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.

10 Tips On Navigating The “Large Enterprise” 

Barring two exceptions – MongoDB and Novasoft – all my career was spent working for really big companies . And all that time my client base was “large enterprise” – companies that operate in multiple countries , have multiple divisions , make billions of dollars of revenue and employ tens or hundreds of thousands of employees . 
Over the last two decades , the biggest obstacle to career success that I have seen is the difficulty in navigating these large complex companies . It’s not just a career success problem – I have seen both my buy side and sell side analyst friends beating their heads against the wall on why “stupid decisions” are made by companies they cover. I have also seen sales and delivery people struggle navigating their client organizations which are large and complex.

Honestly , from time to time – I get frustrated with some inexplicable things about these companies too . And I get asked about these a lot by folks I mentor . And despite all these – I and many others have had good careers in this space . Size can be a significant advantage when used and understood right .

So here is an attempt to share some thoughts on this topic – and as always , strictly my personal opinion

The best way to describe life in a large company is “The Matrix Dysfunction” . This is also the working title of the autobiography I aspire to write some day when I am retired 🙂 

1. Organizational structures will change frequently – make peace with it 

With every change of any arbitrary dimension – number of employees , product lines , countries they operate in , number of senior executives whose ego need a boost and so on – comes the reflexive move that goes “let’s change the structure” . It makes perfect sense on paper to organize everything neatly on a spreadsheet or an org chart PPT every time some such dimension changes – but it is often just counterproductive to push a lot of people through massive change. Change fatigue kicks in and frustration starts creeping up . 

Some day, large companies will hopefully figure out that such org level changes are not needed frequently . But in the meanwhile , the best way to deal with it is to assume it will happen a few times in your career at any large company, and learn to live with it . The analytical ones amongst us will be driven nuts because we won’t get all the answers on the rationale – but if you don’t make peace in reasonable time, you will just tie yourself in knots . If it is any consolation – your daily work might not change much at all in most org changes . 

In any case, natural leaders emerge above the noise and if you are observant , you can figure out who can actually make things happen irrespective of their Stars and Stripes . They often can teach the rest of us what we need to learn.

2. No one “truly” owns anything below the C suite 

The most loosely used phrase in large companies is “I own the P&L for this”. To be fair – this is factually true that someone does indeed own a P&L for compliance reasons  , just that it is not how things work in the field . For example -legal entity  P&L might be owned by a geography , but product lines that drive the revenue are shared services across all geos . So if a critical decision needs to be made , two decision makers become a minimum and they may have conficting objectives to make “their numbers”. For example – a Geo leader might be measured on regional revenue and a product line leader is measured on one specific product’s revenue . As complexity of product portfolio increases – you start to spend more time on internal deal making than deal making with customers . 

This is not as bad as it seems on paper – the design point is one of “healthy tension”. It also is the reason why experienced leaders know that control is an illusion and collaboration is the only way to get things done efficiently. 

3. Policies are built for Efficiency , not Efficacy

When you have a lot of people , you can only make broad rules that can be implemented in reasonable time with low effort . The unfortunate part is that unimaginative leadership some times makes it impossible to have any nuance . A funny example would be a senior executive who runs a billion dollar business might still need her boss to approve a new $500 smart phone for work use .  

The only way to make this work is to build a culture where if a policy “looks stupid” when it’s implemented , you can go up the chain easily and point it out . Best case the policy gets changed – but worst case at least you can get a few exceptions approved . You do need to pick the battles you want to fight so that you don’t exhaust yourself in the process 🙂

4. You may get the impression that finance and HR run the company 

While rest of the organization – the line business – changes from time to time , support functions are generally a stable part of the company . Their job is to support line management – but since line management changes from time to time , and because the support function often are the communicators of the message – the prevailing wisdom in many companies is that finance and HR runs the company . 

A CFO I deeply respect once told me ” When the business leaders run the business well, my job is to report . In that scenario I rarely speak unless spoken to . When they don’t run it well, my job is to get the business to a shape that lets business leaders run it . And at those times the business leaders won’t speak unless spoken to” . He was only half joking 🙂

5. You can use the overhead functions in your favor , or you can lose it 

Overheads come in many flavors – senior execs who are not in line jobs , operations teams , overlay sales teams and so on . Whether you use them to your advantage or not , their cost is allocated across the company . And there are many ways to use them very effectively . 

Many senior executives have relationships with clients that are useful for you . Rather than do all the analysis by yourself , you can perhaps enlist the help of someone in operations and so on . 

