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Digital Skills & AI for Business - CIS200/2 Fall 2026
Course
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Ladislava Knihova
COURSE SYLLABUS
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Digital Skills & AI for Business
Course code: CIS 200/2
Term and year: Fall 2026
Day and time: Tuesdays 11:15-14:00
Instructor: PhDr. Ladislava Knihová, Ph.D., MBA
Instructor contact: ladislava.knihova@aauni.edu
Consultation hours: Tuesday 10:45 – 11:15; Tuesday 14:00 – 14:30
|
Credits US/ECTS |
3/6 |
Level |
Bachelor |
|
Length |
15 weeks |
Pre-requisite |
CIS161, MKT248, MGT245 |
|
Contact hours |
35 hours |
Grading |
Letter grade |
1. Course Description
In a world shaped by rapid digital change, this course equips students with the essential digital skills and AI capabilities needed to thrive in today’s business landscape. It offers a comprehensive introduction to the practical use of digital tools and the transformative potential of artificial intelligence (AI) across key business functions—including marketing, core business operations such as workflow automation, logistics, and customer support, as well as strategic decision-making. Through hands-on projects, real-world case studies, and interactive discussions, students will develop the confidence and competence to apply digital and AI-driven solutions to real business challenges.
2. Student Learning Outcomes
Upon successful completion of this course, students will be able to:
● Identify and apply essential digital tools and AI technologies in business contexts demonstrating awareness of their limitations in light of fundamental AI concepts.
● Analyse how digital transformation and AI can support business strategy and core business processes such as decision-making, marketing, and customer service.
● Collaborate effectively in teams to design AI-enhanced business solutions.
● Communicate complex AI concepts in a clear, accessible, and professional manner.
● Reflect critically on the ethical, strategic, and personal implications of using AI in business.
3. Reading Materials
Required Materials
● Textbooks
Maheshwari, A. Data Analytics Made Accessible. 2025 edition (Kindle Edition)
Harvard Business Review Press (Ed.). (2023). HBR guide to AI basics for managers. Harvard Business Review Press.
Graylin, A., W., Rosenberg, L. Our Next Reality: How the AI-powered Metaverse Will Reshape the World. 2024.
● Articles
Birkinshaw, J. (2025, April 15). Will AI Disrupt Your Business? Key Questions to Ask. MIT Sloan Management Review. https://sloanreview.mit.edu/article/will-ai-disrupt-your- business-key-questions-to-ask/
Harvard Business Review. (2023). Harvard Business Review, September/October 2023. HBR Store. https://store.hbr.org/product/harvard-business-review-september-october-2023/BR2305
McLaughlin, L. (2025, April 7). 10 Urgent AI Takeaways for Leaders. MIT Sloan Management Review. https://sloanreview.mit.edu/article/10-urgent-ai-takeaways-for-leaders/
Renieris, E. M., Kiron, D., & Mills, S. (2022). To Be a Responsible AI Leader, Focus on Being Responsible. MIT Sloan Management Review. https://sloanreview.mit.edu/projects/to-be-a- responsible-ai-leader-focus-on-being-responsible/
Wingate, D., Burns, B. L., & Barney, J. B. (2025). Why AI Will Not Provide Sustainable Competitive Advantage. MIT SloanManagement Review.
https://sloanreview.mit.edu/article/why-ai-will-not-provide-sustainable-competitive- advantage/
Wu, B. H. and L. (2025, June 25). Why Robots Will Displace Managers—And Create Other Jobs. MIT Sloan Management Review. https://sloanreview.mit.edu/article/why-robots-will- displace-managers-and-create-other-jobs/
Recommended Materials
Recommended Articles – Specific Focus
McKinsey & Company. (n.d.). Rewired in action: Digital & AI transformations | Tech and AI | McKinsey & Company. Retrieved 4 January 2026, from https://www.mckinsey.com/capabilities/tech-and-ai/how-we-help-clients/rewired-in-action#/
Scope: Practice-oriented but grounded in extensive empirical work on digital and AI transformations, with many cross-industry cases and capability-building insights relevant for digital skills.
Ramírez, R., Lang, T., Köhler, J., & Mennell, M. (2025). A Faster Way to Build Future Scenarios. MIT Sloan Management Review, 67(2). https://sloanreview.mit.edu/article/scenario-planning-examples/
Focus: In the context of AI-driven business environments, this practical article illustrates how organisations use scenario planning to navigate strategic uncertainty and anticipate future business conditions. Through real-world examples, it demonstrates how strategic tools can be applied in practice and how scenario planning supports managerial insight and strategic decision-making amid rapid technological and organizational change.
Ransbotham, S., Kiron, D., Khodabandeh, S., Iyer, S., & Das, A. (2025). The Emerging Agentic Enterprise: How Leaders Must Navigate a New Age of AI. MIT Sloan Management Review. https://sloanreview.mit.edu/projects/the-emerging-agentic-enterprise-how-leaders-must-navigate-a-new-age-of-ai/
Scope and focus: The Emerging Agentic Enterprise: How Leaders Must Navigate a New Age of AI is an in-depth report exploring how agentic AI systems — autonomous, adaptive, and capable of planning and acting independently — are reshaping organisational boundaries, roles, and management frameworks. Based on a global executive survey and expert interviews, the e-book highlights how traditional distinctions between tools and human decision-makers are blurring, and outlines strategic considerations for governance, value creation, and leadership in AI-driven enterprises.
Tiron-Tudor, A., Labaditis (Cordos), A., & Deliu, D. (2025). Future-Ready Digital Skills in the AI Era: Bridging Market Demands and Student Expectations in the Accounting Profession. Technological Forecasting and Social Change, 215, 124105. https://doi.org/10.1016/j.techfore.2025.124105
Focus: Proposes a new framework of digital and AI-related skills for accounting and similar business functions, comparing market and university expectations.
