Problem-Solving Cheatsheet
Improving your problem-solving is extremely difficult. Too many resources, too many frameworks, and most are far from practical. This post is for consultants who want a simple cheat sheet that work
You know, I struggled with problem-solving as a McKinsey consultant.
Then, I found a cheat sheet that helped me a lot.
Here's how it started...
On my first project, I was given a process workstream. I couldn’t demonstrate my problem-solving abilities.
Then, in my first review, I was given a 'Concerns' rating in my problem-solving dimension.
I got really scared. I knew it was really bad for me.
I wanted to ensure nobody ever questioned my problem-solving.
Since then, I have been obsessed with learning it.
But learning problem-solving was tricky. There were so many resources, techniques, and frameworks, and most of them were far from practical.
I always felt that my answers were too flat and obvious.
But I didn't know how to make them better.
Then, I accidentally found a laminated piece of paper with problem-solving questions listed on both sides.
Since then, whenever I solved problems, I always used that cheat sheet for inspiration. And you know, it helped a lot.
My problem-solving improved dramatically.
Now, after many years, I realize that my problem-solving has improved not because of some magic cheat sheet, but because of the discipline of asking myself the right questions at the right time.
If you want to download that cheat sheet, I actually improved it, added new questions, and turned it into a downloadable guide.
MBB Problem-Solving Cheatsheet
If you are an MBB consultant and want to improve your performance through personalized coaching support:
We will discuss your pain points, challenges, and aspirations. No strings attached, just straight answers to your biggest questions.
If you want to learn more about my coaching program, read this post:
The coaching program for MBB consultants.
This is very true. The best consultants I’ve seen were not necessarily the ones with the most templates. They were the ones who could slow down, isolate the real constraint, pressure-test assumptions, and figure out what mattered versus what was just noise.
This becomes even more important after consulting, especially in tech and AI roles, where the problem is often ambiguous, the data is messy, and nobody hands you a clean case prompt.