> I've found that executives will often try to poke holes in the predictions, especially if the ML solution is risky or potentially threatening to them In part

> I've found that executives will often try to poke holes in the predictions, especially if the ML solution is risky or potentially threatening to them In particular, automation that reduces headcount reduces their justifiable budget and therefore power within the firm, salary and benefits, and external status. For an example of the latter, Havard Business School asks you how many people you are currently managing when you apply for an MBA. This creates a strong incentive to block any attempts at automation or increased efficiency, especially when said inefficiency is not reflected on the KPIs used to gauge the executive's performance. Customer satisfaction and error rates are rarely measured well, nor with a refresh rate sufficiently high to be such a KPI. Blocking is easiest to do by seeding mistrust in the person attempting to build the automation, and in the automation itself. Part of being an effective data scientist/big data engineer/whatever the buzzword du jour is consists of figuring out what KPIs the executive wants to maximise and sell him on that instead. The good old "work on making your boss look good".