Phil Fersht
 
CEO and Chief Analyst 
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You just can't lose... with Chris Boos. Time for an AI reality check
June 22, 2018 | Phil Fersht

There aren't too many people you can listen to today where you feel all those sticky layers of hype just fall away from your brain, as this guy actually knows what he's talking about and (as we English love to put it) he just doesn't mince his words. So, after a terrific meeting with Hans-Christian (Chris) Boos, Founder, and CEO of leading AI platform vendor arago, I pinned him down to share some of his views with the HfS crowd...

Phil Fersht (Founder and CEO, HfS Research): Chris - you've been a terrific guy who adds so much energy and colour to the intelligent automation industry... but can you shed a little light on your story?  How did you find yourself setting up the business in 1995?  Was the focus on intelligent automation back then?  I thought we were all going nuts about ebusiness!

Chris Boos (Founder and CEO, Arago):  Phil - I originally wanted to do AI research at a university and then I saw how slow academic research is today with the way it is financed. I chose to do it inside a company instead. We could control the pace there. We setup arago to research general AI and my belief has always been that general AI is all about automation. If it is intelligence – even the quite boring artificial version – I guess you could say that smart automation was my goal, then.

Most people are surprised about the research phase. But if you look at most people who are doing significant work in AI they all plan or have done a roughly 20-year research phase. The one thing that is special about arago is that we financed it ourselves. We split the company down the middle, half was doing research and the other half was doing projects to get the money. That way we did not only get to do basic work on AI and make money to finance the work, but we also had a testbed for all our components in real businesses. A brilliant idea I cannot take credit for, it was my uncle who founded arago together with me who came up with the model. It worked out really well for us. We did pure research from 1995-2008, then used our own toolset to start automating IT operations and slowly turn it into a product and do some deployments till 2014, then scaled automating IT operations on top of more static approaches while collecting a dataset that is descriptive of all kinds of companies in all kinds of industries and 2017 we finally started applying AI generally in other industries and processes than IT.

By the way, 1995 was before the e-business boom started. I remember we did the first online banking on the web in Europe then and the page said, “your browser should support tables”. Can you still remember these days?

Did you ever expect to be where you are today? 

Absolutely not. I am still being surprised every day. If you had asked me about feeding animals with an AI a year ago, I would have looked at you like you just tole me the aliens had landed. Now we are feeding animals with an AI. This is what is so absolutely fantastic about the industry, there is a new frontier to be pushed further every day.

Fortunately makes up for all the crap you have to hear because everything that has the slightest bit of math inside is called AI these days. I would like to reverse that saying: It is only called AI as long as it does not work. As soon as it works, it gets a real name like “facial recognition” ???? 

So you've been talking about some very real and honest stuff regarding machine reasoning... what's this all about?  

In the area of AI, we have made one mistake since 1954 now. Whenever we found a new algorithm or were finally able to actually apply an algorithm we found a long time ago, we declared this algorithm to be the one solution to everything. This one-size-fits-all approach is not only stupid but lead to AI winters which were periods in research and commerce when no one would touch AI with surgical gloves – except for the crazy ones of course; did I mention 1995 was during such an AI winter? We are making exactly the same mistake again, by declaring deep-learning equivalent to AI. One algorithm will not be a solution for everything, it is a solution for a defined set of problems. This means it will fail miserably at other problems and also have clear limitations in the scope it was originally built for. Let’s stick with machine learning for a bit. The clear limitation is data. At some point, there will not be enough data to describe “now”.

I believe there are three basic sets of algorithms to be considered when building a general AI:

