I was reading a short piece on “A Scorecard for Making Better Hiring Decisions” on hbr.org and came across this line towards the end, wherein the author is referring to artificial intelligence in neural networks:
For learning to occur over repeated trials, there has to be feedback in the system, wherein the computer makes a “guess” about what letter is represented and then gets feedback about whether it is right or wrong.
I was reminded of something I have often pondered, that when it comes to firms submitting proposals to prospective clients, firms face an incredibly challenging situation in trying to get better at proposing solutions for clients. The situation is stacked decidedly in the prospect’s favor, who often has few incentives to be completely honest with vendors and often have many incentives to remain opaque with regard to their true selection process and decision making.
In these situations, vendors can only improve with timely and accurate feedback, something that is often lacking from the interactions with prospects, exacerbated by the fact that many times the vendor may only ever have the single interaction with the prospect (rather than a large number of repeated interactions in which to attempt to collect data).
And no, this is not because we just lost a bid (which we have not) 🙂
“A Scorecard for Making Better Hiring Decisions,” by Ben Dattner