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 !.





Side Projects have been such a blessing !

There is nothing that bores me more than repetitive work ! The irony is that I don’t have this problem outside work. I can eat rice three times a day for rest of my life. I can listen to the same set of Ilayaraja songs every day. I have watched Gandhi movie 30+ times , All 7 seasons of West Wing multiple times on Netflix and probably will do it again this Christmas. I love reading the same books over and over every few years. I can spend months perfecting every last bit of synchronization between my dog and me before we compete in dog shows.

But when it comes to work – I cannot stand repetition !

I get bored very quickly. In the early stages of my career, this kind of worked in my favor – I just switched the areas I worked on every year or two and had a fun time picking up new skills and having fun, and in general my career progressed at a fair pace. In hind sight – I was probably taking way too much risk (while doing this on an H1B visa at the time) , and I had a lot of great managers who never stood in my way when I wanted to do something new. But once I was in leadership roles at work – it became abundantly clear that repetition is a good thing for abundance and it is futile to avoid it. On the bright side – I was also quite lucky that people I got to work with mostly loved optimizing repetitive work and gaining efficiencies. But the problem remained that I still could not feel excited to do encores – and since I generally won’t ask my teams to do anything I wont do myself, this started becoming a bloody pain for me.

And that is where side projects became a life saver for me ! They gave me enough variety and stopped repetitive work from distracting me.

The first thing I tried my hand was in blogging – on what was then called SAP Developer Network. And thanks entirely to Marilyn Pratt, I started enjoying it and later branched to having my own personal blog. Only a small % of my blogs talk about technology or work related stuff – and I mostly write to get things off my chest, as a venting mechanism. So I write on the wordpress app on my iPhone for the most part, never proof read or spell check. But thanks to some very loyal and generous readers , it has always been a fulfilling activity. What I realized is that it also benefited my work some how. Many a time, I have landed at airports across the world where someone recognized me and chatted, and I have had clients give me business because they googled me and found my POV on a topic of mutual interest. All this is net goodness – but 500+ blog posts (and podcasts and videos and all), I should say that it is hard to find motivation to write these days. From 2 blogs a week at one point, I barely write one post every other month now.

Ollie and me

Then I picked up my old hobby of dog shows. I was quite successful as a competitor growing up in India and have won my fair share of top awards – and lost more than I care to count. My claim to fame was my german shepherd who could go to our corner store and buy milk by himself. The local news paper and TV channel there had that story covered when I was in college. What I realized in the second round was that my love for dogs is now significantly more than my love for dog shows. Long story short – I did not push myself or my fur kids hard enough and we mostly just hang out and cuddle.  Again – there were many positives to take to work. One is increased patience and the other is the invaluable lesson that any problem can be solved by breaking it into several smaller problems and solving each and putting it back together.  And a side “benefit” – having dog hair on your trousers are a great conversation starter with fellow dog lovers at work 🙂

My day job these days is to run a small “hyper-growth” business at IBM ( pretty sure if you name a buzz word, I have it in my portfolio – Cognitive, predictive, IOT..check, check and check) – with more people than I have ever led before, and a target with more zeros than I have ever managed before. There is something new to learn every day, to say the least. Obviously, doing such a business profitably includes a fair mix of repetitive work, and first of a kind work. Thanks to the long plane rides – my coding and math skills are somewhat kept sharpened through all this 🙂

bee school

As luck would have it – I also got the best side project I have ever had in my career. I am the executive sponsor for our consulting school for senior managers – which we lovingly school “The Bee School”. There is nothing more fulfilling than joining hands with our very passionate and skilled Learning and Education team, and a bunch of enthusiastic fellow executives to teach and learn from our next generation of leaders. It has been an eye opening experience for me to see how an education program is designed from ground up, how we train the trainers, how we execute the training and how we learn and adapt quickly. I am seriously thinking of being a full time educator now. The best part of the week we spend with a hundred senior managers is the Cognitive hackathon night where people who have no prior coding background build a virtual agent using Watson and realizing how easy modern technology is , and how much value it adds to clients ! I have to thank my “big boss”  Ismail Amla for the opportunity .

