Teachers’ Day – A few fond memories

Today is September 5th , and in India it is celebrated as “Teachers’ day” , in honor of Dr. S. Radhakrishnan who was the second President of India and probably the best comparative religion/philosophy scholar of his time .

My late grandfather, R. Easwara Pillai, was a history professor in University of Kerala. He was the first teacher I ever met – and till date he remains the best teacher in my mind . His ability to explain History , Politics and Economics in simple terms with lots of examples and stories was what kindled a life long interest for me in those subjects .

In Kindergarten ( At Kayankulam St.Mary’s ), my favorite teacher was Ms Kochurani . It amazes me that I remember her name but can’t remember her face any more. But I do remember her visiting our home every few weeks and bringing story books for me to read !

In elementary school (Chinmaya Vidyalaya ) , my favorite was Mrs Nirmala Mathrubhootam. She would take us out of the class room and make us gather around the big trees in the school yard and talk to us about nature , how trees get their food and how they help clean up the pollution.

In junior high (Christ Nagar), there were two stand out teachers . Both of them taught English – Mr Appukuttan Nair and Rev Fr Berthold CMI . The former took away my fear for the language and the latter taught me the nuances of “Spoken English” . I had no idea at that time how impactful those lessons would be in my future.

Pre-Degree ( Govt Arts College ) had some super star teachers – the two I remember the most are Prof Mohan Kumar who taught Organic Chemistry and Prof Jayaprakash who taught Physics. They had absolute mastery over their subjects and demanded excellence from their students .

Then came four years of Mechanical Engineering (T.K.M college) and the first time I really understood that the world has as many bad teachers as it had good teachers. For me there is no doubt who had the most impact on me – that was Prof Nasser who taught us Automobile Engineering . What put him in a class of his own was his passion for the subject – he loved cars and it showed in how he would explain the design principles .

And finally MBA ( IMK , Kerala ) which was probably the two years I enjoyed the most as a student. There were two professors that I gave “rock star” status right after their first lecture, and I can safely say that I have not seen anyone better in those subjects ever since . One was Dr Kevin who taught financial management and the other was Prof Kalyanaraman who taught Statistics . Even today I refer back to my old lecture notes from their classes to refresh the first principles. A close second to these two was the late Dr MNV Nair who was the dean of management studies , and his classes on strategy management were brilliant . He – and Dr Kevin – encouraged us to challenge them and I (and many others) did and learned from that experience . I remember him telling me after a debate on business law that he lost that ” You did well,young man . I am fiercely loyal to my own ideas – but only till someone proves me wrong” .

I have left off several great teachers in the list – but I am grateful to all of them . I will echo Dr. Radhakrishnan’s point of view as my parting note – “Teachers should be the best minds in the country”.


The new Uber CEO’s primary challenge

I think Uber board picked an amazing leader as the new CEO , despite all the leaks and drama and all around it . With adult supervision from the new boss and hiring experienced leaders to work for him in various functions , I think a lot of their current problems with culture , litigation , board politics, driver retention etc will get resolved .

Solving the current problems is unfortunately just table stakes really . The fundamental question in my mind is whether Uber has a sustainable business model . How long can they capture growth by subsidizing costs when we are talking in multiple billions ?

Clearly, they have made some mis-steps by trying to optimize for market share at all costs . So getting out of some international markets was a necessary step , and I expect more of the same for near future . Getting out of leasing also seems like a smart thing to do .

I am a firm believer that driverless cars will become a mainstream reality soon – between google , Tesla , uber and many others putting their might behind it – it’s only a matter of time . But that time is not in next couple of years . So for foreseeable future , they have to subsidize human drivers and figure out better ways to retain drivers . And then there will be a period where self driving cars and human driven cars will do-exist . And some time in far future – perhaps they can switch to mostly driverless cars ( assuming they have the legal and political backing to do so in the major markets ). Will investors agree to bleeding money for that long ?

Also – when they do mostly driverless cars, wouldn’t they just incur even more costs for owning ( or leasing ) and maintaining a big fleet ? And my guess is that insurance cost will be quite high for the in-between period where they need both human driven and self driving cars .

