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2025/2026  BA-BHAAV6032U  AI for Sales Management

English Title
AI for Sales Management

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 100
Study board
Study Board of General Management
Course coordinator
  • Milena Micevski - Department of Marketing (Marketing)
Main academic disciplines
  • Management
  • Marketing
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 30-01-2025

Relevant links
Learning objectives
At the end of the course the student should be able to:
  • Identify relevant theories, concepts and models discussed in class and in the context of AI in Sales Management.
  • Understand the value of and be able to discuss the assumptions in connection to theories, concepts and models that underlie the study of AI for Sales Management.
  • Critically assess and apply the theoretical knowledge on AI and sales management to practical business cases.
  • Understand, reflect upon, and contrast different forms of AI that can be used in the sales setting.
  • Describe and reflect upon the challenges and opportunities for sales organization in relation to implementation of AI.
  • Understand and critically evaluate the impact AI has on privacy, ethics and biases in the context of sales organization’s internal and external environment.
  • Follow academic conventions in your written presentation.
Course prerequisites
None.
Examination
AI for Sales Management:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Written assignment
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
Duration 2 weeks to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
If the student fails the ordinary exam, the course coordinator chooses whether the student will have to hand in a revised product for the re-take or a new project.
Course content, structure and pedagogical approach

Artificial intelligence (AI), manifested by machines that mimic certain aspects of human intelligence (HI), is increasingly utilized in sales and service and considered the most impactful source of change. For instance, robots have automated many parts of our lives, Gen AI based virtual bots turn customer service into self-service, big data AI applications are used to replace portfolio managers, and social robots such as Pepper are used to replace human greeters to welcome customers in customer-facing services. These developments clearly show that we are in the fourth industrial revolution in which technology is blurring the boundary between the physical, digital, and biological spheres.

 

Although AI affects multiple business areas, its biggest impact pertains to marketing and sales. Following this, the goal of this course is to equip students with relevant knowledge for contributing to the academic discourse on AI and how it affects the sales organization and sales employees, but also its business partners and customers.

 

The course provides the basis for development of knowledge and understanding of how AI is developed and utilized in sales settings and also the challenges and opportunities that go hand in hand with the application of AI in sales settings.

 

In its broadest terms, the objective of this course is to develop a managerial perspective regarding AI in sales settings. The emphasis is on the decision-making processes pertaining to engagement in and later implementation of AI in sales organizations. The course will prepare students for a challenging career in digitally enabled business environments.

 

More specifically, the course is designed to:

  • Connect the state of the art of AI in general with sales management in particular and by doing so enable you to contemplate on the future developments and applications of AI in the sales management context in a more informed way.
  • Enhance your understanding of the role AI has for sales management.
  • Enhance your understanding of the broader impact AI has on the organization's internal and external environment (e.g. employees, customers).
  • Connect the current insights, approaches, and developments in the area of AI and sales management and ground them in academic traditions.
  • Showcase the challenges and opportunities presented by different mindsets about and approaches to AI in sales organizations.
  • Provide you with tool-box of theories and methods to understand the real importance of AI for your sales organization and develop AI solutions accordingly.
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Classic and basic theory
  • New theory
  • Teacher’s own research
Research-like activities
  • Discussion, critical reflection, modelling
Description of the teaching methods
The course uses blended learning: that is, we combine online material and lectures with in-class discussions and workshops. Blended learning (the mix of online and offline platforms) creates a powerful leaning environment for students, which we intend to use to its fullest potential.

The course consists of online lectures and materials, and online/offline case-based and general discussions. The class is highly interactive both with a corresponding expectation that students engage in these interactions.
Feedback during the teaching period
Group assignments are used to give students a better overview of whether they are following the expected learning curve. In an extended classroom-teaching situation groups can voluntarily present their projects. Feedback is a provided for all the case studies and assignments students engage in with an aim to highlight strong points and correct and guide the improvement of the weaker points in the assignment. At the end of the course a Q&A session is planned.
Student workload
Preparation 120 hours
Exam 50 hours
Teaching 38 hours
Expected literature

Indicative literature (more literature will be announced upon enrolment):

  • Brynjolfsson, E., & Mcafee, A. N. D. R. E. W. (2017). The business of artificial intelligence. Harvard Business Review.
  • Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.
  • McClure, C. E., Epler, R. T., Schmitt, L., & Rangarajan, D. (2024). AI in sales: Laying the foundations for future research. Journal of Personal Selling & Sales Management, 44(2), 108-127.
  • Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135-146.
  • Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.
  • Singh, J., Flaherty, K., Sohi, R. S., Deeter-Schmelz, D., Habel, J., Le Meunier-FitzHugh, K., ... & Onyemah, V. (2019). Sales profession and professionals in the age of digitization and artificial intelligence technologies: concepts, priorities, and questions. Journal of Personal Selling & Sales Management, 39(1), 2-22.
  • Robinson, S., Orsingher, C., Alkire, L., De Keyser, A., Giebelhausen, M., Papamichail, K. N., ... & Temerak, M. S. (2019). Frontline encounters of the AI kind: An evolved service encounter framework. Journal of Business Research.
  • De Keyser, A., Köcher, S., Alkire, L., Verbeeck, C., & Kandampully, J. (2019). Frontline Service Technology infusion: conceptual archetypes and future research directions. Journal of Service Management, 30(1), 156-183.
  • Keegan, B. J., Canhoto, A. I., & Yen, D. A. W. (2022). Power negotiation on the tango dancefloor: The adoption of AI in B2B marketing. Industrial Marketing Management, 100, 36-48.
  • Campbell, C., Sands, S., Ferraro, C., Tsao, HYJ, & Mavrommatis, A. (2019). From data to action: How marketers can leverage AI. Business Horizons.
  • Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020). Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. Berkman Klein Center Research Publication.
  • Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS quarterly, 75-105.
  • Van der Borgh, M., Xu, J., & Sikkenk, M. (2020). Identifying, analyzing, and finding solutions to the sales lead black hole: A design science approach. Industrial Marketing Management, 88, 136-151.
Last updated on 30-01-2025