Artificial Intelligence as Innovation Accelerator

Abstract

The origin and emergence of innovations has been a central question in research and practice for decades. In this context, technological progress plays a decisive role: Existing technologies and capabilities serve as building blocks for the innovations of tomorrow. Over time, the advancing technological evolution thus accelerates itself [1]. Being one of those building blocks, artificial intelligence is used to develop innovations that serve the needs of humans, the needs of other technologies, or both, for example in healthcare, the financial industry, and future mobility.Recently, artificial intelligence has been given another area of application: It can also be used to anticipate future developments; not as a predictor for the future, but as versatile tool for analyzing big data sets in order to detect weak and strong signals of change, emerging trends, and newly-developed technologies [2]. Leading companies have been performing these analyses manually since years and for this reason, among others, were found to be more likely to outperform the industry [3]. However, with the ever-increasing amount of data that needs to be analyzed for this purpose, it has become inevitable to automate this task [4].The areas of application are manifold [4]: The corporate environment can be continuously monitored in order to detect relevant changes at an early stage, leaving companies with more time to develop adequate response strategies. Strategic competitive intelligence can be used to observe and classify known market players, start-ups, and venture capital investments. In the front-end of innovation, consumer insights can be modeled and clustered to suggest ideas for product, service, and business model innovations. A risk radar helps to monitor and assess the probability and impact of current risks, allowing individual risk mitigation strategies to be developed.When using artificial intelligence for innovation management - for example, as algorithms and features in a digital innovation platform - it is important to achieve early success with simple use cases. In the next step, the depth of integration and the complexity of the applications can be successively expanded. In order to permanently establish the use of artificial intelligence, it is important to build trust; all parties must be involved in the lean start and agile implementation process from the very beginning.In the medium term, this technology will lead to a large number of new applications. There are already first successful applications of artificial intelligence in innovation management for generating and testing new business ideas, evaluating start-ups, assembling teams, and supporting branding and positioning of innovations. In the long term, we will see artificial intelligence become an integral part of fundamental decision-making processes. For example, research and development is already being performed on a new technology that develops and executes profitable investment strategies and decisions fully automated. As a consequence, future developments can not only be anticipated, but also considerably influenced in the near future.