Other Recommended Learning Materials
● LinkedIn Learning & Coursera Educational Courses on relevant topics
4. Teaching methodology
This course applies a learner-centred, design-driven instructional approach that integrates design thinking principles with hands-on experimentation using digital tools and AI-driven applications. The learning environment is structured to foster curiosity, iteration, and problem-solving, enabling students to think critically and creatively about how technology can solve real-world business challenges.
Each session follows a pedagogical arc that mirrors the design thinking process:
● Empathise & Define: Students begin by exploring user needs, digital trends, and business pain points through real-world case studies and guided reflection.
● Ideate: In collaborative tasks, students generate potential AI-supported solutions and experiment with digital platforms such as ChatGPT, Canva, Trello, or GPT- integrated Sheets.
● Prototype: Teams create low-fidelity mock-ups, data visualisations, or chatbot scripts, engaging with tools and AI applications in ways that simulate actual business workflows.
● Test & Reflect: Students present ideas, receive peer and instructor feedback, and refine their solutions through iterative improvements.
The course further incorporates project-based learning and task-oriented seminars to build both foundational digital skills and strategic thinking. Throughout, students are encouraged to critically evaluate AI’s role in shaping business processes and to reflect on their personal AI readiness and ethical responsibility.
This methodology supports the development of critical thinking, effective communication, and responsible digital action, while preparing students to lead and contribute in AI-augmented business environments.
5. Course Schedule
|
Date |
Class Agenda |
|
Session 1 Tuesday |
Topic: Introduction to Digital Skills and AI for Business Description: The first session introduces key concepts of digital transformation and the growing role of AI in business. Students explore why digital and AI skills are essential today and try out tools powered by large language models (LLMs), such as ChatGPT and/or Perplexity.ai, in a hands-on activity focused on team-based conflict resolution. Tool of the Week: ChatGPT and/or Perplexity – introduction to generative AI, AI-assisted research, and verification. Mini-task: Describe digital transformation in a company of your choice. Seminar Activity: Map of essential digital and AI skills (via Miro or a similar collaborative platform). Reading: Chapter 1: Wholeness of Data Analytics (Maheshwari, A. Data Analytics Made Accessible. 2025 edition), pp. 19 – 49 Assignments/deadlines: N/A |
|
Session 2 Tuesday |
Topic: Digital Productivity, Collaboration & AI-Enhanced Workflows Description: Session 2 introduces students to essential digital productivity and collaboration tools, with a focus on how AI can support the organisation of information, teamwork, and everyday business workflows. Students explore how digital workspaces integrate documents, tasks, shared knowledge, and AI-assisted functions, while considering where human input and oversight remain essential. Tool of the Week: Notion / Notion AI (where available) – digital workspace, knowledge organization, and AI-assisted workflows Mini-task: Design a simple team workspace for a small business, including tasks, shared knowledge, and one AI-supported activity. Gamified Activity: Productivity challenge – compare a manual and an AI-assisted workflow. Reading: Chapter 2: Business Intelligence Concepts and Applications (Maheshwari, A. Data Analytics Made Accessible. 2025 edition), pp. 50 – 72 Additional Reading: Gilbert, R. M. (2019). Inclusive Design for a Digital World: Designing with Accessibility in Mind. Apress. https://doi.org/10.1007/978-1-4842-5016-7 Assignments/deadlines: CW1 / Deadline: From Sep 07, 11:59 PM to Nov 04, 11:59 PM (local time) - ongoing |
|
Session 3 Tuesday |
Topic: Data Literacy, Analysis & AI-Assisted Insights Description: Session 3 develops foundational data literacy and introduces AI-assisted data analysis. Students explore data quality, basic analysis and visualization using spreadsheets and AI-supported tools. Particular attention is given to interpreting AI-generated insights critically, checking outputs against the underlying data, and recognising the risks of incomplete, biased, or misleading data. Tool of the Week: Google Sheets + GPT for Sheets (or another current AI-assisted spreadsheet tool). Mini-task: Analyse and visualize a small business dataset (e.g., coffee shop sales) and use AI to identify possible patterns or insights. Verify whether the AI-generated interpretation is supported by the data. Discussion: Can we trust AI-generated data insights? Reading: Chapter 3: Data Warehousing (Maheshwari, A. Data Analytics Made Accessible. 2025 edition), pp. 73 - 84 Assignments/deadlines: CW1 / Deadline: From Sep 07, 11:59 PM to Nov 04, 11:59 PM (local time) - ongoing |
|
Session 4 Tuesday |
Topic: Fundamentals of Artificial Intelligence & Prompt Engineering Description: Session 4 introduces the fundamentals of artificial intelligence, with particular emphasis on effective interaction with generative AI. Students explore how prompt structure, context, clarity, specificity, and tone influence AI-generated outputs. Through practical prompting activities, they learn to formulate purpose-driven prompts, compare alternative formulations, and critically evaluate the quality and suitability of AI responses for different business contexts. The session also introduces the importance of verification and human judgement when working with generative AI. Tool/Workflow of the Week: ChatGPT or another current LLM – structured prompting, prompt refinement, and comparison of outputs. |
|
|
Mini-task: Explain the same AI concept to a child, a grandparent, and a CEO. Compare how audience, tone, vocabulary, and level of detail change the prompt and the output. Video Resource: Selected excerpts from Andrew Ng’s AI For Everyone; How to Get Started with SEO and GEO from LinkedIn Learning’s Digital Marketing Foundations. Reading: Chapter 4: Data Mining (Maheshwari, A. Data Analytics Made Assignments/deadlines: QUIZ 1 |
|
Session 5 Tuesday |