There is a flip side to this – when things go bad for a company , the axe falls on overhead functions . But when done using broad rules and without taking input from people in the field – often this leads to “penny wise, pound foolish” decisions . Again – the only real solution is a culture that makes leaders have a constant pulse of actual execution , and staff feeling free to let leaders know the potential impact of decisions . Easier said than done !

6. You need to develop a healthy working relationship with your manager , but You need multiple mentors to thrive 

Since things change frequently , and since you are living in a multi dimensional matrix – you need a set of long term mentors to act as your compass . This is especially true for people new to the large company set up . Managers keep changing , but long term mentors will provide stability and insight since they have experience living through changes . Goes without saying – you need to mentor someone too . Pay it forward !

7. Very few people know everything that happens 

This is more people looking from the outside . A large company is not a democracy – decisions get taken and communicated at many different levels of the matrix . So it won’t be unusual for a senior executive to find out something important  only after it shows up in the press . Not ideal – but communication is not efficient when size and complexity of a business is large . I make peace with it by assuming two things 

1. If we agree that on a “need to know” basis of working , 90% of the time things work fine 

2. Half of the rest , you can get through informal channels if you build a good network within the company 

And some times inertia is the sole reason why something happens . I know a client who spent millions of dollars on sponsoring basketball games long after they sold off the particular product line that was targeted for the basketball campaign . It took a long time  to come to that realization ! 

8. Responsibility and authority may not be correlated 

This is perhaps the hardest part to get used to for managers . The decision on salaries , transfers, career progression etc might not be taken directly by an employee’s direct manager – but the manager often needs to be the one communicating and defending a decision . 

One way I have seen managers deal with it is to ask someone else – like an HR rep – to do the hard conversations . I am not a fan of this approach . Good or bad – managers have to own the responsibility of communicating with their team . You won’t become a true leader till you get comfortable telling good and bad news with poise , honesty and candor .

9. A template for everything !

To operate a big company , you have to minimize variance whereever possible from a top down perspective . There is no use fighting the need for consistency – otherwise you just paralyze the decision making process . The solution for me is two things 

1. Ruthlessly prioritize so that your hardest hitting points come clear on the template 

2. Find opportunities other than formal reviews to get buy in for your ideas . All the more important that you build relationships early and often 

10. Execution rules , Strategy drools 

I can’t emphasize this enough. Large companies tend to have a lot of people who work on pure strategy – some good , and several just academic and far removed from reality . You will also keep hearing strategy was awesome , but execution did not match . I call BS on this every time – a strategy that cannot be successfully executed is a bad strategy , but perhaps a good hope ! 

Tactically – I have always favored the approach of “if you can do something ethical and legal to make your client and your team successful , do it – and ask for forgiveness if you fail”. Best case – you might end up changing a made policy and make a new best practice . Worst case you get slapped a bit , but still can sleep better . Not a bad trade in my book.

I will end this with a bonus tip . Not every one is a good match for any given role for ever . Just as your employer can fire you – you can fire your employer too . If you keep your skills sharp and have an open mind, the world around us has a lot of opportunities. So if you find that there is no way you can make it work in your current role despite your best efforts   – you should consider quitting and starting afresh elsewhere, within or outside your employer  . Nothing is worth being miserable all the time . Leave on your own terms and respectfully without burning bridges ! 

How much will we trust AI ?

Most of my adult life was spent doing consulting work for clients around the world on topics like information management and analytics. For last few years, I have also been involved in IOT, AI etc. When I look back – one thing has been common for all “data projects”, and that is the importance of TRUST…or lack there of. Billions of dollars have been spent creating BI systems ,data warehouses, data lakes, cubes, reports, workbooks, blah blah – and yet at the end of the day, spreadsheets still prevail at EVERY SINGLE COMPANY I know !  I wrote about this in the past.


But why ?

There are good reasons why users don’t trust data they get from BI, like

  1. In most cases, users cannot see how it ties back to source systems
  2. Transformations and enrichments to the data are not transparent to users
  3. Users do not know how well the system has been tested
  4. The tools may not be as intuitive as a spreadsheet….and many more

Every single one of these problems in “Classic BI” has a solution – which either a product or a service can solve. An IT expert can probably show what transformations happened, or create some reconciliation reports for example. Or a data lineage tool can trace back from report to source. So with some additional cost, we can minimize the trust issue – though the cost may eventually become prohibitive to build trust at scale.

Past is set in stone …most of the time, at least 🙂

The reason we could do all of this is because we are essentially looking at things that already happened – which are kind of “set in stone”. And the information we got from such systems had finite values as answers like “sales in north america was $10M” which is based on basic arithmetic . If I asked the same question again tomorrow, I will get the exact same answer of $10M. If I did not – I would know right away that something bad happened. If a bad decision was made –  for the most part, it is possible to trace back and validate the data and confirm if it was indeed bad, and prevent it from happening in future.

