Finding the right innovative ideas

For two decades I have listened to, learned from, and worked with some of the world’s leading technology innovators. I quickly noticed that many of the brilliant innovators in the life sciences industry struggle with the same issues. From the sidelines, the innovation process seems simple, misleading many to think: “Why can’t innovators just pick an idea and run with it?” Unfortunately, the innovation process is daunting and complex. With a multitude of ideas available everywhere, innovators need to sift through a lot of noise and clutter to find the right idea.

Once an idea is selected, innovators need to consolidate the right data in a way that allows them to effectively analyse and use it. There is no shortage of information available to innovators to help them with this task. In fact, when one considers the wealth of siloed information and data that is both privately and publicly available, one can understand how this “overload” can be considered one of innovation’s biggest obstacles.

Once innovators filter through this maze of data, they are then challenged with identifying the initiatives that have the highest probability of success and allocating the right resources. The decision to move ahead or ”kill” a project, especially when unsure about the potential outcome, can be difficult and even scary.

Most innovators that can make it past these obstacles then need to circumvent potential resistance from colleagues and superiors. Many find it difficult to train, coach and mentor innovators as well as operational and clinical teams on how to deploy, adopt and embrace innovation. Sometimes this means recognizing why something does not work so you can re-direct your focus on what does. Engagement and feedback loops are critical in ensuring innovation adoption and in reducing the change management effort required.

To succeed in innovation, an organization needs to: generate new ideas, evaluate and select which ideas to pursue, stay ahead of new technologies and trends, accelerate timelines and ensure initiative effectiveness and profitability. But most of all, organizations need to create a culture of “embedded innovation” that engages employees and drives participation on a daily basis.

One of the ways innovators encourage a culture of innovation is by embracing technology. Artificial intelligence (AI) and machine learning (ML) are essential in life sciences because they are one of the proven ways to accelerate the innovation process. AI allows innovators to search, merge and interpret the different and vast amounts of data and ideas. Deploying an AI-driven platform can provide organizations with the ability to semantically search past and existing internal as well as external data sources, providing the organization with useful insights. Thanks to machine learning, these insights can be personalized according to how users leverage the platform. Artificial intelligence can also help organizations to identify barriers and innovation projects trends, making it possible to provide insights that offer answers and tools to support innovation. With the help of AI, innovators feel supported and can more easily recommend projects, facilitate the prioritization of innovation efforts, and manage the projects and resources that will have the greatest impact.

Finally, a well-trained AI platform can work to identify missing elements from innovation initiatives and allow innovators to capitalize on these learnings. With AI assistance the entire decision-making process can be conducted more rapidly than ever before, allowing innovators to more quickly and easily “pick an idea and run with it”.