My latest side project is to build up a garden – flowers, veggies , anything that grows. The last time I did this was when I was in grade school. What makes this a fun project is that I live in AZ, where the summer temperature is a cool 100F to 120F. I have a willing co-conspirator in my mother in law who has a beautiful garden in India. My core design principle was “hyper growth” – so I had to do my research on what plants will thrive in our climate, which ones will yield flowers and fruit the fastest, and how to build for some future scale in case this actually works.

We already have some success – yesterday we ate the first egg plant and tomato from the garden . And there are about 20 different shades of hibiscus flowers in the yard. The ROI for these vegetables is now something like every $5 of veggies cost about $500 ! But the sheer satisfaction of seeing “hyper-growth” is priceless 🙂

There are some good lessons I could potentially take to my work – 1. Time spent in upfront research saves a lot of rework ( The few plants we lost in the process were all mistakes that we could have avoided if we took time to read more and ask around a bit more). 2. Automation is a good thing and something you need to put in place upfront ( everything on timed automatic irrigation now). 3. You cannot just trust process and automation if you want solid results ( Till plants take root, you need to water extra , give them support, remove weeds and generally treat them like a little baby 🙂 )

The current gardening project should keep me going for a little . But I am already thinking of my next side project – and one idea is to buy a German Shepherd puppy and train for Schutzhund/IPO. I am inspired by 7 year old Samantha – watch this video and see for yourself. Or perhaps start writing my book – The Matrix Dysfunction !



SapphireNow 2017 – My 2 cents

First off – thanks to SAP for having me at the event as a blogger. In my day job, I no longer work on SAP technologies closely. But most of the clients I have are SAP shops and often talk to me about SAP given my heritage. Sapphirenow keynotes and executive meetings with the board members were enlightening for me. Here is my quick commentary.

Hana – done and dusted ?

As I walked into the show floor, the first thing that caught my attention was that there were hardly any signs for Hana. After the last 6 years of Hana overdose, I was pleasantly surprised to see this. I have always maintained that success of Hana will be when SAP does not explicitly talk about it, and it just becomes a part of everything they do. To be clear – Hana is not a part of everything they do quite yet – most of their customers are still not on Hana, but SAP probably feels confident that the tipping point on adoption has happened and momentum will keep building. Congrats to all my friends who worked tirelessly on it.

Indirect Access – SAP heard the community loud and clear !

Bill McDermott needs to be commended for taking on the indirect access issue heads on in his opening keynote. Without that, the whole empathy thing would have sounded hollow. Now the proof of the pudding is about SAP following through and changing the perception in the ecosystem. I heard it loud and clear from Bill in a private meeting that he is committed to making it happen and I trust him.

All hail SAP Leonardo – just don’t ask me what it exactly is 🙂

This Sapphire was all about SAP Leonardo ! Unfortunately, I cannot quite explain what it really is under the hood. From the slides I saw, it seemed like everything but the kitchen sink was thrown into this package – analytics, block chain, cloud…. . To be honest, it seemed like SAP did not have the time to think through a crisp message before the event. I am sure this will be a lot better by TechEd. I should say – while admittedly not being a marketer myself – I doubt SAP will keep this name “Leonardo” for long. It sounds like a cool internal project name , but I doubt this will catch widespread imagination. I heard this from several people on the show floor too, including several of SAP’s largest customers.

SAP loves partners !

The keynotes were totally partner friendly – We had senior execs from Dell, intel, Google, IBM et al show up on stage at various points. But it was rather light on customers. Perhaps this was a conscious decision given the customers would have wanted to talk about their existing (boring?) – mostly on premises –  systems, where as SAP wanted to position their future with cloud and AI and all that. I trust the balance will tilt towards customers as pilot projects for Leonardo get completed. While I have no idea of commercial impact, the Hana startup program was a good thing for Hana adoption. I was surprised at not seeing it being repurposed to get Leonardo a similar lift. Perhaps more at Teched?