  1. Machine learning.  The ability to learn to recognize patterns and associate positive actions with them. This is like evolution, everything that behaves favorably survives, everything else dies. Adding a temporal memory to this system was what started deep learning with long-term-short-term-memory networks. But there are many other learning algorithms.
    In biology, we call this “instinct” and all species have them.
  2. Natural Language Processing.  Now this is where it gets tricky, because language has so much compression. Think of how many different pictures you can imagine on the 3-byte input of “cat”. Your implicit knowledge of context and an internal argument narrows what you understand when you hear “cat” in a conversation down to a very likely correct interpretation. Machines, unfortunately, do not understand anything, and they also lack all the context. This is why NLP is still one of the hardest parts of AI. There is no way to “learn” the meaning of language through machine learning (yet), especially because the context, is so volatile. This is why the hype around chat-bots has lead to a lot of disappointed customers. They work well if you can predict the dialog with a high degree of certainty, for example, if you offer a telephone line where people can call in sick you know that there are only so many ways to say “I am not feeling well” and the only result you want out of the dialog is “when will you be back”. But all other more general cases are very difficult. This is why you have to change your language structure quite drastically if you want Alexa, Google Assistant et al to do anything for you. There are a lot of very advanced algorithms in this area which are mainly very advanced statistics to create probable context, probable synonym, probable XYZ and then math this to a pre-determined understanding structure. These are the least self-reliant algorithms in the AI family.
    In biology, language was the single differentiating factor that made us as a species outperformed everything else on the planet. We no longer had to go through long cycles of observation as well as trial and error to have only the ones with the right solution to a problem survive. We could simply tell each other “if you see a Tiger, run away” and no evolutionary iteration was needed. This is a huge advantage which machines are completely missing.
  3. Machine Reasoning.  The one you were actually asking about. This was how AI started. The idea was to make a logical argument to find a solution to a given problem. The first attempt was to use decision trees to “write down the one and only answer for every situation”. This does obviously not work, because the more interesting a problem is the more different ways of reaching a solution there are. The industry moved from decision trees to decision graphs. Then we found out that logic does not govern the world and that ambiguity, contradictions, overlapping information, wrong information and unexpected events have a huge influence on how to really solve a problem. The type of algorithms that create a solution by outputting a step-by-step execution instruction for a more complex task by choosing the best step to take out of an existing pool of options and then the next and so on are called machine reasoning. The limitations of these algorithms used to be in the knowledge base because the maintenance effort of such knowledge bases grew exponentially and the benefit grew polynomially.
    In the world of biology, this is called “imagination” of if we want to be less philosophic the ability to simulate a bit of the future in our heads to make the right choices of what to do in order to reach a defined goal.

Looking at only one algorithm set to solve all problems in the world seems dumb, yet if you read about AI, it seems that machine learning has become synonymous with AI. This literally guarantees a bursting bubble once the limits of data availability are reached. My prediction is early 2019.

We have set out to combine these algorithms to produce a single engine with a single data pool to mitigate these problems and this is why we started in IT automation and are expanding to more and more complex automation across all kind of different industries.

And you've been quite poignant regarding your views on AI actually substituting human intelligence and how unrealistic a "singularity" is - can you share some of your candid thoughts here with our readers?  

The entire debate about singularity does not make sense, Phil. We pretend that simply by rebuilding the electrical part of the brain we get a self-conscious self-reliant entity, why should that happen? If you build the skeleton of a dinosaur you don’t get a dinosaur either.

Ok, to put this down in numbers. A large neural network used in deep learning has about a million nodes today. Is uses up the power of half a powerplant. An average human brain has 84 billion neurons and uses 20 Watts. According to Moor’s law, we can achieve rebuilding this by 2019 and I am one of the guys who believes that Moor’s law will hold. Yet, that is not all there is to the brain. The brain also has a chemical system creating a literally infinite number of configuration of the brain’s 84 billion neurons. Infinite because the chemical system is completely analogue. And then for good measure, there are a lot of well-reviewed research papers arguing that the brain also must have a quantum mechanical system injecting probabilities. So there are two entire dimensions we are missing before we can really reproduce a brain-like structure.

And even if we could… We don’t understand or know what consciousness and self-awareness are, do we. So how do we think we can build it? Is “by accident” really a good explanation? “Build the field and it will come” is definitely not the answer here. This is why all the talk about killer AIs and ethical machines is far too early. I am not saying there will never be a super-intelligent AI, but not in the new future.

That does not mean that current AI technologies cannot outperform us at tasks we have already mastered. Tasks that we as humans already have the experience for and thus tasks we can transfer to the machine. But why would we mind? A crane is outperforming my weight-lifting ability everyday and I think that is perfect, I have absolutely no desire to become a crane, do you?

What will AI truly evolve into over the next 10-15 years, based on your experience of the last two decades?  Is there any real reason why change will accelerate so fast?  Are just getting caught up in our own hype?

What will happen is that automation leaves the constraints of standardization and consolidation. With AI systems based on today’s tech, we can automate tasks, even if they only occur once and even if they have never been posed like this before.

I think I was a bit too abstract here. I believe that AI will make any process that we have mastered and that is not entirely based on language autonomous. Machines will most likely do 80% of what we are doing today. Which means that our established companies get a fair opportunity to catch up with the tech giants. This is why I believe that we need RPA as a transition technology, because it basically puts an API to everything that there is in the corporate world. On top of that, we can use AI to automate almost everything allowing every enterprise the wiggle-room to actually evolve 

So what's your advice to business and IT professionals today, Chris - how can we advance our career as this intelligent automation revolution takes hold?

I think in IT we are in a unique position. What click-data was for commerce, IT ops data is for the enterprise. IT ops data describe everything a company is doing and thus forms the foundation of applying the next generation of automation and autonomy.

The only thing we as IT professionals really have to do is open our minds. If we do so, we can revolutionize much of the business and not be the “laggards” who are slowing everything down as we were in the ecommerce revolution.  You know I am German, so I get to be blunt: I think we have to “grow a pair” and take on the risk of automating everything from IT, otherwise business will do it for us and then who needs IT?

