AI & Data driven - Innovation management

In the 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 innovation potential in a company. No 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 conjunction with foresight, working with trends and strategies, will fall behind those companies that do in the medium term.

Companies that do not use data and artificial intelligence in conjunction with foresight, working with trends and strategies, will fall behind those companies that do in the medium term.

What is behind it?

Data-driven innovation management means recognizing and using the power of data, its correlations and development in order to specifically promote innovation in a company and drive it forward at the right time. The focus here is on using and analyzing data to gain valuable insights, identify trends at an early stage and make well-founded decisions that lead to new products, services or processes.

The data basis for this is crucial and can be diverse, depending on the context, 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. 

Considering trends alone would be inadequate in any case. With regard to foresight, it is important to look at several data categories, take a broad approach and include as much data as possible.

This is also where AI and machine learning (ML) become a key component of data-driven innovation management. AI algorithms can process large volumes of data quickly and efficientlyand 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 recognized by human analysis alone. It does this in a completely unbiased way, without emotions and without BIAS.

The AI & data-driven approach should be applied at all stages of innovation management. This approach is particularly well suited to the front-end phase, in which information on trends, signals, markets, future scenarios and research have a significant influence on the innovation strategy. The Synthesis AI in AnyIdea quickly extracts the most relevant information despite the wealth of data and enables well-founded strategic decisions to be made on this basis.

In subsequent phases, AI & data-driven innovation management can make an important contribution to the identification and definition of future customer problems, idea generation and validation. AI can also help with the creation of prototypes and simulations towards PoC and implementation in order to visualize ideas and check their feasibility. This enables companies to test and validate ideas at an early stage before investing in development.

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Further interesting topics:

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