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BCS Generative Artificial Intelligence Award
Accurately assessing the potential of generative AI is becoming a core strategic capability. Grounded knowledge of underlying concepts connects technological developments with real business value.
Key topics
- Structure and operating principles of generative models.
- Prompt engineering and evaluating AI-generated outputs.
- Security requirements, data protection, and regulatory frameworks.
- Embedding generative AI into existing business workflows.
- Overview of leading AI solutions and current market developments.
Prerequisite
An interest in digital innovation and openness to technological change are all that is needed to get started.
Target group
Suited to managers, consultants, IT specialists, and specialist departments with a digital focus who want to understand and strategically apply generative AI.
A solid conceptual grasp of generative AI creates reliable decision-making foundations and reveals new pathways to sustainable value creation.
Course content
Core concepts of AI and generative AI
- Key concepts and characteristics of generative AI systems
- Overview of common applications and use cases
- The role of machine learning as a foundation for generative AI
How generative AI systems work
- Stages of the generative AI process from input to output
- The significance and processing of training data
- How transformer architectures support the learning process
- The impact of feedback on model behaviour and quality
Prompting: Guiding AI effectively
- The role and influence of prompts on AI-generated outputs
- Different prompt types and how to apply them situationally
Quality assurance and output evaluation
- Why systematic quality checks are essential for generative AI results
- Methods for validating and verifying AI-generated outputs
Ethics, regulation, and responsible AI use
- Ethical principles in the development of generative AI systems
- Legal and regulatory requirements at a glance
- Strategies for mitigating common AI-related risks
Up to 100% funding for your continuing education
Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.
Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.
What does the EXIN BCS Generative AI certification teach?
- The course covers core concepts of generative AI, enterprise application scenarios, and an assessment of opportunities and risks – including the technologies behind generative models and responsible AI use in professional settings.
Who is the Generative AI certification designed for?
- The course targets professionals from IT, business, project management, and digitalisation – particularly those who need to evaluate, classify, or strategically apply generative AI.
What prior knowledge do I need for the Generative AI exam?
- Basic familiarity with digital technologies and AI concepts is an advantage. Programming experience is not required – the focus is on understanding, applying, and evaluating generative AI.
What topics does the Generative AI course cover?
- Topics include generative models, large language models, practical use cases, ethical considerations, risks, governance, and responsible AI deployment in organisations.
How does Generative AI certification strengthen my career?
- Verified AI competence sharpens professional profiles in digitalisation, innovation, and strategy – organisations increasingly seek experts who can assess generative AI clearly and deploy it responsibly.
How are companies using generative AI?
- Generative AI is applied in content creation, automation, data analysis, and knowledge management – enabling organisations to boost efficiency and build new digital products and services.
Is generative AI relevant beyond IT roles?
- Absolutely – business, marketing, analytics, and management roles all benefit. Many use cases emerge outside traditional IT: communication, product development, and process automation are prime examples.
What competencies does generative AI training develop?
- Participants build understanding of generative models, the ability to evaluate AI applications, and skills for assessing risks and opportunities – alongside the expertise to make well-founded strategic decisions about modern AI.
