Artificial intelligence (AI) is about imbuing machines with a kind of intelligence that is mainly attributed to humans. Extant literature-coupled with our experiences as practitioners-suggests that while AI may not be ready to completely take over highly creative tasks within the innovation process, it shows promise as a significant support to innovation managers. In this article, we broadly refer to the derivation of computer-enabled, data-driven insights, models, and visualizations within the innovation process as innovation analytics. AI can play a key role in the innovation process by driving multiple aspects of innovation analytics. We present four different case studies of AI in action based on our previous work in the field. We highlight benefits and limitations of using AI in innovation and conclude with strategic implications and additional resources for innovation managers.

Civic challenges such as urban mobility and energy problems offer new corporate innovation opportunities. However, such challenges are wicked and difficult to tame. They require novel solutions that account for and integrate contradictory perspectives within the local innovation ecosystem of firms, governments, and citizens. This article presents a successful civic innovation crowdsourcing project case study, in which multinational firm Bombardier encouraged a global civic crowd to co-create visionary solutions to the challenge of future mobility in crowded cities around the world. Bombardier recruited a global crowd of 900 individuals and facilitated the citizen development of more than 215 solutions of unique firm value. We explore the process and outcome of this crowdsourcing project and derive actionable design principles for a three-phased civic innovation crowdsourcing process including: (1) crowd construction, (2) crowd knowledge acquisition, and (3) crowd knowledge assimilation. This process enables the crowd to integrate members' diverse and contradictory knowledge proactively at both the team and individual levels. Additionally, the crowd is able to balance extension of existing local solutions and exploration of path-breaking technologies and solution concepts.

This supplementary case follows up on an innovative R&D approach by Beiersdorf,a skin care and cosmetics company. The case relates what happened to the product launched by Beiersdorf, to its Nivea line, following the events of the A case, and how the commercial success of the product informed thinking by leaders in R&D for the future.

The case describes the efforts of Beiersdorf, a worldwide leader in the cosmetics and skin care industries, to generate and commercialize new R&D through open innovation using external crowds and "netnographic" analysis. Beiersdorf, best known for its consumer brand Nivea, has a rigorous R&D process that has led to many successful product launches, but are there areas of customer need that are undervalued by the traditional process? A novel online customer analysis approach suggests untapped opportunities for innovation, but can the company justify a launch based on this new model of research?