Reviews 4,2
Reviews 4,5
100% fundable

AI+ Program Director – Practitioner™

Taking responsibility for AI programmes means having direction, structure, and a clear view of what works. This training combines strategic thinking with hands-on classification and builds the confidence to manage complex, data-driven initiatives.

Key topics

  • How modern AI programmes are structured and governed.
  • Regulatory requirements, ethics, and accountability frameworks.
  • Embedding AI into existing business processes.
  • Interface management between specialist functions and technology.
  • Assessing value, risks, and scalability systematically.

Prerequisite
Those familiar with digital business models or experienced in IT or data-driven projects can join directly – no deep technical background required.

Target group
Leaders and programme managers who want to not just support AI initiatives, but actively shape and own them.

Structured knowledge, clear methods, and a trained perspective on AI programmes enable leaders to unlock technological potential in a targeted, responsible, and sustainable way.
 

course content
  • What distinguishes AI, ML, and deep learning from one another
  • How the AI lifecycle is structured and where it connects with real-world practice
  • The broader societal significance and impact of AI
  • Use case: Triage system (AI in emergency services)
  • Case study: Recommendation systems in retail – personalisation as a competitive advantage
  • Hands-on exercise: Building a simple AI classifier with Teachable Machine
  • Frameworks for aligning AI strategy: AI canvas, value vs. feasibility matrix
  • How to identify processes that benefit from AI – repetition, data richness, personalisation needs
  • Prioritisation methods: weighted scoring, risk-adjusted ROI
  • Use case: AI in finance – fraud detection systems
  • Case study: AI-driven project management system from a programme director’s perspective
  • Hands-on exercise: Building a Trello board and prioritising AI opportunities within a given scenario
  • Core principles for deploying AI responsibly
  • Recognising and reducing bias in AI systems
  • Use case: Fairness review in AI-supported recruitment processes
  • Case study: Mitigating algorithmic bias in credit scoring models to ensure fair lending
  • Hands-on exercise: Analysing model fairness and bias using Google’s What-If Tool in Google Colab
  • AI project planning with CRISP-DM
  • Strategic choice: build in-house, buy, or partner?
  • Key tools for AI project management at a glance
  • Use case: Predictive maintenance in manufacturing asset management
  • Hands-on exercise: Simulating an AI project in Asana
  • Ensuring data quality and effective data governance
  • Building and operating data pipelines for AI
  • Handling sensitive data responsibly
  • Use case: Retail warehouse management – AI-driven reordering and demand forecasting
  • Case study: Data security in healthcare – protecting patient privacy in AI-based systems
  • Hands-on exercise: Setting up Airbyte Cloud and building a basic data pipeline
  • Evaluating AI solutions systematically
  • Selecting and managing external providers
  • Use case: Choosing an AI provider for predictive maintenance in a production facility
  • Hands-on exercise: Assessing AI vendors using a structured selection template in Google Sheets
  • Legal and regulatory frameworks at a glance
  • Identifying and systematically reducing bias in AI systems
  • Use case: Fairness in facial recognition within law enforcement systems
  • Case study: Ensuring compliance in AI applications in financial services
  • Hands-on exercise: Bias testing and fairness assessment with KNIME and Google PAIR Facets
  • AI-specific project management tools in practice
  • Data management tools and their applications
  • Case study: AI workflow management in retail – using PM tools for AI implementation
  • Hands-on exercise: Using Asana to map project timelines, set up tasks and milestones for an AI initiative
  • Building AI teams and guiding them through change processes
  • Stakeholder communication and expectation management
  • Use case: Leading an AI rollout in a large manufacturing company
  • Hands-on exercise: Visualising stakeholder communication strategies with Miro and identifying key influencers
  • From pilot project to full organisational deployment
  • Assessing the AI maturity level of an organisation
  • Use case: Rolling out AI-powered recommendations globally in retail
  • Hands-on exercise: Creating a scaling roadmap with Lucidchart to map the key steps for expanding AI initiatives
  • Upcoming AI technologies and their strategic relevance
  • Use case: AI in autonomous vehicles – where is the technology heading?
  • Hands-on exercise: Getting started with Hugging Face Transformers for NLP and TensorFlow for deep learning applications
  • Task brief and framework for the final project
  • Presentation of results & structured feedback
  • Final assessment & certification – approach, process, and feedback mechanism

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.