Artificial intelligence (AI) has left the labs of computer scientists and entered into mainstream business. Many of tomorrow’s business leaders expect AI to have disruptive impact on a variety of industries and individual businesses, but have little understanding what AI actually is, or how it can be practically applied. In the present lecture, future leaders will learn what they should know about AI in general, and in the domain of Financial Management & Management Control in particular. Among other important topics, the following fundamental questions will be addressed:

Fundamentals
● What is AI and how can it be practically applied?
● What is the difference between AI, deep learning, machine learning, and robotics?
● What is an algorithm?
● How does a deep learning algorithm work?
● What are the main drivers of AI progress?
● How ‘intelligent’ are AI systems today?
● How will Quantum Computing change the field of AI?
● What are the main risks and ethical considerations?
● How can we understand how AI systems come up with their decisions?

Applications in Financial Management & Management Control
● What are the main business domains where AI can be used today?
● How can AI be applied in financial management and management control (financial
forecasting, risk assessment, portfolio management, fraud detection, churn
prediction, budget allocation, etc.)?
● How to harness the power of AI without specialist data science skills?
● What are the first steps in using AI in financial management and management
control?
● How can organizations best prepare for the use of AI?
● How do world-leading corporations use AI?
● How can medium-sized enterprises (SMEs) profit from AI?
● What are the main challenges in applied AI projects?
● What can and can’t be done with AI technology existing today?

Strategy & Leadership
● How can AI drive customer and organizational value?
● How can key AI opportunities be identified in organizations?
● How to get started with AI?
● Organizational transformation: How to accompany change, understand and address
fears, and empower stakeholders in a participatory fashion.
● What are AI’s essential consequences for the strategic leadership of organizations?

Not assuming any mathematical or technical background of participants, a practical understanding of AI is established. The demonstration and explanation of examples in numerous domains enables participants to identify opportunities and to leverage the benefits of this fascinating and game-changing technology for their future organizations. AI’s impact on our society and the future of organizations becomes evident. By considering the essentials that tomorrow’s executives need to know, participants will be equipped with the comprehensive understanding and the confidence necessary to make informed strategic decisions regarding AI.

After completing this class, students will have the ability to:
● identify key AI opportunities in organizations in the domain of Financial Management
& Management Control
● develop coherent narratives to convey their ideas regarding AI in the domain of
Financial Management & Management Control
● develop presentation for applied AI projects to pitch their proposals
● make informed strategic decisions regarding AI in the domain of Financial
Management & Management Control

In order to successfully complete this course, you have to attend at least 80% of its class time. Attendance on campus WU is expected; this class does not offer online sessions.

Please note that certain units are coachings at which only the respective groups have to be present.

Teaching/learning method(s):

● Immersive interactive lecture (including theory input, open class discussions,
interactive teamwork)
● (TBA) Guest talks by world-leading AI researchers (University of Oxford) and
experienced industry experts (TBA)
● Group projects: Development and delivery of a presentation to pitch a proposal for
an applied AI project

Please note the following schedule:

Session 1: Input 

Session 2: Input 

Session 3: Input 

Session 4: Coachings (individual appointments)

Session 5: Final Presentations

Wolfgang Frühwirt is an Associate Member of the Oxford-Man Institute of Quantitative Finance (University of Oxford, Department of Engineering Science) where he works with the Machine Learning Research Group under Professor Stephen Roberts. Wolfgang holds two PhDs (both data science-based) and a master’s degree in business. Operating at the intersection of multiple complex domains, he combines technical and business know-how (Managing Partner at object a GmbH) with psychological expertise (Private Practice in Psychotherapy and Executive Coaching). His current research interests lie in the Future of Work and AI's impact on the human subject and society in general. He is also interested in probabilistic machine learning and applied neuroscience.

At Oxford University Wolfgang has conducted multiple projects in applied neuroscience and artificial intelligence (AI). His most recent work, as Co-Principal Investigator of an Oxford University study, combines probabilistic machine learning methods to support healthcare organizations in creating data-driven strategies for dealing with automation technologies such as AI.

Latest Publication in AI & The Future of Work:

Fruehwirt & Duckworth (2021) Towards better healthcare: What could and should be automated?
Technological Forecasting and Social Change, Volume 172

https://www.sciencedirect.com/science/article/abs/pii/S0040162521003991

Latest Publication in Crypto & Quantitative Finance:

Fruehwirt, W., Hochfilzer, L., Weydemann, L., & Roberts, S. (2021). Cumulation, crash,
coherency: A cryptocurrency bubble wavelet analysis. Finance Research Letters, 101668.
https://www.sciencedirect.com/science/article/abs/pii/S1544612320303421

Executive Coaching & Business Coaching: https://www.fruehwirt.wien/executive-coaching-wien/

Organizational Development: https://www.object-a.team

Personal Website: https://www.fruehwirt.wien/

Assessment :

• Workshop presentation (25%)
• Final presentation (50%)
• Final report (25%)

Grading scale:

Excellent (1): 87.51%<100.00%
Good (2): 75.01%-87.50%
Satisfactory (3) 62.51<75.00%
Sufficient (4) 50.00%<62.50%
Fail (5) <50.00%

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