... For decades, the Dunning-Kruger effect has been understood to mean that people with low ability, those who know the least, are the most overconfident, while high performers are more realistic. Hundreds of studies have replicated the classic finding, illustrating that people who do well on multiple-choice tests overpredict their performance by less than those who do badly.
In conjunction with the adoption of generative AI and AI assisted development I had a very interesting discussion with a co-worker in my company. He is specialized to analyze bussines processes and to do management coaching. We discussed the role of AI in our SDLC, and what could/should be improve that is known in theory since quite a while.
There is a strong focus on technology at the moment. Which model performs better, how to manage knowledge - vector stores vs. markdown ... But considering the vast and fast changes in the last months technology will be outdated sooner rather than later.
But there are things more resilient to time. For example how to approach and solve a problem.
Process
flowchart LR
Start(["⚡ Problem"])
Stop([🛑 Stop])
Delegate["📥 Delegate 😋"]
Solve["🛠️ Solve ⚙️"]
Solved(["✅ Solved"])
RealProblem{Real?}
MyProblem{Mine?}
Start([Problem]) --> RealProblem
RealProblem --> Stop
RealProblem -->|Yes| MyProblem
MyProblem --> Delegate --> Stop
MyProblem -->|Yes| Solve --> Solved
classDef A fill:#e6f7ff,stroke:#0099cc,stroke-width:2px;
classDef B fill:#f1f8e9,stroke:#5cb85c,stroke-width:2px;
class Solve,Delegate A;
class RealProblem,MyProblem B;
Yes - processes evolve and AI could be a pivoting point here as well. But in the end - I do not even think the process needs to change much; rather the focus of the human role ....
