AI & data driven - innovation management

In a digital age, data-driven innovation management is more than just a buzzword. Rather, it is a MUST; it is the central lever for exploiting the innovative potential in a company. None production Plans without product and production data, no purchasing Plans without material and supplier data and no Sales Plans without product, market and customer data. Companies that do not use data and artificial intelligence in connection with foresight, working with trends and strategies will fall behind the companies that do so in the medium term.

Companies that do not have data and artificial intelligence associated with Foresight, the Working with Trends and Strategies In the medium term, will fall behind those companies that do so in the competition.

What is behind it?

Data-driven innovation management means recognizing and using the power of data, its connections and development to specifically promote innovations in a company and drive them forward at the right time. The focus here is on using and analyzing data to gain valuable insights Trends early to recognize and well-founded decisions To be able to meet that lead to new products, services or processes.

The database for this is crucial and can be diverse, depending on the context and area of activity and objectives of a company. Whether internal data such as customer data, surveys or social media interactions, or external sources such as market research reports, automated AI interviews, patents, scientific papers or trends.

Taking trends into account alone would be inadequate in any case. When it comes to foresight, it's important to look at multiple categories of data, set yourself up broadly, and include as much data as possible.

At this point, AI and Machine Learning (ML) are also becoming a key component of data-driven innovation management. This is how AI Algorithms can Process large amounts of data quickly and efficiently, understand and extract valuable insights and patterns that are difficult or impossible to recognize through manual analysis. AI helps to make the innovation process more efficient by providing new perspectives and insights that cannot be identified on human analysis alone. This is completely unbiased, without emotions and without BIAS.

The AI & data-driven approach should be used in all Stages of Innovation Management Find application. This approach is particularly suitable in Front-end phase, in which information on trends, signals, markets, future scenarios and research significantly influences the innovation strategy. Die Synthesis of AI in AnyIdea Despite the wealth of data, it quickly extracts the most relevant information and makes it possible to make well-founded strategic decisions based on this.

In subsequent phases, AI & data-driven innovation management can make an important contribution to identifying and defining future customer problems, finding and validating ideas. AI can also help with the creation of prototypes and simulations in order to visualize ideas and verify their feasibility towards PoC and implementation. This enables companies to test and validate ideas early on before investing in development.

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Further topics:

• What is synthetic AI?
• Which data and data categories are taken into account?
• Can I also add my own data to AnyIdea?