AI-Driven Transformation & Strategic Decision Making for Managers
Introduction
The aim of the training course “Strategic Decision-Making for Executives Using Artificial Intelligence” is to introduce executives and managers to modern strategic decision-making methodologies based on Artificial Intelligence (AI). The Course combines data science techniques, predictive analytics and generative artificial intelligence (Generative AI), providing participants with the tools and knowledge they need to improve strategic planning, risk management and business performance. Participants will become familiar with modern platforms and tools, such as Artificial Intelligence applications, AutoML platforms and Decision Intelligence Frameworks, and will understand how these can be used to support complex management and business decisions.
The Course has a strongly practical focus and includes demonstrations of applications on real business data, case studies from international organisations and guided exercises showing how AI tools can be integrated into management and decision-making processes. Upon completion of the Course, participants will have gained a solid practical understanding of how Artificial Intelligence can be integrated into their organisation, helping to improve efficiency, competitiveness and innovation.
Scientific Director
Sotirios Bersimis,
Professor, Department of Business Administration, University of Piraeus
Target Audience
The Course is aimed at managers, senior executives of businesses and organisations, and professionals actively involved in strategic decision-making, the management of business operations and the design of organisational development and digital transformation initiatives. In particular, the Course is aimed at:
- Chief Executive Officers (CEOs) and General Managers.
- Senior executives and heads of business units.
- Directors and Heads of Department.
- Strategic Planning and Business Development executives.
- Innovation and Digital Transformation executives.
- IT directors and managers (IT Managers).
- Finance and Performance Audit executives.
- Human Resource Management executives.
- Business and organisational development consultants.
- Heads of digital transformation and innovation projects.
The Course is also suitable for entrepreneurs, owners of small and medium-sized enterprises, self-employed professionals and executives in the public or private sector who are involved in business planning, performance indicator monitoring, risk management, the implementation of innovation projects or the adoption of new technologies.
Participants should have an interest in and/or responsibility for improving organisational performance, using data for decision-making, introducing Artificial Intelligence technologies, managing change and enhancing the competitiveness and innovation of their organisations.
Admission Requirements
Participants must have:
- Higher education
- Very good knowledge of English
- Basic computer skills
- Internet access
- A personal email account
Holding a higher education degree (from a university or an equivalent Greek or foreign institution) is a key selection criterion.
Delivery Method
The Course is delivered exclusively in person in a classroom at the University of Piraeus. An attendance register is kept during the lectures, and Educational Material is provided that covers all the thematic units of the Course and includes: academic notes, lecture presentations, application examples, case studies, practical exercises, laboratory scenarios, worksheets, bibliographic references and scientific articles, as well as educational material on modern tools for Artificial Intelligence, Business Analytics, Predictive Analytics, Machine Learning and data-driven decision-making.
Successful completion of the Course requires attendance of all Teaching Units, passing the examination on the Course content and payment of the fees in full. In addition, total absences may not exceed 10% of the scheduled teaching hours
Type of Certificate
Trainees who successfully complete the Course are awarded a Training Certificate by KEDIVIM of the University of Piraeus or a Certificate of Successful Attendance.
See a sample of the certificate awarded here
Units
Teaching/Thematic Unit 1: Decision Intelligence and AI-Driven Management Frameworks
1.1 Introduction to Decision Intelligence (DI – a discipline combining Artificial Intelligence (AI), data science and managerial decision theory to improve business decisions).
1.2 Decision Intelligence lifecycle (data collection, analysis, decision modeling and monitoring).
1.3 Data-driven decision-making vs intuition-based management approaches.
1.4 Overview of decision intelligence platforms.
1.5 Case study
1.6 Workshop
1.7 Discussion
Teaching/Thematic Unit 2: Predictive Analytics Applications for Strategic Planning
2.1 Predictive Analytics (use of statistical algorithms and Machine Learning (ML – systems that learn patterns from data) to forecast future events).
2.2 Introduction to AutoML (Automated Machine Learning – platforms that automatically build predictive models) used by organizations in Switzerland and Sweden to democratize analytics usage among managers.