Topic: AI Applications in Business: Value Creation, Workflows & Agentic AI Description: Session 5 explores how AI creates value across business functions, including marketing, finance, operations, and customer service. Through sector examples and case analysis, students examine different roles AI can play in business – from assisting and recommending to automating workflows and taking limited autonomous action. The session introduces agentic AI and considers its opportunities, risks, decision boundaries, and the continuing need for human oversight. Tool/Workflow of the Week: ChatGPT or another current AI platform – research, analysis, and business problem-solving workflows. Mini-task: Analyse one AI business case using a SWOT framework. Identify where AI assists, recommends, automates, or acts, and where human oversight is required. Case/Video Content: Selected McKinsey example on AI in business. Reading I: Chapter 5: Data Visualization (Maheshwari, A. Data Analytics Made Accessible. 2025 edition), pp. 107 - 122 Assignments/deadlines: CW1 / Deadline: From Sep 07, 11:59 PM to Nov 04, 11:59 PM (local time) CW2 / Deadline: Sep 28 by 11:59 PM (local time) |
|
Session 6 Tuesday |
Topic: AI in Marketing, Personalisation & Customer Service Description: Session 6 explores how AI is transforming marketing and customer service through personalization, conversational AI, and AI-assisted customer interactions. Students examine how AI can support customer journeys, recommendations, service responses, and routine customer-service tasks, while considering the importance of transparency, privacy, trust, and effective human handoff. Particular attention is given to the capabilities and limitations of AI in emotionally sensitive customer interactions. Tool/Workflow of the Week: Conversational AI and chatbot design using a Mini-task: Design and test a basic customer-service chatbot for a selected business scenario, including at least one situation in which the conversation should be escalated to a human. Reading: Chapter 6: Decision Trees (Maheshwari, A. Data Analytics Made Accessible. 2025 edition), pp. 124 - 143 Assignments/deadlines: CW1 / Deadline: From Sep 07, 11:59 PM to Nov 04, 11:59 PM (local time) - ongoing |
|
Session 7 Tuesday |
Topic: Advanced Prompt Engineering: From Prompts to Adaptive AI Interaction Description: Session 7 builds on students’ experience with prompt engineering and explores how prompting can evolve from one-shot instructions towards iterative and adaptive AI interaction. Students experiment with AI as a tutor, coach, mentor, and thinking partner, examining how role definition, questioning strategies, feedback loops, and progressive task design can shape the quality of AI-assisted learning and problem-solving. The session also considers the limitations of AI-generated feedback and the importance of maintaining human judgement and active engagement. Tool/Workflow of the Week: ChatGPT, Claude, or another current LLM – adaptive prompting, Socratic interaction, and feedback loops. Seminar Activity: AI as a Personal Tutor – test and compare prompting strategies such as a Personal Tutor, Socratic Mentor, Practice Generator, or Skill Gap Analyzer. Mini-task: Choose one skill you would like to improve. Design an AI interaction that teaches rather than simply gives you the answer. Test it and evaluate whether the AI genuinely adapts to your responses. Discussion: When does AI support learning – and when does it replace thinking? Reading: Chapter 7: Regression (Maheshwari, A. Data Analytics Made Accessible. 2025 edition), pp. 144 - 161 Assignments/deadlines: N/A |
|
Session 8 Tuesday |
Topic: E-commerce, AI Shopping & Online Business Models Description: Session 8 explores e-commerce and online business models in an increasingly AI-mediated shopping environment. Students examine how digital storefronts, platforms, recommendation systems, conversational shopping assistants, and AI-powered product discovery are reshaping the customer journey. The session considers how businesses can create value when customers increasingly discover, compare, and evaluate products through AI interfaces rather than traditional search and browsing. Tool/Workflow of the Week: Shopify or a similar e-commerce platform + an AI shopping or product-discovery interface. Mini-task: Design a simple digital storefront for a selected product or brand and map how a customer might discover the same product through a traditional search journey versus an AI-assisted shopping journey. Discussion: What happens to the traditional customer journey when AI becomes the shopping interface? Reading: Chapter 8: Artificial Neural Networks (Maheshwari, A. Data Analytics Made Accessible. 2025 edition), pp. 162 - 172 Assignments/deadlines: N/A |
|
|
Mid-term break October 26 – October 30 2026 |
|
Session 9 Tuesday |
Topic: Data Privacy, Responsible AI & AI Governance Description: Session 9 explores data privacy, responsible AI, and the emerging governance requirements for the use of AI in business. Students examine key principles of GDPR and the EU AI Act, including AI literacy, transparency, human oversight, and responsible use of AI systems. International perspectives such as the CCPA are used for comparison. Through practical scenarios, students consider how organizations can balance innovation, business value, customer trust, and regulatory responsibility. Framework of the Week: AI Ethics Canvas – evaluating an AI application from the perspectives of data, transparency, fairness, human oversight, and accountability. Mini-task: Evaluate a selected AI tool or business use case using the AI Ethics Canvas and identify one key risk, one safeguard, and the person or role responsible for oversight. Roleplay: Business vs customer – a privacy and transparency dilemma involving the use of AI. Discussion: Who is responsible when an AI-supported business decision causes harm? Voices from Experts: “To Be a Responsible AI Leader, Focus on Being Responsible” by Elizabeth M. Renieris, David Kiron, and Steven Mills, MIT Sloan Management Review. Reading: Chapter 9: Cluster Analysis (Maheshwari, A. Data Analytics Made Accessible. 2025 edition), pp. 173 – 190 Assignments/deadlines: CW3a / Deadline: Nov 02 by 11:59 PM (local time) |
|
Session 10 Tuesday |
