AI in Innovation Management - What’s Happening?

How AI Can Help Innovators Now

As innovation is increasingly seen as a cost center, and innovation professionals are expected to deliver better results with fewer resources - the potential of AI to plug the gaps looks promising. But it’s not all about cost cutting.

As our panel discussed, AI is already being used by innovation teams in a range of impactful ways, from speeding up processes to helping them achieve things that simply weren’t possible before. Here are a few examples.

Improved access to information speeds up research

Question and answer models such as ChatGPT provide quick, concise and relevant responses to queries, replacing lengthy internet searches. This is particularly useful during market research phases, when teams want to quickly get up to speed with new topics.

Supercharged ideation processes

The creative potential of generative AI can help teams to generate hundreds of ideas for new projects, products and solutions in a matter of minutes. Although not all of these ideas will be on the right track, AI can help teams to think outside the box and come up with possibilities they wouldn’t have considered before. A bigger funnel of initial ideas also enables innovators to be more selective with the ones they take forward, resulting in higher quality end results.

Efficiency and productivity gains

AI helps teams carry out key areas of the innovation process - such as conducting market research, ideating, and testing at scale - much more quickly and easily. With AI, teams can cut delivery timelines for these processes from several months to a single day. Although it’s still important to keep humans in the loop to check and verify AI outputs, the technology can give teams a real head start on many different aspects of their work.

Boosting creativity for all team members

Generative AI models are democratizing creative processes, making it easier for everyone to create written and visual materials - not just specialist designers. This is hugely valuable when team members want to quickly spin up a design or prototype, or generate materials for a presentation.

Demonstrating a project’s potential

AI enables teams to generate workshop materials in real time, making these sessions more interactive and impactful. This also helps them to get buy-in for a project among the people in the room, for example, by creating visualizations that help stakeholders to understand an innovation’s potential future impact, and secure the backing of leadership.

Connecting the dots within an organization

AI is hugely impactful in innovation management, as it helps large organizations to analyze their innovation data and keep track of their operations across their different portfolios, projects, R&D activities and scouting efforts.

By providing a single overview of all innovation activity in an organization, innovators across an organization can see what work is currently underway, and what has already been done. This ensures work isn’t repeated, and captures learnings and research findings from previous projects for use in the future.

Releasing time to focus on the human side of innovation

By automating some elements of the innovation process, team members can release their time to focus on things only a human can do. This might be crafting a story around innovation that they can use to inspire leadership, and ultimately secure backing for a project within the organization.

AI in action - 3 innovation case studies

Each of our panelists also shared a recent project where they applied AI to an innovation project or process. This enabled them to tackle problems in new ways and achieve significant gains.

From research to product launch in a fraction of the time - Jan Beranek

The context: Working with an insurance company which wanted to explore the problem of aging in place for the current aging population.

The results: The team used an AI platform to carry out initial research into the problem, successfully identifying seven submarkets they could target with potential new products and solutions. Using the power of AI, they then quickly generated 200 ideas for specific products. The client picked 10 of these to explore within a wider business case, eventually launching market tests for two solutions.

The benefits: Thanks to their use of AI throughout this process, the insurance company became 2-3 times more productive, with their innovation team saving 40 hours of work per week. The process resulted in a successful new product launch, gaining 50 customers in three weeks with a cost of acquisition of $7 for a $300 service. The insurance company also had the added benefit of the 190 ideas that weren’t selected, that they could potentially explore in the future.

World building and scenario planning in real-time - Stefan Rass

The context: The client was one of the largest filter manufacturers in the world, whose filters are typically used in internal combustion engines. With the phasing out of fossil fuels, they wanted to see how to apply their filters elsewhere.

The results: The team undertook a world building exercise, creating different scenarios on the future of filter manufacturing to look for innovation opportunities. By using AI, they were able to evolve the world in line with changes and current developments going on in the real world, even doing this in real-time during live sessions with clients.

The benefits: The use of AI to create future scenarios in real-time ensured these scenarios were as accurate as possible, giving relevance to the innovation opportunities that were identified. This helped to secure buy-in for new ideas, giving the manufacturer confidence to explore uses for their filters outside of internal combustion.

Creating a knowledge management platform to centralize projects and capture learnings - Christian Mühlroth

The context: The client was a large automotive OEM with more than 10 different brands. Each of these brands operated as a separate business line, with its own innovation manager and innovation team. This led to a duplication of effort, with the brand teams independently researching trends, customer insights and technologies, even though there was a lot of cross-over between their segments.

The results: Building an AI-based knowledge platform made it possible to connect the dots for innovators across the organization’s different brands. This enabled innovators across the business to share information on emerging technologies, competitor analysis, and the projects in their innovation pipelines, saving time and money as work was not repeated.

The benefits: As well as promoting collaboration, and securing efficiencies by preventing work from being duplicated, the knowledge platform also helped to champion innovation within the organization. The team made the decision to open up the platform to everyone in the business, helping to shine a light on their work and boost the profile of innovation.

Navigating the Impact of AI on the Team

Although the impact of AI is undeniable, the technology is still in its early stages. For now, this means that AI still requires an expert human touch, to guide its outputs and fill in the gaps where it is unable to produce results, or lacks the ability to produce results of sufficient quality.

As Jan notes, innovators have common expectations that AI should help them increase speed and reduce costs, but the impact of the technology will depend on their team’s maturity levels. If a team has a large number of junior or relatively inexperienced people, they will likely see significant productivity increases. But the more advanced the team, the less value AI is able to add as experienced team members are able to work more quickly.

“I’m seeing the most success with teams who understand that AI isn’t 100%. Like for ideation. One of my clients says: ‘This AI isn’t going to be 100%, but even if it’s 80% but 100 times faster it’s going to enable us to do things that weren’t possible before’.” - Jan Beranek

Job roles will change - but will the robots replace us?
No conversation about AI would be complete without the age-old question: will the machines replace us? For Stefan Rass and Jan Beranek, it’s more a case of considering how our roles will change as a result of AI.

Stefan sees a future where white collar workers will have more and more tools at their disposal to help them with their work, giving them more of a managerial role. Some types of innovators, such as foresight practitioners, will concentrate more on the human side of their work, creating narratives and stories around innovation, instead of focusing on desk research and process-based tasks.

This draws out a key possibility of AI - that it can help all of us become more creative, and enable us to build things that weren’t possible before.

Jan sees the role of the innovation manager changing, with the practice of innovation itself moving away from separate, specialist teams and instead being integrated within different business units in an organization. According to Jan, the innovation manager role is already moving into delivery as traditional processes such as ideation and validation are now being automated:

“Every professional services process that deals with data, or mixes different data sets, adding transformation and creativity on top…will be automated. The question is not if, it’s on what timeframe,” he says.

Christian takes a stronger position that even as innovation roles evolve, some jobs will still be lost as this “always happens with larger technology revolutions.” So as innovation practitioners, whilst we need to be positive about the use of AI in innovation we should also be clear and honest with ourselves about what lies ahead as the technology continues to grow in strength and capability.

To be continued, that's for sure.

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