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 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

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 be able to:

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

  • Learning activities

    Workshops on campus, project work, video lectures and self-paced learning units, exercises with data and software, self-study. Active participation is required.

  • Teaching support

    Learning platform Canvas, Microsoft Teams.

  • 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.

    Bettencourt, L. A. & Ulwick, A. W. (2008). The customer-centered innovation map. Harvard Business Review, May 2008, 109-114.

    Cooper, R.G. (2009.) Effective gating: Make product innovation more productive by using gates with teeth. Marketing Management, April 2009, 12-17.

    Easingwood, C., Moxey, S., & Capleton, H. (2006). Bringing high technology to market: successful strategies employed in the worldwide software industry. Journal of Product Innovation Management, 23, 498-511.

    Frattini, F., De Massis, A., Chiesa, V., Cassia, L., & Campopiano, G. (2012). Bringing to market technological innovation: What distinguishes success from failure. International Journal of Engineering Business Management, 4, 15.

    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.

    Maurya, A. (2012). Running lean (2nd Ed.). Sebastopol, CA: O’Reilly.

    Ooi, K. B., Tan, G. W. H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., ... & Wong, L. W. (2025). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, 65, 76-107.

    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.

    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.

    Vial, G., Cameron, A. F., Giannelia, T., & Jiang, J. (2023). Managing artificial intelligence projects: Key insights from an AI consulting firm. Information Systems Journal, 33, 669-691.

    Waheeduzzaman, A. N. M. (2008). Market potential estimation in international markets: A comparison of methods. Journal of Global Marketing, 21, 307-320.

  • 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.

    Grading: A-F

  • About use of AI

    Assessment and mandatory activity: 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. Active participation includes workshop preparation (readings and project work), presence during the whole workshop, completion of the workshop tasks, and documentation of the workshop results.

    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 in order for the participant to be assessed in the course.

  • Teaching hours

    • Workshops on campus: 20 hours,
    • Exercises with data and software: 10 hours,
    • Project work on the semester-long case project: 130 hours,
    • Flipped classroom/self-study/syllabus literature: 90 hours.