Topic: AI and Decision-Making: Decision Support, Human Judgement & Accountability Description: Session 10 explores how AI supports business decision-making through prediction, forecasting, pattern recognition, and recommendation. Students examine the difference between AI-assisted and AI-automated decisions and consider when human judgement should remain central. Particular attention is given to uncertainty, over-reliance on AI, automation bias, and the need to verify AI-generated recommendations before acting on them. Tool/Workflow of the Week: Power BI and/or spreadsheet-based forecasting – exploring AI-supported business insights and decision support. Reading: Chapter 10: Association Rule Mining (Maheshwari, A. Data Analytics Made Accessible. 2025 edition), pp. 191 – 203 Additional Reading: Ross, M., & Taylor, J. “Managing AI Decision-Making Tools: A Framework to Determine When and How Humans Need to Stay Involved.” In HBR Guide to AI Basics for Managers, pp. 139–145. Assignments/deadlines: CW3b / Deadline: Nov 9 by 11:59 PM (local time) |
|
|
Holiday – Struggle for Freedom and Democracy Day – November 17, 2026 (no classes) |
|
Session 11 Tuesday |
Topic: AI-Enhanced Project Management & Digital Collaboration Description: Session 11 explores how digital and AI-enhanced tools can support project planning, coordination, communication, and teamwork. Students examine how AI can assist with task breakdown, scheduling, prioritization, progress tracking, and team communication, while considering the risks of over-automation and the continuing importance of human coordination, accountability, and decision-making. Tool/Workflow of the Week: Trello, Notion, Microsoft Planner, or another current project-management platform with AI-supported features. Mini-task: Create a simple project plan for a digital marketing or AI-related business project, including key tasks, responsibilities, milestones, and dependencies. Use AI to suggest or refine the plan, then critically review its recommendations. Reading: Chapter 11: Text Mining (Maheshwari, A. Data Analytics Made Accessible. 2025 edition), pp. 205 – 219 Assignments/deadlines: N/A |
|
Session 12 Tuesday |
Topic: Future Trends in Digital Business: Agents, AI Co-workers & Human–AI Collaboration Description: Session 12 explores emerging developments that are likely to shape digital business and the future of work. Particular attention is given to agentic and multimodal AI, AI co-workers, human–AI collaboration, digital twins, physical AI, and immersive technologies. Rather than treating individual technologies as isolated trends, students consider how they may reshape business processes, organizational roles, skills, and managerial responsibility. Reading: Chapter 12: Naïve Bayes Analysis (Maheshwari, A. Data Analytics Made Accessible, 2025 edition), pp. 220 – 234 Assignments/deadlines: N/A |
|
Session 13 Tuesday |
Topic: Final Project Workshop: Agentic AI for Business – Design & Governance Description: Session 13 is dedicated to the development of the final Agentic AI for Business project. Teams refine their proposed agentic AI solution through structured peer review, instructor consultation, and collaborative problem-solving. Particular attention is given to the agent’s role and decision boundaries, business value, human-in-the-loop governance, risks and safeguards, and the clarity of team roles and responsibilities. Workshop Focus: Business problem → agent role and capabilities → decision boundaries → human oversight → business value → risks and safeguards. Reading: Chapter 13: Web mining (Maheshwari, A. Data Analytics Made Accessible. 2025 edition), pp. 235 – 241 Assignments/deadlines: CW4 – Draft / Deadline: Dec 07 11:59 PM (local time) |
|
Session 14 Tuesday |
Topic: Final Project Presentations – Agentic AI for Business Description: Session 14 features final team presentations of the Agentic AI for Business projects. Each team presents a designed agentic AI solution for a real or realistic business context, demonstrating how AI agents can support business processes and decision-making while remaining within clearly defined boundaries of human oversight. Presentations focus on business value, agent capabilities, decision logic, governance, risks and safeguards, and team collaboration rather than technical implementation. Format: Team presentation – 15 minutes + Q&A, supported by a structured presentation deck. Teams may also include a demo, prototype, or short video where appropriate. Reflection Prompt: What did designing an Agentic AI system change about your understanding of managerial responsibility, teamwork, and decision-making in an AI-augmented business environment? Reading: Chapter 14: Social Network Analysis (Maheshwari, A. Data CW4 – Final version / Deadline: Dec 14 by 11:59 PM (local time) |
6. Course Requirements and Assessment (with estimated workloads)
|
Assignment |
Workload (hours) |
Weight in Final Grade |
Evaluated Course Specific Learning Outcomes |
Evaluated Institutional Learning Outcomes* |
|
Class Participation |
42 |
10% |
Active engagement in discussion, tool exploration, peer feedback, and teamwork. |
3 |
|
CW1: AI Glossary (Pairs) |
8 |
10% |
Define and present 6 essential AI terms clearly and accessibly, based on literature research and real-world business relevance. |
1,2 |
|
Quiz 1 Session 4 (Individual) |
10 |
10% |
Assess understanding of core digital tools, prompt design, and AI fundamentals. |
1 |
|
CW2: Prompt Engineering Reflection (Individual) |
10 |
10% |
Explore how tone influences AI-generated responses by crafting and comparing prompts in two distinct tonalities and reflecting on their effectiveness in a business context. |
1,2 |
|
CW3a: AI in Action: Solving a Real- World Challenge (Team Project) |
20 |
25% |
Apply digital and AI tools to address a practical business scenario in a team setting. |
1,2,3 |
|
CW3b: AI in Action: Solving a Real- World Challenge (Individual Reflection on Team Project) |
10 |
10% |
Reflect critically on individual learning and contribution to the team project. |
1 |
|
CW4: Agentic AI for Business – Design & Governance (Team Project) |
50 |
25% |
Design and present an Agentic AI system for a business context, demonstrating how semi-autonomous AI agents can support decision-making and business processes under human supervision, with clear governance, value creation, and team collaboration. |
1,2,3 |
|
TOTAL |
150 |
100% |
|
|
*1 = Critical Thinking; 2 = Effective Communication; 3 = Effective and Responsible Action
7. Detailed description of the assignments
Assignment 1
CW1: AI Glossary (Pairs)
You will work in pairs to create a glossary of six essential AI terms relevant to digital skills and business applications. Each student is responsible for three terms, while the pair jointly prepares one presentation and presents together in class.
