Using technology to enhance innovation isn’t new but AI is a potential game-changer. New research outlines seven areas where companies can best harness its power.
The concept of open innovation (OI), first introduced two decades ago by the organizational theorist Henry Chesbrough, has led to revolutionary changes in innovation management techniques and practices. The previously closed-door, secretive model of business innovation has given way to external flows of information fueled by co-creation with customers, recognizing the value of cross-sector expertise and embracing the use of licensed technologies.
At the same time, artificial intelligence (AI) has evolved into self-adjusting neural networks that can identify patterns and predict behavior without human intervention. Oceans of external data gathered from customers and competitors can pinpoint trends and future needs, transforming innovation from a slow, costly process hindered by the limits of human capacity into a constantly evolving flow of information that can be accessed using natural language via an Alexa-style interface.
This knowledge, invisible to the naked eye, has the potential to transform the field of OI. Innovation requires information to be connected in novel ways; AI-fueled OI allows managers to do this constantly. It extends and complements conventional innovation processes and human practices, generates ideas, launches new products and processes and, ultimately, accelerates innovation.
Oceans of data gathered from customers and competitors can pinpoint trends and future needs
According to the Boston Consulting Group, 91 percent of innovation leaders are already using AI to identify new business opportunities, with 92 percent using it to support innovation decisions. Amazon, Google and Microsoft are all developing business models to offer remote AI as a cloud-based utility.
But what does this mean in practical, everyday terms for businesses? How can small and medium enterprises embrace the benefits of AI to boost competitiveness?
Seven ways to boost innovation
Esade’s Xavier Ferràs, together with Petra Nylund and Alexander Brem from Universität Stuttgart, have summarized in The Oxford Handbook of Open Innovation the crucial areas where businesses can implement AI to enhance innovation.
1. Business model innovation
Databases such as Crunchbase, which process real-time data from hundreds of thousands of global start-ups, can be data-mined to identify the business models associated with sector-specific technologies. The analysis can reveal which models attract the most investment or achieve the highest growth rates, and also be used to predict the business models most likely to succeed.
2. Technology detection
Scientific research databases can be analyzed to identify emerging fields of knowledge receiving high levels of attention. This information can be analyzed to predict the time frames of converting ideas to marketable technology, and the types of products that can be developed around it.
3. Technology trajectories
For technology already on the market, AI can assess where sectors are turning their attention, the areas receiving the most investment and the fields that are likely to be consolidated.
4. Strategic detection
Past and current events available in news databases and digital networks can be analyzed to identify the investments, products and strategic directions of competitors.
5. Predictions of results
Historical data within an industry (such as historical sales, social and political analysis and demographics) can be used to optimize sales forecasts and predict the business results of competitors.
6. Market research
AI can analyze data from millions of online social media posts and comments to provide instant analysis of brand perception, detect emerging trends, intercept new ideas from competitors and segment user groups based on the information they share in real-time.
7. Prediction of events
Health events, such as the possibility of heart attacks or susceptibility to disease, can be predicted with increasing accuracy thanks to the growing number of databases containing information on human genetic codes and the availability of clinical records. This information can also be used by businesses in the life sciences arena to develop preventative and customized treatments.
New possibilities
As the use of AI crosses the realm from streamlining operations to driving innovations, businesses of all sizes will gain access to previously unthinkable levels of insight. The core competencies of a company — decisions on expansion, products and patents, research and development — will all be driven by AI.
This fascinating field will continue to disrupt innovation. However, as AI adds increasing levels of value to business, the importance of ensuring it also adds value to society should never be overlooked.
AI offers new research and practice scenarios to a field, that of Open Innovation (OI), where almost everything had already been said. Introducing AI into OI practices opens a new field of experimentation, obtaining new flows of information and knowledge from metadata, which can lead to new ideas and projects, beyond those obtained in the traditional scope of business or sector operations.