And finally... if you were made the Emperor of AI for one week and you could make one change to mankind, what would it be, Chris?

Mankind? That is too big for me… It would have nothing to do with AI, I would force people to think rationally for at least 50% of the day instead of 0.5, but let’s not go that far or people will think I am a cynic.

Let’s say I was made king of AI in the enterprise world for one day. I would decree to stop every POC, POV, Pilot, or whatever other terms you can find for trying to be half-pregnant and force people to start doing things in production right away. There simply will not be enough speed if we keep on “trying”. As master Yoda said, “Do or do not, there is no try”. We really need to adopt this behavior pattern.

Thanks for your time today, Chris.  Am looking forward to sharing this discussion with our community.

Accenture, IBM, Cognizant, Infosys, Wipro and TCS lead the first Digital OneOffice Blueprint
June 10, 2018 | Phil FershtMelissa O'BrienAnirudh PillalaSaurabh Gupta

Digital is all about an organization's ability to respond to the needs of their customers as those needs happen - or even be smart enough to anticipate those needs before they happen. This is all enabled by interactive technologies to create those touchless interfaces with the customers.  Smart analytics and AI enable organizations to anticipate these needs based on the ability to recognize patterns and inferences over time, but nothing can really substitute for human intelligence to bring customers, suppliers and employees closer together, unimpeded by frustrating silos and legacy processes. 

Remember, every broken process chain, or poorly converged dataset, slows down an organization's ability to do business in real-time and stay ahead of its market.  Traditional barriers between front, middle and back offices hinder the true ability of companies to operate in this real-time, responsive and anticipatory digital fashion, which is why we coined the term "OneOffice", where the unification of digital business models, intelligent automation, analytics and creative talent is happening before our very eyes.

The HfS Digital OneOffice Framework (see below) describes how organizations must integrate their digital customer interfaces with their operations in order to fulfill and anticipate their customers' needs. It is the organizational end-state to survive and succeed in a world where digitized processes dictate how responsive, agile, cost-effective, predictive and intelligent firms have to be to stay competitive.  

To this end, we have delved deep into all the four dimensions of the Digital OneOffice, and conducted deep analyst discussion to aggregate service provider performance at delivering the sum of the Digital OneOffice parts:  

  1. Digitally driven front office
  2. Digital underbelly
  3. Intelligent digital support functions
  4. Predictive digital insights

HfS Premium subscribers can click here to access their full copy of the 2018 Blueprint Report: Digital OneOffice Services

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So how did the Winner's Circle service providers fair?

Accenture

Strengths

  • Well-rounded portfolio across OneOffice: Accenture has the best performance overall across the OneOffice portfolio, and a breadth of industry expertise to complement it. Accenture placed in the Winners' Circle for each of the Blueprint studies used to compile this OneOffice assessment.
  • Strong marketing operations capabilities to support integrated digital OneOffice offerings.  Accenture has 16,000 business-focused staff dedicated to delivering digital marketing assignments - a considerable asset that goes well beyond the firm's IT delivery.
  • Strong intelligent automation capabilities. Acquisition of GenFour and exciting partnerships, with significant investments, with the likes of Automation Anywhere, Blue Prism and IPSoft.
  • Winning with thought leadership: Accenture is well-known as a thought leader across many of the change agents as well as within individual industries. 
  • C-Suite relationships beyond IT.  Digital business and intelligent automation decisions are largely being driven by both IT and business C-Suite executives in the Global 2000.  Accenture has the combination of strategic relationships outside of IT, in addition to the managed services execution. 
  • Leveraging creative assets for CX and UX design: Accenture has developed an industry-leading focus on becoming a customer experience expert, as evidenced by its 30+ design agency assets, by the broadest portfolio of digital design assets in the services industry (click here for a full list of digital M&A in services.)

Challenges

  • Size can work in its disfavor: Its size and success have given Accenture a reputation as a premium, high cost, and less responsive organization. In particular, for smaller companies, just this perception in the market can steer buyers instead toward more niche specialized agencies and the attention, flexibility, and experience they receive from a smaller provider.
  • Finding the right culture balance: Accenture is well known for its results-driven, traditional consultancy culture, which will need to be balanced out or effectively blended with the more left-brain focused acquisitions in order to retain creative talent and remain generally effective.
  • Proving to the industry it can deliver the end-to-end Digital OneOffice portfolio: There is no doubt that Accenture can pick up strategic work and execute for clients, but being able to demonstrate to the industry it can deliver both the strategic design integrated with complex operational delivery - at scale - is still in its infancy.  Many of its competitors will fight hard for execution work where Accenture is delivering the high-end design and consulting. It needs to demonstrate the "one-stop OneOffice shop" is where it wins.