What is next for UI and API in the machine to machine world?

SAP did an exceptionally good job with UI with Fiori etc – and Sam Yen and team should be incredibly proud of that. It was also an expensive effort to get SAP to look cool.  Now with Bernd and Hasso painting the vision of automation, ML etc – I would love to hear what is the next evolution of SAP UI. When most of the back office and factory processes are automated – there won’t be human beings keying in things into a beautiful screen for most of the processes. And for the few screens that do remain in future, screens cannot be static either – and will need to learn user behavior and adapt. Just looking at how many screens get used today and making decisions based on it might not be fit for future. I have a similar question on APIs. Future is mostly about machine to machine interactions and AI. The way APIs are designed are not fit for this kind of future – machines for example do not need anything more than a binary protocol to talk to each other. Sapphire is a business setting – so perhaps these things will find a place at next teched.

Time to rethink demos a bit ?

I will just make two points on keynote demos. Putting sensors in drills and showing how it works in real time on a live stage is something I applaud. What I thought was less impressive was the articulation of the business case. If I am renting a drill – the renting company cannot give that drill to anyone else. So – what is the point of charging per hole drilled ? What if I rent a drill, keep it for a week and drill 2 holes ? Its not a logical business model and it detracts the people watching it. I hope SAP just finds a real life customer case to demo next time.

From the beginning of time, SAP has demonstrated dashboards. I love dashboards – and the ones showed this time were visually impactful too – and maybe had great ML behind the scenes. It just did not show that way – I think its time SAP moved to some other way of making the message hit home.

SAP makes a much needed entry into AI

Hana brought the speed and technical simplification SAP needed. Now SAP is taking the next logical step to make applications smarter. SAP leaders I met here were all realistic on the effort and skills needed to make this happen. Ariba choosing Watson as its cognitive engine, and Google Tensorflow being endorsed in a big way by SAP are all signs that SAP clearly understands that AI is an ecosystem play.  In my mind – SAP can’t get the skills and IP needed to keep up with this market without a string of acquisitions. SAP has always had a strong services ecosystem around it for decades – but they historically have been low key on role of services in creating value. The future of applications might not all be pre packaged like how ERP traditionally worked – and the significant need for domain expertise etc is readily acknowledged now. 

The one aspect where I have some pushback for SAP is the inside out view that AI sits inside SAP systems. While there are valid scenarios like invoice and payment matching where all data sits inside SAP – it is not realistic that a company can model real world with just SAP data. Again – I think its just a matter of time for the vision to mature.

It was fantastic being here – and catching up with old friends and making new ones. It was also fascinating meeting several people I have known only on social media – thanks for introducing yourselves ! Till next year !

IT Services industry – time for empty talk is over !

Every day I read at least one article that talks about Indian IT companies planning to hire several thousand people locally in US , and multiple articles about digital transformation disrupting multiple industries. What is abundantly clear to me is that jobs and careers as we have known them will get disrupted in an unpleasant way and society will have to pay a big price to pay . 

Global IT services business was built on “your mess for less” philosophy, not on advisory and consulting type business . 

If you, the client,need to spend a million dollars to process invoices or make collections  ,then  I – the service provider – have a combination of cheaper labor ,infrastructure  scale and technology that lets me do it for cheaper . So I will do it for cheaper and give you a better deal while still keeping a healthy margin for myself . And then I will keep optimizing my process and technology so that I make better margins – some of which I will pass on to you too . Now that you love the model for invoice processing , I can also make this work for you in say maintaining your ERP system . I will just hire more people , and use the learning from the invoice process work and keep making my business better while you have a healthy cut on your costs too . 

