NASSCOM 2017: Indian IT services paralyzed by Trump, but being a deer in the headlights is not an option

When, in history, has there existed a market that just keeps relentlessly growing at 5-10% each year, with profit margins consistently at a 15-20% level; and for well over a decade? Yet you attend the annual flagship Indian IT conference only to experience an atmosphere of acute paranoia and paralysis.  Is change really that frightening?

Even most clients are openly declaring they haven’t had their budgets reduced – many simply aren’t ready to make investments while there is such uncertainty surrounding the market because of an unpredictable US President.  Even NASSCOM itself adds to the uncertainty by deferring its usual business outlook

However, acting like a deer in the headlights is not an option.  The smart strategy is to expect the worst and make measures now to get in front of it…. don’t let the juggernaut, that is a protectionist US administration, squash you flat in your tracks.

However negatively this could turn out for some of the Indian IT services industry – here are six simple ways to reinvest some of those bloated warchests, before those greedy investors who got rich off your spoils demand to cash in their chips…

1) Invest internationally beyond the US.  Those Indian IT majors in the strongest position are those that are least reliant on their US clientele for future growth.  In fact, HfS estimates $7 Trillion in B2B digital expenditure by 2020 – with only $2bn being in the US (traditionally 50% of worldwide IT spend came from the US, but digital spending – both B2B and B2C – is changing that picture dramatically). For example, the British PM is already deep in discussions with Modi about closening UK/Indo ties even further in the wake of Brexit. The UK has the potential to become a major digital hub, fuelled by Indian talent.  While Brexit appears like a terrible idea on paper, change forces action and these actions will be all about increasing the flow of trade and talent with emerging nations and creating new wealth. We also see a real appetite for digital business model investments and automation by Australian businesses – and many of the Asian nations are only too happy to move from zero to hero to take advantage of the humongous digital B2B expenditure in Asia/Pacific and the rest of the world.



In addition, many of the European regions, such as Nordics and Germany, are now rapidly exploring more global resources to support their digital growth. If America – as it appears – is on the path of becoming a protectionist anti-globalization country for the next four years, perhaps its time to broaden your horizons?

2) Invest in a smarter onsite/offshore model that gets you closer to your customer’s customer.  Yesterday’s IT services model was all about helping legacy traditional enterprises keep their lights on by maintaining clunky old ERP implementations keep operating, adding extra sauce to spaghetti code and keeping an eye on server outages from afar.  Tomorrow’s winners have moved all this stuff into the cloud and automated much of their infrastructure management.  The future growth is working much closer to your customers to help them design and implement digital business models by building mobile applications, testing customer sentiment, forging partnerships and developing APIs with new digital business partners and communities.  Technology skills such as DevOps, Agile, Hadoop, Blue Prism and Automation Anywhere are the watchword, and a global race is on to access these skills.  Moreover, the developers need to be closer to the business designers and customer strategies of the clients to make this effective.  So Indian IT majors need to focus on developing these skilled resources where all their clients are situated, in addition to India itself.  This will require re-investing some of that lovely cash sitting around – and, heaven forbid – take a small margin sacrifice for a few quarters.

3) Partner with digital agencies to get it done.  Be realistic for once and accept the fact that most customers are not going to come to you to design highly creative digital business solutions.  You have an IT services brand, not a creative digital brand.  Most clients will go to the advertising firms, the Design Thinking consultancies and the digital specialists for that work.  However, all those firms are pretty clueless when it comes to actually communicating their business designs to technology firms and having them just get it done. This is where you can really do well – by working with these agencies and consultancies as their IT partner – bring them into your clients and they will being you into theirs!  Believe me, most the digital firms worth acquiring have already been hoovered up by the Accentures and Deloittes… most the stuff left on the market is overpriced, too small, and most their nose-ringed designers will jump ship the moment you buy them.

4) Become great intelligent automation intermediaries to manage broad automation and analytics environment for enterprises. Clients are crying out for providers to partner with them on their automation journeys – in fact, 45% of buyside operations leaders, when polled privately, view rolling out automation in tandem with their service provider as adding the most quality to their service relationship (see below). Several of the leading Indian heritage IT services firms are making impressive strides with their enterprise analytics and automation solutions – such as Infosys with MANA, TCS with Igneo and Wipro’s Holmes – the key now is their ability to twin their solutions with the cream of the third party intelligent automation apps, such as Automation, Blue Prism, UiPath, Workfusion, Redwood, Antworks etc to become their clients’ intermediary for automation and analytics value. While some proprietary tools and bots can add great value, especially when aligned to specific industry processes, clients want to have the choice of adding their own independents tools to enjoy the biggest impact on their process value. The Indian IT leaders need to become great partners and facilitators in these emerging environments – they have the development talent in spades and the passion to bulldoze their way to the front of this market.

