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Foresight | AI-Driven Innovation

Leveraging AI To Spot Opportunities In Foresight & Innovation

Recently, we have been exposed to a lot of information about the new and rapidly developing field of artificial intelligence (AI) and machine learning which is likely to change many things about the way we work and exist in the future.

This technology is a well-known enabler of a variety of innovations serving the needs of humanity, be it in healthcare or mobility. Just recently we saw and experienced ourselves how AI can be applied to foresee developments and analyze large data sets thus discovering new windows of opportunities through business-relevant trends and technologies. However, how can you apply complex AI algorithms to steer innovation management in a company and add value to the business?

In this article let’s explore how AI adds value to companies trying to develop new products and services given the ever-changing environment as well as those pursuing efficiency and costs cutting to keep business up and running in the long term.

Why AI in Foresight & Innovation Management

For many companies, innovation is one of the top priorities. The more complex the organization, however, the more difficult it often becomes to keep a clear overview of all the innovation projects, teams, and products in the making. Therefore, bigger players usually opt for dedicated software tools and solutions to sort things out and gain needed transparency for the innovation pipeline. This comes together with motivation for an offensive strategy when companies want to tap into new product and service development and thus grow their businesses even further.

However, business growth is not the only motivation for companies to sort out their innovation processes. Since over the last 5-10 years, most companies and industries were mainly growing, today they need to look into ways of gaining more efficiency, maybe saving some costs and ensuring better investment decisions as to innovation projects. In our context, this is a defensive strategy when companies are trying to defend what they have been accumulating and working on for quite some time. Moreover, enabling innovation management and foresight at any company also allows us to look at and update the innovation roadmap to understand what the future may hold and how to act upon it.

Thus, strategists and innovation managers are trying to spot areas where their companies can “play” in the future. And for this, some tools are required. AI-powered innovation management platforms are a fit for this need. As the amount of data that has to be analyzed in the enterprise is growing exponentially, be it research, patents, intellectual property, competitors, startups, investment news, or political updates, companies feel the need to not only collect it all but also analyze. Large players usually do this by investing a lot of money into establishing separate teams of scouts who produce huge reports about trends or technologies. Due to specially designed tools, a large part of this process can be automated. Companies can unleash capacities for sensing and interpreting the insights rather than searching and analyzing large bulks of data. But how do you get started?

How to Get Started: The Known Unknowns Matrix


The known unknown matrix

A thought framework of the Known Unknowns is a good way to kick off improvements in innovation management at a company as well as its foresight activities. This framework is not a mere play of words but a powerful tool to break down what is happening in the innovation pipeline of your organization and discover windows of opportunities.

Known Knowns

The first step in leveraging AI is to collect and connect relevant innovation data by writing down all the Known Knowns. Although it sounds easy, it could also be a challenging task given the size of the organization but also the way such information is managed and if it is managed internally. Moreover, such knowledge is usually implicit and tacit and therefore is difficult to extract/distill. Oftentimes, such information is simply not collected in a clear and structured way across the organization.

Therefore, at this stage, all the Known Knowns i.e. customer needs, market or technology trends are collected. Then, they are being evaluated together with the customer based on a variety of criteria such as the current market situation, stage of technology adoption, potential impact, strategic fit, or time to market. The same is done for the internal processes. Companies usually start by compiling a list of existing innovation projects to get a foundation for further AI algorithms.

Thus, at the end of the Known Knowns stage, companies are able to create both perspectives, an external one dealing with market and technology push or market pull and an internal one referring to what the company is currently working on and where its investments are going. Upon building this foundation, we start applying first data analytics to that and gaining first powerful insights.

Unknown Knowns

This is the knowledge that companies own but have not shed light on them yet. For example, a portfolio of innovation projects that you are working on but which is not too easy to keep in sight for a globally distributed organization. Here data analytics comes into play as it helps identify these Unknown Knowns. One simple but compelling tool is an innovation graph which is connecting technology and market trends to innovation projects from the pipeline. This is possible owing to the previous step of collecting all the necessary data. Upon connecting the dots, we also analyze them based on the criteria needed and display the results through color-coding on the graph.

This tool also enables one to easily spot outliers, for example, trends with a very high impact but totally disconnected from the innovation pipeline. It helps to drive a discussion on why there is nothing going on around that trend while everyone agrees on its importance and relevance. A simple but actionable step to take here is to create a list of outliers that are of importance but not acted upon yet to at least start a discussion or run an ideation campaign for them.

Innovation graphs and networks

Such visual representation and analysis also allow us to look at things the other way around by discovering questionable priorities in the innovation portfolio. Here we are going away from the offense strategy and are looking at defense. By zooming in on the trends of very low importance but surrounded by a variety of projects, we can question the efficiency of such investments. Why are we investing so much here if we agree that this is not really the future, at least for us as the company? Are we still doing the right thing? This gives an opportunity to look at the projects again and see if the company can do something about it, merge them to release some resources, and direct investments in other more promising areas to achieve tangible business impact.

Known Unknowns 

There is a huge amount of things in the world that we know we don't know and it is actually fine. As innovation managers or strategists, it is important, however, to at least create an easy way to learn about these topics. A good way to tackle this constant need for new knowledge is to have a streamlined solution allowing one to tap into each trend or technology and get to know more about it, e.g. recent growth of the trend, the companies and startups working in the field, latest news. It is vital to first get a high-level understanding of the trend to make well-informed decisions regarding it in the future.

Unknown Unknowns 

The Unknown Unknowns are directly empowered with AI and represent a holy grail of automated foresight. These are the things we don't know we don’t know. Wouldn't it be cool if AI could just tell you what are these Unknown Unknowns that are also of importance to you and your company? Unfortunately, the AI algorithms right now are unable to analyze the importance of a trend for you, however, they can find some new topics and fields for you to further explore and synthesize.

AI decision making

What Tools to Use? Software-Supported Foresight

Despite all of the fears regarding AI replacing human jobs, the algorithms are still not that advanced leaving some meaningful work to innovation managers and strategists. Software-enabled solutions are there to release the most precious resource, time, for specialists to focus on critical and actionable tasks instead of operational and research ones. One should therefore look for suitable innovation management solutions enabling a company to discover new opportunities, on the one hand, and also streamline processes already in place, on the other.

Given that there are some easy-to-use tools for almost every category of the Known Unknowns matrix, the main question remains on how can the software search new topics automatically and through an AI algorithm.

At ITONICS, for example, this search for new topics is implemented through an army of very efficient and narrowly specialized AI bots. They are a virtual representation of an unlimited number of trend and technology scouts. By analyzing as much as possible, users can find topics of which they were not even aware in the beginning. This is not about detecting signals but rather about the automatic detection of new topics. These bots are working to notify you when there is a new topic coming up in the scientific literature or a new emerging trend. So far, machines cannot interpret these upcoming trends leaving it up to innovation managers or strategists to conduct a more thorough analysis. For companies, however, it means being faster than the competition in identifying, analyzing, and exploring opportunities.

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