IBM

Strengths

  • Strong intelligent OneOffice offering: Market leading capabilities to drive the OneOffice underbelly (automation, security, cloudification) and neural networks (AI, smart analytics, blockchain, and IoT). Impressive development of credible global automation capability and several notable early wins.
  • Portfolio breadth: End-to-end and scaled IT and business process services across front, middle, and back-office.
  • Horizon 4 investments: Very strong investments and IP in horizon 4 (and beyond) technologies that will shape the future (e.g., Quantum Computing).
  • Design Thinking: Has made some considerable investments in recent years, but needs to align more aggressively with OneOffice approach
  • Watson: The analytics/cognitive powerhouse has a significant role to play as a cognitive virtual agent, an analytics resource that has huge scalabiity and a long-term investment area for firms with deep interests in their cognitive capabilities.

Challenges

  • Size can be a disadvantage: IBM is a large and complex organization, which makes it hard to seamlessly deliver all that it has to offer.
  • Translating tech to business outcomes: IBM is often perceived as a technology powerhouse, but one lacking the business translation and context to successfully apply emerging technologies.
  • Agility: Lacks the nimbleness and flexibility of smaller players.
  • Focus on cognitive may impede its ability to compete for design-focused end-to-end deals:  IBM has substantial credibility to drive analytics-driven, cognitive/automation projects, but its lesser focus (over the last couple of years) on true digital design may see it lose out to firms such as Accenture and Cognizant, where digital is firmly established at their core.

Cognizant

Read More »

To keep receiving HfS updates, make sure you register now!
June 10, 2018 | Phil Fersht

Still enjoying life now GDPR's cleaned up your inbox, but now realize HfS is the one you just cannot live without?

Let's be honest, you probably do need to keep up-to-date with the finest change-agent research on RPA, blockchain, AI, and much more, right? Then you really must register here to receive HfS' content, or update your email subscription to keep receiving us.

And time for a real Infosys Saliloquy...
June 07, 2018 | Phil Fersht

Salil Parekh, recently appointed CEO and Managing Director for Infosys took some time out of his busy schedule during his client partner conference to catch up with me to talk about his vision for all things Infosys and the future of services…

Phil Fersht, CEO and Chief Analyst, HFS Research: Welcome to your first HfS interview Salil! Maybe you could take us a little bit back to your early career. When did you get the appetite to lead one of the largest IT services firms in the world? You know, was this something you always wanted to do? Was this planned, or have you always been an opportunist?

Salil Parekh, CEO, Infosys: Thank you, Phil, this was quite an un-planned scenario for me. So, maybe when I finished with Engineering, a Master’s in Computer Science, and I was working with a consulting firm for years. Then we got acquired by a consulting and tech company, so I’d basically been in the same company for 25 years. And then this opportunity showed up a few months ago. It’s a tremendous privilege to have this opportunity. It’s one of those things you dream about, in your career, as you sort of think, ‘Maybe it’s possible,’ but when it happened, at least, for me, it was completely unplanned. So I’m delighted to be here, I wish I could plan such things, but I can’t [laughter].

Phil: So, how would you compare this new Infy experience with Capgemini, you know, both global services powerhouses, one with a Parisian epicentre, the other one Bangalorian, so – what haves been your observations?

Salil: Well, I think, Cap’s a fantastic company. I think I would focus much more on the strengths

Read More »

Finally the industry has credible RPA product benchmarks from 359 superusers
June 01, 2018 | Phil Fersht

As am sure most of you noticed, HfS quietly released the most comprehensive customer satisfaction benchmarking of the 10 leading RPA solutions, authored by Saurabh Gupta, myself and Maria Terekhova.  We covered 359 super users of RPA products (enterprises, advisors and service providers) across 40+ customer experience dimensions across the following 6 key dimensions: 

  1. Features and functionality
  2. Integration and support
  3. Security and compliance
  4. Flexibility and scalability
  5. Embedding intelligence
  6. Achieving business outcomes

As an example, here is how dimension 6, "Business Outcomes" came out looking across the products:

So why did we undertake this research?

Our industry is plagued by many consultants with limited depth in RPA, who have no access to product level data that supports the tough decisions facing enterprises. In addition, most analysts deliver these 2 x 2 matrices which offer very limited insight or value (and all look remarkably similar). It’s time to dispel myths and provide enterprises with unbiased, credible and highly statistically significant data. The HfS RPA customer experience benchmarks are designed to help enterprises with RPA product selection as they formulate their intelligent automation roadmaps.  

It's more than a report... it's an online RPA decision-support tool

In addition to the report, HfS is also launching an online RPA decision-support tool for enterprises to enable client-specific due diligence on RPA providers. This tool will allow HfS clients to customize the decision criteria and associated weights from the available 40+ customer experience dimensions. It will provide clients a customized report detailing the top three RPA products that the client should consider, based on the rich insights that HfS collected as a part of the RPA study. HfS analysts are also supporting RPA clients through collaborative ThinkTank sessions, half-day workshops designed to problem-solve and validate strategies. These ThinkTanks go beyond the data where HfS analysts can share HfS IP, perspectives, and experiences on RPA tool selection, best practices, and common pitfalls to avoid.