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5) Keep investing in start-ups. One of the best cultural shifts in the Indian IT industry in recent times has been the emergence of the start-up scene in Delhi, Mumbai, Bangalore and other areas. Ambitious Indian IT talent is no longer desperate to walk that slippery steep treadmill of the IT juggernauts – many of whom are already too big, clunky and corporate for their own good. Moreover, tech investors are fed up having to invest $20-100m in US start-ups to develop one product or technology, when you can get the same value from the likes of India, China or Eastern Europe for a fraction of the cost. Having heard about the 400+ emerging startup firms who are already members of Saurabh Srivastata’s network (the original founder of NASSCOM), it gives me real hope for India’s future that the next generation of IT talent is already being healthily incubated.

6) Just make a plan and stick to it.  The one big element of NASSCOM which I found most infuriating was the lack of a plan from most of the service providers.  Most are simply playing a game of denial and react.  This is a recipe for failure.  Accept the fact there will likely be some uncertainty for six months before some new draconian measures are forced on businesses seeking to do business with the US.  Net-net, it’ll be more expensive to deliver services to US clients and also harder to send your own talent over there to train US staff and manage projects. So set aside funds to hire more people in the US and budget for a margin squeeze on future US contracts.  And forecast a 10-25% hit on deal flow due to longer decision cycles and US clients veering away from using highly visible offshore services suppliers.

Bottom-line: Take the tough blows now to roar to the front of the global IT industry when sanity returns

While the global IT world waits with baited breath, paralyzed by the ramblings of an unstable and determined US President, our beloved IT services firms can either remain numbed by fear, or actually use this opportunity to make some key strategic investments and initiatives. Those mountains of cash need to be used sensibly before those greedy investors demand their piece back, so act now, swiftly and decisively to organize an IT business that isn’t so reliant on lifting and shifting labor to and from the US, and puts you in the driving seat to lead in the $7 trillion dollar digital world, where automation is native and access to skills absolutely critical. India has a great shot at emerging as the world’s great IT pioneer, and so much more than a low cost labor provider for greedy legacy US corporates. Trump won’t be around forever, and he might actually be doing India a massive favor without ever realizing it…

(Cross-posted @ Horses for Sources)

No hype, just fact: What artificial intelligence is – in simple business terms

What is artificial intelligence?

Image from Wikimedia Commons

Artificial intelligence, machine learning, cognitive computing, deep learning, and related terms have become interchangeable jargon referring to AI. Although it’s hard to believe, the level of marketing hype around AI has even surpassed digital transformation.

To break through the hype and nonsense, I asked the Chief Data Scientist of Dun and Bradstreet to explain AI in straightforward business terms. It’s a complicated assignment, so I went to Anthony Scriffignano, one of the smartest, most accomplished data scientists I know. Anthony is a brilliant communicator, making him an ideal candidate to explain AI.

In the short video embedded above, Anthony gives a succinct introduction to AI for business people. Watch the video and enjoy un-hyped truth about an important topic.

Explain artificial intelligence for us

If there’s nothing else that our industry is good for, it’s creating terms that people can use that have ambiguous meaning, and can be taken to mean almost anything in any situation. And this is certainly one of them. So, it’s one of those things that you understand, but then when you try to define it, scholars will disagree on the exact definition. But, artificial intelligence collectively is a bunch of technologies that we run into. So, you’ll hear “AI.” You’ll hear “machine learning.” You’ll hear “deep learning,” [or] sometimes “deep belief.” “Neuromorphic computing” is something that you might run into, or “neural networks;” “natural language processing;” “inference algorithms;” “recommendation engines.” All of these fall into that category.

And some of the things that you might touch upon are autonomous systems ─ bots. Sometimes, we will hear talk of… Well, Siri is probably the most obvious example that anybody runs into (or any of the other ─ I won’t try to name them all because I’ll forget one), but things of that nature where you have these assistants that try to sort of mimic the behavior of a person. When you’re on a website, and it says, “Click here to talk to Shelly!” or “Click here to talk to Doug!” You’re not talking to a person; you’re talking to a bot. So, those are examples of this.

Generally speaking, that’s the broad brush. And then if you think about it as a computer scientist, you would say that these are systems processes that are designed to do any one of several things. One of them is to mimic human behavior. Another one is to mimic human thought process. Another is to “behave intelligently” ─ you know, put that in quotes. Another is to “behave rationally,” and that’s a subject of a huge debate. Another one is to “behave ethically,” and that’s an even bigger debate. Those are some of the categories that these systems and processes fall into.

And then there are ways to categorize the actual algorithms. So, there are deterministic approaches; there are non-deterministic approaches; there are rules-based approaches. So, there are different ways you can look at this: you can look at it from the bottom up; the way it just ended; or regarding what you see and touch and experience.

How do terms like machine learning, AI, and cognitive computing relate to one another?

