## The Pattern I Keep Encountering As people have begun to face the need to use [[Atlas/Notes/Ideas/AI]] tools in their day-to-day, we've all seen the myriad of responses. Whether the change is big or small this is always the case, however, with this shift things seemed different. It seems that with AI there's a distinct shift in dynamic. People express hesitation in the form of questions like these: - "Does this solve the right problem?" - "How does this fit what I'm already doing?" - "What happens to human judgment we're replacing?" - "Will I get in trouble for using this?" When viewed through the "traditional" lens of change management this kind of resistance seems typical: - Hesitation = barrier to overcome - Questions = resistance to work through, things to clarify - Caution = lag in adoption; cultural mismatch ## What Changed My Thinking The difference with AI that doesn't seem to fit the familiar narrative is that the most effective implementations don't need to come from the most technically sophisticated approaches. Real success seems to reside where people are clear about the safety to experiment and fail, where people have felt comfortable asking questions, and where people have let go of their preconceived notions about "who" _should_ be good at this. They've come from: - Understanding how humans actually process information - Identifying what makes workflows sustainable vs. just faster - Treating resistance as valuable data, not obstacle ## The Realization This ought not be unique to those who are "tech focused". When someone says "I'm not sure about this automation," or "will I get in trouble for using this tool" they might be noticing: - Context the system can't see - Judgment steps we're about to eliminate - Friction that's about to compound - Cultural norms that transcend directives This made me realize that what could be framed as resistance was actually people voicing parts of their lived experience that they otherwise might not have language for. To ignore this reality would be to ignore valuable information - not simply a signal that we might need to change our approach, as with many functional aspects of working with AI this was a clear example of [[Hesitation as Data|hesitation as data]]. To take in the information and make the best decision given the real circumstances is [[Intelligence vs. wisdom|wisdom]] not resistance. Looking at it this way caused me to rethink what skills matter most right now while so many organizations are making big technical changes. As many have said, people with particular expertise will likely still excel with the help of AI in their given field and this can result in those without deep technical backgrounds might feel behind. The reality is much more inclusive and encouraging for anyone with a spark of imagination. Folks who aren't "technical" often bring valuable insight to AI (and digital transformations broadly) like: - Experience seeing how tools get adopted—or abandoned - Understanding of human-centred design from working with end users - Natural tendency to ask "should we?" alongside "can we?" Moreover - the people who pause might be the ones we should listen to more carefully. --- > [!faq] The Questions This Raises: - What if hesitant teams are not behind in (AI) adoption, but are asking the right questions for where they are? - [[The most powerful questions in (digital) transformation are "should we" and "why", not "can we".|What if pausing to ask "why?" and "should we?" is the competency that matters most right now?]] - What might have to change at a given organization to treat this [[Hesitation as Data|data]] the same as the other KPIs? - [[How do we distinguish valuable hesitation from fear based resistance?|How do we distinguish valuable hesitation (sensing missing context) from fear-based resistance (avoiding change)?]] ---