Not sure how to correctly extrapolate here – but my best guess is that for next decade it is not going to make profitable revenue with the “cheap taxi” business alone , while also capturing significant chunks of the global market.

While uber is getting out of some geographic markets, it’s definitely entering some adjacencies – like trucks and boats. But the business model is still the same – so all the problems with the economic model of taxis should apply to boats and trucks too .

First mover advantage is with uber – but there is always the significant risk of fast followers who can learn from Uber’s mistakes and avoid the heavy initial capital investments and expenses .

If private markets are fine with all this and Uber just chooses to remain privately held for a very long time – none of this might be an issue . But going to public markets without proving out their business model seems like mission impossible . Even in private market, my suspicion is that they cannot sustain the $69B valuation given all the economic issues . And a loss in valuation might start a round of talent attrition which might make it really hard to execute on whatever roadmap the new CEO puts in place .

So all things considered – I think the main challenge for the new Uber CEO might not be the things in the news now . They are all no doubt important problems to solve , and unless he solves them first – there might not even be a chance to change the business model . But the true fight in my mind is to figure out how to run this business as a sustainable enterprise , while preserving as much of the valuation as possible .

Given the size of the short term and long term challenges for uber – I hope the CEO, the board and the staff of Uber have the stamina to do a few marathons back to back !

Vishal Sikka leaves Infosys – An arranged marriage that ended in a divorce !

Just as I was about to hit the sack yesterday night , I got the news that Vishal has submitted his resignation and moved to a executive vice chairman role . I was not surprised – for at least the last year, I felt it was just a when question, not an if question .

The story of Vishal’s tenure at Infosys and his exit yesterday follows the plot of the average Indian “flood of tears, and well dressed rich people” TV serial . It goes like this in general …

Groom’s parents finds a beautiful and highly educated bride for their son and parades her around friends and family . Then at some point, the in-laws get buyer’s remorse ( jealousy of bride being smarter than their own kids and immediate family is the usual story line ) and starts a routine of mental torture . The dutiful young bride tries to make it work despite the hostile environment for a long time, due to her kindness of heart and respect for tradition – but finally with the support of friends and mentors , says “screw it, I am divorcing the guy”. And even at the divorce court , the teary eyed guy says “But I still love you” while he signs the court paperwork . He might even break into a long monologue about how his parents didn’t do the right thing , but he wishes his now ex-wife well ! The newly free young woman also does her monologue on how hard she tried to make it all work , but realized the abuse was too much and life is too short to stay at it . And throughout the story you keep seeing crying children who are torn apart .

There is a very cruel joke about the “in-laws – bride” story – which goes “I don’t care if my brother dies in the process , All I want is to see my sister-in-law’s tears” .

Well – it was quite the drama, to say the least . I applaud Vishal for hanging in there all this time and putting a brave front to the external world even when the founders did everything they could to undermine his position .

Infosys is an iconic company . When people of my generation came out of college , we wanted to join one amongst TCS, Wipro or Infosys . Apart from Vishal and many other ex-SAP colleague, I have several friends who work there and who care deeply about the company .So it is painful for me to watch this , even though I am not directly affected by it .

Culture change is a hard task for anyone – and there are more failures than successes when it comes to large scale transformations . It was a brilliant experiment to bring in a software veteran to turn around a services company . Some experiments succeed and some fail – this one failed rather miserably and there are probably many reasons for it . But it should not be forgotten that it’s easy to fail and really hard to succeed , and by adding distractions – the founders and the media certainly didn’t increase the odds of success .

In hind sight , there are perhaps things Vishal could have done better on a few aspects .

1. Instead of hiring most of his old team from SAP labs , perhaps he could have targeted top tier consulting companies to find leadership talent . That would have been a harder sell for sure than convincing loyal friends . Most of the SAP talent left he hired left any way – and several of legacy Infosys leaders also left .

2. The whole $20B target was a bad idea as it was unrealistic . It just proved to be a distraction for the company than a motivation . I don’t blame him for setting a high goal for his team internally – doing so externally seemed misguided . Why didn’t the board counsel him on that ?

