Veidlapa Nr. M-3 (8)
Study Course Description
AI Integration and Business Transformation
Course Code
SZF_165
Branch of Science
Economics and Business
ECTS
3.00
Target Audience
Business Management; Health Management; Information and Communication Science; Management Science; Marketing and Advertising; Political Science
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Pauls Jansons
Structure Unit Manager
Ieva Puzo
Structural Unit
Faculty of Social Sciences
Contacts
20080719
About Study Course
Objective
The goal of this course is to prepare students for the effective use of artificial intelligence (AI) in business management. Students will gain in-depth knowledge of AI solutions and their strategic applications, and acquire practical skills in AI project management and business process optimization. The course will develop competencies in AI strategy development, data management, and addressing ethical issues, enhancing students' ability to lead and implement AI-driven organizational changes and find close alignment between AI and business needs.
Preliminary Knowledge
Business Management Fundamentals: Basic knowledge of business process management Understanding of organizational structures and management principles Project management fundamentals Technology Understanding: Basic digital literacy Ability to work with business software Understanding of basic data analysis principles Analytical Skills: Ability to analyze business problems Critical thinking Basic understanding of statistics
Learning Outcomes
Knowledge
1.1) Students are well-versed in AI solutions for business management, understand their potential and limitations, and are familiar with the latest industry trends. 2) Students have comprehensive knowledge of AI strategy development and implementation, allowing them to competently discuss and argue for the application of various AI solutions in organizations. 3) Students have mastered at least 3 tools designed for business process optimization. 4) Students are familiar with at least 5 different AI project management methodologies and can choose the most appropriate one for each specific case. 5) Students have acquired in-depth knowledge of AI ethics principles and governance practices, enabling them to develop guidelines for responsible AI use in their organizations.
Skills
1.1) Develops and documents a comprehensive AI strategy for the organization, including detailed situation analysis, specific implementation steps, measurable outcome indicators, and required resource calculations. 2) Plans and implements AI projects by creating detailed project plans, applying both Agile and traditional project management methodologies, documenting progress, and measuring project outcomes. 3) Evaluates and compares AI solutions by conducting functionality analysis, cost-benefit assessment, technical requirements validation, and implementation risk assessment. 4) Creates and implements data governance strategy for AI initiatives by defining quality criteria, establishing security requirements, and ensuring regulatory compliance. 5) Manages and coordinates AI implementation change processes by developing change management plans, establishing measurable indicators, and organizing necessary training programs.
Competences
1.1) Students are able to develop a comprehensive AI strategy for an organization, taking into account its specifics, goals, and resources. 2) Students are able to critically analyze and evaluate various AI solutions, choosing the most appropriate ones for specific business needs. 3) Students can develop and implement a data strategy that supports AI initiatives, ensuring the necessary data quality, security, and regulatory compliance. 4) Students can manage organizational changes related to AI implementation, creating an innovation-oriented organizational culture and ensuring the development of necessary skills within the team.
Assessment
Individual work
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Title
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% from total grade
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Grade
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1.
Individual work |
-
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Final test students will create and present a comprehensive plan either for:
a) Transforming an existing company using AI technologies, or
b) Developing a new AI-based business that solves a specific business problem. It will demonstrate ability to apply course concepts to real-world scenarios, synthesizing knowledge from various lectures. Develops presentation and business planning skills crucial for AI implementation in organizations.
Performance on the final test will be graded by following criteria: Quality and feasibility of the AI strategy/business plan (30%)
- Depth of AI concept application (10%)
- Presentation clarity and professionalism (30%)
- Innovative thinking and problem-solving (30%)
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Examination
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Title
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% from total grade
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Grade
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|---|---|---|
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1.
Examination |
-
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-
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1. Brief, automatically-graded multiple-choice questionnaires will be administered at the end of each lecture to assess immediate comprehension of key concepts, encourage regular engagement with the course material, and provide immediate feedback, with each questionnaire contributing equally to 40% of the total course grade.
2. A comprehensive test covering all course topics will assess overall understanding of AI concepts, theories, and applications in business management through multiple-choice questions (50% of grade) and a written case study analysis (50% of grade). The case study component will be graded according to these criteria: Problem Identification, Application of AI Concepts, Analysis and Critical Thinking, Recommendations, Communication. Total contribution 25% of total grade.
3. Final test students will create and present a comprehensive plan either for:
a) Transforming an existing company using AI technologies, or
b) Developing a new AI-based business that solves a specific business problem. It will demonstrate ability to apply course concepts to real-world scenarios, synthesizing knowledge from various lectures. Develops presentation and business planning skills crucial for AI implementation in organizations.
Performance on the final test will be graded by following criteria: Quality and feasibility of the AI strategy/business plan (30%)
- Depth of AI concept application (10%)
- Presentation clarity and professionalism (30%)
- Innovative thinking and problem-solving (30%)
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Study Course Theme Plan
FULL-TIME
Part 1
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Introduction to Artificial Intelligence and Its Impact on Business
Description
Annotation: 1) Current state of AI technology
2) AI's impact on various industries
3) Case studies of successful AI implementation in business
Topics covered during the class: How can AI transform business operations and strategy in your industry?
What for technologies you can use today or what for technologies you should pay attention to?
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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AI Strategy and Roadmap Development
Description
Annotation: 1) Assessing organizational readiness for AI implementation
2) Identifying high-impact AI use cases
3) Prioritizing AI initiatives
4) Developing an AI roadmap
5) Change management for AI adoption
Topics covered during the class: How would you assess your organization's AI readiness and develop a strategic roadmap for AI implementation?
