Navigating AI: Strategy and Impact

Course Director: Sebastian Krakowski

WELCOME TO COURSE M07314!

AI strategy without buzzwords. We start with problems, not tools.
No coding required. Just clear thinking, hands-on experimentation, and honest conversations about what AI means for organizations and for you. This is more doing than listening, so come ready to participate.
Bring curiosity, a laptop, and a willingness to try, learn, and iterate. If something fails, we learn. If it works, we make it better. And we help each other out along the way.
Let's get to work.

Please note that this is an online-only course.

Tentative syllabus. This document and any PDF version are subject to change. The timetable, companies, live cases, guests, and TA remain to be confirmed.

For administrative questions regarding enrollment, scheduling, exam formalities, or special needs accommodations, contact the Course Administrator. For GNAM-related questions, contact the GNAM point of contact.

Post questions about materials and concepts in the designated course forums, then reach out to the Teaching Assistant for remaining questions.

For any other inquiries or concerns that are clearly idiosyncratic or personal, please direct them to the Course Director.

Please note that the Canvas inbox is not monitored.

The course contents, including the ILOs, stated in this syllabus are the ones that apply for this semester. If course information stated elsewhere, such as on Canvas, SSE Portal, or with your home institution differs from those stated here, the current syllabus takes precedence.

If you have been granted educational support because of a documented disability, please contact the Course Administrator as soon as possible so that adequate accommodations can be made (e.g., if a student is granted extended time to take an exam, the student must inform the Examination Office at least 14 days in advance). Contact specialneeds@hhs.se if you have questions about being granted educational support or need a copy of your
educational support certificate. Read more at Special Needs on the Portal.

Generative AI tools were used to develop and revise this syllabus.

Content

This course explores how organizations and individuals can strategically navigate AI, moving beyond adoption to transformation. Through an iterative approach combining theory with hands-on experimentation, students build, test, and refine prototypes and workflows to measure impact and extract learnings.
The course addresses three core dimensions: competitive strategy in the age of AI, organizational transformation and implementation, and human-AI collaboration. A central principle is problem-first thinking. Students learn to identify and validate genuine challenges before considering technological solutions, developing skills in recognizing real needs, testing assumptions, and strategically evaluating when and how AI can drive meaningful impact. Team-based live case work forms the backbone of the course. Teams investigate a problem, run experiments, build usable prototypes, and present evidence and recommendations.
Individual memos and a final paper examine how AI reshapes work, skills, and professional development.

No coding, technical background, or prior AI coursework is required. A genuine interest in strategy and organizations, willingness to experiment, and readiness to work in an international team are expected. Students need a laptop, reliable internet, an office suite, access to the course tools, a working camera, and clear online meeting audio and video (e.g., Microsoft Teams or Zoom) in an environment where they can hear and speak without interruptions or disrupting class. Check early that your institution does not block the course tools.

UN Sustainable Development Goals (SDGs)
The course most directly relates to the following UN Sustainable Development Goals:

**Goal 4.** Builds digital and AI literacy and promotes lifelong learning through a globally relevant, practice-based course.

Goal 8. Examines how AI reshapes work, shifts tasks toward creativity, and affects productivity and growth.

Goal 9. Develops innovation capability through AI-enabled experimentation and problem-solving.

Goal 16. Addresses AI ethics, accountability, bias, transparency, and responsible governance.

Goal 17. Emphasizes collaboration, reciprocity, and shared learning within teams and with external partners.

Format and structure
This is a fully online course. Sessions are held via online meeting tools such as Zoom or Microsoft Teams. Readings and preparations support discussion, analysis, and applied work in class. The course combines sessions on strategy, AI, and organizations with coaching and pitching sessions, final team presentations, and topic and course synthesis.

Regular low-stakes quizzes may be administered during sessions without prior notice, as part of the active participation assessment.

Active engagement is expected at all sessions and through collaborative work between sessions, and constitutes a major component in assessment and grading. Auditors are not
accepted.