3. Innovators dilemma proved to be real . Like Vishal – I also think the future of the consulting business is where the distinction between products and Servcies blur . So he invested and ring fenced innovation on products . But in the overall picture – that was a tiny portion of the company and the larger legacy business just didn’t transform quickly . It also didn’t help that the product business didn’t take off at rocket speed – and had multiple leaders quit in a short period.

But then hind sight is always 20/20. If anything we should applaud Vishal for his bold vision of future of the company and the industry . And he has always been a big proponent of customer focus .

It’s a good lesson for the rest of the industry is understanding the challenges of culture changes . Every large company has its “antibodies” that will attack anything new – for good and bad reasons . Having demonstrated this in public , I wonder if Infosys can now attract top caliber candidates for its leadership ranks any more . My best guess – not that what I think matters – is that they will make an internal candidate the full time CEO , and base that person out of their HQ in Bangalore . That could be one of the younger founders too I guess .

As for Vishal – I really hope he and Vandana take a long vacation , catch-up on sleep and so on . They deserve a break away from all the stress . When he comes back , I think the best option for the world will be to have Vishal as a VC and professor .

Good luck, V !

Re-learning leadership , again

For the most part, I have had a pretty good career so far – not spectacular by any stretch of imagination , but can't complain either . And I attribute most of it to having great leaders who helped me grow.

My interest in leadership started for a simple (and awkward) reason – in the early part of my career, I had some really awful managers. My solution was to stand up for what was right in my mind and often leave the company as a result. So by the time I was given a leadership role – I was determined that I should not let any one in my team go through the trouble I had in the past. Roughly at the same time – I also had the good fortune to see what great leadership looks like (finally!) and it helped set my expectations more appropriately.

One thing became abundantly clear to me over time – learning how to be a good leader is a journey and never a destination . There are no "here are 12 things to do" that serves as a magic bullet . You need to constantly calibrate where you are and seek the needed help to improve. This unfortunately doesn't mean that I followed through on it – I have some ways to go 🙂

Thanks in a large part to the less than stellar leadership I got when I started out – I have become a big fan of mentoring young men and women who are starting out in their careers. I also spend a lot of my time mentoring first line managers . This serves two purposes – the highest energy comes from the entry level colleagues and I get to channel it for the good of the business , and I don't become a bottleneck to the process since the first line managers get a better perspective on why their success is totally dependent on the success of their team.

To enable this behavior – I have long had a rule that anyone can get 15 mins on my calendar , no questions asked. Not everyone takes me up on it – but several do. And it does get overwhelming at times.

This is when my friend Stephanie Anderson, an HR leader in IBM, gave me some invaluable advice . She told me "You cannot mentor everyone – you need to let others help you". Pretty straightforward and I should have known it – but the truth is that I did not . I am pretty good with delegation – as any of my direct reports can vouch for . But when it comes to mentoring , clearly I sucked at delegation . So thanks to Steph giving me timely feedback – I have woken up to the reality and have started enlisting the help of others to help mentor more of our younger colleagues . Thanks Steph ! And since no good deed should go unpunished , I am now pushing a bunch of mentoring requests to Steph as well 🙂

The first few years of my professional life was actually quite calm – I learned programming and project management and got to apply it at projects and had the time to develop my skills. I did not have to do much more than take classes couple of times a year to stay on top of it . Then it started changing – technology started moving at faster pace and I realized I need to get into a "learning is for all of your life" paradigm . And that has only helped me in my life – actually on personal front too . Folks starting out today don't have the luxury I had of starting slow !

For a long time, I wondered why I was signed up for classes like "executive presentations" and "executive negotiations" when I was not even close to being an executive . But in hindsight – pushing me to take those classes was one of the best things my mentor Ken Englund did for me more than a decade ago. It taught me that the sooner you learn things – even if they are hard and they don't apply immediately to your work – the faster you make an impact . And trying new skills in early part of your career is a lot less risky than trying them later.

