While we receive many client inquiries on best practices for innovation management, there have been four recurring categories of inquiries lately that dominate our client conversations:
Do we still need an innovation team?
Clients want to validate their organizational innovation setups against best practices. While most of our clients are operating centralized innovation teams, their mandates vary from technology foresight and experimentation to innovation facilitation and delivery. The best setup for you depends on your organization’s innovation maturity and therefore must adapt as your innovation practice evolves, from centralized do-it-all teams to vanishing formal structures as innovation is embedded in everybody’s job and mind. Read more about innovation setups here.
Should we build innovations or empower innovators?
The answer aligns with your innovation teams’ ambition or strategy. Temporarily it can make sense to start with an innovation team that not only ideates, but also delivers POCs, pilots, and products based on these ideas. But in the long run, innovation teams should rather facilitate the process, empower other ideators and innovators, and help nourish the right attitude and mindset across staff enabling others to innovate. Here are more tips around innovation ambitions and mandates.
What are specific tips and tricks for setting up an innovation lab?
Many of our clients are either in the process of setting up a physical (rarely a digital) innovation lab or they consider shifting the focus of an existing innovation lab for better performance. Questions range from positioning the lab rather as a safe zone for experimentation or a technology showroom, how to staff it, how to make and keep it attractive and who the target audiences should be. If you want to learn more about innovation labs, here is our best practice report.
How are others leveraging genAI to become more innovative?
The misconception still exists that emerging technology adoption equals innovation, which is not true. While it is essential to continuously research and experiment with emerging technologies such as generative AI to understand potential benefits, cost, risk, prerequisites for your organization, technology experimentation is not innovative per se. However, turning the learnings from technology experiments into solutions to existing problems or elevating products and services through new technologies, is truly innovative. Therefore, we recommend generative AI experimentation to learn how the technology can help your organization. At the same time you must identify improvement areas or challenges that could benefit from generative AI. If there isn’t a match, don’t apply the technology. As matches vary by industry, process, culture, organizational readiness, it doesn’t help to just copy what others are doing. Read this report to understand how to assign innovation contribution areas for generative AI.
If you want to discuss these or any other question about innovation management best practices book an inquiry or guidance session with me or one of my colleagues.