So take time to delve into the realities of RPA and some of the findings may just surprise you

The industry is still struggling to solve challenges around the process, change, talent, training, infrastructure, security, and governance. Our mission at HfS is to dispel this confusion and uncover the truth to successful RPA deployment. It's time to separate the hype and propaganda from reality - and here is the reality!

Premium HfS subscribers can access the HfS Benchmarking Report: Detailed Assessment of the 10 Leading RPA Products here

The G2000 is still cost-obsessed, but getting there now depends on process robotics, predictive data, OneOffice alignment and a whole lotta pain
May 27, 2018 | Phil Fersht

However which way we look at it, driving out costs from business operations still dominates the directives of C-Suites across the Global 2000 - just revisit our 2014 study to see how little has changed. Fast forward to today, and the only real differences, since then, are the methods to slake this thirst for cost elimination, as traditional operating models are no longer delivering much more than incremental value.

Our new State of Operations and Outsourcing Study, conducted with KPMG, covers the dynamics of 381 operations leaders from the Global 2000 and reveals these rapidly changing C-Suite directives to drive out their number one nemesis: cost.

Click to Enlarge

Traditional cost savings models are running out of steam, as robotics, predictive analytics, OneOffice and cognitive become the new operating value levers

Little tweaks here and there to delivery locations and headcount allocations are becoming less and less effective, as it becomes clear only the fundamental rewiring of underpinning data repositories - and the digitization of manual processes - are going to progress operations to a place where real efficiencies can be enjoyed. In addition to fixing data and manual processes that clearly hit that old cost button, C-Suites are also recognizing the dire need have their customer needs being addressed by their employees as and when they occur (OneOffice), and also to invest more in cognitive tech and machine learning to drive more value from their current pool of talent:

Click to Enlarge

Cost reduction mandates still fall well short, but expect to see them improve as data-driven initiatives bear fruit

The perennial issue here is clearly one where C-Suites rarely feel exhilarated by the cost reduction impact of their operations leaders.  Of all their mission-critical directives this year (see above), none disappoints them as much as their ability to impact cost reduction (only 28% are very satisfied), while there are much larger numbers of C-Suite leaders already a lot happier with their robotic process investments (40% 'very satisfied' and a further 30% 'satisfied').  However, as we continue to see this strong impact in these areas aligned to robotics, OneOffice, and predictive analytics, surely it's merely a test of time until we see these initiatives having greater visibility, in terms of ironing out unnecessary costs and inefficiencies in the system.

The Bottom-line: It's taken several decades, but our enterprises finally have no choice but to make fundamental changes to the very make up of their processes, data, and people if they are going to survive 

Ever since my first blog 11 years ago (right here), we've pretty much repeated the same conversation that's been continually refined over the years.  The only game changers have been the gradual need for less people to run operations as cloud-based software platforms take-hold, offshore talent is optimized, and the more recent introduction of robotic process automation solutions to remove manual workarounds and create broader digital processes, that can be aligned with common business outcomes and metrics. 

However, these changes are more fundamental than merely slimming down the number of cooks in the kitchen and making the food taste better:  it's forcing a complete rethink from ambitious firms to redesign operating frameworks where revamped business processes are enabling true digital business models, where emerging AI capabilities can be weaved in... where innovation is native to the culture of the firm and its people. Yes, it's redesigning the entire kitchen, not merely hiring some better chefs with better recipes. 

The toughest challenge is fixing many years of poorly-constructed data repositories, where the corporate IT ancestors that built them have likely long-since departed, and other IT stormtroopers from the midst of time have plastered on countless workarounds and spaghetti coding to keep the back end (somehow) functioning.  These are the deep, murky areas where it's frighteningly difficult for many firms to take the risk of investment and change to find their way out of the dark data ages.  Somehow ripping out the very fabric of what got you here is what you may have to do to survive in the future... and that can be one very painful, risky and costly experience.  Sure, you can keep papering over those yawning cracks, but the wallpaper just isn't working like it used to... 

The why, the what and the how of the HfS Digital OneOffice
May 21, 2018 | Phil FershtSaurabh Gupta

We've talked a lot about the HfS Digital OneOffice operating framework - it's the HfS vision for the business operations endstate for digital organizations:

The Digital OneOffice is where teams function autonomously across front, middle and back office functions to promote broader processes with real-time data flows that support rapid decision making. It’s where front, middle and back offices will cease to exist, as they will be, simply, OneOffice.

Why Digital OneOffice?

Digital organizations must have an operating framework that maps out how they have to operate in the future. Traditional operating models, while creating some incremental productivity value if managed effectively, struggle to drive the unification of digital business models with emerging technologies across a business's operations:

A true digital business cannot succeed without unifying front, middle, and back offices
Traditional approaches (organizational restructuring) have failed to have a purpose beyond incremental efficiency / productivity 
The Digital OneOffice is the organizational end-state to survive and succeed

What is the Digital OneOffice?