So when last fall when our North America managing partner Ismail Amla asked me to sponsor the core consulting school for senior managers , I jumped in with both feet. I still wonder why he chose me given he was fairly new to IBM at the time and we didn't know each other very well at the time . In any case I said yes before he changed his mind 🙂

I was also taking over a new day job running a large ( well large for me, not really that large for IBM) portfolio in parallel . I sure had my moments of stress – but it was the best experience in my time at IBM bar none .

To begin with, I had no idea how much care and effort it takes to put on a comprehensive learning event – and the Pre and post school activities . Fortunately I was paired with experienced learning experts like Debi Steinbacher , Lorraine Rapuano and other colleagues . We also managed to find a team of volunteers from amongst the partners and associate partners in the firm to be the teachers . It's pure magic when a team of passionate people come together with a common purpose – and now Bee School has taken a life of its own and is growing from strength to strength . And here is a shout out to Pooja and Andrea for coming up with the name "Bee School" !

My favorite part of these schools are the "ask me anything" sessions . When you can ask and answer hard and often uncomfortable questions, you start growing !

Last week at dinner, Lorraine told me "you are the Zen master of Bee school" given how I apparently had a calming influence during the chaotic times we went through in preparation . Well, if I was Zen – it's only because I had, and continue to have, full confidence in the amazing team around to me . And also along the way , I learned that it's foolish to stress about things you can't control 🙂

Success breeds success – and the confidence I got from being part of this team that put together Bee School led me to start a second learning initiative that I lovingly call the T-school which is where we focus on technology training , like AI and IOT . We ran the pilot couple of weeks ago and it was a lot of fun hacking AI solutions with 30 of our new engineers . And again – it only happened because we brought together a team that was super passionate about the cause and leaders from the business took time out of their day job and came in as teachers. I lucked out having a great partner in Andreana Miller from our learning team and a bunch of new friends from our global team .

And in the process, the Bee school got a fantastic upgrade too . Susan Wedge , a dear friend and a great leader of our public sector business , took over Bee school sponsor role from me . I can't wait to see her take it to even greater heights !

Not only was the investment in learning good for my soul and fun for all of us – it had some side benefits too in my day job . I now have a MUCH better appreciation of what great looks like and how iteration is far superior to aiming for upfront perfection . And best of all – there are now several new ideas for making our clients more successful . It's just fascinating watching what happens when high potential people are given the tools and freedom they need . Pure magic !

One last point before this plane lands in SFO – we all know that asking for help is a good thing . What I realized in the past few months is that asking for help should not be just to your senior management – a lot of help can and does come from your team as well . I can't tell you how cool it is to see my young engineers and consultants jump in and solve problems with high quality when I requested their support . And their energy is infectious – and has convinced me beyond a shred of doubt that I have more help to ask 🙂

Googler’s Screed

Or perhaps I should say Xoogler's screed instead , now that he got fired .

Strangely, the first thing that hit me was the word "screed" since the last time I heard the word was couple of decades ago in engineering college . Perhaps "manifesto" associates itself with "communism" and hence "screed" seemed to be the more appropriate word.

I went through a series of emotions as I read about the screed . It started with anger, and that is the only reason I did not start typing my views on day 1. I needed time to process the information . And doing that increased my respect for journalists who had to break the news without much time to look at it at depth , as well as Sundar Pichai who had to take a decision quickly on what to do with the guy who wrote the document . I am glad I took the time before posting a rant – it was quite educational to read many different opinions and talk to many fellow engineers, male and female .

One thing is abundantly clear – Google was put in a no-win situation. If they didn't fire the guy, they would get painted as anti-women . If they fired the guy – they will be accused of shutting down diversity of thought.

There were a few points in the debate on both sides that I thought were rather weak – like Damore's first amendment rights , and whether he should have written such a document during work hours. Google is not a government entity and first amendment should not be a big consideration in letter or spirit in this context . The guy attended a google class on diversity and wrote it in response . If google offered the class during work hours , I can't blame him for writing a response during work hours and circulating it .

