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Course

INN365 Artificial Intelligence from a Business Perspective

Credits (ECTS):10

Course responsible:Joachim Scholderer

Campus / Online:Taught campus Ås

Teaching language:Engelsk

Course frequency:Annually

Nominal workload:250 hours

Teaching and exam period:This course starts in Spring parallel. This course has teaching/evaluation in Spring parallel.

About this course

Artificial intelligence (AI) has become a central driver of innovation in modern organisations. The aim of this course is to provide participants with the knowledge, skills and general competences to implement AI in organisations and lead business development projects related to AI. The course consists of five parts:

· Introduction: AI as the latest wave of digital transformation

· AI for efficiency improvement: task analysis, use case development, assessing efficiency gains, planning and implementing AI projects

· AI for top-line growth: from technology to product, assessing customer and user acceptance, estimating market potential, developing marketing strategies for AI products and services

· Change management: developing organisational AI capabilities, stakeholder management, compliance management

· AI strategy: maturity models, partnering and platforms, strategic foresight

Practical work with real and current cases is a key part of the course. Participants will work in project teams on a semester-long project assignment, consulting a case company on their AI strategy.

Learning outcome

A student, upon completing the course, will have the following overall learning outcomes defined in knowledge, skills, and general competence:

Knowledge

After completing the course, participants will have gained:

  • Advanced understanding of the capabilities of modern AI,
  • In-depth knowledge of AI-related innovation strategies.

Skills

After completing the course, participants will be able to:

  • Identify which tasks in an organisation can be automated with AI,
  • Assess efficiency gains and develop a business case,
  • Develop AI-enabled product and service concepts,
  • Assess their market potential and develop a strategy.

General competence

After completing the course, participants will also be able to:

  • Lead AI-related development and implementation projects,
  • Manage stakeholders and regualatory compliance,
  • Use foresight techniques to prepare for changes in technology and market.

  • Learning activities
    In line with NMBU's learning philosophy,this will be an interactive course combining a case project with workshops, lectures and demonstrations. Active participation is required.
  • Teaching support
    All project teams will receive supervision during their work on the project assignment.
  • Syllabus

    Alon, I., Haidar, H., Haidar, A., & Guimon, J. (2025). The future of artificial intelligence: Insights from recent Delphi studies. Futures, 165, 103514.

    Ångström, R. C., Björn, M., Dahlander, L., Mähring, M., & Wallin, M. W. (2023). Getting AI implementation right: Insights from a global survey. California Management Review, 66(1), 5-22.

    Brynjolfsson, E., Chandar, B., & Chen, R. (2025). Canaries in the coal mine? Six facts about the recent employment effects of artificial intelligence. Palo Alto, CA: Stanford Digital Economy Lab.

    Christensen, C. M., Hall, T., Dillon, K., & Duncan, D. S. (2016). Know your customers’ jobs to be done. Harvard Business Review, 94(9), 54-62.

    Davenport, T. H., & Mittal, N. (2023). Stop tinkering with AI. Harvard Business Review, 101(1), 116-127.

    Handa, K., Tamkin, A., McCain, M., Huang, S., Durmus, E., Heck, S., Mueller, J., Hong, J., Ritchie, S., Belonax, T., Troy, K. K., Amodei, D., Kaplan, J., Clark, J., & Ganguli, D. (2025). Which economic tasks are performed with AI? Evidence from millions of Claude conversations. arXiv: 2503.04761.

    Kim, Y., Blazquez, V., & Oh, T. (2024). Determinants of generative AI system adoption and usage behavior in Korean companies: Applying the UTAUT model. Behavioral Sciences, 14(11), 1035.

    Kolbjørnsrud, V. (2024). Designing the intelligent organization: Six principles for human-AI collaboration. California Management Review, 66(2), 44-64.

    Pettersson, M. O., Björkdahl, J., & Holgersson, M. (2025). Profiting from AI: Evidence from Ericsson’s pursuit to capture value. California Management Review, 67(4), 5-20.

    Salmon, P., Jenkins, D., Stanton, N., & Walker, G. (2010). Hierarchical task analysis vs. cognitive work analysis: Comparison of theory, methodology and contribution to system design. Theoretical Issues in Ergonomics Science, 11(6), 504-531.

    Schilke, O., & Reimann, M. (2025). The transparency dilemma: How AI disclosure erodes trust. Organizational Behavior and Human Decision Processes, 188, 104405.

    Sonntag, M., Mehmann, S., Mehmann, J., & Teuteberg, F. (2024). Development and evaluation of a maturity model for AI deployment capability of manufacturing companies. Information Systems Management, 42(1), 37-67.

    Tomlinson, K., Jaffe, S., Wang, W., Counts, S., & Suri, S. (2025). Working with AI: Measuring the applicability of generative AI to occupations. arXiv: 2507.07935.

    Varoquaux, G., Luccioni, S., & Whittaker, M. (2025). Hype, sustainability, and the price of the bigger-is-better paradigm in AI. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (pp. 61-75). New York: ACM.

  • Assessment method

    Project assignment conducted in groups of up to five participants during the teaching period (weight: 100%).
    No re-sit examination will be arranged in this course.

    Karakterregel: Letter grades



  • About use of AI
    K3 - Full use of AI. Use of AI is permitted, but must be in accordance with the guidelines for use of artificial intelligence (AI) at NMBU.

    Descriptions of AI-category codes.

  • Examiner scheme
    An external examiner will control the quality of the syllabus and the principles for the assessment of the project assignment.
  • Mandatory activity

    There will be five workshops with mandatory participation. Participants must actively participate in at least four of the five workshops.

    The mandatory activity is valid only for one semester. If a participant would like to retake the course, the mandatory activity must also be retaken. The mandatory activity must be approved for the participant to be assessed in the course.

  • Teaching hours

    Lectures, demonstrations and case work on campus (2x2 hours) in calendar weeks 6, 8, 9, 11, 12, 15, 17, 18. Workshops on campus (4 hours) in calendar weeks 7, 10, 13, 16, 19.