We’ve all heard a lot about AI recently, fuelled by the arrival in our daily lives of OpenAI’s ChatGPT. Without getting drawn into the “shiny object” hype around this important technology, I’d like to offer a different perspective – how AI is used for innovation.
Innovation is integral to meeting some of the challenges the world is facing right now. As a practitioner who has been working with machine learning and, more recently, AI and Large Language Models (LLMs) such as ChatGPT in the field of innovation, I’d categorise the usage of AI for innovation into three main stages.
Stage One: Connecting the Dots
We founded our company on the belief that humans can solve any problem when supported by the right technology. We swiftly understood that basic AI / Machine Learning – could help reduce complexity and duplication in the basic process of generating and gathering ideas. It could also accelerate collaboration and transparency by helping people who had similar ideas find each other in large, complex organisations and work together. The machine made the connection that the humans created.
This ability to make connections also means that ideas that don’t work right now aren’t lost or forgotten. Instead, they live in the system memory and when, in the future, a company is looking for an idea in that area, the AI can connect that historic idea with the current need for a solution. Recently, one of our engineering customers solved an urgent issue with an idea that had first been captured four years previously.
Stage Two: Understanding the patterns
Open innovation is an approach to problem solving that involves organizations asking questions (Challenges) to, and capturing the input of thousands of experts (Solvers). It’s a powerful proposition and can solve a staggering array of problems. These include helping to create cleaner drinking water in developing countries to protecting astronauts on space walks, and many more in between. However, knowing the nature of the thousands of Challenges they have solved as well as the specific skills and expertise these Solvers have is extremely difficult.
Simple tagging and key words could never uncover the rich details required to truly understand questions like; Are there trends or patterns in Challenges and solutions?
What skills, knowledge, and experience have Solvers demonstrated that is not claimed on a CV?
This can work both ways. People could list a skill on a CV that they actually don’t have, but also, the rigid structure of a CV can lead to the vital information required on a Solver’s overall skill-set being left off.
AI has helped achieve this. Through analysis of data, covering more than 20 years of solutions to Challenges, it is possible to see precisely what skills are contained in a community, how trends in problems have changed over time, and the evolution of AI Challenges over time. It would be impossible to understand the rich complexity of a crowd without AI reading, understanding, and categorizing information. This process of clustering would have taken thousands of hours of work and still wouldn’t have been as comprehensive.
Stage Three: Drawing the picture
The emergence of Generative AI (GenAI) has heralded a further boost to innovation. It can assist with approaches to writing or refining problem statements or potential solutions and can also reframe problem statements to direct the question to different crowds. This becomes incredibly useful for making a technical topic more accessible to non-technical Solvers, inviting a broader range of perspectives when seeking ideas. This will play a critical role in encouraging more diverse groups to participate in the innovation process and will make ii faster and simpler to launch innovation Challenges.
Given the scale of some of the innovation Challenges the world is currently facing, it’s my belief that AI can be a powerful aid to human ingenuity and creativity and play a vital role in addressing those challenges.
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