Attendance
In case of a scheduling conflict between SSE course sessions, the general rule is to attend the mandatory session. Attend all live sessions prepared, with your camera on, and contribute to
your team and to other teams. All session times are Stockholm time; check the time difference before enrolling. Notify the Course Director in advance of an absence.
Repeated absence lowers the active participation grade. Sessions are not recorded, except team pitches, which may be recorded for feedback.
Not completing the pre-course survey by the deadline, or missing the first session without prior notice, may lead to automatic failure in meeting the course requirements. Students who persistently miss sessions or do not respond to contact may also fail to meet the requirements for a passing grade.

Course Administrator: Anna.Burell@hhs.se

After successfully completing the course, students should be able to:

1. Analyze how AI changes competition and evaluate strategic choices for defensible competitive advantage.
2. Design approaches for implementing AI in organizations, addressing workflows, capabilities, incentives, and human-AI roles.
3. Frame real problems through empathetic stakeholder inquiry and creative, problem-first thinking, and judge when AI adds value.
4. Plan and run experiments with usable prototypes, and revise decisions based on evidence.
5. Critically evaluate information and AI outputs, and address the ethical and governance implications of AI use.
6. Collaborate and manage shared work in diverse teams, give constructive feedback, and communicate recommendations clearly.
7. Reflect on how AI reshapes work and skills, and develop a deliberate approach to your own AI use and learning.

General entry requirements

Short, curated readings (typically 2-3 per session) including scholarly articles and practitioner pieces. All materials will be provided digitally before the course begins; the complete reading list will be released in advance. Supplemental resources may be shared during the course. There is no mandatory textbook.

Attendance and Assessment

Assessment has three components: active participation (30 points), team project (30 points), and individual project (40 points). Submit all required work by the stated deadlines. Late submissions may not be considered or may lower the assessment. If you anticipate a problem, contact the course team before the deadline.

Submit documents as PDF through Canvas unless specified otherwise by the course team and hand-in instructions. Include your name or team name and member names on the title page.

Active participation
Active participation involves thoroughly preparing for each session, contributing constructively to discussions, collaborating with peers, and supporting other teams' work. Reciprocity and shared learning are core values in this course. Quality matters more than quantity, though the latter assumes a certain level of the former.
Being a reliable teammate is a baseline expectation. Assessment also considers engagement beyond your own team, such as specific feedback on another team's experiment evidence, constructive questions during presentations, or help that enables a peer to make progress. Strong contributions generate follow-on activity: questions, comments, and actions by others in sessions, online session chat, and the shared course channels.

Valuable contributions apply course ideas, challenge assumptions respectfully, connect evidence across sessions, and improve decisions. A clarifying question counts when it advances shared understanding. Simply rephrasing content or discussing unrelated topics does not count.

Short low-stakes quizzes may be used in sessions as part of the participation evidence.

In general, professionalism is a central aspect of evaluation. For further guidance, see NACE's competency on professionalism. What is Career Readiness

Intuitive guidelines for assessment criteria:
• Below Pass: Insufficient preparation or constructive engagement.
• Pass: You regularly said relevant things.
• Good: You regularly said relevant things, and we remember you for it.
• Very Good: You regularly said relevant things, we remember you and the specific things you said, and we learned from them.
• Excellent: You regularly said relevant things, we remember you and the specific things you said, we learned from them, and you consistently enabled others to contribute and learn too.

Team project

Teams and case allocations are confirmed at the start of the course. Teams work with live cases on organizational challenges related to AI strategy and implementation. Team number and sizes depend on enrollment. Agree early on roles, availability, ambition, communication, and how to resolve disagreement. Teams scope problems, run experiments, build usable prototypes, and iterate. For coaching sessions, prepare short pitches where you state the problem, your proposed solution, the available evidence, and decision(s) on which you need feedback.

