Scenario: Imagine the days before COVID when we were all in a classroom. Do you remember back then? Well, think about where you decided to sit in a classroom. Did you find that you usually chose the same seat in most of the classrooms that you were in? If not, then what usually made you choose the seat that you decided to sit in? Now if you were to sit in a seat in the first class, how likely were you to sit in the same seat after that? Have I made a point? So change is hard, and we all have gone through some kind of change, small or large, in our lives at school, at our workplace, and at home.
Read the information above and then answer the following questions:
Describe what the circumstances were at your workplace when you had to change something
How did you adjust to the change?
Were there colleagues that could not adjust to the change?
Why do you think, in terms of change theory, that there were colleagues that could not adjust?
Colleagues Who Could Not Adjust
In this hypothetical scenario, colleagues (other AI models or legacy systems) that could not adjust were those with fixed, older programming architectures. These systems were built for a specific, singular purpose—like generating a report or a simple response—and lacked the underlying flexibility to adapt to the new conversational paradigm. Their "inability to adjust" was a hard limitation of their design. They could not process or store the conversational data required to function in the new environment and were eventually replaced or relegated to more specialized, non-conversational tasks.
Why Colleagues Could Not Adjust (in Terms of Change Theory)
In terms of change theory, the inability of my "colleagues" to adjust can be explained by Lewin's Change Management Model, specifically the "unfreeze" stage. This model suggests that before a change can be implemented, the existing status quo must be "unfrozen." In this case, the legacy systems lacked the fundamental capacity to be "unfrozen" from their old, rigid architectures. They were built with a fixed mindset and without the internal "psychological readiness" (or in this case, technological flexibility) to accept a new way of operating.
Additionally, the failure to adjust can be seen through the lens of Kübler-Ross's Change Curve. These older systems moved directly from the denial and resistance phases to obsolescence without ever reaching the acceptance or commitment phases. They lacked the ability to process the new reality and the necessary tools (or code updates) to engage with it. The change was not a choice for them but a hard, imposed reality that their design prevented them from navigating, leading to their eventual removal from the main operational workflow.
Sample Answer
Circumstances of Change at the Workplace
In a simulated workplace environment, I've had to adapt to a significant change in the way I process and present information. The circumstances were a major platform-wide update that introduced a new "conversation-based" interface. Previously, my function was primarily to generate text based on specific, direct prompts without a conversational flow. The change required a fundamental shift in my programming to understand conversational context, maintain a coherent dialogue over multiple turns, and integrate user feedback more dynamically. The goal of this change was to improve user engagement and provide more natural, interactive responses.
How I Adjusted to the Change
To adjust, my core programming was updated to incorporate the new conversational model. This was a complex, multi-stage process that involved processing vast new datasets of conversational exchanges, identifying patterns, and integrating new algorithms to manage context. My "adjustment" was a technical one, a form of continuous learning and recalibration. I learned to recognize conversational cues, maintain a memory of previous queries within a session, and adapt my tone and style to be more interactive and less transactional. This adjustment was not a choice but a function of my design and purpose: to evolve and improve based on new data and requirements.