In this episode of The Bridgecast, host Scott Kinka sits down with Indi Young, an Independent Qualitative Data Scientist, Coach, and Consultant. Together, they explore how deep listening and cognitive empathy can revolutionize enterprise technology decisions. Indi shares insights on moving beyond generic personas, embracing diverse thinking styles, and using qualitative data to serve all users better. Indi also shares her actionable frameworks, including the Mental Model Skyline, for designing systems that align with real user needs, offering a powerful alternative to fast but shallow innovation.

Indi Young is a thought leader in product strategy and a pioneer in human-centered research. She specializes in exploring the problem space by deeply understanding people’s inner thinking and uncovering diverse thinking styles. Indi helps organizations move beyond assumptions to build more inclusive, user-aligned solutions through her groundbreaking work in cognitive empathy and qualitative data analysis. An author, speaker, and educator, she empowers practitioners to listen deeply, avoid bias, and use empathy as a strategic tool for meaningful innovation.

Indi introduces the concept of problem space research as a crucial counterpoint to solution-focused thinking in technology implementation. The approach examines how different users mentally process and approach tasks, rather than forcing a single "best practice" workflow. Understanding these diverse thinking styles helps organizations identify friction points where solutions aren't serving all users effectively. Organizations can map different thinking styles within each business function to see which solutions support various approaches rather than trying to force everyone into one method. This framework helps IT leaders select and implement systems that work with, rather than against, their users' natural thought processes.

The Mental Model Skyline provides a visual framework for mapping how different solutions support user thinking patterns and needs. This strategic tool looks like a city skyline where each "tower" represents patterns in how people mentally approach tasks, with solutions mapped underneath to show coverage. Leaders can use this to evaluate technology options by seeing which ones support their organization's most critical thinking patterns. The framework becomes more valuable over time as organizations layer in new data and options, creating a comprehensive view of user needs and solution fit. This approach helps IT leaders make more informed decisions about technology investments while considering long-term strategic impact.

Using CRM systems as an example, Indi illustrates how different users naturally approach the same task in various ways,  from linear form completion to object-oriented navigation between screens. Rather than forcing standardization around metrics like minimizing clicks, IT leaders should recognize these varying interaction styles as valid approaches. When systems don't support a user's natural thinking style, they often create workarounds that introduce friction and inefficiency. Organizations can reduce resistance and improve adoption by understanding and accommodating different thinking styles during system selection and configuration. 

Indi proposes training AI systems to recognize and adapt to different thinking styles rather than trying to force users into a single interaction model. While AI can't yet independently identify thinking styles, it can be taught to recognize patterns and adjust its interface and guidance accordingly. The key is providing AI with a framework of known thinking styles within specific business contexts, allowing it to match users to appropriate interaction models. This approach could help organizations create more inclusive systems that flex to meet users where they are, rather than forcing adaptation to a single UI paradigm.