Teams are asked to submit periodic updates demonstrating genuine progress. Each update requires an updated live homepage, a working prototype, an updated shared experiment tracker with metrics and results, and written comments on at least one other team's tracker. Pass requires clear evidence of new learning and action (experiments run, results, interactions with the company, clients, or users), implications, and concrete next steps to mitigate risk.

The final team deliverable contains a concise pitch deck PDF (with an optional appendix), a live and accessible homepage, and a usable and accessible prototype. Explain the problem, proposed solution, tests, results, limitations, changes, and recommended next steps. Include evidence showing how contact with people confirmed, changed, or challenged your intuition, and AI
-supported or desk research. Distinguish observed behavior and credible traction from vague interest, simulated feedback, and assumptions.

Teams are also asked to present the project to the live case companies and the class (external guests and faculty may also join sessions). Assessment considers the clarity and credibility of
the opportunity, the quality of evidence and iteration, handling of questions, and how effectively you applied course principles. Success is not defined solely by the outcome in terms of the perceived viability of your solution. It is equally defined by the process, i.e., purposeful and disciplined behavior: taking informed risks, testing, iterating based on evidence, and learning from each step.

Regarding team dynamics, we ask that you communicate clearly and respectfully. Assign roles and responsibilities, including leadership (which can rotate). If conflicts arise, resolve them
through constructive dialogue. Expect that a member may drop out, and do not let a non-responsive member slow the team down. As a last resort, teams can jointly ask a member to leave, with the Course Director informed immediately. Faculty is contacted only in severe
cases.

We ask that you treat the information you receive from the organization confidentially, since the materials can be of a sensitive nature. Agree on a responsive contact person, a real problem owner, access to stakeholders, and a relevant workflow or artifact. If company access is limited, seek comparable users elsewhere and explain these limits in your work and presentations.
Intuitive guidelines for assessment criteria:

• Below Pass: Mainly conceptual or simulated work without a usable artifact or credible learning evidence.
• Pass: A clear problem, usable prototype, real stakeholder engagement, and documented tests and limitations.
• Good: Credible problem-solution fit and evidence that informs iteration.
• Very Good: Well-designed task-based tests or comparisons, traceable results, and substantive revision.
• Excellent: Strong evidence of behavior or commitment, critical treatment of contrary results, and persuasive strategic and organizational judgment.

Individual project

The individual project develops your perspective on the future of work and AI through three structured memos and a final paper.

Memos

Each memo is a concise, analytical reflection on course topics, discussions, and your own evolving thinking. The memos serve as building blocks for the final paper: they document your learning in real time, capture what shifted in your understanding and why, and create a trail of evidence that only you could produce. Memos are submitted as PDF via Canvas at the stated deadlines.

Final paper

The final paper develops a coherent, evidence-based argument about the future of work and AI. Combine relevant scholarly research, course concepts, and evidence from your memos and project experience. Use high-quality sources, such as journal articles from the FT50 list or rated 3 or higher in the AJG list, and judge each source's relevance.

You choose the specific scope, but the paper must substantively address the future of work. Possible angles include (but are not limited to): the changing nature of tasks and jobs, skill demands and human capital, labor market displacement and creation, wages and productivity, human-AI collaboration in professional settings, organizational redesign of work, or the implications for your own career and field. Your key argument should be a non-obvious and interesting one that a reasonable person could disagree with.

What distinguishes this paper from a standard literature review is the integration of reflexive practitioner inquiry: your memos provide first-person evidence of how your own thinking, assumptions, and practices evolved through direct engagement with AI during the course. The paper should weave together academic and practitioner sources with your documented experience to produce an analysis that is both theoretically grounded and personally situated.

Typical length is 2,000-3,000 words (excluding figures, tables, references, and appendices). Submit as PDF via Canvas by the stated deadline.