I am an engineer myself and hire engineers to work with me – and it was extremely painful to read the document and realize it was a fellow engineer who wrote it . It just felt like someone did the profession a big injustice – and perhaps it's an over reaction . In any case – I would not hire a person as an engineer in my team if I suspect a significant lack of empathy . Not going to belabor the point – this is a brilliant take on it and you should read it .

The tone of the manifesto is quantitative and dispassionate from what I could interpret . When criticizing it, however, there seems to be a penchant in media to refer to it as "quasi-professional" and "pseudo-scientific" and so on . Even though the opposing arguments looked strong to me , trying to attack the tone of the writer as opposed to his central ideas and facts(?) diminished its effectiveness.

While I can't say I have a first hand understanding of what it is like to be a woman in tech – I can extrapolate from what I went through as an immigrant and have no doubts how difficult it must be . I also grew up in a family of strong women in India who fought all odds to thrive in a male dominated society . I was not in the minority growing up in India – and my appreciation for its value only happened after I moved to USA in my early twenties .

When I first came to this country, I faced a fair bit of Discrimination as an Indian programmer in the midst of mostly white male programmers – insults to my intelligence , the food I ate , the music I liked , my accent and so on were common place. I also had some very kind managers and friends and co-workers who considered me as one of them and helped me cope . For the most part, I don't feel it anymore – I developed a thick skin over time and larger number of Indians are there in the workforce now for me to feel alone.

Damore is absolutely entitled to his opinions like the rest of us – we live in a free country. But as an adult, he should also know that actions have consequences.

I think where he lost the plot of having a good debate – instead of the storm he caused – was in quoting studies and stating that all of it applied only to populations and not individuals , but then going on to make recommendations that don't follow that thread of logic . That gave me the impression that he was not arriving at a conclusion by building an argument ground up, but just finding a way to substantiate what he always believed . Irrespective of the content , that is not the hallmark of a good engineer .

He does state that he is supportive of an inclusive workforce and agrees that sexism exists. Unfortunately the recommendations are either too vague , or not backed by consistent logic. It came across like "Current diversity program sucks, so let's get rid of it. No diversity program is better than a partly effective one". Huh ?

The charitable side of me wants to believe it was mostly ignorance and lack of skill that caused him to write it the way he did , as opposed to totally evil intentions . In any case – he earned the backlash fair and square in my opinion .

To begin with, Google had a lousy episode recently of telling DOJ that 100K USD is too much money to spend on compiling payroll information for gender equality. Now if they also did not fire the guy who wrote the awful memo – it would have been an even bigger nightmare.

I do grant one thing Damore raised . If you hold conservative views in Silicon Valley, it's rare that your views will resonate in the work place, and there is a good chance you will be out-shouted . Unless of course you are someone like Peter Thiel . It's an extremely left leaning place and the lack of inclusion is not just about gender, it's about diversity of thought too .

One thing google needs more than anything to keep its leadership in the market is retaining and attracting top talent . They cannot afford to risk a bunch of their talent walking away if they think google doesn't support their ideology . There is no non-compete in CA and many of these engineers are already rich and will find multiple jobs quickly with google on their CV . Even if no one walked out of the door per se , which development manager would choose to have Damore in their team after his views became public ?

If forced to choose between the support for gender diversity and thought diversity – I firmly think gender diversity should win every time . Ideologies evolve with time and mistakes can be corrected relatively quickly , but gender doesn't follow that path. Solve the gender diversity and it will be fair game to have absolute focus on thought diversity .

In my view, Sundar Pichai absolutely did the right thing by firing the guy – but google leadership , HR and PR departments should get a B- for how it was handled . As a friend mentioned on Facebook – the only thing worse than scheduling the all hands was canceling it .

The net goodness out of this episode is that it sparked debate yet again on the importance of diversity . The sad part is that without such incidents, it doesn't get the attention it deserves.