The goal of this assignment is to define and present six essential AI terms clearly and accessibly, grounded in literature research and real-world business relevance.
❖ Assignment Description
In this first coursework assignment, you will work in pairs to create a glossary of six essential AI terms that are shaping today’s business and digital landscape. The focus is on AI-related terminology such as concepts, tools, models, or processes that appear throughout the course.
Your goal is to deepen your understanding of foundational and emerging AI concepts, while learning to explain them clearly and accessibly—an essential digital competency for anyone working with or alongside intelligent technologies.
Start with a brief literature review, drawing on:
● Required course readings (e.g. Data Analytics Made Accessible, Our Next Reality)
● Curated articles from MIT Sloan Management Review, OpenAI, McKinsey, or similar
● Reputable sources such as academic journals, white papers, or industry blogs
Each of the six selected terms should include:
● A correct source citation in APA 7th format
● Each of the six selected terms should include:
● A clear and concise definition (max. 100 words)
● A real-world business example or application in context
● A correct source citation in APA 7th format
● Where appropriate, a simple self-explanatory visual (e.g. diagram, icon, process flow, or metaphorical illustration) to support understanding
Each pair will deliver a 5-minute joint in-class presentation, in which both students actively participate, explaining the relevance and real-world significance of their selected terms in business or marketing contexts. You may support your explanation with a short visual, metaphor, or analogy to aid understanding. Use simple visuals in a professional-looking PPT template.
Important rule on terminology selection: If a term has already been presented by another pair, subsequent teams must approach it from a different business perspective, industry, or managerial angle. Repeating an already presented explanation or example will not be accepted. A shared class glossary will be maintained during presentations to track covered terms and perspectives.
CW1: Assessment Criteria Breakdown
|
Assessed area |
Percentage |
|
1. Content: Accuracy and completeness of definitions, relevance of selected terms, and depth of understanding demonstrated in explanations. |
40% |
|
2. Presentation: Clarity, organization, and effectiveness of the in-class presentation; originality of perspective and avoidance of repetitive explanations; ability to engage with the audience and answer questions; and effective use of simple, self-explanatory visuals to support understanding where appropriate. |
30% |
|
3. Research and Examples: Use of appropriate examples or case studies to illustrate the terms. |
20% |
|
4. Professionalism and Timeliness: Adherence to formatting guidelines, timely submission of materials, and overall professionalism in both the written and presented work. |
10% |
|
TOTAL |
100% |
Assignment 2
CW2: Prompt Engineering Reflection – The Power of Tone (Individual)
This assignment focuses on developing your prompt engineering skills by exploring how variations in tone and style influence the quality and effectiveness of AI-generated output. You will practice crafting purpose-driven prompts, experiment with tonal differences, and reflect on their impact—essential competencies for effective AI-assisted communication in business contexts.
❖ Assignment Description
● ∙ Choose a realistic business use case (e.g., replying to a customer complaint, drafting a motivational message to your team, summarising an ESG report, creating a product pitch).
● Write a single prompt for that use case.
● Rewrite your prompt in two different tonalities using the combinations from the handout Tonality in Conversation with Generative Language Models (e.g., friendly + professional vs authoritative + expert).
● Run both prompts using ChatGPT or another AI language model. Save the responses.
● Write a short reflection (150–200 words) answering:
o How did the tonal differences affect the response?
o Which version better suited your intended business goal—and why?
o What did you learn about tone as a tool in AI communication?
o What does this teach you about communicating with AI in business settings?
CW2: Assessment Criteria Breakdown
|
Assessed area/Criterion |
Description |
Percentage |
|
1. Reflection & Insight |
Evidence of critical thinking, observation, and learning |
40% |
|
2. Prompt Quality |
Clear, task-appropriate prompt with well-applied tonal variation |
30% |
|
3. Relevance of Tone Choices |
Match of tone to business context, purpose, and medium |
20% |
|
4. Language & Structure |
Professional formatting, coherence, and grammar |
10% |
|
TOTAL |
100% |
|
Assignment 3a
CW3a: AI in Action: Solving a Real-World Challenge (Team Project)
Assignment Description
In this team project, you will apply what you have learned so far to design a practical, AI- enhanced solution to a real-world business problem. Working in small teams (3–4 students), you will identify a challenge in marketing, operations, customer service, HR, or e-commerce—and use digital and AI tools to address it creatively and effectively.
Your task is to explore the potential of digital tools to streamline processes, improve decision- making, or add customer value. The outcome should be a short team presentation demonstrating your proposed solution, the tools used, and a simple prototype, demo, or mock-up.
This is your opportunity to experiment with tools, solve a relevant business scenario, and demonstrate your growing digital confidence in front of your peers.
What You Will Do:
1. Select a real or realistic business scenario (e.g., onboarding customers, streamlining internal processes, automating feedback collection).
2. Choose relevant tools introduced in class (e.g., ChatGPT, Canva Magic, Trello, GPT for Sheets, Intercom chatbot builder, Power BI, Copy.ai, etc.).
3. Design your AI-powered solution, clearly showing how the tool(s) improve a process, enhance decision-making, or create value.
