I had worked with IBM until earlier last year as Watson architect.

Watson is:

1. A brand - almost everything that has anything to do with analytics, gets labelled Watson. From cloud services to healthcare co-developments, to statistical software (i.e. SPSS) to anything (e.g. an e-commerce suite).
1.1 There also is "Watson Machine Learning" which is nothing more than SPSS in the cloud. (But of course you can use it for doing ML stuff)

2. A research project, which developed Watson as the system for the Jeopardy gameshow event and is still continuing to develop this system/technology further

3. There is a Watson core system, nowadays codenamed "Watson Discovery Advisor", which is architecturally pretty close to what was the original research system. There are only a handful of systems built on this architecture. It has nothing to do with the cloud services on bluemix, but is a massive parallel pipline in which pretty decent NLP gets combined with a set of hypothesis evidence scorer (various algorithms, from grammatical reasoning like simple LAT to ontological reasoning, lookups, tuple- and triple searches, indri, fulltext and other search approaches). This architecture is used for example, to power the Watson Cancer product. Every project like this is super expensive... speaking of 20 million +

4. A set of APIs in IBM's cloud platform ("Bluemix").

4.1 There we have NLC, a semantic text classifier (I personally find it quite good), a chatbot-system which pretty much is NLC with continuous training plus a handwritten dialog tree ("Conversations")

4.2 a machine-learning-ranking based search system ("R&R", "Discovery"), which combines an Elastic Search with a query language, paragraph handling, an NLP pipeline (see 4.3) and a supervised machine learning based ranker. The ranker is used to train the search to find relevant answers for a given natural language question (i.e. long tail question answering)

4.3. A NLP pipeline, which is NOT UIMA based, but based on the AlchemyAPI aquisition (which brought in the public taxonomy) and a SIRE based statistical machine learning relationship extraction approach. There is a training environment called "Watson Knowledge Studio" which lets you either program (dictionaries, rules) or train (click instances on sample texts) the NLP to your type system. This is running on Bluemix as "NLU" or in "Discovery"

4.4 And other stuff like image classifiers (party brought through the AlchemyAPI acquisition, partly from IBM's research in Almaden), Speech 2 Text, Text 2 Speech, Translation

6. An on-premise software called "Watson Explorer" which is a mixture of the Vivissimo aquisition (a search engine + portal + data integration) and IBM's homegrown UIMA pipeline and a nice analytical interface (data science tool for textual analytics - correlational analysis on text, NLP features and metadata with deviations, timelines, etc). This pipeline can be programmed with a rule- and dictionary based dev environment AND with the supervised machine learning based training environment Watson Knowledge Studio (->5)

7. Very few "products" like Watson for Oncology or ICPA, which are based on a variation of the above technologies and sold through specific channels

8. A Watson Health brand, dedicated to applying the Watson ideas to the healthcare market. They "own" Watson for oncology. They also have a big set of custom cloud APIs (some quite good and complex), like body part and drug tagging, adverse event detection, ICD10 coding, etc.

There is a huge sales folk in IBM, who does not understand the complexity and the breadth of portfolio the company has. Very often this results in overselling. The big Watson projects are pitched as a "this is what you get" plus the Watson APIs on the Bluemix cloud are shown as a "this is how easy and cheap you can start"... sales "forgets" to mention, that there is a huge gap between playing around with APIs and developing large systems like Watson for oncology.
Or they are pitching the big Watson story and then selling Watson Explorer, just because this is, what they get their sales-quota for.

Also the stuff requires really skilled people to actually forge a customer specific Watson system. I have seen many projects gone bad, just because some product managers think "this is my product and we solve the problem with just this" - instead you would have needed a few good techies and an skilled architect to develop a solution, leveraging multiple different APIs and a combination of cloud and on-prem (Watson Explorer) to achieve a good result for the customer.

If you know what the technology can do, do not oversell it, and have skilled people working on it, you can built great things. I myself have lead the implementation of a good dozens of pilots and a handful of production projects (which are still active!)