MAN 5244 Generative AI for Business
MAN 5244 Generative AI for Business
This course provides business executives with a comprehensive understanding of Generative AI (GenAI) and its transformative potential across various business functions. Participants will gain practical knowledge of GenAI tools, prompt engineering techniques, and real-world applications, enabling them to leverage this cutting-edge technology for strategic decision-making and driving business growth.
The course will mainly be lectured in English, but lectures in Norwegian may occur. The exam can be written and delivered in English or Norwegian.
After completing the course, students will be able to:
- Explain the fundamental concepts, principles, and history of Generative AI
- Describe key GenAI models, their capabilities, and current developments
- Understand the implications of GenAI as a non-human resource in the workplace
- Identify strategies for integrating GenAI into business operations
- Understand the principles and best practices of prompt engineering
After completing the course, students will be able to:
- Apply GenAI tools for specific business tasks such as content creation, management, data analysis, and product development
- Craft effective prompts for various GenAI applications
- Analyze the potential impact of GenAI on different business functions and models
- Develop strategies for effective human-GenAI collaboration to enhance productivity and innovation
- Evaluate the ethical implications and potential risks associated with GenAI adoption
After completing the course, students will be able to:
- Critically assess the potential of GenAI for their organization's strategic objectives
- Develop strategies for responsible and effective implementation of GenAI within their organizations
- Communicate effectively about GenAI capabilities and limitations to various stakeholders
- Navigate the ethical and societal implications of GenAI in a business context
- Understand the current and potential future legal frameworks governing GenAI use in business
Mandatory list of themes of the course:
GenAI Foundations and Business Integration
- Core principles and key concepts of Generative AI
- Current state-of-the-art GenAI models and their capabilities
- Strategic considerations for GenAI adoption in business
- Integration approaches and impact on business models
Prompt Engineering and Communication
- Fundamentals and best practices of prompt engineering
- Advanced techniques for crafting effective prompts
- Methods for optimizing prompts across different GenAI models
- Quality control and output refinement strategies
GenAI Applications
- Content creation tools (text, image, video, sound, virtual world generation)
- Code generation and automation tools for work
- GenAI-Powered data analysis
- Customized agent and personal assistant
Management and Strategy
- GenAI-assisted decision making processes
- Workflow automation and optimization
- Human-GenAI collaboration strategies
Implementation and Ethics
- Strategic planning for GenAI integration
- Risk assessment and mitigation
- Legal and ethical considerations
- Security and privacy frameworks
Target group:
- Middle and upper middle managers who want to learn how to apply GenAI in their work, their team, and their department
- Senior leaders and entrepreneurs who aim to strategically incorporate AI into their businesses
The course is conducted total of approx. 75 resource hours. There are two (2) on-campus modules, one is consisting of three (3) full days and the other of two (2) full day class sessions. The course will employ a blend of lectures, case studies, and hands-on exercises to facilitate learning. Lectures will provide theoretical foundations and industry insights, while case studies will illustrate real-world applications of GenAI. Hands-on exercises will allow participants to experiment with GenAI tools and develop practical skills. The course will foster active participation, encouraging discussions and sharing of experiences among participants.
The students are evaluated through a term paper, counting 60% of the total grade and a 72-hour individual home exam with own computer counting 40%. The term paper is written in groups of 1-3 students. All evaluations must be passed to obtain a certificate for the course.
For the term paper, the guidance/supervision offer is estimated at 2 hours per. group.
Please note that while attendance is not compulsory in all modules, it is the student's own responsibility to obtain any information provided in class that is not included on the course homepage/ itslearning or other course materials.
The term paper is included in the degree’s independent work of degree, cf national regulation on requirements for master’s degree, equivalent to 9 ECTS credits per. 15 credits course. For the Executive Master of Management degree, the independent work of degree represents the sum of term papers from the courses/programmes.
In all BI Executive courses and programs, there is a mutual requirement for the student and the course responsible regarding the involvement of the student's experience in the planning and implementation of courses, modules and programmes. This means that the student has the right and duty to get involved with their own knowledge and practice relevance, through the active sharing of their relevant experience and knowledge.
Software tools
NotebookLM, Poe (including most GenAI models), COZE, Perplexity, GAIforResearch.com
Students will develop a strategic plan for applying generative AI to a specific challenge or opportunity within their current company or a chosen organization. This plan should demonstrate a deep understanding of the course content, including:
- GenAI Fundamentals: A clear understanding of generative AI models, their capabilities, and limitations.
- Business Context: A thorough analysis of the chosen company's business needs, goals, and existing processes.
- GenAI Application: A detailed proposal for how generative AI can be used to address the identified challenge or opportunity. This should include specific use cases, potential benefits, and a realistic implementation roadmap.
- Ethical Considerations: A thoughtful discussion of ethical implications, including data privacy, bias, and responsible AI deployment.
- Implementation Strategy: A practical plan for integrating generative AI into the company's existing infrastructure and workflows. This should include resource allocation, potential risks, and success metrics.
Bachelor degree, corresponding to 180 credits from an accredited university, university college or similar educational institution
The applicant must be at least 25 years of age
At least four years of work experience. For applicants who have already completed a master’s degree, three years of work experience are required.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 40 Grouping: Individual Duration: 72 Hour(s) Comment: Individual 72 hours home exam, counting 40% of the total grade Exam code: MAN 52441 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 60 Grouping: Group (1 - 3) Duration: 1 Semester(s) Comment: Term paper, counting 60% of the total grade. Exam code: MAN 52442 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 42 Hour(s) | |
Webinar | 33 Hour(s) | |
Prepare for teaching | 50 Hour(s) | |
Student's own work with learning resources | 125 Hour(s) | |
Examination | 150 Hour(s) | Term paper and home exam |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 15 ECTS credit corresponds to a workload of at least 400 hours.