4. Prepare a short team presentation (5–7 minutes) that includes:
o The problem you identified
o Your proposed solution
o Tools used and why
o A simple prototype, visual mockup, chatbot sample, or data example
o Lessons learned and challenges faced
Deliverables:
● Seminar presentation (PPT SLIDES)
● One-page concept summary outlining the business case, tools used, and key benefits
CW3a: Assessment Criteria Breakdown
|
Assessed area |
Percentage |
|
1. Relevance and originality of the business problem |
20% |
|
2. Effectiveness and creativity of the AI-based solution |
30% |
|
3. Quality and clarity of the presentation |
25% |
|
4. Use of appropriate tools and justification |
15% |
|
5. Teamwork and contribution balance |
10% |
|
TOTAL |
100% |
Assignment 3b
CW3b: Individual Reflection on the Team Project CW3a
After completing the group project, you will write a short individual reflection (300–400 words) that deepens your learning and connects your experience to your personal development. This reflection should capture what you contributed to the team, how the collaboration shaped your understanding of AI tools, and how the project helped you discover your personal AI-business advantage.
Assignment Description
After completing your group project, you will write an individual reflection (300–400 words) that covers two key areas:
1. Your experience and contribution to the team: What you learned from the collaborative process, challenges faced, and how your understanding of AI tools evolved through the project.
2. Your personal AI-business advantage: Based on the course so far, what do you see as your unique strength or opportunity when it comes to using AI in business contexts? How do you plan to apply this in your future studies or career?
This reflection helps consolidate learning, deepen your self-awareness, and highlight your ability to critically evaluate both teamwork and the role of AI in business.
CW3b: Individual Reflection on the Team Project CW3a – Assessment Criteria Breakdown
|
Assessed area |
Percentage |
|
1. Insightful reflection on personal learning, growth, and team contribution |
35% |
|
2. Critical evaluation of the team project solution and the use of AI tools |
25% |
|
3. Clarity and originality in identifying personal AI-business advantage |
25% |
|
4. Structure, language, and submission quality |
15% |
|
TOTAL |
100% |
Assignment 4
CW4: Agentic AI for Business – Design & Governance (Final Team Project)
❖ Assignment Description
This capstone team project reflects the latest evolution in applied artificial intelligence: Agentic AI — AI systems that can plan, decide, and act semi-autonomously while remaining under human supervision.
In teams of 3–4 students, you will design an Agentic AI system for a real or realistic business context, focusing on business value, decision logic, governance, and collaboration, not technical implementation.
You are not expected to code or build software. The emphasis is on managerial thinking, strategic design, and responsible AI use.
What You Will Do
Your team will:
- Select a business context, such as:
- Marketing & branding
- Customer service
- HR & recruitment
- ESG & sustainability
- Operations or internal processes
- Education & training
- Design an Agentic AI system, clearly defining:
- The agent’s role and mission
- What the agent can decide or do autonomously
- What actions require human approval
- How often and in what situations the agent acts
- Define Human-in-the-Loop Governance, including:
- Who supervises the agent
- How escalation and intervention work
- How errors, bias, or misuse are handled
- Where responsibility and accountability lie
- Explain Business Value, addressing:
- Efficiency gains
- Customer or employee experience
- Strategic relevance
- Reflect on Risks & Limits, such as:
- Ethical concerns
- Over-automation
- Data sensitivity
- Trust and transparency
- Demonstrate Team Collaboration, explicitly describing:
- Individual team roles
- How responsibilities were divided
- Key collaboration challenges
- How the team resolved disagreements or coordination issues
- Deliver a 15 minute team presentation during the final session. A live demo, prototype, or short video are encouraged but optional.
Required Output
Team Presentation (PPT)
● Length: 15 minutes (+ Q&A)
● Slides: approx. 12–15 slides
● Format: PowerPoint / Canva / PDF
Mandatory slide structure
- Business context & problem
- Why this problem matters
- The Agentic AI concept (role & mission)
- Agent capabilities (what it does autonomously)
- Human-in-the-loop governance model
- Decision boundaries (AI vs human)
- Business value created
- Ethical risks & safeguards
- Team roles & responsibilities
- Collaboration challenges & lessons learned
- Limitations & open questions
- Key takeaways for managers
CW4: Assessment Criteria Breakdown
|
Assessed area |
Percentage |
|
1. Quality of Agentic AI design & business logic: Relevance of problem, clarity of agent role, decision logic, business fit |
30% |
|
2. Governance, ethics & human oversight: Human-in-the-loop, responsibility, ethical risks, safeguards |
20% |
|
3. Team collaboration & role clarity: Defined roles, contribution balance, cooperation, conflict handling |
20% |
|
4. Presentation quality & structure: Logical structure, clarity, |
20% |
|
5. Critical reflection & managerial insight: Lessons learned, limitations, managerial perspective, future implications |
10% |
|
TOTAL |
100% |
8. General Requirements and School Policies
General requirements
All coursework is governed by AAU’s academic rules. Students are expected to be familiar with the academic rules in the Academic Codex and Student Handbook and to maintain the highest standards of honesty and academic integrity in their work. Please see the AAU intranet for a summary of key policies regarding coursework.
Course specific requirements
There are no special requirements or deviations from AAU policies for this course.