They’re not synonymous. So, cognitive computing is very different than machine learning, and I will call both of them a type of AI. Just to try and describe those three. So, I would say artificial intelligence is all of that stuff I just described. It’s a collection of things designed to either mimic behavior, mimic thinking, behave intelligently, behave rationally, behave empathetically. Those are the systems and processes that are in the collection of soup that we call artificial intelligence.

Cognitive computing is primarily an IBM term. It’s a phenomenal approach to curating massive amounts of information that can be ingested into what’s called the cognitive stack. And then to be able to create connections among all of the ingested material, so that the user can discover a particular problem, or a particular question can be explored that hasn’t been anticipated.

Machine learning is almost the opposite of that. Where you have a goal function, you have something very specific that you try and define in the data. And, the machine learning will look at lots of disparate data, and try to create proximity to this goal function ─ basically try to find what you told it to look for. Typically, you do that by either training the system, or by watching it behave, and turning knobs and buttons, so there’s unsupervised, supervised learning. And that’s very, very different than cognitive computing.

What does “training a model” mean?

So, a model is a method of looking at a set of data in the past, or a set of data that’s already been collected, and describing it in a mathematical way. And we have techniques based on regression, where we continue to refine that model until it behaves within a certain performance. It predicts the outcome that we intend it to predict, in retrospect. And then, assuming that we can extrapolate from the frame we’re into the future, which is a big assumption, we can use that model to try to predict what happens going forward mathematically.

The most obvious example of this that we have right now is the elections, right? So we look at the polling data. We look at the phase of the moon. We look at the shoe sizes. Whatever we decide to look at, we say, “This is what’s going to happen.” And then, something happens that maybe the model didn’t predict.

So, now we get into AI. The way some systems work, not all, is they say: “Show me something that looks like what you’re looking for, and then I’ll go find lots of other things that look just like it. So train me. Give me a webpage, and tell me on that web page which things you find to be interesting. I’ll find a whole bunch of other web pages that looks like that. Give me a set of signals that you consider to be a danger, and then when I see those signals, I’ll tell you that something dangerous is happening.” That’s what we call “training.”

Why is training models complicated?

Sure. So imagine that I gave a whole bunch of people, and the gold standard here is that they have to be similarly incentivized and similarly instructed, so I can’t get, you know, five computer scientists and four interns … You try to get people that more or less have either they’re completely randomly dispersed, or they’re all trying to do the same thing. There are two different ways to do it, right? And you show them lots and lots of pictures, right? You show them pictures of mountains, mixed in with pictures of camels, and pictures of things that are maybe almost mountains, like ice cream cones; and you let them tell you which ones are mountains. And then, the machine is watching and learning from people’s behavior when they pick out mountains, to pick out mountains like people do. That’s called a heuristic approach.

When we look at people, and we model their behavior by watching it, and then doing the same thing they did. That’s a type of learning. That heuristic modeling is one of the ways that machine learning can work, not the only way.

There’s a lot of easy ways to trick this. So, people’s faces are a great example. When you look at people’s faces, and we probably all know that there are techniques for modeling with certain points on a face, you know, the corners of the eyes. I don’t want to get into any IP here, but there are certain places where you build angles between these certain places, and then those angles don’t typically change much. And then you see mugshots with people with their eyes wide open, or with crazy expressions in their mouth. And those are people trying to confound those algorithms by distorting their face. It’s why you’re not supposed to smile in your passport picture. But, machine learning has gotten much better than that now. We have things like the Eigenface, and other techniques for modeling the rotation and distortion of the face and determining that it’s the same thing.

So, these things get better and better and better over time. And sometimes, as people try to confound the training, we learn from that behavior as well. So, this thing all feeds into itself, and these things get better, and better, and better. And eventually, they approach the goal, if you will, of yes, it only finds mountains. It never misses a mountain, and it never gets confused by an ice cream cone.

How is this different from traditional programming?

The original way that this was done was through gamification or just image tagging. So, they either had people play a game, or they had people trying to help, saying, “This is a mountain,” “This is not a mountain,” “This is Mount Fuji,” “This is Mount Kilimanjaro.” So, they got a bunch of words. They got a bunch of people that use words to describe pictures (like Amazon Mechanical Turk).

Using those techniques, they just basically curated a bunch of words and said, “Alright, the word ‘mountain’ is often associated with there’s a high correlation statistically between the use of the word ‘mountain’ and this image. Therefore, when people are looking for a mountain, give them this image. When they’re looking for Mount Fuji, give them this image and not this image.” And that was a trick of using human brains and using words. That’s not the only way it works today. There are many more sophisticated ways today.

Video: When does AI make sense for business?

Please see the list of upcoming CXOTALK episodes. Thank you to my colleague, Lisbeth Shaw, for assistance with this post.

(Cross-posted @ ZDNet | Beyond IT Failure)