The Digital OneOffice focuses on real-time customer and employee engagement. OneOffice is:

Collaborative (Collective outcomes)
Unified (Without silos and hierarchies)
Dynamic (Agile and scalable)
Intelligent (Predictive, not reactive)
Responsive (Real-time)
Simple (Touchless and autonomous)

How to achieve Digital OneOffice?

The Digital OneOffice is the framework for achieving a true digital organization:

CX is not just fancy UI. Make CX the core of all your business operations from front to back.
Cost reduction is not a strategy. Drive organizational alignment and metrics that measure value creation, not only cost reduction. 
Weed out the people unprepared to change. Invest in an inclusive talent strategy, based people who want to learn and share.
Your tech infrastructure is everything. Automate, digitize, cloudify, and secure your organizational underbelly.
Build co-innovation relationships and shed legacy relationships. The partners who got you’re here may not be the ones to take you where you want to go.
Stop kicking the intelligent technology can down the road. It’s all here and now you need to make decisions on where you go with it
Stop thinking about the Future of Work. It’s already here...act now!

The Bottom-line: Traditional operating models have been focused on incremental improvements, not creating genuine frameworks for digital organizations

While traditional models such as outsourcing, shared services and global business services promote incremental efficiencies based on centralization of support functions and use of offshore to lower operating costs, none of these models have provided an ideal endstate for ambitious digital organizations.  Without having a true picture of how you want to operate in the future, you will be perennially be searching for short-term fixes to drive out further costs, and never be able to map out a strategic journey that will bring together your two most critical assets: your customers and employees.

Is RPA officially the new outsourcing?
May 17, 2018 | Phil Fersht

Just as many enterprises were running out of places to find more and more hidden costs they could quickly remedy through (yet) more outsourcing, along came their perfect new toy to unearth costs they had never thought possible to eliminate: RPA.  

Yes, folks, this stuff is just the thing to keep you occupied for the next few years to keep your greedy CFOs at bay - and even includes the word "robot" to conjure up images of human work displacement, creating hours upon hours of repetitive (robotic) debate at conferences from people who literally sprung from seemingly nowhere to become lifelong experts in this new dark art. 

And, oddly, most of these new RPA maestros seem to be exactly the same people who were hawking the delights of outsourcing just a couple of years ago.  Maybe the connection between outsourcing and RPA is a lot closer than we think?  So let's have a gander at the new findings from the 2018 State of Operations and Outsourcing study, conducted with KPMG across 381 Global 2000 organizations, where we questioned operations leaders about their intentions to keep investing in RPA and outsourcing. 

This data shows the tranche of operations leaders making significant investments in RPA and outsourcing, sliced by industry sector:

Financial services firms, where outsourcing is most mature, are showing voracious appetites to go down the RPA path

While banks and insurers are showing the smallest appetite (10%) to keep pursuing aggressive outsourcing strategies, they are right at the front of the queue (50%) when it comes to RPA.  Insurers were one of the first industries to explore BPO and offshoring twenty years ago, so it's little surprise that RPA is so appealing to these firms, where they can find completely new ways to mimic highly repetitive, intensive processes, plagued by manual workarounds, using smart

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Sticking to his Nitin
May 06, 2018 | Phil Fersht

Let's be honest, the services business needs dynamic leaders, if we're ever going to step up to being these innovators and true partners we keep claiming we are.

One such character I have enjoyed getting to know over recent years is Nitin Rakesh, who spent a good part of his earlier career at Syntel, eventually taking the CEO mantle for three years, until moving over to Mphasis just over a year ago, to revitalize the $1bn financial services focused IT services firm, which spent many years are part of HP, before being divested. 

Nitin is also very active in the thought leadership sphere as chairman of the IT services council for NASSCOM, and serves on the advisory broad for [email protected] (among other activities). But one of the things you'll get to know about Nitin is his brain typically works faster than most mortals, especially when it comes to his favorite topic about aligning technology to the needs of the customer, and working those desired outcomes right through to the back office, which is a philosophy very close what we believe in at HfS, with our Digital OneOffice conceptual framework

So let's hear a bit more from Nitin about how to get ahead in today's IT services industry, and what we need to do to be effective in the wake of intense competition and the leveling off of traditional IT services...

Phil Fersht, CEO and Chief Analyst, HFS Research: Good morning, Nitin. It's great to have you on here. To start with, I'd love to hear a bit more about you personally - you’re a technical guy, you're an engineer at heart. So how did you wind up running a billion-dollar IT services firm? Tell us where this all started and why you've been so successful at it.