Here is the course outline:
1. Introduction to Digital Skills & AI for Business
Sep 1 11:15am .. 2pm
The first session introduces key concepts of digital transformation and the growing role of AI in business. Students explore why digital and AI skills are essential today and try out tools powered by large language models (LLMs), such as ChatGPT and/or Perplexity.ai, in a hands-on activity focused on team-based conflict resolution. Tool of the Week: ChatGPT and/or Perplexity – introduction to generative AI, AI-assisted research, and verification. Mini-task: Describe digital transformation in a company of your choice. Seminar Activity: Map of essential digital and AI skills (via Miro or a similar collaborative platform). |
2. Digital Productivity, Collaboration & AI-Enhanced Workflows
Sep 8 11:15am .. 2pm
Session 2 introduces students to essential digital productivity and collaboration tools, with a focus on how AI can support the organisation of information, teamwork, and everyday business workflows. Students explore how digital workspaces integrate documents, tasks, shared knowledge, and AI-assisted functions, while considering where human input and oversight remain essential. Tool of the Week: Notion / Notion AI (where available) – digital workspace, knowledge organization, and AI-assisted workflows Mini-task: Design a simple team workspace for a small business, including tasks, shared knowledge, and one AI-supported activity. Gamified Activity: Productivity challenge – compare a manual and an AI-assisted workflow. |
3. Data Literacy, Analysis & AI-Assisted Insights
Sep 15 11:15am .. 2pm
Session 3 develops foundational data literacy and introduces AI-assisted data analysis. Students explore data quality, basic analysis and visualization using spreadsheets and AI-supported tools. Particular attention is given to interpreting AI-generated insights critically, checking outputs against the underlying data, and recognising the risks of incomplete, biased, or misleading data. Tool of the Week: Google Sheets + GPT for Sheets (or another current AI-assisted spreadsheet tool). Mini-task: Analyse and visualize a small business dataset (e.g., coffee shop sales) and use AI to identify possible patterns or insights. Verify whether the AI-generated interpretation is supported by the data. Discussion: Can we trust AI-generated data insights? |
4. Fundamentals of Artificial Intelligence & Prompt Engineering
Sep 22 11:15am .. 2pm
Session 4 introduces the fundamentals of artificial intelligence, with particular emphasis on effective interaction with generative AI. Students explore how prompt structure, context, clarity, specificity, and tone influence AI-generated outputs. Through practical prompting activities, they learn to formulate purpose-driven prompts, compare alternative formulations, and critically evaluate the quality and suitability of AI responses for different business contexts. The session also introduces the importance of verification and human judgement when working with generative AI. Tool/Workflow of the Week: ChatGPT or another current LLM – structured prompting, prompt refinement, and comparison of outputs. Mini-task: Explain the same AI concept to a child, a grandparent, and a CEO. Compare how audience, tone, vocabulary, and level of detail change the prompt and the output. Prompt Lab – rewrite one business prompt using two different tonalities and compare the resulting AI outputs. Brief current topic: From traditional search to AI-mediated search and Generative Engine Optimization (GEO). Video Resource: Selected excerpts from Andrew Ng’s AI For Everyone; How to Get Started with SEO and GEO from LinkedIn Learning’s Digital Marketing Foundations. |
5. AI Applications in Business: Value Creation, Workflows & Agentic AI
Sep 29 11:15am .. 2pm
Session 5 explores how AI creates value across business functions, including marketing, finance, operations, and customer service. Through sector examples and case analysis, students examine different roles AI can play in business – from assisting and recommending to automating workflows and taking limited autonomous action. The session introduces agentic AI and considers its opportunities, risks, decision boundaries, and the continuing need for human oversight. Tool/Workflow of the Week: ChatGPT or another current AI platform – research, analysis, and business problem-solving workflows. Mini-task: Analyse one AI business case using a SWOT framework. Identify where AI assists, recommends, automates, or acts, and where human oversight is required. Case/Video Content: Selected McKinsey example on AI in business. Voices from Experts: “Will AI Disrupt Your Business? Key Questions to Ask” by Julian Birkinshaw, MIT Sloan Management Review. |
6. AI in Marketing, Personalisation & Customer Service
Oct 6 11:15am .. 2pm
Session 6 explores how AI is transforming marketing and customer service through personalization, conversational AI, and AI-assisted customer interactions. Students examine how AI can support customer journeys, recommendations, service responses, and routine customer-service tasks, while considering the importance of transparency, privacy, trust, and effective human handoff. Particular attention is given to the capabilities and limitations of AI in emotionally sensitive customer interactions. Tool/Workflow of the Week: Conversational AI and chatbot design using a current chatbot platform (e.g., Landbot, or similar). Mini-task: Design and test a basic customer-service chatbot for a selected business scenario, including at least one situation in which the conversation should be escalated to a human. Discussion: Can AI truly empathize with customers? Reflection prompt: When does useful personalisation become intrusive? |
7. Advanced Prompt Engineering: From Prompts to Adaptive AI Interaction
Oct 13 11:15am .. 2pm
Session 7 builds on students’ experience with prompt engineering and explores how prompting can evolve from one-shot instructions towards iterative and adaptive AI interaction. Students experiment with AI as a tutor, coach, mentor, and thinking partner, examining how role definition, questioning strategies, feedback loops, and progressive task design can shape the quality of AI-assisted learning and problem-solving. The session also considers the limitations of AI-generated feedback and the importance of maintaining human judgement and active engagement. Tool/Workflow of the Week: ChatGPT, Claude, or another current LLM – adaptive prompting, Socratic interaction, and feedback loops. CW2 Debrief: Selected examples and lessons learned from the Prompt Engineering Reflection. Seminar Activity: AI as a Personal Tutor – test and compare prompting strategies such as a Personal Tutor, Socratic Mentor, Practice Generator, or Skill Gap Analyzer. Mini-task: Choose one skill you would like to improve. Design an AI interaction that teaches rather than simply gives you the answer. Test it and evaluate whether the AI genuinely adapts to your responses. Discussion: When does AI support learning – and when does it replace thinking? |
8. E-commerce, AI Shopping & Online Business Models
Oct 20 11:15am .. 2pm
