lifecycle. The course emphasizes stakeholder management, organizational development, and the leadership competencies needed to drive transformation in the face of disruption. Critical to successful execution, students also master securing blended financing from private, public, and charitable sources, with emphasis on structuring outcome-based financing mechanisms that align stakeholder incentives and ensure project completion through measurable impact milestones.

Course 10 • 7 Weeks

The strategic capstone integrates all learning from the program into a comprehensive, real-world collaborative project that addresses a complex disruptive challenge facing an organization or sector. Working in teams with diverse strategic and technical track students, participants work on their own projects to recommend innovative solutions in their local or regional communities. They will apply AI in the design stage, scenario models, and the implementation strategies. Groups will then formally present recommendations to local stakeholders and funding partners. The capstone emphasizes ecosystem approaches, strategic integration, cross-functional collaboration, and the communication skills necessary to influence senior leaders and drive organizational change. Students demonstrate mastery of strategic frameworks while building practical experience in leading complex transformation initiatives.

No. Accrediting bodies have strict rules about how credit hours are defined, assigned, and overseen. When a student is enrolled in two separate for-credit programs simultaneously, it creates conflicts in how those credit hours are governed and attributed. Institutions cannot properly comply with accreditation requirements in that scenario. However, it is possible to take a for-credit program like the Applied AI Fundamentals certificate and a (not-for-credit) Lab at the same time.
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The Graduate Certificate in Applied AI Fundamentals and the MSc in Strategic AI and Innovation form a stackable learning pathway. The Graduate Certificate builds a foundation in applied AI, data, and systems thinking. The MSc builds on that foundation to develop strategic decision-making for complex organizational challenges. You can complete the Certificate as a standalone credential or continue into the MSc.
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No. Admission follows a set sequence: you apply to and complete the Graduate Certificate in Applied AI Fundamentals first. Certificate graduates can then apply to the MSc in Strategic AI and Innovation.
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“Stackable” means the Graduate Certificate in Applied AI Fundamentals is a complete, independent credential that also counts as the first stage of the MSc in Strategic AI and Innovation if you choose to continue. This is different from an embedded certificate, which exists only within a degree program and cannot be earned on its own.
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No. Both the Graduate Certificate in Applied AI Fundamentals and the MSc in Strategic AI and Innovation are designed for professionals from technical and non-technical backgrounds. No prior AI or coding experience is required.
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In both programs, AI is a tool for analysis and decision-making. Learners work with AI-powered tools and interactive dashboards rather than building models or writing code, so no prior AI or coding experience is required.

In the Certificate, AI powers the analysis that helps learners understand how disruption spreads across interconnected systems. Learners use AI-driven tools to trace cascading risks, map vulnerabilities, and see how key indicators respond to changing conditions, then apply those insights to adaptation and mitigation strategies. The Certificate also examines how the design and governance of AI systems can reduce or amplify risk.

The MSc shifts the focus from understanding disruption to planning and leading organizational adaptation and mitigation strategies. Learners explore how to evaluate, select, and govern AI solutions, weigh AI's capabilities against its limitations, and combine human judgment with AI-driven insight. They also use AI-powered analytics for risk assessment, scenario planning, and stress testing as they design organizational responses and plan their implementation.

Across both programs, the emphasis is on interpreting what AI reveals, understanding its limits, and using it responsibly to make sound decisions.