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  • Writer's pictureJennifer Chase

Navigating AI using Cognitive Diversity

AI is here to stay and success will require a change in many operations such as drug development as well as mindset. But even before you create the algorithm, select & clean data then train the AI, or select the right platform you must first identify what is the problem you are trying to solve. It's the same but different with humans.

For all of us, whether we are the CEO or an administrator, we approach problem-solving in one of two ways and it’s based on a sliding scale of how leaders use structure and control.

Problem-Solving Style is known under different names such as cognitive or thinking style and defines an individual's instinctual approach to how they tackle challenges. Problem solving-style has nothing to do with how good we are at something. It's hard wired into us by the late teens and far less visible and subtler than conventional diversities like age or ethnicity.

On one end of the scale you have a style like a Thomas Edison (invented the light bulb) who preferred to problem solve by using a structured, incremental approach - does things better. And on other there is the style preferred by Anita Roddick (Founder of the Body Shop who shaped ethical consumerism) who had a more unstructured approach challenging existing boundaries - does things differently. No one approach is better than the other, it all depends on the problem needing to be solved. Both styles are needed and nobody has 100% of any specific trait. Bridging and Coping Expertise are essential leadership tools which enable seamless communication and collaboration across various style domains and cultures. While you can hone these skills, your problem-solving style remains innate.

Implications for the AI Era

It's still very early days in the AI revolution but a few learnings from what we know already about problem solving style today provide a good starting place. A broad spectrum of perspectives is likely to lead to innovative AI solutions. Cognitively diverse teams can minimize biases in AI algorithms, curate richer training data, and ensure a user-centric design. Furthermore, the resilience and adaptability inherent in cognitively diverse teams are invaluable in the ever-evolving realm of AI. As well in helping with fostering a talent retention culture by refining recruitment processes to boost cognitive diversity within their leadership.

How these will play out on an individual, team or corporate level is too early to say. We know that people with different styles view threats differently with those having a preference for unstructured problem see threats coming more externally (eg competitors), while not surprisingly, for those with a more structured problem solving style see threats coming from internally such supply as chain issues. Styles are complimentary or they clash, especially if there is a big gap between two individuals styles. Two employees with significantly different problem solving style, see problems differently and more often will lead to talent retention issues. We know that people leave jobs because they don’t feel valued, miss a sense of belonging, or don’t feel their contributions are being considered, very often due to differences in style. Problem solving style in talent acquisition takes on a new relevance as the shift towards a greater focus on essential skills (traditionally the soft skills) continues. Certain jobs and company cultures require leaders to possess a specific problem solving style. What these will be for integrating AI are still open for discussion.

Chase Partners LLC has fine-tuned a method to harmonize hard and soft skills, and hones in on tailoring them perfectly for distinct leadership roles.

You can download it here

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