2.3 Demonstration of international tools.
2.4 Explanation of how such tools reduced decision time in international companies.
2.5 Case study
2.6 Workshop
2.7 Discussion
Teaching/Thematic Unit 3: Generative AI Applications for Managerial Productivity
3.1 Generative AI (AI systems capable of producing reports, analysis and recommendations based on user instructions).
3.2 Demonstration of international tools: ChatGPT Enterprise (AI assistant used by global consulting organizations) Microsoft Copilot (AI assistant embedded in Microsoft productivity tools) Notion AI (AI knowledge management assistant)
3.3 Explanation of productivity gains achieved internationally.
3.4 Prompt Engineering (structured method of writing effective AI instructions). Participants will learn the CLEAR Framework (Context-Length-Examples-Action-Result) used by US consulting companies.
3.5 Process Mining (analysis of digital process data to identify inefficiencies). Used in Germany by Siemens to improve operational processes.
3.6 AI limitations: Hallucinations (AI generated incorrect information presented as valid). Participants will learn validation techniques used in international organizations.
3.7 Case study
3.8 Workshop
3.9 Discussion
Teaching/Thematic Unit 4: AI Innovation Strategy, Governance and Risk Management
4.1 AI Innovation Strategy (structured approach for adopting AI technologies to create competitive advantage).
4.2 AI governance frameworks (policies, ethical AI principles and compliance requirements such as EU AI Act).
4.3 Risk management practices (data privacy, cybersecurity risks and AI model bias mitigation techniques).
4.4 Case study
4.5 Workshop
4.6 Discussion
Teaching/Thematic Unit 5: Workshops
Understanding theory through laboratory application
Learning Outcomes
Upon completion of the Course, trainees will be able to: Describe the fundamental concepts of Artificial Intelligence (AI), Predictive Analytics and Decision Intelligence. Explain the role of data in the strategic decision-making process. Recognise the capabilities and limitations of modern AI tools in organisational management. Understand the basic principles of AI governance, risk management and regulatory compliance (EU AI Act). Identify opportunities to use AI to improve operational performance, innovation and competitiveness.
Upon completion of the Course, trainees will be able to: Use modern Artificial Intelligence tools and AutoML platforms to support business decisions. Analyse business data and draw useful conclusions for decision-making. Evaluate alternative business scenarios on the basis of data and predictive models. Design basic AI-supported decision-making frameworks. Apply Prompt Engineering techniques for effective interaction with Generative AI tools. Prepare an initial plan for the adoption and use of AI solutions in their organisation.
Upon completion of the Course, trainees will be able to: Integrate Artificial Intelligence technologies into strategic planning and management processes. Support well-founded, data- and analysis-driven decision-making. Lead digital transformation and organisational innovation initiatives. Assess their organisation’s readiness to adopt AI solutions and prioritise related investments. Promote the responsible, secure and ethical use of Artificial Intelligence. Contribute to building a culture of innovation and continuous improvement through the use of data and advanced analytical tools.
Assessment
Learning outcomes are assessed through:
1. Trainees’ participation in practical exercises and workshops during the Course. 2. Solving case studies based on real business scenarios. 3. An individual or group application project, in which trainees will be asked to analyse a business problem and propose solutions using Artificial Intelligence tools. 4. Presentation of the project results and discussion of the proposed solutions. 5. A short final knowledge test (multiple-choice or short-answer) to confirm that the learning outcomes have been achieved.
Discount Policy
The following discount categories apply:
- GROUP A (-50%): For unemployed persons
- GROUP B (-40%): For group registrations of more than ten (10) people through a company.
- GROUP C (-30%): For students
Please note that:
- Discounts are not cumulative.
- Each trainee may benefit from only one discount category.
- Participants entitled to a discount must submit the supporting documents that will be requested.
Course Fee after discount
|
Discount |
GROUP A | GROUP B |
GROUP C |
| Course Fee | 500€ | 600€ |
700€ |
Payment of Fees
Fees are payable in a single payment into an account of the Special Account for Research Funds (ELKE) of the University of Piraeus.
| Duration: | 1 Months / 26 Hours |
|---|---|
| ECTS: | 1.01 |
| Field of Study: | Technology & Informatics |
| Language: | Greek |
| Scientific Director: | Sotirios Bersimis |
Educational Material
| Electronic Notes |
|---|

