So pretty much like any major enterprise system from the likes of IBM, SAP, Oracle, ...
So by platform I mean, lets say you train a NERC model using Watson Knowledge Studio. Obviously this model has to be "deployed" somewhere so you can call it using an API. They host it for you and they bill you per API call. Anyone can go create their own entity type system and manually annotate a training dataset. So it's definitely a re-usable platform, you don't need to pay for any IBM consultancy to use it. I found that the NLP offerings have many problems, and that the documentation alone is not enough to help resolve all of them. So eventually, IBM will just tell your employer you're stupid and that's why it's not working as it should and you should pay IBM to come in.
But make no mistake, these are all just standard machine learning tools that have been "packaged" so end-users can use them through a web front end. It is in no way, whatsoever, getting any input from any AI/Neural Network/Database/whatever you want to call it/ thing called "Watson".
I personally think it's disingenuous because when people hear Watson they think Jeopardy and they think that somehow that technology is involved when they use any of the Watson.* products.
The use of the Watson name is a deliberate attempt to take advantage of the Jeopardy game. It's a name that has cachet, and I've seen just enough of the marketing perspective to know that marketing will push very hard to reuse a successful name.
I think federation would be a better term. There was a core set of APIs and hardware that might be called "Watson proper" but each market segment would be handled by a different organization. And then there was the proliferation of odd ball things out of research or little groups looking for growth/stability that get Watson branded.
Sometimes we'd be the first time a team relaizes there is already something doing what they've been building.
Eventually they scrapped the project because it not only took a ton of employee time to talk with IBM's team to get it set up and working, it also cost a significant chunk of money and wasn't as good as what the people who already worked at the company thought they could do themselves.
With all due respect to the people that work at IBM, I just can't imagine IBM's sales and consulting cultures to work well with deploying AI. I don't know firsthand, but from what I've heard and what I would guess anyway, a lot of the people selling Watson and actually on the front lines working with it probably aren't that knowledgeable about AI/ML/whatever. I just don't see how you could determine a project's feasibility or effectiveness without having a sharp conceptual knowledge of the actual AI algorithms and what potentially what kind of data is needed to make them shine.
Suppose a university admissions department offers paper surveys to prospective students at the end of on-campus tours. In an effort to improve admitted student yield (the percentage of students that actually attend the university after being accepted), the university wants to be able to scan these surveys' text digitally and then perform sentiment analysis to determine how excited the student is about attending the university, or more directly, how likely the student is to matriculate. The university doesn't have any people capable of doing this, so they get into contact with Watson.
How much will the salespeople at Watson pry into the questions of the survey, demographics and culture of the school, or the sample size? Will they ask about statistics such as acceptance rate, yield, and which students are most likely to matriculate (based on quantifiable metrics)? Even the type or color of paper and text field sizes on the surveys on could affect the feasibility of the project regarding OCR, or bias the responses toward short answers. I would argue that a lot of knowledge about the project would be necessary before a sales quote or even the feasibility of the project itself could be considered, but would a salesperson know to ask these question? Would they even be incentivized to ask? Would the consultants know that certain questions could make OCR hard or sentiment analysis a wash? Would a statistician be consulted to see if the same or better results could be obtained from simple analysis of GPA, ZIP, and test scores?
I'm sure everybody at Watson is pretty technically competent - and to be sure, I'm sure for most consulting and sales that IBM does, I wouldn't have to make the following qualification. But to be brutally honest, I think the type of people who are familiar enough with AI to be the person you want working at Watson in consulting and sales probably are using those skills as developers and data scientists. And even then, again with all due respect to IBM employees (and I know IBM puts out a lot of great research), those people might not also be at IBM either.