Veidlapa Nr. M-3 (8)

Course Description Status: Approved

Course Description Version: 26/27.R.4.00

Description Period: 2026./27 academic year rudens semestris

Study Course Accepted: -

Study Course Description

AI Integration and Business Transformation

Main Study Course Information

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 the structures and management principles of organisations. Project management basics. Understanding technology: basic digital literacy. Ability to work with business software. Understanding the basic principles of data analysis. Analytical skills: ability to analyze business problems. Critical thinking. Understanding basic statistics.

Learning Outcomes

Knowledge

1.Students will analyse the potential and limitations of AI solutions in business management, identify current industry trends and explain how they affect the company’s strategic decisions.

Individual work and tests

Lecture tests • Knowledge Check assesment

2.Students will demonstrate comprehensive knowledge of the design and implementation of AI strategies, as well as assess the benefits and risks of different approaches.

Individual work and tests

Knowledge Check assesment • Lecture tests

3.Students will discuss in a reasoned manner the suitability of AI solutions for different organisational situations using critical thinking and comparative analysis.

Individual work and tests

Lecture tests • Knowledge Check assesment

4.Students will apply at least three AI tools designed to analyze and optimize business processes and explain how the results obtained affect decision-making.

Individual work and tests

Knowledge Check assesment • Lecture tests

5.Students will compare at least three different MI project management methodologies and choose the most appropriate approach based on the specific business situation.

Individual work and tests

Lecture tests • Knowledge Check assesment

6.Students will collect and interpret in-depth knowledge of AI ethical principles and governance practices, as well as develop guidelines for responsible use of AI in their organization.

Individual work and tests

Knowledge Check assesment • Lecture tests • Enterprise transformation Plan

Skills

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.

Individual work and tests

Enterprise transformation Plan • Presenting the theme for final test

2.Plan and implement AI projects by establishing a detailed project plan, applying both agile and traditional project management techniques, documenting progress and measuring project results.

Individual work and tests

Enterprise transformation Plan

3.Assess and compare AI solutions through functionality analysis, cost-benefit assessment, verification of compliance with technical requirements and assessment of implementation risks.

Individual work and tests

Enterprise transformation Plan

4.Establish and implement a data management strategy for AI initiatives, defining quality criteria, implementing security requirements and ensuring regulatory compliance.

Individual work and tests

Enterprise transformation Plan

5.Manage and coordinate processes for changing the implementation of AI by developing a change management plan, identifying measurable indicators and organising the necessary training programmes.

Competences

1.Students are able to develop a comprehensive AI strategy for an organization, taking into account its specifics, goals, and resources.

Individual work and tests

Enterprise transformation Plan

2.Students are able to critically analyze and evaluate various AI solutions, choosing the most appropriate ones for specific business needs

Individual work and tests

Enterprise transformation Plan

3.Students can develop and implement a data strategy that supports AI initiatives, ensuring the necessary data quality, security, and regulatory compliance.

Individual work and tests

Enterprise transformation Plan

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.

Individual work and tests

Enterprise transformation Plan

Assessment

Individual work

Title
% from total grade
Grade
1.

Enterprise transformation Plan
20.00% from total grade
10 points

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%)
2.

Presenting the theme for final test
10.00% from total grade
10 points
  1. Present a comprehensive plan for the selected topic for the g ala test either: (a) Transforming an existing business using AI technologies or (b) creating a new AI-based business that addresses a particular business problem.
  2. Presenting a general solution and a high-level roadmap, presentation not longer than 3 min

Examination

Title
% from total grade
Grade
1.

Lecture tests
35.00% from total grade
10 points

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.

Knowledge Check assesment
35.00% from total grade
10 points

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.

Study Course Theme Plan

FULL-TIME
Part 1

  1. Lecture

Modality
Location
Contact hours
On site
Auditorium
2

Topics

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?

  1. Lecture

Modality
Location
Contact hours
On site
Auditorium
2

Topics

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?

  1. Lecture

Modality
Location
Contact hours
On site
Auditorium
2

Topics

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?

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

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?

  1. Lecture

Modality
Location
Contact hours
On site
Auditorium
2

Topics

AI-Driven Decision Making
Description

Abstract: 1) Role of AI in complementing human decision-making 2) Predictive analytics and predictions 3) Prescriptive analytics for optimization 4) Balancing real-time decision support system 5) AI recommendations with human judgment 6) Present the problem found for final work and a high-level roadmap for the final work. Lesson questions: What ways can AI complement and improve decision-making processes in your particular business context? How would you integrate AI-generated insights with human expertise into strategic decision-making? What are the potential limitations and risks of relying on AI to make critical business decisions?

  1. Lecture

Modality
Location
Contact hours
On site
Auditorium
2

Topics

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?

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

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?

  1. Lecture

Modality
Location
Contact hours
On site
Auditorium
2

Topics

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?

  1. Lecture

Modality
Location
Contact hours
On site
Auditorium
2

Topics

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?

  1. Lecture

Modality
Location
Contact hours
On site
Auditorium
2

Topics

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?

  1. Class/Seminar

Modality
Location
Contact hours
On site
Auditorium
2

Topics

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?

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

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?

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

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?

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Final exam presentations

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Final exam presentations
Total ECTS (Creditpoints):
3.00
Contact hours:
30 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