Veidlapa Nr. M-3 (8)

Course Description Status: Approved

Course Description Version: 25/26.P.1.00

Description Period: 2025./26 academic year pavasara semestris

Study Course Accepted: -

Study Course Description

Management, Implementation and Strategic Development of Artificial Intelligence Projects in Organisations

Main Study Course Information

Course Code
SZF_242
Branch of Science
Other engineering and technologies
ECTS
3.00
Target Audience
Business Management; Management Science
LQF
Level 7
Study Type And Form
Full-Time

Study Course Implementer

Course Supervisor
Linda Alksne
Structure Unit Manager
Ieva Puzo
Structural Unit
Faculty of Social Sciences
Contacts

Dzirciema Street 16, Riga, szf@rsu.lv

About Study Course

Objective

The course is designed to prepare managers who know how to implement, manage and develop AI solutions in organizations. The course prepares for effective AI project management and implementation in organizations by developing the skills to link AI solutions to business objectives, processes, and real needs. Students learn the full cycle of AI project management from problem definition and design thinking to data strategy, experiments, MVP development, maintenance, and building a long-term AI strategy.

Data analysis, strategy, experimentation and a man-centred approach.

Preliminary Knowledge

A general understanding of the basic operating principles of organisations and basic experience in project or process management are recommended for successful completion of the course. Interest in AI applications, digital development and innovation will make the learning process much easier. Analytical skills and experience in implementing digital initiatives will be an advantage, but technical data or programming expertise is optional.

Learning Outcomes

Knowledge

1.on the lifecycle of AI projects and its fundamental differences from traditional IT systems development

Individual work and tests

Development of an AI project plan based on a real organisation scenario

2.about data management principles, creating a data strategy, and basics of data flow architecture in the context of AI solutions

Individual work and tests

Development of a data strategy and experiment model

3.on the design of experiments, iterative prototyping and testing methods for the gradual improvement of the solution

4.on AI model assessment approaches, monitoring processes and long-term maintenance mechanisms

5.on the stages of developing the AI strategy, linking it to the objectives of the organisation and methods of impact modelling

Individual work and tests

Definition and development of an MVP concept or prototype • Development of an AI project plan based on a real organisation scenario

6.on the principles of design thinking in the development of AI projects, in particular for the provision of usability, workflow and human centred solutions

Skills

1.able to identify AI usage capabilities and accurately define the problem to be solved based on organization processes and data reality

2.able to develop a complete design plan for AI including MVP concept, experimental structure and test scenarios

Individual work and tests

Presentation of the project in the final examination period • Definition and development of an MVP concept or prototype

3.able to analyse data flows, assess data quality and assess its impact on a potential AI solution

4.structure AI initiatives according to the organisation’s strategic priorities and processes

5.apply design thinking techniques to develop usable, practical and sustainable AI solutions that are easily integrated into the work environment

6.model risks for AI projects, identify implementation barriers and develop solutions to mitigate them

Individual work and tests

Risk analysis and structuring of implementation phases

Competences

1.manage the full lifecycle of the AI project and coordinate the work of an interdisciplinary team

Individual work and tests

Presentation of the project in the final examination period

2.develop an organisation-appropriate framework for AI strategy, defining development directions and expected impacts

Individual work and tests

Presentation of the project in the final examination period

3.assess the feasibility of the AI project, resource needs and operational risks

4.ensure integration of AI solution into organization’s work processes and long-term maintenance

5.reasoned decision-making based on data, impact analysis and results of experiments

Assessment

Individual work

Title
% from total grade
Grade
1.

Development of an AI project plan based on a real organisation scenario
20.00% from total grade
10 points
2.

Definition and development of an MVP concept or prototype
20.00% from total grade
10 points
3.

Development of a data strategy and experiment model
20.00% from total grade
10 points
4.

Risk analysis and structuring of implementation phases
20.00% from total grade
10 points

Examination

Title
% from total grade
Grade
1.

Presentation of the project in the final examination period
20.00% from total grade
10 points

Study Course Theme Plan

FULL-TIME
Part 1

  1. Lecture

Modality
Location
Contact hours
On site
Study room
2

Topics

Foundations for AI deployment in organizations
Description
  • Phases of the cycle of AI projects and key features
  • of the role and cooperation structure of interdisciplinary teams
  • initial assessment of the Organization’s processes, objectives and AI readiness

  1. Lecture

Modality
Location
Contact hours
On site
Study room
2

Topics

Definition of the problem and development of AI applications
Description
  • Formulating a precise and measurable problem
  • Real needs to technological opportunity
  • cost/benefit analysis and anticipating the impact of the solution
  • practical framework for applying AI across different sectors

  1. Lecture

Modality
Location
Contact hours
On site
Study room
2

Topics

Design thinking in AI project management
Description
  • Role of a human centered approach in the early stages of AI projects
  • identify user needs and analyze processes
  • define usability and accessibility criteria
  • integrate solution into human workflow
  • Design thinking cycle in AI product development

  1. Lecture

Modality
Location
Contact hours
On site
Study room
2

Topics

Data strategy and data flow architecture
Description
  • Identification of data sources and quality criteria
  • data preparation, cleaning and validation
  • data monitoring processes
  • principles for robust data flows and infrastructure building