And Jerry Says : A Path to SUCCESS with Advanced Analytics

Folks, I am very proud and happy to have my dear friend Jerry Kurtz do a guest blog on my site. Jerry runs the Cognitive and Analytics businesses in my portfolio, and is a long time IBMer. He has been in this field for 30 years across SAP, Managed Business Process Services and Analytics and now Cognitive for last few years. He is a man of many talents outside work too – a very good singer – he is the lead singer of the “midlife crisis band”, a competitive golfer, and an overall good dude to hang out with. He lives with his wife Amy, his daughter Emily , Son Adam and his 3 year old fur kid Baxter, a Chocolate lab. You can find him on twitter as @jerry_kurtz 

Jerry with Fish

Take it away, Jerry !

I will start with sharing highlights of my beliefs regarding the “10 Fundamentals of Successful Advanced Analytics Programs”.  I hope to go deeper into each of the 10 in subsequent posts.  Also, for the purposes of this blog, I will not define each element of analytics.  Rather than keeping “predictive” separate from “optimization” and “prescriptive” separate from “cognitive”, let’s just call it all “Advanced Analytics”, shall we?  We shall…

Analytics Screens

Fundamental #1 – Establishing a “Balanced” Advanced Analytics Strategy. Any analytics strategy must have three basic things.  These three basic things may seem like “motherhood and apple pie” to some of you, but it’s amazing to see how many times we have seen Fortune 500 companies make mistakes on these basics.  More on case studies in future blogs.  Your analytics strategy MUST HAVE:

  1. A Business Capabilities or “Use Case” roadmap that answers the question “what solutions do we need to implement for our BUSINESS, USERS, and BUSINESS PARTNERS to achieve our business goals”? This is the value side of the equation.
  2. An Information Foundation roadmap that ALIGNS very tightly with the Business Capabilities roadmap. A strong data foundation does not in and of itself create value (with few exceptions), it ENABLES the full range of business capabilities and VALUE to be rolled out over time.  The above two strategies MUST be aligned with each other to maximize value.
  3. An Organization / Governance approach and roadmap that also aligns with overall business strategy and the above elements of the analytics strategy. We have seen that the “technology can be the easy part”. It is often the organizational structure, culture, and related politics that gets in the way of success.

Fundamental #2 – Establish a program goal of “10X value to cost” and “Self-funded”If you have the right level of executive sponsorship and you scope analytics programs properly, you can target at least $10 of hard value for every $1 of program cost.  Also, if you prioritize business capabilities the right way, self-funding is achievable within 90 days of program start. If your analytics strategy is not meeting these metrics, you should probably rethink your strategy.

Fundamental #3 – Think and Act with “Parallelism”Self-funded analytics programs can’t be achieved by working “serially”.  We have seen clients say, “we need to get our data fixed first, then do basic Business Intelligence and Reporting, THEN we will do some advanced analytics”.  In today’s world, however, parallelism is key. For example, some advanced analytics can help fund other elements of the program.  New business capabilities can help fund data transformation.

 Fundamental #4 – Having the right level of business sponsorship Without going into too much detail (yet) I will summarize my experience that the most successful analytics programs have senior and clear business sponsorship / ownership.  In the last couple years, my most successful analytics roadmap / implementation program was sponsored by the global CFO.  Just an example.

Fundamental #5 – Picking the right place to start  If you have 50 new innovative ideas for Advanced Analytics use cases, the best place to start is usually on use cases that (1) have strong executive sponsorship / business need, (2) have data readily available to solve the problem, even if in multiple sources, (3) LOW COST but HIGH VALUE for Phase 1 (e.g. Proof-of-Value).  Again, this may seem basic, but we see mistakes all the time.  Last year, I walked into a client that had started with a global management dashboard across 10 countries.  Very expensive and very time consuming.

Fundamental #6 – Scaling beyond science projects For now, let’s just say that the technology aspects and “finding smart people” will be the easy part.  The “soft stuff” will make or break the project.

Fundamental #7 – Embrace diversityI grew up in the ERP market where there was a fair amount of homogeneity across project resources e.g. similar background, similar training backgrounds, etc.  During my last several years in the Advanced Analytics space, I have met hundreds if not thousands of people, and I can best summarize them by saying that “they are all from different planets”.  While the incredible diversity in this space can make it much more difficult to assemble a “winning team”, I personally LOVE the challenge and so should you.