Nitin Rakesh, CEO Mphasis: Thank you for that, Phil. I think I am an engineer at heart, I love building stuff. Early on I started experimenting with newer areas - as I came out of college, back in the days in the early '90s looking at how do you apply technology to things like image processing, character recognition. Those were very early days of artificial intelligence because you are teaching the software how to actually recognize handwriting.

So I think early on I got really excited about the impacts technology can have on our daily lives, and how we can change the world surely but certainly. I think from then I’ve never really looked back even though I've done a few stints in financial services. How do you apply technology and innovation? Back in the day, in the mid '90's, there was a field which is now also pretty prevalent called ‘Technical Analysis of the Markets’. And that was nothing but pattern recognition to see how do you analyze human behavior looking at the patterns in stock markets or their price behaviours.

So I think the theme started to get clearer to me over the years, but I've been lucky that I was at the right place at the right time as well. More importantly, I am really passionate about applying technology to everyday problems and ended up running a technology services company.

Phil: We got to know each other when you were at Syntel, but you've since taken over Mphasis, and now it's free of the HP empire (or former empire). So how is that business refocusing itself... and where are you taking it?

Nitin: I think this company has got some unique capabilities despite having gone through both shareholders in the last 12 years. I think we have retained and maintained our focus on applied technology. The company was founded by two ex-Citi bankers, so the focus was always applying tech to financial services and banking.

One of them was a business leader and the other one was a technical leader, a CTO. I think they built a techno-functional mindset into the business more than just a functional approach to applying problem-solving. I think it was always about embedded technology. And I think under EDS and HP, some them flourished, but some of them were impacted due to the overall global empire of HP, and the fact that we were a small piece of their overall business.

But as I came onboard about a year ago, we do have a fairly progressive shareholder who encouraged us to find our footing based on our areas of strength. What we've really been doing over the last 12 to 18 months is, essentially, differentiating ourselves by being an applied-tech firm that focuses on looking at how to apply new technologies to everything that banks, insurance companies and financial services firms do.

This is really about looking at, in the current age, how we make every enterprise customer-centric for their end customers and consumers, and how do you apply technologies to help them get closer to their customer in order to improve customer experience, reduce downtimes, offer targeted products and services with hyper-personalization?  And all of this at a lower cost, with a fast time to market. So that's kind of the mantra that we've set for ourselves.

Phil: A billion dollars in revenue: Surely, Nitin, that should be the ideal size to be big enough to be dangerous, but small enough to be sort of nimble and disruptive. What does this mean though, in reality? Can you share an example or two of how you can disrupt with your clients, while also delivering the bread-and-butter work that keeps the machine going?

Nitin: Absolutely Phil. That's a great positioning statement! We actually use a variation of that quite often. But I think our positioning almost always is that of a 'champion challenger". And from that, one, we obviously have the agility and the customer-centric focus on our side. We aim to give clients a personalized white glove service experience and we continue to invest significantly in our capabilities to stay ahead of the curve. In fact, there are multiple examples where we've been fairly nimble - but also aggressive - about going back to our clients and proposing to them things that challenge how they run their current operations, whether technology or business.

I'll give you a small example: Why should we not apply something like predictive analytics to an offering as standard as infrastructure application management? Why should we not turn AMS or an IMF into a big data analytics problem, and why should we wait for something to fail or break, so that we can go and fix it, which is (let's face it) the traditional IT outsourcing model?

So, I think, from that perspective, it means that we end up shrinking the overall footprint of the ITO team, but that's okay with us because I think that's the right thing to do for the customer. So, I think from our perspective, we've been fairly aggressive in moving clients along this journey of applying technology to traditional services as well.

And given that our scale is normally a fraction of some of the very large players, we are able to go back in and propose something very creative, even if it means that it actually shrinks the core and has an adverse impact on us as well. I just think that's the right thing to do. So that's how we are able to challenge the status quo, and in the process, carve out a position for ourselves.

Phil: One of the big discussion topics we talked about at our recent New York FORA summit centered on emerging technologies like automation, machine learning not being an end - they are just a means to get from one place to another. So, what are these places? What - in your view - is the real end-game for clients these days?

Nitin: Great question, Phil. I think I'm a big believer in the fact that every next technology isn't anything more than a tool, and what you do with it depends on how you are able to align it with one or two objectives. I talked about the fact that one of the biggest reasons why we are seeing fairly high degrees of disruption, especially in consumer-facing industries, is because, over the years, enterprises became so complex in the way they ran their back office systems and operations, that almost every business that's been around for 25-30 years is essentially run back-to-front what that means that the back office determines when you can launch the next product, the back office determines what's the next recycle for you to be able to make changes to your system, so you can have the new functionality.

