Certified AI Manager

Source: https://www.tuwien.at/en/ace/programs/seminars-conferences/technology-innovation/certified-ai-manager Parent: https://www.tuwien.at/en/ace/programs/seminars-conferences

© TU Wien - Academy for Continuing Education

Certified AI Manager

Successfully identify, plan, and implement AI projects

Optional: Examination and personal certification “AI Manager” in accordance with ISO/IEC 17024

Key Facts

• Duration: 4.5 days (36 hours)
• German course: 25–26 June + 3–4 September 2026 (in person) & 18 September 2026 (online project presentation and feedback)
• English Course Schedule: 12–13 November + 10–11 December 2026 (in-person) & 18 December 2026 (online project presentation and feedback)
• Location: TU Wien Academy (+ 1 online session)
• Language: German/English

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What to Expect

Why pursue training as a Certified AI Manager?

Since February 2, 2025, companies that provide, develop, or use AI systems in certain areas are required to ensure, to the best of their ability and to an adequate extent, personalized AI competence. With the practice-oriented Certified AI Manager program, you can demonstrably meet the requirements of Article 4 of the AI Act.

Artificial intelligence has gained enormous importance in recent years and will decisively influence and transform business development in the years to come. The key is to meaningfully integrate the competitive advantages offered by these new opportunities and potentials into company processes.

Why Choose TU Wien Academy: Quality & Knowledge Transfer

Do you want to confidently identify, evaluate, and manage AI projects while relying on the highest quality in professional training? As a university, we are committed to high standards of quality and independence. The excellence of our research is reflected in the quality of continuing education at the TU Wien Academy.

The Certified AI Manager program at TU Wien Academy goes beyond basic knowledge transfer: you will work on an AI project from your own professional practice, present your results at the end of the program, and receive qualified expert feedback. Optionally, you can complete the examination for certification as an “AI Manager” in accordance with ISO/IEC 17024 through Austrian Standards. Lecturers from TU Wien and Fraunhofer Austria share their experience from applied research and industry projects, supporting you to learn in a practice-oriented way through your own project.

The Vienna University of Technology is Austria’s largest research and educational institution in engineering and natural sciences. More than 4,000 researchers work across five research focus areas in eight faculties on “technology for people.” The content of our programs is derived from this excellent research. As an innovation driver, TU Wien strengthens the business location, enables collaborations, and contributes to the prosperity of society.

What You Will Learn in the Certified AI Manager Program

• You will become fluent in the fundamentals, functioning, and limitations of AI, and be able to distinguish hype from real value.
• You will gain the ability to make informed decisions when evaluating AI applications, addressing make-or-buy questions, and navigating regulatory requirements.
• You will be able to identify, assess, and strategically prioritize AI deployment opportunities—from an idea pool to clear investment decisions.
• You will be capable of structuring the planning of AI projects, including data requirements, project setup, and legal and ethical considerations.
• You will learn to select, evaluate, and manage AI projects through to implementation, including roles, architecture, and vendor selection
• You will be able to assess the economic viability, risks, and operations of AI systems and make scalable management decisions.

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Your Benefits

• Practical Industry Focus: Case studies presented by experienced instructors who bring concrete applications from industry.
• Hands-On Training: The program gives you the opportunity to apply what you’ve learned directly in your own project.
• Top Experts: Learn from researchers and project leaders at TU Wien and Fraunhofer Austria, who support you in developing a project alongside the course.
• Networking Opportunities: Exchange ideas with other decision-makers and leaders, and build valuable professional connections on site.
• Flexibility and Variety: The combination of in-person days and virtual sessions allows the program to fit well into your work schedule.
• Future-Proof Expertise: Acquire AI skills that prepare you for forward-looking careers.
• Optional Recognized Certification: After completing the program and passing the exam, you can receive an additional certification from Austrian Standards.

