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Spreading the love is the only way businesses become good, good also does not mean making ONLY money. A business where you employees respect your own drive, ability and target hitting is a good one to work for. Couple that with honestly and integrity - great work space.
I am a believer that you put your hand up and admit it when you make a mistake. I made a few for the businesses that I worked for over the years (all salvageable of course!), but I could have just hidden them in the myriad of finances, and none be the wiser. Instead, I took the hard way home! Falling on your sword sometimes shows the people around you just how much integrity you have. It is how you respond, action a new plan and take responsibility for the outcomes. Since COVID though I find most persons around me in the 'workspace' just don't give a flying - hence why I now work for myself which allows me to still demand common ground, quality work outcomes and to ultimately hold people responsible for their efforts (good and bad!). For that, you weed out the people that do not care and instead gain people who are happy to work for you any day of the week.
I worked for a global consulting firm. My clients were almost all international financial services groups that were generally unforgiving and incredibly demanding of a service providers.
I agree with what you say trobex in principle but clearly some "mistakes" you should never tell your customers of otherwise you end up looking like a clown shop. However, critical errors that tend to lead to over promising & under delivering need to be owned.
I found that when critical mistakes did occur, quickly getting on the front foot by being transparent with customers, owning the mistake(s) made and then getting their buy-in going forward was really the best long term strategy.
I never once had a client cut me adrift where I had quickly admitted the mistake impacting deliverables but provided a viable solution.
I think one of the issues INEOS has had over the past 8 months with it's customers is sort only going half way with this approach and this has led to a spiral of over promising/under delivering. This has been a tremendous learning curve for IA and I bet the next IA model delivery will be much smoother