Guidance on memos and the final paper

• Show learning and decisions. Explain what changed in your thinking, why, and your next concrete steps. Do not write a diary of activities or vague plans.
• Demonstrate progression. Compare your starting assumptions with later evidence, including contrary results, and explain how your judgment or behavior changed.
• Be selective and analytical. Focus on the most consequential insights. Analyze assumptions, limits, and implications. Avoid summarizing known material or restating general concepts.
• Cite precisely. Name the specific readings, data, sessions, and peer feedback that informed your thinking. Apply frameworks, do not just list them.
• Connect reciprocity and relevant experience. When it strengthens your analysis, note how you helped or received help, and draw on relevant prior experience.
• Be transparent and genuine. You do not have to agree with the course materials or perspectives presented, but criticism should be well-founded and substantiated.

Intuitive guidelines for assessment criteria:

• Below Pass: Anyone could have written it without taking this course.
• Pass: Any student who took this course could have written it.
• Good: Only you could have written it, and it draws on the course. Your perspectiv e and the course material come together.
• Very Good: Only you could have written it, and only after taking this course. The course visibly changed how you think.
• Excellent: Only you could have written it, and only after taking this course. Your insights plausibly shaped how others in the class thought and wrote about the same topics.

Paper structure

You have flexibility, but a natural approach might include: (1) an introduction framing your question or angle on the future of work, (2) a brief grounding in relevant literature and course concepts, (3) your analysis and argument, integrating insights from your memos and course experience, and (4) a conclusion with implications for your own professional development.

Grading

Composition of the final grade

Active participation (ILOs 1, 2, 5, and 6): 30 points

Team project (ILOs 1-6): 30 points

Individual project (ILOs 3, 5, and 7): 40 points

Total: 100 points

Grading scheme

Excellent: 80-100

Very Good: 70-79

Good: 60-69

Pass: 50-59

Fail: 0-49

To pass, submit all required work and earn at least 50 points overall, including at least 15/30 in active participation, 15/30 in the team project, and 20/40 in the individual project.

Academic integrity, AI policy, and referencing

Academic integrity

Follow the applicable SSE academic integrity and disciplinary rules and the current student handbook. Credit other people's ideas, words, data, and assistance. Fabricated sources, invented fieldwork, or presenting generated material as observed evidence are unacceptable. When in doubt, cite the source.

AI policy

The use of generative AI tools is permitted and encouraged in this course. Learning to work effectively with AI is itself a course objective. However, the following requirements apply:

• Verify. AI generates plausible language, not guaranteed facts. You are responsible for the accuracy of everything you submit. If an AI gives you a claim, a citation, or a number, assume it is wrong unless you can verify it independently. AI works best for topics you already understand.
• Disclose. Include a brief AI-use statement with each assessed submission, or state that no generative AI was used. Identify the tool and model or version where known, its purpose, and the kinds of prompts or instructions used. Explain what you checked and what you decided yourself. Example: "I used a generative AI assistant to propose counterarguments, then revised the argument and verified the sources myself."
• Exercise judgment. Use AI to support learning, experimentation, and critique. You remain responsible for the argument, evidence, and decisions. Preserve meaningful practice of the skills in your learning contract. A polished generated text without your own verified analysis will not meet the assessment criteria.

As a summary: AI is a powerful tool, but one that requires skill, skepticism, and transparency. Or, more bluntly: “AI lies continuously and well” (Mollick, 2024). Use it, but verify everything and make the work your own.

Referencing

Cite all sources you use. Statements that are not your own observations or experiences must be substantiated with references. You may use any standard referencing style (e.g., Harvard author-date or Vancouver numbered), but use it consistently throughout each assignment, including a complete bibliography.

Find the course schedule in TimeEdit

Have you been granted educational support because of a documented disability? If so, please contact the course director as soon as possible so that adequate accommodations can be made.

Contact specialneeds@hhs.se if you have questions about being granted educational support or need a copy of your educational support certificate.

For more information, visit the Specific Educational Needs page on the Student Hub (login required).