Slaughters pilots AI software with Autonomy founder Lynch. Plus: the struggles of IBM Watson

Jump into the “Waaaay Back Machine” while I set the scene:

 

• The  Watson technology timeline actually began in 2008, but its Jeopardy win on TV didn’t come until 2011. At that point, IBM announced that it would turn Watson into both a product and a service within two years.

 

 

• By 2013, Watson-related news stories were saying IBM was already targeting healthcare and customer service with many other segments to follow as Watson morphed from a supercomputer application to the IBM cloud.

 

 

• The biggie came in 2013 at the Mobile World Congress when Ginni Rometty, Chairman and CEO of IBM, announced a $1 billion investment in Watson as well as a $100 million fund to promote third-party Watson development. 
• Given that these announcements were five years after the first Watson news, three years after the Jeopardy win, and took place at the new IBM Watson HG in NYC, I think we can date Watson’s commercial debut from that time.

 

 

In the legal industry, most of the attention has been on ROSS Intelligence. Built on the Watson cognitive computing platform, ROSS developed a legal research tool that ostensibly enables law firms to slash the time spent on research, while improving results. The legal technologists such as Richard Susskind have forecast that this technology will “dismantle” the legal profession in favor of “increasingly capable systems”.

Only a few writers, such as James Wood, have said “Whoaaaaaa!! Let’s avoid the hype and distortion in a hugely complex subject. Predictions in this field have often proved widely off the mark, both over and under-estimating the impact of technology dramatically. A sea-change? Let’s hang on”.  But boy …. sea change sure sells!! Just check out the topic agenda at any legal industry conference.

So now it is now we are three years later still. Watson has been around in some form for eight years, and the creation of IBM’s Watson business unit was three years ago. So is Watson a success?

Well, it is very difficult to tell from IBM’s reported financials whether Watson is a success or not. As I noted to clients in my IBM report earlier this year, the way IBM segments its revenue makes it difficult to see whether the company’s so-called strategic initiatives are making money. This is especially the case for Watson, whose results are not only mixed up with revenue decidedly not from what IBM calls “Cognitive Computing”: IBM also splits Watson revenue across two different segments. So while IBM continues to talk a good game for Watson, it isn’t at all clear whether Watson is delivering much in the way of sales.

Or at least it was not clear until now.

Bob Cringely … a tech angel investor with a laser-like mind when it comes to software analysis … recently sent to clients some internal IBM data concerning Watson product registrations (verified with the source redacted):

 

NOTE: almost anyone can try Watson for free, but to do so, requires first registering with IBM. There is pretty much no entry barrier for these registrations. If a single development group inside a corporation decides to give Watson a try, that counts for the whole company: they are in.

Bob’s comments on the numbers in the chart above:

• Clients are IBM corporate and institutional customers – everything from banks to universities to government agencies.
• So far, precisely, 500 of these have registered as Watson users. Bob says that number, in itself, is questionable: “Precisely 500, really? But let’s accept it”. 
• The client target for 2016 was 8,145, of which 500 represents six percent, so Watson is presently missing its target client registrations by 94 percent.
• IBM business partners are companies that resell IBM’s products or services. Watson business partners are specific to Watson. This chart says there are 329 Watson business partners, though IBM’s 2016 target is 4,047. Admittedly, 2016 is far from over, but at this point, IBM appears to be 92 percent behind its target.
• For a program that is at least three years old, this level of sales performance is dismal. If only eight percent of the companies that are supposed to be selling Watson have even minimal experience with the technology, it’s difficult to say it is even broadly available. Certainly, cloud-like sales increases of 30+ percent per year aren’t happening for Watson.
• At what point will IBM admit this? Not until it is forced to.

Bob’s overall view:

“If I were to hazard a guess why these numbers are so bad (understand this is only a guess), it’s because the most prominently missing Watson customer is IBM itself. Big Blue has made the most fundamental mistake in high tech: they don’t eat their own dog food”.

IBM very carefully paints an image of itself based on hype, spin, and half-truths. It will tell you cloud is up 60 percent, but not give actual apples-to-apples numbers. Its cloud sales are heavily loaded with services business. The company’s analytic sales are heavily loaded with mainframe software revenue. The only clear information is IBM’s total revenue is still dropping. If IBM’s revenue is falling, that means it is losing business faster than the new services can cover.

And as I said long ago, Watson is a solution in search of a problem. Watson was a marketing stunt someone thought could be turned into a $20 billion per year business. It doesn’t matter if Watson is great technology or not. What matters is IBM has been killing its old businesses and needs several $20 billion-per-year new businesses to replace them.

Bob’s concluding remarks:

What’s not yet clear is how big these replacement businesses can eventually be. Remember they have to add up to $80-100 billion for IBM’s rebirth story to be a clear success. This is darned near impossible for any company to do.

Can IBM make more than $20 billion in revenue from cloud? Can IBM make more than $20 billion from Watson/analytics/cognitive? Can IBM make more than $20 billion each on security, mobile, and social? What if IBM can only make $20 billion on security, mobile, and social combined? Then, IBM needs to make $40 billion on cloud and another $40 billion on Watson/analytics/cognitive. Remember Amazon’s AWS is already close to being a $10 billion-per-year business. Is it reasonable for IBM to grow to twice AWS’s size? No. It’ll be lucky to reach half the size of AWS.

For all of us that crunch numbers and avoid the hype, spin, and half-truths of press releases and planted stories, success depends on context. IBM needs the Watson business unit to generate $20 billion per year for many years. We can look at IBM’s businesses individually and believe they are successful and growing. Looking at this growth in the context of IBM’s massive size suggests total revenue five years from now that is half of what it used to be and half of what Ginni Rometty says it will be. That is nowhere near what IBM presently describes as being successful. This is like the old saying the “operation was a success, but the patient died.”

We should have known that when Stephen Pratt jumped ship after only 9 months running all of IBM cognitive computing efforts, something was up.

Artificial intelligence has always been hyped by its often-charismatic enthusiasts. Today the media pundits, the spin doctors, and the technologists have made the rise of artificial intelligence “inevitable”. And it seems every company can do AI. Companies with massive data fed into AI systems – like Google, Facebook, Yahoo, and others – make headlines with technological successes that were science fiction even a decade ago.

Yes, people will (and are) making money off AI. Apple, Google and Facebook clearly are. But it is a tough business model. You need to understand the perspective, the scale of technology and the brutal realities of selling into your business market. IBM’s revenue took a huge hit as its corporate customers shifted their buying habits toward software and services delivered through the Internet. So it had to scramble and bet its future on new-breed offerings including the artificial-intelligence and analytics services.An example of a successful AI business model: