Andy Lin

The most important thing I learned beyond the thesis above is giving them the opportunity to perform in an area they don’t currently feel is possible. Their willingness to try, be awkward in the beginning but get better over time, is a strong predictor of high performing talent that can be developed into general leadership, outside their current area of expertise.

More and more organizations understand the power of the growth mindset individuals vs those that have deep experience in an area but show no growth or evolution. Highlighting your history of learning, applying and mastering new skills, teaching or enabling others, and continuing to grow will grab the attention of the right employer. Want to really get noticed? Be open to a lower base salary and tie a bonus to your performance. Makes it pretty easy to say yes to peeps willing to bet on themselves.

This might be unpopular, but whenever I am taking my first step in learning something new, I find the free content on YouTube to be quite valuable. Just to get the overview and general landscape. The number of views, subscribers, and comments/reviews directs me to what I believe to be the most credible, relevant and helpful content. A critical step in the process is to actually apply what you learn to see if you understood and validate the training content. Honestly, I’ve gotten deep enough with Youtube content that I rarely need to find paid training as I complete my learning by getting hands on and doing the work vs continuously trying to absorb training.

A Product Mindset combined with Agile works well here as in the vast majority of software endeavors in the vast majority of cases. Implementing AI in particular with dated waterfall methodologies that view things through a “project” lens will make it harder to get to success. So much experimentation as well as trial-and-error is required with AI that trying to preplan everything against FIXED scope, budget and timeline is at odds with the learning and tuning inherent to AI.

AI enable humans to work on the nuanced, non-deterministic, “fuzzy” sorts of problems. While a lot of focus is trying to use AI for better decision making, better predictions, etc, modern AI can really help humans focus on problems that probably shouldn’t be solved via an algorithm. At least not yet… The rote, mundane and soul-sucking activities and process execution should be outsourced to AI. When one thinks of this as a small group of individuals for a short period of time, the return may not be impressive. But when you consider this benefit at scale, how much better can the world be when our collective creativity and brain power is now focused on solving the “outter edge” problems that can make real impacts and break throughs?

Yes, but the reality is, deadlines can creep up and extra effort is required despite doing the above. I’ve found it quite effective and helpful to answer the team’s questions before they ask them… 1) Why is this date important? 2) What is the value to the project, the client, the team for meeting this deadline? 3) What is in it for them, especially if their discretionary time is required? And probably equally important, answering the question of “What happens if the deadline is missed?” In parallel with the above, negotiating scope to give the team a better chance of success by a deadline is not only practical, but also gives the team a bit more comfort that they are being setup to succeed, not fail.