Course Contents

1. Fundamentals: Terminology & How AI Works

• Fundamentals: Terminology & How AI Works
• Introduction to AI: Key terms clearly explained – AI (Artificial Intelligence), ML (Machine Learning), DL (Deep Learning), GenAI
• Brief overview of AI development
• Distinguishing hype vs. real value
• Technical functioning
• How AI learns from data
• Training and testing of AI systems
• Common learning types: Supervised, unsupervised, and reinforcement learning
• Typical training risks: Overfitting, underfitting
• Limits of technical feasibility
• Overview of AI subfield
• Machine Learning & Deep Learning, including neural networks
• Natural Language Processing (NLP), e.g., sentiment analysis, LLMs
• Computer Vision (CV): classification, detection, segmentation
• Generative AI & Foundation Models
• Make-or-buy decisions for AI solutions
• Relevant AI laws and regulations
• Data acquisition and usage: Open Data, Data Governance Act, GDPR, AI-generated content
• EU AI Act: risk classes, obligations, and documentation requirements\

2. Requirements Analysis, AI Use Cases & Project Planning

• Identification of AI Use Cases
• Proven methodologies for AI projects: CRISP-DM, Data Product Canvas
• AI potential analysis in your own environment
• Strategic prioritization of use cases
• Quick wins vs. long-term AI initiatives
• From Raw Data to Quality Data:
• Relevant data sources within the organization
• Data quality & responsibilities
• Building an appropriate dataset
• Annotation, including the involvement of domain experts
• Basic infrastructure requirements
• Ownership, responsibilities & data strategy
• Interactive Exercise (1|4)
• Initial situation & problem statement, including justification of AI relevance
• Project plan, resource requirements, and preliminary legal assessment
• Liability Fundamentals:
• AI liability guidelines
• Penalties, ethical challenges, and risks
• Gender, diversity, and inclusion consideration

➔ Transfer task between Day 2 and 3:

• Identify an AI application or data sources in your own environment.
• Submission: Data Product Canvas + short report (approx. 3 pages)

3. Selection, Evaluation & Implementation of AI Methods and Systems

• Selection of the Right AI Project
• Decision criteria from a management perspective
• Stop/go criteria for AI projects
• Roles and Execution of an AI Project
• Key roles in AI projects (e.g., ML Engineer, Data Engineer)
• Considering user perspective and acceptance
• Evaluation and testing of AI projects
• Deployment of AI solutions
• Interactive Exercise (2|4):
• Derive functional, technical, and legal requirements and identify relevant data sources
• Draft a high-level AI architecture as a basis for solution evaluation
• Includes Data Product Canvas and CRISP-DM methodology
• AI Vendors:
• Evaluation and traceability of externally developed AI models
• Framework for selecting external providers
• Vendor lock-in & dependency risks
• Cost models of AI vendors (license, subscription, pay-per-use, token-based billing)

4. Implications & Economic Relevance

• Evaluation & Operations
• Assessing Return on Investment (ROI)
• Feasibility and risks of AI projects
• Monitoring strategies
• KPIs for AI projects
• Interactive Exercise (3|4)
• Compare possible AI approaches and vendors, including cost, risk, and compliance assessment
• Make a well-founded decision for the model/solution, including resource allocation and system integration
• Best Practices
• Industry use cases
• Success factors & lessons learned
• Scaling AI within the organization
• Interactive Exercise (4|4)
• Define measurable actions & KPIs, including quantitative targets
• Evaluate expected results and ROI as a decision basis for management
• Maintenance & further development of AI models (lifecycle management)

5. Online – Presentation of Results & Expert Feedback

After the four training days, you will prepare your project presentation. In an online session, you will present it to the instructors and an expert panel to receive feedback.

This ensures you are optimally prepared if you later wish to obtain certification as an “AI Manager” through Austrian Standards in accordance with ISO/IEC 17024.

Target Audience

This training program is aimed at:

• Project and process managers who lead AI-based projects or will be involved in future projects
• Specialists and executives, CEOs, and organizational developers responsible for strategic business development who want to understand how AI can impact their organization
• IT managers
• Business consultants
• Entrepreneurs

No prior knowledge of computer science or AI is required.