The back office determines how much flexibility do you have, and so on and so forth. Whereas if you look at the new age, truly digital companies, they actually put the end customer in the middle of everything, and work backward from that. So how do you really pivot the focus of large enterprises from being functionally operationally back-office driven, to being customer-driven. And that's how you should think of applying all new technologies, whether it happens to be analytics, which should give you the ability to understand every customer, or whether

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Offshore outsourcing died with Trump. Now value-based partnerships are rising from the ashes...
April 28, 2018 | Phil Fersht

What a difference an election makes.  When we ran our State of Operations and Outsourcing study in 2014 (mid-way through President Obama's final term), Global 2000 enterprises were still planning to increase their short-term investments in offshoring their IT by more than 20%.  When we re-ran the study in 2016, offshoring intent was clearly dropping to a 12% intended increase (which is a realistic number for a saturating market), but this year it has nose-dived to a mere 5% increase, which is a clear result of the anti-offshoring sentiment that has hurt offshore-centric deals:

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I discussed this trend with one of the lead partners at ISG, the offshore outsourcing industry's largest deal advisor, and he shared that Trump's stance against offshoring was considerably slowing down the deal cycle for his firm, and he was even seeing some outsourcing deals going to the likes of Accenture and IBM because it created the façade that work was not being offshored (even though it was).  Yes, this is the kind of stuff that happens when a president likes to get fast and loose with his twitter account! 

However, while Trump's open attacks on American firms using offshoring stoked panic into many paranoid C-Suites, what really transpired was a rapid shift in how US firms are viewing their partnerships with global service providers. Today's reality is technology has become core to business competitiveness by creating new revenue channels made possible by interactive communications technologies with customers, by simplifying business operations to support the business with real-time data, and by supporting broader processes that respond to the needs of customers, as they occur.

Offshoring may be slowing, but the services business is in its best shape for four years

The healthy trend here, for the future of IT and business services, is the fact that the industry finds itself on the healthiest growth footing since 2013 - so clearly offshoring is no longer the primary driver behind IT services investments:

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President Trump merely speeded up the development of global services from a cost-reduction to a business-value proposition

Many enterprise leaders are clearly no longer thinking, "How can we shave some more cost off our annual IT budget by moving more work to India?".  Instead, they are thinking, "How can I get quality services delivered at competitive prices that take advantage of the cloud, automation, and global talent."  The subtle shift here is clearly one from an obsessive focus on low cost, to one of getting quality services as the industry matures, where there are many leverage points to find productivity gains, beyond merely relying on FTE rates.  The more pricing shifts towards outcomes, volumes and KPIs, the less visible offshoring becomes as a cost-lever. 

When you buy electricity, do you care where the supplier houses its generators?  When you use public cloud services, do you bother to question Google, Amazon or Spotify where they house their massive data farms?  It's the same when engaging with IT services firms to get work done: business operations leaders are barely thinking about where they are located anymore - and all President Trump has done is shifted the optics, compelled the leading India-heritage firms to make substantially more onshore staff investments - which they needed to do in any case - as the nature of IT work is driving the need for greater client intimacy and physical proximity between service delivery staff and client staff. 

Traditional outsourcing is being replaced by partnering, and "offshoring" is not even part of that conversation

Our recent study looking at digital transformation to the OneOffice reveals that the majority (57%) of the highest quartile of performers in the Global 2000 (based on revenue and profitability) view their primary service providers as supporting their digital transformation roadmaps, as co-innovation partners helping them achieve co-defined business outcomes.  Only a third viewed their service providers solely as a resource to provision skills and scale via a headcount model:

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This data speaks volumes - enterprises digital leaders need providers which can work with them to achieve outcomes that are increasingly challenging - most no longer requisition 500 developers per year to code in ABAP for strategic initiatives - that is a commodity practice today, usually delegated to lower level manager to lead.  Nearly all G2000 firms, today, have a Chief Digital Officer tasked with taking their companies through significant business model change, enabled by smart technology provided by partners which understand what is required.  Whether the talent for these strategic projects resides in Bangalore, Basingstoke, Bucharest or Baton Rouge is moot - this is about getting results where top talent is hard to source, and the location is just not very relevant anymore.

The Bottom-line: Trump did us a favor and ripped off the legacy Band-Aid for the services industry

Trump's stance on offshore outsourcing sparked two behaviors which have set up the future of services to be far more value-driven and business oriented: All the major Indian-heritage service providers have been aggressive adding 10,000+ staff right across North America and Europe.  Several are also embarking on ambitious acquisitions of niche onshore digital firms (both creative and tech-driven) to engage themselves higher up the foodchain within their clients and be considered for more lucrative digital engagements where there are deeply engaged with their clients redesigning business models that need sophisticated technical support.  So while the industry suffered from a couple of flat years trying to squeeze the last vestiges of life out of a dying body-shopping model, the new reality is a global delivery model that is now embedded in engagements where the focus is much more on business value and outcomes than prehistoric effort-based inputs.  We are also entering an era where the likes of Cognizant, Infosys, TCS and Wipro will cease to be called "Indian providers" and merely be referred to as global IT services firms.  Location is irrelevant... expertise most definitively is not.