Generative artificial intelligence (AI) tools like ChatGPT Or Slab are changing the way creative work is done, particularly in industries that rely on innovation.
However, the use of AI in the innovation process requires careful consideration. Our research shows that the key to success lies in understanding and leveraging the distinct but complementary roles that humans and AI play.
Innovation is vital for any business that wants to succeed today. Actually, 83 percent of companies consider innovation a top prioritybut only 3% are ready to make this priority a reality. This shows how companies need to improve their approach to innovation.
Innovation is about solving complex problems that lead to real improvement. It’s not just about coming up with good ideas, it also involves knowledge workwhich is the process of using information to create something valuable.
Generative AI can help companies prepare to innovate by facilitating knowledge work, but its full potential in this area is still limited. not fully understood.
(Shutterstock)
Design Sprint
Our team, composed of academic researchers with expertise in emerging digital technologies and a practitioner experienced in leading human-centered innovation projects, conducted a study detailed study of how generative AI was used in design sprints in three organizations. (The study is available as a pre-print and has been submitted to a journal for peer review).
A design sprint is a quick, structured process for solving important problems that helps teams test whether a product, service, or strategy will work. Sprints are useful because they reduce the risks and costs of traditional product development
In a design sprint, a small team of five to seven employees from different areas works together intensively for a few days to solve a problem. Their work is coordinated by a facilitator who organizes activities, guides the team, monitors progress, ensures that objectives are clear and that time is used effectively.
The first stage of a design sprint focuses on understanding and defining the problem, while the second stage involves creating and testing a solution. Both steps require teams to use two key types of thinking:
-
Divergent thinkingwhich means coming up with many different ideas and possibilities.
-
Convergent thinkingwhich means refining these ideas to identify priorities or solutions.
Our study examined how the facilitator used generative AI tools like ChatGPT, DALL-E 3 or Uizard to help the team effectively engage in divergence and convergence.
(Cédric Martineau, Carverinno Conseil)
AI and humans work together
In divergent thinking activities, we found two main benefits to using generative AI. First, it encouraged teams to explore more possibilities by providing basic ideas as a starting point. Second, it allowed team members’ unclear ideas to be rephrased and synthesized, ultimately leading to better communication within teams.
One participant told us:
“Sometimes we had a lot of ideas and the AI summarized them into concise text. This allowed us to understand what was happening. This gave us a basis, there were many fragmented ideas that everyone had contributed to, and now we had a text that we all agreed on. In this way, we started from the same base which served as a springboard to move forward.
The real value of generative AI therefore lies not in the contribution of brilliant new ideas per se, but in the valuable synergies that have emerged from the process. Team members used their contextual knowledge and remained in charge of the process while AI helped them better convey their ideas, expand exploration, and resolve potential blind spots.
(Shutterstock)
Make better informed decisions
We noticed different dynamics in convergence activities where teams had to make decisions after demanding idea generation sessions. By this point, team members were generally mentally exhausted. Generative AI was particularly helpful in doing the heavy lifting during this part.
AI helped manage the information-intensive tasks needed for team alignment, such as restating, summarizing, organizing, comparing, and ranking options. This reduced mental strain on team members, allowing them to focus on important tasks. like evaluating ideas. In this process, the team was responsible for:
- Verifying AI Outputs to ensure the content was accurate and useful. For example, ChatGPT and Uizard helped create draft scenarios and draft prototypes to validate their concept, but the team still needed to refine them to achieve the project’s goals.
- Add their own ideas and contextual nuances to guide final decisions, taking into account factors such as feasibility, ethics and long-term strategic impact.
One participant said:
“Sometimes the AI focused on details that were insignificant to us… Sometimes we needed less general synthesis and more personalized input.”
Overall, this form of human-AI collaboration in converged activities helped the team make more informed and confident decisions about which problem to focus on and which solution to pursue. This gave them a feeling of control over the final results of the sprint.
One participant said:
“For crucial phases like decision-making or voting on something important as a success factor, if we relied solely on AI to determine what is important, there would be rejection. We are in a better position to know. We are the employees who will implement the final solution.
Challenges and opportunities
According to research on cognitive automation And intelligent automationWe found that generative AI was a great help in handling cognitively demanding tasks, such as rephrasing poorly articulated ideas, summarizing information, and recognizing patterns in team members’ contributions.
One of the key challenges of using generative AI in innovation is ensuring that it complements, rather than replaces, human involvement. While AI can act as a useful companion, there is a risk that it will reduce team engagement or project ownership if overused.
The design sprint facilitator told us:
“Feasibility must be balanced with desirability. You could technically automate most of the process, but that would remove the need for fun, interaction and the doubts of humans would not be taken into account; Additionally, humans need to take ownership of the problem – all of these elements are essential in a human-centered innovation process.
Therefore, regularly assessing the impact of AI in this process is crucial in order to maintain a healthy balance. Automation should improve creativity and decision-making without undermining the human knowledge that is at the heart of innovation.
As AI continues to develop, its role in innovation will grow. Businesses that integrate AI into their workflows will be better equipped to meet the rapid demands of modern innovation. But it is important to understand both the strengths and limitations of AI and humans to ensure this collaboration is effective.
This article was co-written by Cédric Martineau, CEO and innovation management consultant at Carverinno Consulting.
