This resonates with me. I was tasked in my organization to apply ML to most of the processes and sub teams..just because. People hand me over spreadsheets with

This resonates with me. I was tasked in my organization to apply ML to most of the processes and sub teams..just because. People hand me over spreadsheets with 10 or 100 records expecting black magic. Many companies need to create a stream line process before they embark into the ML path. And once they decide to take that trip from what I have seen is that hiring ML engineers and DS, may not make sense at the very beginning. What I believe is that path from Analytics, then API/black box products from Cloud vendors selling "MLaaS" where they use tons of data to offer solutions in specific areas, such as finance, manufacturing, etc. From there pivot and continue to invest more in ML if it makes sense.