AI Manager Workshop
Initiate and implement meaningful projects (intensive)
Overview:
In this 5-day, interactive and intensive workshop you will gain knowledge about the potential and the possibility of meaningful initiation of projects with artificial intelligence.
You will learn to understand the trends, the underlying technologies and to identify challenging AI projects. You will then be able to prepare your project team and your organization for AI initiatives, structure projects and communicate the potential of AI solutions.
A practical simulation and input from experts will enable you to successfully manage AI projects from conception through prototyping and MVP to implementation.
Target group:
Executives, product and project managers who want to gain knowledge of artificial intelligence, learn how to use it in their day-to-day work and successfully manage complex AI projects in their company.
This AI Manager workshop is also aimed at developers, engineers and technologists who want to inspire their organization with their innovative ideas and initiate projects.
5 days at a glance:
- Understanding advanced technologies & interpreting data
- Identify challenging AI project candidates
- Preparing the project team and organization for complex projects and building strong coalitions
- Structuring and managing AI projects effectively
- Evaluate scaling potential and develop sustainable AI strategies
Day 1: Understanding advanced technologies & interpreting data
- Introduction to digital transformation and process automation:
- Differences and connections between digitalization and digital transformation.
- Best practices and current trends in process automation:
- Fundamentals and relevant developments in the field of digitalization.
- Data economy:
- Benefits and potential of data-based process innovations and business models.
- AI as a game changer:
- The basics of predictive and generative AI, use cases in the financial sector, the use of chatbots and the language of prompts.
- AI-based process automation:
- Demo of a use case for automated evaluation (95% time saving).
- Methods for prioritizing digital use cases and projects:
- Tools for selecting and evaluating the most promising digital projects.
- Keeping an overview:
- Keep the organization up to date with the help of Gartner’s Hype Cycle, the Technology Radar and the Use Case Canvas.
Day 2: Identify challenging AI project candidates
- Identification of high-impact projects:
- Evaluation of the strategic benefits of complex AI initiatives.
- Innovation management:
- Use of AI to develop new business models and services.
- Economic trends and fundamentals of digital business models:
- Strategic approaches to digital transformation.
- Customer needs and idea generation:
- Methods for identifying customer needs and sources for generating ideas for digitalization and automation projects.
- Practice-oriented idea generation methods:
- Application of Design Thinking to solve complex challenges.
- Goal formulation and vision development:
- Use of the Golden Circle / Why-How-What model to develop a clear vision.
- Business modeling and valuation:
- Modeling and evaluation of digital business models including relevant metrics and business cases.
Day 3: Preparing the project team and organization for complex projects and building strong coalitions
- Effective management of organizational change in the digital age
- Methods for dealing with resistance, organizing stakeholder interests, using the viral effect and promoting self-organization.
- Integration of innovations through theoretical sessions, extensive exercises and moderated discussions.
- Digital change management based on Kotter:
- Introduction to the principles of change management according to the Kotter model.
- Stakeholder management and force field analysis:
- Identification of stakeholders and analysis of forces that support or hinder change.
- Strategies for dealing with resistance in digital projects:
- Techniques and best practices for overcoming resistance in digital transformation projects.
- Pitch and communication in conflicts:
- Effective communication and presentation techniques in conflict situations.
Day 4: Structuring and managing AI projects effectively
- Risk management:
- Identification and management of risks in complex AI initiatives.
- Risks of technology & service provider lock-in & strategies to avoid them
- Integration of AI into existing systems:
- Technical strategies for seamless integration and interoperability.
- Agility and agile (management) methods:
- Introduction to agility and overview of agile methods and frameworks.
- Kanban, Scrum and Lean Start-up: Basics and possible applications.
- Agility toolbox: Understanding agile tools and frameworks and classifying their use in different project phases.
- VUCA world and digital transformation management:
- The challenge of the VUCA world and strategies for dealing with it – (volatility, uncertainty, complexity, ambiguity).
- Hypothesis-driven project start:
- Identification of critical hypotheses for a resource-saving project start.
- Methods for identifying and validating critical hypotheses in a project.
- Methods for testing critical hypotheses.
- Visualization of projects and product ideas:
- Minimum Viable Product (MVP), Proof of Concept (POC), iterations and prototyping – introduction and differentiation.
Day 5: Evaluating scaling potential and developing sustainable AI strategies
- Strategies for scaling AI solutions:
- Development of scalable infrastructures and processes.
- Spin-offs of own AI solutions and scaling of business models:
- Development and marketing of new business opportunities based on the AI solutions developed beyond the boundaries of the company.
- Scaling AI use cases with the help of platform dynamics:
- Develop innovative, scalable business models through the use of marketplaces and platforms.
- Overview of successful platform examples such as Airbnb, Amazon, Delivery Hero and their transformative impact.
- AI governance and sustainability:
- Development of guidelines for long-term success and ethical standards.
- Measurement of goodwill:
- Advanced KPIs and ROI analyses for AI projects.
- Final presentation & pitch exercises:
- Presentation of the strategies developed and feedback round.