The differentiation became one of price charged by the provider – and hence buyers just needed to compare rate cards for the most part to make a determination . The last couple of decades went along this way – with minor differences . There came a point where low price alone wasn’t differentiating enough – so some kind of price + value add was needed to keep this motion going . That came in the way of pricing for outcomes , rebadging employees , adding some freebies like proof of concept work , etc . 

What didn’t change was that success was still tied largely to headcount – how many people could the customer take off their books , and how many the provider could add . Even if the contracts were outcome based , the costs were mostly head count based . This model could adapt to slow changing technology – but not for massive shifts in technology . For example – when BPM came along a decade ago, a ten member team probably got shifted to an eight member team . But when AI becomes mainstream – with visual recognition , pattern matching and so on – we may only need 2 people to do that job , or perhaps none ! The current model is not built for that kind of disruption – and companies and people will both have a price to pay .
I look at “we will hire thousands of employees in US” as mostly (not solely) a PR exercise . While protectionism will very slightly delay the inevitable – the job losses in US and elsewhere won’t be because India and China have cheaper labor . It will be because automation will take over routine repeatable jobs , and there just won’t be enough people with the skills needed for the higher end jobs given we have not prepared as a society and as an industry for such a tipping point . 

These higher end jobs unfortunately cannot be done in near future in India ( not sure about other countries but can’t be that different ) because the quality of the mass produced engineers are not anywhere close to the levels needed for taking these jobs on scale .  Revamping the education system world wide should be priority one – and this can’t be done via formal education system alone . 

It will be a rather weird world ahead of us till we hit the next equilibrium point . Job losses will not be because there aren’t enough jobs that need to be filled  – it will be because we don’t have enough people with the new skills . 

Automation will light up the industry candle on both ends – clients will automate a lot of their work , and service providers will automate a lot of their work . This affects all levels of current services organization hierarchy . For the most part , I expect the fundamental services business model to change . Just a few examples in no particular order to make the point

1. Services companies became large and profitable by optimizing large resource pools – like a thousand ETL programmers to support a hundred  projects . Large SI companies historically did not need too many people with multiple skills – so it was quite possible to be a top class ETL programmer without ever needing data modeling skills for example . When the need changes to micro specialization by client , and smaller and shorter projects , it no longer makes it possible to optimize this way . So I expect the large service providers to be more of general contractors and the actual skills to be provided by individuals and smaller companies . Some very niche skills alone might get housed in these larger SIs – and the differentiation will mostly be IP based 

2. Neither clients nor providers will need a lot of managers purely for administrative reasons like reporting , aggregating etc . I do expect project management to become even more important though . 

3. When robots take some of the the jobs of humans – HR will need skills to integrate human machine interactions . While it might sound funny today , I do expect people with “robot recruitment expert” in future 🙂 . Finance will need to deal with the newtradeoffs between balance sheet and P&L when robotics become mainstream. Similarly for a service provider differentiating primarily on IP – the CFO will start watching balance sheet a whole lot more than she does today . 

4. For near term at least – countries like India are better off transitioning to products from services . But in the mid to long term , there won’t be much of a difference between what is a product and what is a service . Again , the skill requirements and business model changes are significant and fairly disruptive 

5. The scope of services provided will fundamentally change . As clients automate their plants and further increase the complexity of their supply chain and talent mix – how many services companies can help them on the front line ? The “manufacturing vertical” of most services firms today deal with mostly back office functionality of manufacturing companies – that won’t be very useful in just a couple of years 

6. Consultants , Advisors and buyers’ agents will need a lot more depth than the high level digital transformation power points, and rate card benchmarks to earn their keep . The level of knowledge needed to implement actual robotics in a company is not the same as “here are the 7 steps” on a power point chart you can impress a senior exec with today.

You get the idea – it’s not just the line employees who will get impacted , the entire length and breadth of the services ecosystem will get disrupted . And given the high spending power of this segment in the society and economy at large – the impact will be wide spread . In other words, the time to act has probably passed us already and we better start at least now . 