What criteria would you use to prioritize AI initiatives in your organization?
How can change management principles be applied to ensure successful AI adoption?
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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AI Ethics and Governance
Description
Topics covered during the class: What ethical framework would you propose for AI use in your organization, and how would you ensure its implementation?
How can bias in AI systems be identified and mitigated?
What strategies can be employed to balance AI transparency with intellectual property protection?
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Class/Seminar
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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The Future of AI and Emerging Technologies (Guest lecture)
Description
Topics covered during the class: How might emerging AI technologies impact your industry in the next 5-10 years, and how can your organization prepare?
What potential synergies exist between AI and other emerging technologies like quantum computing or IoT?
How might the role of human workers evolve as AI capabilities advance?
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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AI-Driven Decision Making
Description
Annotation: 1) AI's role in augmenting human decision-making
2) Predictive analytics and forecasting
3) Prescriptive analytics for optimization
4) Real-time decision support systems
5) Balancing AI recommendations with human judgment
Topics covered during the class: In what ways can AI augment and improve decision-making processes in your specific business context?
How would you integrate AI-driven insights with human expertise in strategic decision-making?
What are the potential limitations and risks of relying on AI for critical business decisions?
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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AI in practice in AI in Customer Experience and finance
Description
Annotation: 1) AI-driven customer segmentation and personalization
2) Chatbots and virtual assistants in customer service
3) Predictive customer behavior modeling
4) AI in content creation and curation
5) Measuring ROI of AI in marketing initiatives
6) AI in finance planning
Topics covered during the class: How can AI be leveraged to enhance customer experience and improve marketing ROI in your industry?
What are the ethical considerations in using AI for customer behavior prediction and personalization?
How would you measure the effectiveness of AI-driven marketing initiatives?
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Class/Seminar
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Data strategy and AI infrastructure
Description
Annotation: 1) Building a data-driven culture
2) Data governance and quality management
3) Data architecture for AI projects
4) Cloud computing and AI infrastructure
5) Data security and compliance considerations
Topics covered during the class: How would you design a comprehensive data strategy to support AI initiatives in your organization?
What are the key considerations in choosing between on-premises and cloud-based infrastructure for AI projects?
How can organizations ensure data quality and security while maintaining compliance with regulations?
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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AI in Business Process Automation
Description
Annotation: 1) Introduction to AI-driven business process automation
2) Key technologies in AI process automation (RPA, NLP, Machine Learning)
3) Identifying processes suitable for AI automation
4) Implementing AI automation: challenges and best practices
5) Measuring the impact of AI automation on business performance
6) Future trends in AI-powered process automation
Topics covered during the class: How would you assess which business processes in your organization are most suitable for AI-driven automation, and what criteria would you use for this assessment?
What are the potential benefits and risks of implementing AI-driven process automation in a specific area of your business, and how would you mitigate these risks?
How can the success and ROI of an AI automation project be measured, and what key performance indicators (KPIs) would you propose to track its impact on business performance?
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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AI Talent Acquisition and AI project management
Description
Annotation: 1) Roles and skills needed for AI projects
2) Building vs. buying AI capabilities
3) Collaboration between technical and business teams
4) Upskilling existing workforce in AI
5) Creating a culture of innovation and continuous learning
6) Practical work
Topics covered during the class: What strategies would you employ to build and maintain an effective AI team in your organization?
How can non-technical managers effectively collaborate with AI specialists?
What approaches can be used to upskill existing employees in AI-related areas?
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Lecture
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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AI Implementation and Organizational Culture
Description
Annotation: 1) AI project lifecycle and methodologies
2) AI project selection and scope definition
3) Management of AI development and implementation
4) Measuring and communicating AI project success
5) Scaling AI from pilot to production
6) Understanding the impact of AI on organizational culture
7) Key elements of an AI-ready organizational culture
8) Overcoming resistance to AI implementation
Topics covered during the class: How would you design an AI project lifecycle for your organization, considering both technical development and cultural adaptation?
What key milestones and review points would you include?
Describe a strategy for scaling an AI solution from a pilot project to full production in your organization. What potential challenges might you encounter, and how would you address them?
How can leaders effectively manage resistance to AI implementation while fostering an AI-ready organizational culture?
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Class/Seminar
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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AI Transformation Success Stories (Guest Lecture)
Description
Annotation: 1) Challenges faced and overcome during AI implementation
2) Key success factors for AI-driven business transformation
3) Lessons learned and best practices
4) Q&A session with the guest lecturer
Topics covered during the class: What lessons from successful AI transformations can be applied to your organization's AI journey?
How did the featured company overcome resistance to AI adoption, and what can be learned from their approach?
What were the most critical success factors in the AI transformation process?
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Unaided Work
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Modality
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Location
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Contact hours
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On site
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Auditorium
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2
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Topics
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Final exam presentations
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Total ECTS (Creditpoints):
3.00
Contact hours:
22 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Daugherty, P. R., & Wilson, H. J. (2024). Human + Machine, Updated and Expanded: Reimagining Work in the Age of AI. Harvard Business Review Press.Suitable for English stream
2.
Malone, T. W. (2018). Superminds: The Surprising Power of People and Computers Thinking Together. Little, Brown Spark.Suitable for English stream
3.
Iansiti, M., & Lakhani, K. R. (2020). Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Harvard Business Review Press.Suitable for English stream