The 4 keys to creating team accountability
Beyond micromanaging or labeling everything "ASAP," here are 4 most critical things you can do as a leader to help your team become accountable to itself.
^ADJ: Love this: Clear expectations > Quick directives
- Is it clear how we will know if we have been successful?
- Is it clear what “great work” looks like?
- Is it clear what “high quality” looks like?
- Is it clear what “on time” means?
The case against morning yoga, daily routines, and endless meetings
How to maximize 10x work and avoid thoughtless daily 1x work routines
^ADJ: Key question: Imagine the thousands of tasks you did in the past year and sort them by impact. How many of them actually moved the needle?
Building a Compounding Machine
Mark Leonard worked as a gravedigger, a bouncer, draftsmen, furniture removalist, venture capitalist… and then went on to build an $80 billion Conglomerate at Constellation Software.
^ADJ: One of the greatest stories of serial acquisition of all time
Career Advice: Simplifiers Go Far, Complexifiers Get Stuck
“If you can’t explain it to a six year-old, you don’t understand it yourself.” – Albert Einstein
^ADJ: I love this test: My test for spotting complexifiers is look for the following pattern:
- Slow progress on results
- Blamed on everything being difficult or complicated
- With a tendency to find artificial prerequisite activities that sound plausible, but on further examination aren’t
Why You Need A Relationship Dashboard
Legendary entrepreneur Jim Rohn once said, “You are the average of the five people you spend the most time with.” While your abilities, habits and decisions are pivotal to your outcomes in life, your relationships—both positive and negative—might matter most.
Planning fallacy
The planning fallacy describes our tendency to underestimate the amount of time it will take to complete a task, as well as the costs and risks associated with that task—even if it contradicts our experiences.
^ADJ: Good point "While AI can aid in mitigating the planning fallacy, it's crucial to remember that AI models themselves can be overly optimistic or biased based on the data they're trained on."