The first step to change is acknowledging we have a problem – so let’s get that over with and start the creative destruction and the rebuild . The onus is on each of us as individuals to reskill ourselves for the needs of tomorrow . And the onus is on each leader to join hands with each other to collectively define and implement the future of our industry – we cannot solve this one company at a time. This is not the time for empty promises and bravado filled PR  and small ring fenced teams that do “innovation” – we need meaningful action at scale . Let’s go !



So I turned 42 – and woke up to 100s of birthday wishes from family and friends around the world. Its also the first time I have had my parents and my mother in law with us here for my birthday, and that makes it extra special.

As I settled down with my two fur kids to enjoy a cup of coffee, it dawned on me that 42 is my favorite number – thanks to Douglas Adams . When I first read the book, I had my fair share of theories (and fierce arguments with friends and strangers) on why is it the ultimate answer – including the binary representation and all.

Then I found out the truth a few years ago and then I liked 42 even more!

“The answer to this is very simple,” Adams said. “It was a joke. It had to be a number, an ordinary, smallish number, and I chose that one. Binary representations, base 13, Tibetan monks are all complete nonsense. I sat on my desk, stared in to the garden and thought 42 will do. I typed it out. End of story.”

The big lesson that 42 taught me was to not take the world around me – and myself – not too seriously . When we do take ourselves too seriously, the joke is invariably on us 🙂

There are three times every year that I look back and forward – New year, my birthday, and the day I review the business plan for my team at work.

Looking back, its amazing how perspectives change with time – and some times at alarmingly accelerated pace. Some totally random birthday thoughts here.

  1. Growing old is not some kind of slow process – and not something that only happens to others. When I joined TCS out of B School, I was 24 and my boss was 30. We used to call him “The old man”. Joke is on me – I am the old man now 🙂
  2. Not everything that is forced on you is bad. I hated being sent to learn Carnatic music as a kid. I never became a good singer, but the music theory stayed with me and now I appreciate the music a lot. I still tease my mom for forcing me to learn music and she teases me that all I seem to listen to is the music she forced me to learn. The irony is readily apparent to me as I drive my kiddo to her weekend activities, some of which she thinks we force on her 🙂
  3. All I wanted as a young kid was to become a professional dog handler. I was pretty good at the craft – and had my fair share of wins in the ring. I still enjoy dog shows – but I enjoy spoiling my fur kids at home a lot more today than taking them through their paces at a show. From time to time I do feel the urge to take the leash and go into the ring just for fun – and I might still do that one of these days. I am not yet fully over the grief of losing my dear fur kid, Boss, but I am also convinced we will get a pup again in near future.
  4. Life has a way of going in circles. After a degree in mechanical engineering and an MBA, I decided my calling was as a programmer. Then along the way, I got nudged into sales, management and all that I hated as a young developer. Turns out not all sellers and managers are horrible people like I initially thought 🙂 . And guess what – its my technical skills that help me the most as I build my current team.
  5. While I try hard to keep my tech skills sharp, I have also realized that its way more fulfilling to be the one handing out the prizes than being the one gunning for those prizes. Bar none, the best part of my work life is to help my friends and colleagues succeed and be their number one cheer leader. And on personal front, the (more than) equivalent is being the chief cheerleader for my daughter.
  6. I (think I) always knew that life is way too short to spend on unimportant things. What I did not realize very well till recently is that some (previously) small little things have become more important to me over time and some big ticket items from past don’t figure in my top priorities as much any more. I just need a little more conviction to say NO to weekend work phone calls – but I am planning on getting better at this really quick 🙂


So, you want to manage AI projects ?

Project management is hard as it is . There are three aspects of project management that I think need some extra attention when the project is about AI . Pro tip – if it’s your first time , consider buying a Costco membership for the BIG aspirin containers 🙂

The three aspects ( certainly not the only three) are

1. Estimation 

2. Change management 

3. Testing

It’s the very nature of AI based development that makes it harder than traditional project management . Let’s start by making a few assumptions 

Good engineers who can work well with AI are hard to find – but for this discussion I am going to assume skills are not an issue and somehow the PM formed a team with all the right skills. 

