> 1. They bring the A players to the sales process, .. C/D players to write code.
I was wondering why IBM failed to make great progress with WATSON project which was started years ahead of others in ML/DL filed.
Now I know the answer, internal IBM projects are developed with same A/B/C people of the hierarchy, where C players are people who write machine Learning/Deep Learning WATSON code.
Since Microsoft lost consumer space by losing windows phone, it's focus is ONLY Enterprise market which is confirmed by LinkedIn purchase.
Slowly but surely Microsoft will eat IBM's "Enterprise lunch" eventually. (sharing part of lunch with AWS & Google Cloud)
The problem was that Watson was a (largely successful) publicity stunt. In that sense, it falls in the category of sales and IMO is a top notch, A-player, demonstration of hype. It wasn't designed from the start to solve real problems, so it was always going to be hamstrung as a real product, regardless of how good the underlying engineering was.
* Edit to add a link to the papers they published -- https://researcher.watson.ibm.com/researcher/view_group_pubs.... -- which I remember skimming at the time and feeling like they were on par with any other typical ML paper. In general, research departments are run with an entirely different culture than engineering.
When I worked at IBM eons ago, products were very tangible, and often very physical in form, and there was no doubt whatsoever about what they did. Each came with beautiful bound folders for Africa describing functionality and usage. As a Systems Engineer, a part of one's job was being able to drive the configurator and other tools that controlled updates and upgrades and ensured products interoperated correctly.
Watson, it appears, is more the sort of snake oil that one would associate with a flakey, fast moving startup that can promise anything and then somehow make it sort of happen for the early adopters. Its hard to pin down exactly what is IS - which is kind of the point when using it as a sales tool.