Upon successful completion, graduates will be able to:

A. Knowledge and conceptual understanding

• Demonstrate systematic and advanced knowledge of artificial intelligence, machine learning, data science, intelligent systems and computational decision making.
• Critically evaluate theories of business strategy, innovation, organizational transformation, entrepreneurship, competitive advantage and value creation.
• Explain how AI interacts with organizational structures, business processes, markets, labor, regulation, culture and stakeholder interests.
• Demonstrate specialist knowledge of a selected business sector or application domain.
• Critically assess competing theories and models of AI adoption, technology acceptance, digital transformation and organizational capability development.

B. Technical and computational competence

• Design, develop and evaluate AI systems using appropriate programming languages, data structures, statistical techniques and computational tools.
• Apply supervised, unsupervised, semi-supervised and reinforcement-learning methods to complex business problems.
• Develop and evaluate deep-learning, natural-language-processing, computer-vision, recommender-system, forecasting and optimization applications.
• Design appropriate data architectures, pipelines, databases and cloud-based environments for AI applications.
• Apply MLOps principles, including model versioning, deployment, monitoring, testing, documentation and lifecycle management.
• Evaluate the quality, representativeness, security, provenance and governance of data used in AI systems.
• Use appropriate performance measures to assess accuracy, robustness, fairness, interpretability, scalability, cost and business value.

C. Business and strategic competence

• Identify business problems for which AI can produce meaningful operational, strategic or societal value.
• Develop AI strategies aligned with organizational purpose, competitive position, resources, capabilities and risk appetite.
• Evaluate the financial and strategic feasibility of AI initiatives, including costs, benefits, return on investment, productivity and opportunity costs.
• Design AI-enabled products, services, platforms, processes and business models.
• Analyze the effects of AI on organizational capabilities, employment, skills, decision rights, management practices and workplace design.
• Assess AI implementation through appropriate business, operational, customer, employee and societal performance indicators.

D. Research competence

• Identify important and researchable problems at the intersection of AI and business applications.
• Formulate original research questions, hypotheses, conceptual frameworks and theoretical propositions.
• Conduct critical and systematic reviews of academic, technical, business and regulatory literature.
• Design and execute quantitative, qualitative, computational, experimental, design-science and mixed-methods research.
• Conduct field studies, case studies, surveys, experiments, simulations, computational evaluations and organizational interventions.
• Analyze and interpret evidence with appropriate attention to validity, reliability, causality, uncertainty, bias and limitations.
• Produce original research that contributes to AI theory, business knowledge, technological practice or organizational transformation.

E. Responsible AI, governance and risk

• Apply principles of fairness, transparency, explainability, privacy, security, safety, accountability and human oversight.
• Identify and manage risks involving bias, discrimination, hallucination, data leakage, model failure, cybersecurity, intellectual property and misuse.
• Evaluate the regulatory and governance obligations associated with AI systems, including risk classification, documentation, monitoring and human oversight.
• Design responsible-AI controls across the AI lifecycle, from problem definition and data collection to deployment and post-deployment monitoring.
• ssess the broader effects of AI on workers, consumers, communities, markets, competition, sustainability and democratic institutions.

F. Communication, leadership and innovation

• Communicate complex AI concepts and research findings to technical, managerial, academic and public audiences.
• Translate AI research into business cases, product requirements, implementation roadmaps, policy documents, investment proposals and executive recommendations.
• Lead interdisciplinary teams involving data scientists, engineers, managers, domain experts, legal professionals and affected stakeholders.
• Manage organizational change and support responsible adoption of AI technologies.
• Identify commercialization, entrepreneurship, licensing, partnership and technology-transfer opportunities.
• Defend research findings in academic seminars, conferences, peer review, executive forums and a doctoral viva.