Applying AI in Innovation Management

Unconventional. Experiential. Applicable.

Artificial Intelligence (AI) is transforming companies and revolutionizing the way innovation management is approached. With the increasing collaboration between AI and humans, there is a need to re-evaluate how creativity is defined and innovations are developed and managed. This course delves into the implications of using AI for creativity and innovation management. We explore the use of AI along the double diamond framework in innovation management to enrich new product development and other innovative tasks such as idea generation, idea evaluation, concept development and prototyping. In addition, we will reflect on the extent to which AI affects established practices in innovation management and related creative fields, identify current limitations, and explore potential future advancements.

Target Groups

This course is suitable for anyone who is interested in learning more about generative AI and its application along the innovation process and in other fields where creativity is needed. No prior knowledge is required to participate.

Key Topics

Introduction to the basics of human creativity and innovation management
Theoretical introduction to artificial intelligence, including its subsets machine learning, deep learning and neural nets
Deep dive into generative AI, especially Natural Language Processing and Large Language Models
Integrating AI, Creativity and Innovation Management: Joint workshop into utilizing generative AI along the double diamond framework
Critical reflection and outlook of generative AI in the innovation context

Key Benefits

Learning how to integrate generative AI in creativity and the innovation process and other creative fields can provide a wide range of benefits.
By making use of generative AI, individuals and organizations can improve their productivity and efficiency levels significantly, while simultaneously unlocking new ideas and creative solutions that may have been previously unattainable.
Additionally, the use of generative AI can lead to faster prototyping, enabling quicker and more fruitful experimentation in the innovation process and other creative fields.
The integration of generative AI in the innovation process can also reduce costs associated with the process by streamlining tasks and improving overall efficiency.
Moreover, companies can gain a competitive advantage by leveraging the unique capabilities of generative AI to create innovative and differentiated products or services that stand out in the market.

Teaching Methods

Interactive Lectures
Small case studies
Practical exercises
Group discussions and presentations

Structure

Creativity & Innovation

Introduction to creativity & innovation in organizations
The importance of innovation for organizational development
The significance of human creativity
The role of creativity for innovation

The Double Diamond Approach in the Innovation Process

Introduction to the Double Diamond framework
Understanding the four stages of the Double Diamond
Methods and tools used at each stage of the Double Diamond
Applications of the Double Diamond in real-world scenarios

The Basics of Artificial Creativity

What is Artificial Intelligence and why does it matter?
What is the difference between Artificial Intelligence, Machine Learning, Deep Learning and Neural Nets?
What is Artificial Creativity and how can we use it?
How can we apply Artificial Creativity in various industries?

Artificial Intelligence for Creativity & Innovation

Introduction to generative AI and its role in creativity & innovation
The Double Diamond Innovation process with AI
Case studies of successful implementation of generative AI in innovation processes
Best practices for integrating generative AI into innovation workflows

Criticial Reflection of the Role of AI for Creativity & Innovation

Ethical considerations in using AI for creativity & innovation (e.g., the role of intellectual property)
Potential risks and drawbacks of relying on AI for innovation (e.g., considerations for data security, less radical innovations, …)
Balancing the use of AI with human creativity and intuition
Implications for the future of work and the role of humans in the innovation process