  1. Lecture

Modality
Location
Contact hours
On site
Study room
2

Topics

Design and iterative development of experiments in AI projects
Description
  • Methodological structure of experiments
  • Test scenarios, hypothetical validation and models
  • iterative Development cycles in AI Project Development

  1. Lecture

Modality
Location
Contact hours
On site
Study room
2

Topics

MVP development and quick validation
Description
  • Define a minimally viable AI solution
  • early validation methods
  • of the prototyping approach (with and without data)
  • and analyze User response
  • tools, capabilities, and how to use them

  1. Lecture

Modality
Location
Contact hours
On site
Study room
2

Topics

Developing an AI strategy for an organization
Description
  • Mapping AI development directions
  • Organisation process and value chain analysis
  • Investment planning, prioritisation and expected impact modelling
  • integration of AI strategy into long-term development plans

  1. Lecture

Modality
Location
Contact hours
On site
Study room
2

Topics

Monitoring, optimisation and scaling of systems
Description
  • Model evaluation methods and performance indicators
  • identifying variances (drift) and
  • maintaining and extending corrective AI solutions in long-term organizations

  1. Lecture

Modality
Location
Contact hours
On site
Study room
2

Topics

Monitoring, optimisation and scaling of systems
Description
  • Model evaluation methods and performance indicators
  • identifying variances (drift) and
  • maintaining and extending corrective AI solutions in long-term organizations

  1. Lecture

Modality
Location
Contact hours
On site
Study room
2

Topics

Implementation risks and most common errors in organizations
Description
  • Weaknesses in the management of
  • the organisation’s culture and process barrier
  • data and their consequences
  • incoherence of strategy and training initiatives
  • insufficient testing and validation cycle
  • problems of communication and collaboration between teams

  1. Consultation

Modality
Location
Contact hours
On site
Study room
2

Topics

Final draft
Description
  • Development of a complete AI project plan based on a real organisation scenario
  • definition and development of an MVP concept or prototype
  • Development of a data strategy and experiment model
  • risk analysis and structuring implementation phases
  • presentation of the project in the final examination period

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Final draft
Description
  • Development of a complete AI project plan based on a real organisation scenario
  • definition and development of an MVP concept or prototype
  • Development of a data strategy and experiment model
  • risk analysis and structuring implementation phases
  • presentation of the project in the final examination period

  1. Class/Seminar

Modality
Location
Contact hours
On site
Study room
2

Topics

Final draft
Description
  • Development of a complete AI project plan based on a real organisation scenario
  • definition and development of an MVP concept or prototype
  • Development of a data strategy and experiment model
  • risk analysis and structuring implementation phases
  • presentation of the project in the final examination period
Total ECTS (Creditpoints):
3.00
Contact hours:
24 Academic Hours
Final Examination:
Exam (Oral)

Bibliography

Required Reading

1.

Ajay Agrawal, Joshua Gans, Avi Goldfarb. Prediction Machines: The Simple Economics of Artificial Intelligence.Suitable for English stream

2.

Thomas H. Davenport. The AI Advantage: How to Put the Artificial Intelligence Revolution to Work (Management on the Cutting Edge)Suitable for English stream

3.

Ethan Mollick. Co-Intelligence: Living and Working with AISuitable for English stream

4.

Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models: plašs pētījums par MI ietekmi uz biznesa modeļiem un uzņēmumu stratēģiju.Suitable for English stream

5.

Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer: labs pārskats par datu analītiku, DL/ML, noderēs datu stratēģijas un arhitektūras sadaļai.Suitable for English stream

6.

Making Sense of AI Limitations: How Individual Perceptions Shape Organizational Readiness for AI Adoption: 2025. gada raksts par to, ka ieviešana nesākas ar kodu vai infrastruktūru, bet ar cilvēku uzticību, izpratni un jaunās realitātes sagatavošanu. Labs piemērs risku un integrācijas aspektiem.Suitable for English stream

7.

A Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises(FAIGMOE): 2025. gada pētījums ar strukturētu pieeju Gen-AI ieviešanaiSuitable for English stream

Additional Reading

1.

Artificial Intelligence: A Guide for Thinking Humans (labs, saprotams pārskats par to, ko AI var un ko nevar) noderēs, lai students saprot reālus riskus un iespējas.Suitable for English stream

2.

AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference (kritiska grāmata par AI hype un realitāti, kas noder risku sadaļā.)Suitable for English stream

3.

The AI-Driven Leader: Harnessing AI to Make Faster, Smarter DecisionsSuitable for English stream

4.

Hello World: How to Be Human in the Age of the Machine (populārzinātnisks skats uz AI un sabiedrību) labs konteksts ētikas, cilvēkcentrētas pieejas un stratēģijas daļaiSuitable for English stream

5.

Artificial Intelligence for the Real World: Raksts no Harvard Business Review, plaši citēts, apskata MI ieviešanos uzņēmumos un kas strādā vs kas nestrādā.Suitable for English stream

6.

AI implementation strategies: 4 insights from MIT Sloan: MIT Sloan pārskats, 2025. gada, ar rekomendācijām MI ieviešanai dažādās industrijās.Suitable for English stream

7.

The ‘productivity paradox’ of AI adoption in manufacturing firms: Jauns pētījums no 2025., kas ilustrē riskus un īslaicīgas grūtības, kas seko MI ieviešanai reālajā ražošanā. Der kā pretstats (līdzsvars) “visu automatizēsim ar AI” optimistiem.Suitable for English stream