 Fundamental #8 – Teamwork / collaboration – At the risk of being too high level for now, I will summarize by saying that it’s all about resource “mix” including both mix of skills but also personality types.  For example, I would rather work with an A- data scientist who works well with others rather than an A+ data scientist who is always “the smartest person in the room”.

 Fundamental #9 – Analytics practitioners must be life-long learners e.g. “Adapt or Die”As Thomas Friedman explains in his recent book Thank You for Being Late, we have reached a point where technology is changing faster than humans are able to adapt. We and our teams had better keep up with rapid change or we risk becoming obsolete.  This challenge can only be overcome through life-long learning and constant, adaptive change.

 Fundamental #10 – Be Hands OnWe ALL need to find ways to be hands on with analytics technology.  If you are “only” a project leader or a business analyst or a practice leader, you should find ways to “sign-on” and learn your trade at a hands-on level.  Generalists with minimal technology savvy will struggle in the coming years, but “hands-on” specialists will thrive.

I hope you have enjoyed reading this as much as I have enjoyed writing it and sharing with you.

Thank you.


IBM Watson is just fine, thank you !


Over the last couple of days, I have seen a bunch of articles on my social media feed that are based on a research report from Jefferies' James Kisner criticizing IBM Watson.

I am a big fan of criticism of technology – and as folks who have known me over time can vouch, I seldom hold back what is in my mind on any topic. I strongly believe that criticism is healthy for all of us – including businesses, and without it we cannot grow. If you go through my previous blogs, you can see first hand how I throw cold water on hype.

Unlike my usual posts, I cannot claim to be an impartial observer in this case. As much as I am a geek who wants to make my opinions known on technology topics, I am also an IBM executive , and I run a part of IBM GBS business in North America that also includes services on IBM Watson (including Watson Health) . I also own IBM stock via ESPP and RSU. I don't set product direction for Watson – but my team does provide input to the product  managers. So I was in two minds over the weekend about blogging about this – but net net, I think I will go ahead and say some things about this , and as always I am happy to debate it and stand corrected as need be. So here we go.

IBM Watson's primary focus is on enterprise, not consumer !

This should be obvious to most people but perhaps the technical and use case implications are not super clear when they conclude Watson is in trouble.

Lets take an example of something that is often used to make the point in favor of consumer AI tech – Alexa. I often get asked Watson versus Alexa/Google assistant questions. You can tell Alexa or Watson to check the weather and they will both do it. The big difference is – Watson keeps the context of the first question while you ask the second question, and Alexa treats the second question as if the first one was independent of the second one. In the set of use cases Alexa solves, this is not a big problem – but the ability to keep context is important for the use cases that Watson solves, like customer service. In a customer service scenario, you cannot engage in a conversation without knowing and interpreting what has already been said.

That said – it is very easy to combine Watson and Alexa. For example , if you have echo installed at home, you can invoke Watson via a voice command and keep having a conversation without even knowing it is Watson that you are talking to.

While Watson cannot solve every possible customer service scenario – it can solve several and deliver very high value. For example – lets say you are a utility company that gets calls from clients who want to pay a bill, check a balance, find outage restorations etc. Those are all things Watson can do just fine, and leave the high value tasks – like being an energy advisor , or a retention specialist – to expert humans. Imagine the type of value generated for that utility, and the consistent and fast customer service for their clients . Consumer AI does not tackle these kinds of problems – and that is a big difference. There are many such examples like this in enterprise side of the house – like this video about how Watson acts as an expert engineering advisor for Woodside, and H&R block using Watson as a tax expert.

IBM Watson does not share one client's data with another client

This design principle is very key to enterprise clients. Data security and privacy drives a lot of AI decision making. Consumer AI generally keeps the data all users give it and uses it to learn and get better. I am sure those companies have high ethical standards and the data won't get misused. But that is not how enterprises look at their data. It is important for clients to have full trust that their data is not shared with others that they don't want to see it.