Session 8 explores e-commerce and online business models in an increasingly AI-mediated shopping environment. Students examine how digital storefronts, platforms, recommendation systems, conversational shopping assistants, and AI-powered product discovery are reshaping the customer journey. The session considers how businesses can create value when customers increasingly discover, compare, and evaluate products through AI interfaces rather than traditional search and browsing. Tool/Workflow of the Week: Shopify or a similar e-commerce platform + an AI shopping or product-discovery interface. Mini-task: Design a simple digital storefront for a selected product or brand and map how a customer might discover the same product through a traditional search journey versus an AI-assisted shopping journey. Current Example: AI as a visual-content and editing assistant for e-commerce imagery. Discussion: What happens to the traditional customer journey when AI becomes the shopping interface? |
9. Data Privacy, Responsible AI & AI Governance
Nov 2 11:15am .. 2pm
Session 9 explores data privacy, responsible AI, and the emerging governance requirements for the use of AI in business. Students examine key principles of GDPR and the EU AI Act, including AI literacy, transparency, human oversight, and responsible use of AI systems. International perspectives such as the CCPA are used for comparison. Through practical scenarios, students consider how organizations can balance innovation, business value, customer trust, and regulatory responsibility. Framework of the Week: AI Ethics Canvas – evaluating an AI application from the perspectives of data, transparency, fairness, human oversight, and accountability. Mini-task: Evaluate a selected AI tool or business use case using the AI Ethics Canvas and identify one key risk, one safeguard, and the person or role responsible for oversight. Roleplay: Business vs customer – a privacy and transparency dilemma involving the use of AI. Discussion: Who is responsible when an AI-supported business decision causes harm? Voices from Experts: “To Be a Responsible AI Leader, Focus on Being Responsible” by Elizabeth M. Renieris, David Kiron, and Steven Mills, MIT Sloan Management Review. |
10. AI and Decision-Making: Decision Support, Human Judgement & Accountability
Nov 10 11:15am .. 2pm
Session 10 explores how AI supports business decision-making through prediction, forecasting, pattern recognition, and recommendation. Students examine the difference between AI-assisted and AI-automated decisions and consider when human judgement should remain central. Particular attention is given to uncertainty, over-reliance on AI, automation bias, and the need to verify AI-generated recommendations before acting on them. Tool/Workflow of the Week: Power BI and/or spreadsheet-based forecasting – exploring AI-supported business insights and decision support. Mini-task: Forecast traffic for a retail website and use the result to make one business recommendation. Identify what additional information you would want before acting on the recommendation. Discussion: Can AI replace human judgement? Voices from Experts: “Why AI Will Not Provide Sustainable Competitive Advantage” by David Wingate, Barclay L. Burns, and Jay B. Barney, MIT Sloan Management Review. |
11. AI-Enhanced Project Management & Digital Collaboration
Nov 24 11:14am .. 1:59pm
Session 11 focuses on project management in a digital environment, introducing AI-enhanced tools and techniques while Session 11 explores how digital and AI-enhanced tools can support project planning, coordination, communication, and teamwork. Students examine how AI can assist with task breakdown, scheduling, prioritization, progress tracking, and team communication, while considering the risks of over-automation and the continuing importance of human coordination, accountability, and decision-making. Tool/Workflow of the Week: Trello, Notion, Microsoft Planner, or another current project-management platform with AI-supported features. Mini-task: Create a simple project plan for a digital marketing or AI-related business project, including key tasks, responsibilities, milestones, and dependencies. Use AI to suggest or refine the plan, then critically review its recommendations. Reflection: Which aspects of project coordination can be delegated to AI, and which should remain a human responsibility? CW4 Preparation: Teams may begin discussing roles, responsibilities, milestones, and collaboration processes for the final project. |
12. Future Trends in Digital Business: Agents, AI Co-workers & Human–AI Collaboration
Dec 1 11:14am .. 1:59pm
Session 12 explores emerging developments that are likely to shape digital business and the future of work. Particular attention is given to agentic and multimodal AI, AI co-workers, human–AI collaboration, digital twins, physical AI, and immersive technologies. Rather than treating individual technologies as isolated trends, students consider how they may reshape business processes, organizational roles, skills, and managerial responsibility. Trend Exploration: Agentic AI, multimodal AI, AI co-workers, digital twins, physical AI, and immersive technologies; selected developments in IoT and blockchain where relevant. Mini-task: Imagine your AI-enhanced professional role. Identify which tasks AI could assist with, which could increasingly be delegated to AI agents, and which human capabilities should remain central to your professional value. Discussion: What becomes more valuable when AI can do more? Voices from Experts: “10 Urgent AI Takeaways for Leaders” by Laurianne McLaughlin, MIT Sloan Management Review. Current Trend Scan: Selected current industry reports and examples on AI and the future of work. |
13. Final Project Workshop: Agentic AI for Business – Design & Governance
Dec 8 11:15am .. 2pm
Session 13 is dedicated to the development of the final Agentic AI for Business project. Teams refine their proposed agentic AI solution through structured peer review, instructor consultation, and collaborative problem-solving. Particular attention is given to the agent’s role and decision boundaries, business value, human-in-the-loop governance, risks and safeguards, and the clarity of team roles and responsibilities. Workshop Focus: Business problem → agent role and capabilities → decision boundaries → human oversight → business value → risks and safeguards. Peer Review: Teams review another team’s concept, focusing on clarity, feasibility, business value, governance, and potential risks. Instructor Check-in: Each team briefly presents its current concept, key unresolved question, and next step. Mini-task: Agent Stress Test – identify one situation in which the AI agent could make a poor decision or exceed its intended role, and design an appropriate safeguard or escalation mechanism. |
14. Topic: Final Project Presentations
Dec 14 8am .. 10:45am
Session 14 features final project presentations where students pitch their digital and AI-powered business solutions, followed by reflective discussion on their personal AI-business advantage and key takeaways from the course. |