Innovation is seen as highly desirable in business in 2019. However, while most businesses might claim to be innovative, for many it is more of a PR exercise that positions that company as innovative without really delivering.
To truly be innovative involves a different culture and a different mindset. And for innovation to be future-proof and sustainable in the long-term, then Artificial Intelligence (AI) is going to play a major role.
The Rise of Innovation
Innovation has become so important, that industry has emerged to meet this demand. Idea management platforms allow an organization to gather ideas and make it easy to capture, refine and implement those ideas amongst their idea crowds.
It’s an industry that continues to grow. A recent report by IT Intelligence Markets – Global Innovative Idea Management Software Market Research Report 2019-2024 – predicted that by the end of that period, 29% CAGR will be achieved by the innovation management market.
The same report also predicted the innovation management sector will reach US$ 1,519.2 million in revenue by 2024. So it’s clear that there is a growing appetite for innovation. Organizations know the value of innovation and are increasingly preparing to invest in the right processes and tools to ensure they are innovative and are capable of tracking and measuring their innovation success.
Such tools continue to evolve even further, as innovation becomes even more highly valued. The latest idea management platforms are now making tangible use of AI to power innovation programs.
Sustainable Innovation Relies on AI
AI has undoubtedly made a difference to Innovation and Idea Management, adding Machine Learning capabilities to ensure corporate memories for ideas are much longer, leading to truly sustainable and on-going innovation.
Even when using an Idea Management platform, a good idea if submitting at the wrong time. If there isn’t a current need for an idea, then ideas mostly either drift into the ether or stored away somewhere. Using an Idea Management program that leverages Machine Learning algorithms allows the platform to keep building, learning and developing its own memory, which becomes more useful in the future.
If an idea in response to a specific request isn’t quite right at that moment, it could be applied to another area at another time. The right Machine Learning technology can store ideas until such a point until use. This means people looking for solutions will be alert to previously submitted ideas that could work. The ideas are based on the idea management platform’s understanding of their needs and memory of what has been suggested before.
As the volume of ideas grows, it becomes less efficient or even impossible for people to manually make connections between these ideas, whether implemented or rejected. AI tools help solve that problem by highlighting unexpected relationships between ideas.
AI and the Continuing Value of Ideas
Similarly, if an organization is running an innovation challenge across its target audiences, it is to get thousands of responses. The most immediate relevant ideas will be selected. Many ideas may be rejected for different reasons, such as lack of resources or poor strategic fit.
Only a small percentage of those ideas go forward initially. What happens to the remaining ideas? They will be discarded and potentially forgotten, but often there’s still value to be found from ideas that don’t find a home the first time around.
By applying AI to Idea Management, these initially overlooked or rejected ideas fold back into the corporate memory. The corporate memory is with the knowledge that in the future they may be useful. The platform learns about each idea and holds it there until that time. It can be a week, a year or even a decade in the future.
Innovating for today is the machine that feeds innovation for tomorrow – culturally, in capability, process and much more. To do this successfully, it means adopting AI-enabled Idea Management to drive innovation even further.