A lot of the criticism that Watson takes a long time to learn and needs data in a specific format that is hard to do for clients come from this principle being not fully understood. Watson can learn from a given client's data – usually unstructured data – and keep getting better, but will not use company A's data for Company B's system to learn. Even if we ignore Watson and look at data science as a general topic – there is no way to shy away from an AI model having to learn. That is the core of the value prop of AI.

This is not to say every client starts from scratch. In many cases, there is a well established starting point. Lets take a Telco call center as an example. If a client wants to put Watson to augment a telco call center, they don't need to build intents from scratch. Instead, they can use "Watson for Telco" that has hundreds of prepackaged intents and just add of change as needed. Over time, this will be applicable to all industries. These are all repeatable patterns – another point that observers don't seem to notice.

IBM Watson has plenty of successful implementations , including Healthcare 

The Jefferies report calls out MD Anderson project uses that to extrapolate that Watson is doomed. I don't see any mention of Mayo Clinic trials,  Or Barrow ALS study, or  Memorial Sloan-Kettering-IBM Watson collaboration   .  Where is the balanced analysis that led to the dooms day conclusion ?

Watson is in clinical use in the US and 5 other countries, and it has been trained on 8 types of cancers, with plans to add 6 more this year. Watson has now been trained and released to help support physicians in their treatment of breast, lung, colorectal, cervical, ovarian, gastric and prostate cancers. Beyond oncology, Watson is in use by nearly half of the top 25 life sciences companies.

IBM Watson is delivered as APIs that its ecosystem can easily use

When Watson won Jeopardy, that incarnation was largely monolithic. But that is not how Watson works now. It is now a set of APIs. I am under no illusion that IBM will be the only game in town, although I strongly believe we are one of the best. Partners and clients will build Cognitive applications using Watson in a much more productive way because the functionality is exposed as APIs.

This gets painted as a negative by some of the articles. You can't have it both ways. As I mentioned above, where it makes sense to package something for a given industry or domain, IBM or someone in the ecosystem will of course package it. But the decoupled nature is the most flexible way of innovating fast and at scale in my opinion. The fact that billions of dollars of investment is directed into this field is good for IBM and its ecosystem – let the market decide on merits who succeeds and who does not.

IBM Watson some times needs consulting , but it only helps adoption

Let me also point out the role of consulting – be it my team at GBS or another consulting company. Clients are still largely tip toeing into Cognitive computing. They need significant help to understand what is possible and what is not in their industry and their specific company – which is what we call advisory services. The actual integration work is not complex and can be done by in house teams or a qualified SI. The other service I often see that is requested by clients is for design. In some other cases, they also need services for instrumentation (like in IOT use cases).

If we rewind couple of decades and go to the time when SAP was just starting out in ERP, What was the role of consulting ? Did consulting  services help or hinder the adoption of SAP globally ? None of this is any different from any other technology at this stage of its life cycle. So I am not sure why there is an extra concern that adoption will tank due to consulting.

IBM Watson team does great marketing, and we already have amazing AI talent 

To be perfectly clear, I am not a marketer – nor do I have any serious knowledge of marketing other than a couple of classes I took in business school many years ago. However, I am VERY proud of the work IBM Marketing has done about Watson. Its an early stage technology – and that needs a certain kind of messaging to get clients to take notice. If all we did was fancy videos and panel discussions and there were no customers using Watson today, I would have gladly joined the chorus to boo Watson. But that is not the case – All over the place leading companies are using it and as I have quoted above, several are public references.

From what I could learn internally, there are about 15000 of us working on this at IBM. This includes about a third of IBM Research. And we are hiring top AI talent all the time. In fact if you are an AI developer and want to work on Watson, shoot me an email and I will get you interviewed right away. While we of course use job boards etc to attract talent, that is not the only way we find people. We already have more AI folks than a lot of our competition – so perhaps that should be factored in to the discussion on "look at job postings, IBM Watson is short on talent" part of the story.

So why is IBM not publishing Watson revenue specifically ?

I am not an official IBM spokesperson – and I am not an expert on this topic. So this one aspect – I have to direct you to people with more stars and stripes than me in the company.