Now lets look at the world of AI .

To avoid a religious war on terminology – please allow me to use AI in its most generic sense as an all encompassing thing that includes what we call data science, machine learning, cognitive etc. Definitions matter – but for this post humor me and pretend all the right things are covered when I say AI.

Just like with classic BI, we use a lot of data and transformations. However, the fundamental idea now is that we are not just reporting on what happened – we are trying to make the best educated guess on what will happen in future. We are not in the world of “only finite answers” here – instead of the exact sales that happened this quarter in North America, we are often trying to find what are the odds that sales will be greater than $10M next quarter, for example.

Enter the trust issue

On one hand, it is quite useful to have a system that makes such predictions for us so that we have a window to the future. On the other hand, if we do not trust the answer – it is a lot more difficult to explain how the system arrived at the answer. And if the system told me today that I had 80% chances of hitting $10M for the quarter, it could very well tell me tomorrow that I only had 50% chances of making that $10M number.  I and everyone else in my team might think the system is foolish because we can see the math to get to the $10M number we want . Lets say the quarter finished and we did exceed the $10M number – this still does not mean the system was either right or wrong. That is the beauty (and pain) of how probability works !

Can’t the creator explain the creation ?

I am often asked “Can’t you just ask the programmer or data scientist who built this to explain how the system predicted?”. Yes I can – and some times that is all it takes to get the answer. But many a time, they may not be able to give that answer with the precision you expect. AI systems are learning systems (with or without human help) – and they learn and get smarter mostly by going through a lot of data, as opposed to just crunching logic fed by a human. By the time I asked the $10M question the second time, the system might have learned something from a new pattern it detected.

AI can piss you off

A sales forecast, in the larger scheme of things, is probably not going to change the world for most of us. However, if we think of other scenarios like say salary planning  or promotions where an AI system scores everyone in a team on a complex set of parameters and makes recommendations – it is hard to accept a decision that cannot be explained in an easy to understand way. The system may be totally right – or it might have all kinds of bias built into it with the model, or the data it trained with. It might have false positives and false negatives. There are techniques to minimize all these problems – BUT If it cannot explain its results to us as users – how will we know for sure ?

Can you trust machines ?

There is another version of the trust issue – when machines need to make choices that affect us. Lets say you are a factory supervisor driving in a self driving forklift that is picking up a heavy load from one top shelf and putting it in another top shelf, while your workers are walking below its arm. The machine probably has visual recognition capabilities, and can crunch lots of parameters from data and make good decisions. Lets say one day, the machine detects the load is too much to bear and it has two options – flip on the side and injure you, who is sitting inside or drop the load and injure your workers. What should that machine do ? And if you don’t know what the machine will do – or at least know that you can over ride it – will you work with that machine ?

AI – its just like us, except it isn’t 

I also get asked “Well, AI is supposed to think like a human, so why can’t it explain its thought process like we can?”. This is an excellent question and it presents two issues. 1. We don’t all think alike – even in the fictitious forklift example, I am sure different people will choose differently.  And 2. We often take a decision, and find an explanation for the decision later if someone asks. We can’t always explain our decisions very well either to someone else except for simple cases. And finally, we make poor decisions too. So mimicking human thinking as-is perhaps is not the best way to think about AI either 🙂

AI is everywhere, and mostly harmless

I am of course not generalizing that all AI scenarios run into a trust ( or ethical or moral) issue. Many don’t – for example an AI algorithm might predict how much longer a device will work before battery runs out. I doubt I will have a trust issue if I see it work approximately well for first few times. And there are several of those kinds of “little” AI solutions all around us – and many might not ever be visible to us. We just take them for granted ! Even in the sales forecasting or promotion examples – over a period of time, we may trust what the system tells us. But the trouble is – will we give it enough time to let it work long enough to earn our trust ?

So what can we do, really ?

Just like other projects, AI projects need some basic education and expectation setting for stake holders before we embark on them. Unlike basic math, and if-then-else logic – statistics concepts needs a bit more hand holding. People tend to use terms like confidence, significance, sampling etc loosely and it is very easy to set wrong expectations with stake holders even with the right intentions. And then there is the issue of trust, and its ethical and moral considerations. It is important to discuss these thoroughly upfront, and during the projects . When done right – and transparently – AI can and does add significant value to us. Its on all of us in this industry to make sure we let AI earn trust the right way !.