Artificial Intelligence As An Innovation Accelerator

Artificial Intelligence As An Innovation Accelerator

Thanks to artificial intelligence (AI), emerging trends or new technologies can be recognized at an early stage – a significant competitive advantage, especially in times of crisis. But how can AI be used properly?

AI is unbiased

Deviations from the status quo are often perceived as a threat by humans. There are countless examples of this, from the steam engine and industrialization to the advent of the automobile; Today, the topics include renewable energies, digitalization, and AI. An AI does not know this human bias. And that is their big advantage because an AI only acts based on facts. Once set up, any number of AI bots can observe an unlimited number of topics and search fields for companies – with an important economic effect. This is because an AI greatly reduces the costs of predictions and can make companies more productive.

New trends ahead

The technologies around AI and machine learning are now very advanced so that they can fundamentally change business models through forecasts. For example, important signals can be recognized at an early stage in the front-end of innovation (i.e. the early phases of the innovation process) with the help of AI. This leaves companies more time to analyze them and work out new growth opportunities with the right answers. This can lead to a significant competitive advantage, particularly in turbulent markets. For example, an AI can predict the failure of systems and machines or how traffic and goods flows develop under certain conditions.

All competitors at a glance

With strategic competition monitoring based on Machine Learing, well-known market players, but also startups and venture capital investments can be observed and classified. The observation can also be applied to geographical areas such as regions and countries. Typical questions here include, for example, “Who develops the technologies that I need?”, “Which companies occupy which subject and technology fields and why?” and “Where is the market going?”

AI in innovation management

Collaboration tools such as road mapping or trend/technology radars are a quick and easy introduction to innovation and strategic planning. A risk radar helps to assess and monitor the effects and likelihood of current risks. These findings in turn serve companies as the basis for developing individual risk reduction strategies. When using AI in innovation management – for example as AI algorithms and features in a digital innovation platform – it is first of all important to achieve first quick successes with simple use cases. In the next step, the depth of integration and complexity of the applications can be gradually expanded. In order for AI to be implemented on a permanent basis, it is important to build trust.

What’s the future like?

The term “AI augmentation” describes the short-term expected development in this area very well: The human being will still be in the center, and around him the AI ​​will expand his skills, deepen and increase the speed extremely. In the short term, this will create a multitude of new applications. There are already first successful uses of AI in innovation management to generate and above all to check new business ideas, to evaluate startups, to put together teams for innovation projects, as well as to support branding and the positioning of innovations. In the long term, research is going one step further: It is currently developing a new technology that processes investment strategies and decisions fully automatically and AI-controlled.


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