This training program is open to anyone interested in expanding their professional skills. The practical examples and exercises are chosen so they map well to the daily work of Sales Assistants; participation is expressly open to everyone, regardless of current professional role. The course teaches methods, tools, and content that can be applied in many different work contexts.
The following typical tasks from the daily work of Sales Assistants serve as worked examples in the exercises; they can be adapted to other fields of activity during the course:
- Quote folders
- CRM upkeep
- Weekly reports
- Conditions maintenance
- Order entry
Content
- Fundamentals and benefits of market analysis
- Components of modern market analysis: data, sources, and AI
- Marketing-relevant analysis fields: overview and classification
- Analyzing demand and dynamics: reading trends, seasonality, and signals
- Competitive analysis: evaluating offerings, messaging, and channels
- Understanding target audiences: data, patterns, and access points
- Defining market segments: attractiveness, potential, and focus areas
- Developing customer personas: goals, barriers, and triggers
- Mapping the customer journey: phases, touchpoints, and friction points
- Consolidating insights: transforming large datasets into clear recommendations
- Reporting and visualization: preparing and presenting results effectively
- Future outlook: trends and next steps in AI-driven market analysis
Learning Objectives
- Apply AI tools for efficient market data analysis.
- Identify and interpret market trends and customer behavior patterns.
- Develop data-backed marketing strategies and actionable recommendations.
- Effectively communicate market analysis findings through reports and visualizations.
Course 2: KI Officer (AI Officer)
Content
- Introduction to the role and responsibilities of an AI Officer
- Overview of the EU AI Act and relevant legislative frameworks
- Ethical guidelines and accountability in AI development and deployment
- Risk management and compliance strategies for AI applications
- Data privacy and security considerations in AI systems
- Ensuring transparency and explainability of AI models
- Implementing audit and certification processes for AI
- Practical use cases and best practices in AI implementation
- Documentation and reporting standards for AI projects
- Stakeholder management and effective communication in AI initiatives
- Developing training and awareness programs for AI adoption
- Change management and implementation strategies for AI solutions
- Evaluating AI systems: key metrics and performance analysis
- Future perspectives and continuous development in AI management
- The indispensable role of critical thinking in AI management
- Tools and techniques for fostering critical thinking in AI contexts
Learning Objectives
- Comprehend the strategic role of an AI Officer.
- Understand and apply relevant AI regulations and ethical principles.
- Implement robust risk management and compliance frameworks for AI.
- Develop strategies for the responsible and transparent deployment of AI.
- Effectively manage AI projects and communicate with stakeholders.
Career Prospects
This program prepares individuals for roles that bridge technology, strategy, and management. Graduates will be well-equipped for positions in market research, marketing management, AI strategy, AI governance, compliance, and IT project management. The skills acquired are applicable in various industries undergoing digital transformation and AI integration.