Since AI functionality is exposed as APIs in many cases , I will also make another “simplistic” assumption to make my point about PM that engineers don’t need to be big time mathematicians and statiticians to use the available APIs .  

Just so we are clear – you do need great skills , and you do need some experience with the math to do AI projects well . I am just making these assumptions to keep the focus on PM in this post . For good measure – I am going to use waterfall language here, but there is no difference if the project uses Agile 

Let’s start with estimation . The first step of the process is to define phases like say development and testing , and use some historical data to figure how much effort and time it takes . This is easy for many parts of development like web/mobile/DB etc since there are plenty of past projects to give realistic guidelines . Then we get to the AI part – let’s say just one model is in scope that uses Machine learning .

Now we run into the first issue – whether the model we chose is the right one for the app we are building . In many cases – we won’t know till we actually test it . And even if it works ok , it might not be valuable enough of an insight that it throws back to the app . So whatever we estimate – there is a good chance that while developing we will need to switch drastically to something else and that will invalidate prior estimates . This is on top of all the other things that can go wrong in the actual design of the app . 

Even if the model technically works – performance can become a dog . Not all models support parallel processing . In many cases , another statistical method might need to be considered after you realize performance is going to be a problem . Ergo – potential to rework is lurking throughout .

Then there is the second issue – which is that unlike traditional projects , source data can fundamentally change the fate of an AI project . Data is already a problem in traditional projects  – unclean data can increase ETL effort for example . But it’s kind of easy to estimate ETL effort after you profile source data . But even if data is technically clean – your model may not find it useful . You probably will miss data you need , or need data from additional sources and so on . And there is no good way to know this prior to putting the data through your model . It’s also not uncommon for your base application to change its paramarters significantly because your model needs different information eventually than what you originally designed . 

In other words – your dev and testing team can drive you nuts if you walk in as PM with “traditional” expectations. Now to be fair – it’s not because your engineers are willfully . They just have a harder time debugging when things go sideways – and a lot of things go sideways . The Costco membership helps with less expensive beer over weekends where you reflect your career choices  🙂

These two issues already make it hard to estimate and test an AI project . Now let’s look briefly at the change management aspect . 

Explaining what an ML model does in business terms is a non trivial challenge to begin with . Now to explain why the model doesn’t work as planned and why it needs to be reworked – without guarantees it will work the next time – makes it a much harder problem . Even when things work well – and if a business person asked you to explain why the model arrived at the result it did , its often hard to explain .

Good PMs educate their stake holders – AI projects can make them look like a tenured college professor 🙂 

So how does a PM mitigate these issues ? I will offer a few thoughts 

1. Start by educating yourself on fundamentals of AI . You can even create little chatbots etc with minimal to no coding . Get a feel for the actual work upfront before you take over the project . Make sure all your non technical people like Business Analysts do this too 

2. Set the expectation with the client on the nature of the AI projects before the project starts . You can do this with real examples using simple models that you can mock up in Excel and then help the client extrapolate what will happen if something goes wrong 

3. Insist on highest quality engineers . If you have to develop something from scratch today – developers need experience with multiple frameworks and know their trade offs . AI models add a layer of complexity on top and have their own trade offs . Having past experience won’t eliminate all the issues I called out – but it will help minimize the mistakes , and when things go wrong you will recover faster . 

4. Aim small , Miss small . Try hard to resist the temptation of trying to build a complex system in one go. Make small goals and course correct as you go . 

5. Double down on data quality . Encourage your ETL team and data scientists to keep looking at data , visualize them differently and identify problems as early as possible . 

6. Create a support system for you and your team . It’s a new field and everyone needs help from time to time . If you don’t plant your shade trees up front , you will face nothing but grief later 

I am very curious to hear your thoughts and comments !