Your Instructors

Andreas Steiner

Scientific Staff Member and Project Leader at the Production and Maintenance Management Research Area, TU Wien; Data Scientist at Celairion GmbH

\ Andreas Steiner supports industrial projects as a research staff member, focusing on data processing and artificial intelligence. His expertise spans both industrial data analysis and industrial AI.

Theresa Madreiter

Project Manager at Fraunhofer Austria and Lecturer in the Production and Maintenance Management Research Area at the Institute for Management Sciences, TU Wien\ \ Theresa Madreiter leads industry-focused projects with a focus on data analysis and the identification of Generative AI use cases. Her research interests include predictive and prescriptive maintenance, knowledge discovery from text, semantic technologies, NLP, predictive data analytics, and machine learning.

Completion / Certification

Upon successful completion of both seminar blocks, you will receive a certificate from TU Wien Academy. In-person attendance and active participation in group exercises are required to earn the certificate.

MBA Credit Recognition

The Certified AI Manager program can be credited toward the Executive MBA Operational Excellence & AI. For more information, please contact mba@tuwien.ac.at

Personal Certification “AI Manager”

Beyond completing the training at TU Wien Academy, you can optionally obtain the “AI Manager” certification to officially validate your knowledge and practical skills in artificial intelligence (AI) in accordance with ISO/IEC 17024 through the independent certification body Austrian Standards.

Certification

Completion of this program qualifies you to take the “AI Manager” examination (ISO certificate) through Austrian Standards, based on the international standard ISO/IEC 17024. After certification, you may officially use the title, for example on LinkedIn or in your CV.

The TU Wien Academy training program prepares you specifically for the oral examination, which is conducted online, lasts approximately 45 minutes, and consists of two parts:

• A project presentation
• An oral knowledge test

Upon passing the exam, you will receive the international personal certification according to ISO/IEC 17024 as an AI Manager. Re-certification is required every 3 years.

Dates & Registration

German Course Schedule\ 1st Block: 25–26 June 2026, 9:00–17:00, at TU Wien\ 2nd Block: 3–4 September 2026, 9:00–17:00, at TU Wien\ Additional online session (half-day) for project presentation and feedback: 18 September 2026, 13:00–18:00, online\ Registration deadline: 18 June 2026\ \ English Course Schedule\ 1st Block: 12–13 November 2026, 9:00–17:00, at TU Wien\ 2nd Block: 10–11 December 2026, 9:00–17:00, at TU Wien\ Additional online session (half-day) for project presentation and feedback: 18 December 2026, 13:00–18:00, online\ Registration deadline: 5 November 2026

Additional dates available upon request.

Participation Fee

Includes:

• Training materials (digital)
• Seminar beverages (water, tea, coffee) and refreshments during coffee breaks
• Certificate of participation, including details on content and instructors

*Price reductions for members of the TU Wien community. Details provided during registration.\ **Corporate rate: 20% discount for 3 or more participants – contact your TU Wien Academy representative:: short-courses@tuwien.ac.at\ ***Book this training bundled with another TU Wien Academy seminar and receive a 25% discount. Contact your TU Wien Academy representative: short-courses@tuwien.ac.at

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Questions?

Feel free to contact us. We're here to help you without complications.

Lisa Martina MüllerMSc

Phone: +43 1 58801 41745

Other seminars that may interest you:

• Developing an AI Strategy – Seminar: 19–20 June 2026

Important Dates

• Program Start: June 2026 (DE), November 2026 (EN)
• Registration Deadline: 18 June 2026 (DE), 5 November 2026 (EN)
• Early Bird Discount: until 31 March 2026 (DE), until 30 July 2026 (EN)
• Location: TU Wien Academy, Vienna
• Language: German/English
• Secure your spot

Important Dates

• Program Start: June 2026 (DE), November 2026 (EN)
• Registration Deadline: 18 June 2026 (DE), 5 November 2026 (EN)
• Early Bird Discount: until 31 March 2026 (DE), until 30 July 2026 (EN)
• Location: TU Wien Academy, Vienna
• Language: German/English
• Secure your spot