Author: Yugal Joshi

Software Eats World, AI Eats Software … Ethics Eats AI? | Sherpas in Blue Shirts

Marc Andreessen’s famous quote about software eating the world popped up often in the last couple of years. However, the fashionable and fickle technology industry is now relying on artificial intelligence to drive similar interest. Most people following AI would agree that there is a tremendous value society can derive from the technology. AI will impact most of our lives in more ways than we can think of today. In fact, it often is hard to argue against the value AI can potentially create for society. Indeed, with the increasing noise and real development around AI, there are murmurs that AI may replace software as the default engagement model.

Artificial intelligence may replace software

Think about it. When we use our phone or Amazon Alexa to do a voice search, we simply speak, hardly using the app or software in the traditional sense. A chatbot can become a single interface for multiple software programs that allow us to pay our electric, phone, and credit card bills.

Therefore, artificial intelligence replacing software as the next technology shift is quite possible. However, can we rely on AI? Or, more precisely, can we always rely upon it? A particularly concerning issue is that of bias. Indeed, there have been multiple debates around the bias an AI system can introduce.

But can AI be unbiased?

It’s true that humans have biases. As a result, we’ve established checks and balances, such as superiors and laws, to discover and mitigate them. But how would an AI system determine if the answer it is providing is neutral and bereft of bias? It can’t, and because of their extreme complexity, it’s almost impossible to explain why and how an AI system arrived at a particular decision or conclusion. For example, a couple of years ago Google’s algorithms classified people of a certain demography in a derogatory manner.

It is certainly possible that the people who design AI systems may introduce their own biases into them. Worse, however, is that AI systems may over a period of time start developing their own biases. And even worse, they cannot even be questioned or “retaught” the correct way to arrive at a conclusion.

AI and ethics

There have already been instances in which AI systems gave results for which they weren’t even designed. Now think about this in a business environment. For example, many enterprises will leverage an AI system to screen the resumes of potential candidates. How can the businesses be sure their system isn’t rejecting good candidates due to some machine bias?

A case of this type could be considered an acceptable, genuine mistake, and it could be argued that the system isn’t doing it deliberately. However, what happens if these mistakes eventually turn into unethicality? We can pardon mistakes but we shouldn’t do the same with unethical decisions. Taking it one step further, given that these systems ideally learn on their own, will their unethicality become manifold as the time progress?

How far-fetched it is that the AI systems become so habitually unethical that users become frustrated? What are the chances that humanity stops further developing AI systems when it realizes that it’s not possible to create AI systems without biases? While every technology brings a level of evil with the good, AI’s negative aspects could multiply very fast, and mostly without explanation. If these apprehensions scare developers away, society and business could lose AI’s tremendous potential positive improvements. That would be even more unfortunate.

As the adoption of AI systems increases, we will likely witness more cases of wrong or unethical behavior. This will fundamentally question and push regulators and developers to put boundaries around these systems. But therein is a paradox: developing systems that learn on their own, while putting boundaries around that learning – quite a contradiction. However, we must overcome these challenges to exploit the true potential of AI.

What do you think?

AI: Democratization or Dumbing of Creativity? Pick your Cause | Sherpas in Blue Shirts

The earlier assumption that artificial intelligence (AI) would impact “routine” jobs first is not holding ground anymore. Indeed, we might be deluding ourselves by thinking that the time in which AI could be the most used interface to engage with technology systems is far in the future. But I’m getting ahead of myself. Let’s look at what’s happening today in the creative sector.

IBM Watson was used last year to create a 20th Century Fox movie trailer. Adobe Sensei is putting the digital experience in the hands of non-professionals. Google is leveraging AI with Autodraw to “help everyone create anything visual, fast.” Think about anyone, any one of us, taking a picture and then telling photo editing software what to do. No need to work with complex brushes, paints, or understanding of color patterns.

AI and creative talent

This is scary for creative people such as graphic designers, digital artists, and others who may consider artificial intelligence a job killing replacement of their skills, despite pundits’ claims that it will “augment” human capabilities. Antagonists proclaim that AI systems will at best reduce the overhead with which creative people deal. But removing overhead is just the first step; the next step is surely the creative. More so, AI systems can ingest so much data, and will increasingly rely on unsupervised learning to correlate so many behavioral traits to create compelling creative content that a human creative artist cannot possibly fathom or understand. Thus, these artists will begin leveraging AI systems to create exceptional, “unthinkable” user experiences that have no “baseline” of reference, but soon may be replaced by these very systems.

AI and businesses

Businesses have always struggled to hire highly skilled creative professionals, and have paid through the nose to secure and retain them. That they will be able to leverage artificial intelligence to take charge of creativity to drive messages and communication to their end users, rather than relying on creative experts, will make them extremely pleased. As the intent of most AI systems is to enable non-specialists to perform many tasks by “hiding what is under the hood,” businesses might not need as many specialist human creative skills.

However, despite this seeming upside of AI systems, their under the hood nature will create problems of accountability. The complexity of their deep learning and neural networks will become such that even the teams developing the systems won’t be able to provide answers to specific decisions they make. Thus, if something goes wrong, where should the blame be placed? With the creation team, or with the AI system itself? The system won’t care – after all, it’s just a technology – and the team members will argue that the system learned by itself, far beyond what was coded, and they cannot be held accountable for its misdeeds. This is scary for businesses.

AI and the impact beyond business

Imagine the impact this will have on the society. Although you can track back how any other technological system arrives at an answer, AI systems that are now supposed to run not only social infrastructure but also much our entire life won’t be accountable to anyone! This is scary for everyone.

I don’t want to add to the alarmist scare going on in the industry, but I cannot be blind to successful use cases I witness daily. People will argue that AI has a long way to go before it becomes a credible alternative to human-based creativity. But, the reality is that the road may not be as long as is generally perceived.

Digital Transformation: The Rise of New CEO, the Chief Everything Officer | Sherpas in Blue Shirts

There’s a mind-numbing alphabet soup of C-level titles in today’s enterprises. Beyond the standards, there’s also Chief Digital Officer, Chief Robotics Officer, Chief Automation Officer, Chief Cognitive Officer, Chief Customer Officer, Chief Experience Officer, and so on.

The Chief Executive Officer (CEO), of course, is the one on whom the organization most depends, as “the buck stops there.” Historically, the CEO typically engaged with the Chief Finance Officer, Chief Strategy Officer, and Chief Operating Officer. The CEO set the long term strategy, and the organization executed it. And the CEO was considered great if he or she could be the face of the organization to drive broader strategy, business development, and market leadership.

Digital transformation and disruption is evolving the role of CEO

But digital disruption is driving massive transformation in the shape and flavor of the CEO’s role. While expected outcomes continue to be largely the same, today’s CEO must be technologically savvy, and must possess a techno-centric business view, not only to be the champion of digital transformation but also to take along the other C-level executives.

As digital transformation shrinks “front-to-back” processes, the CEO needs to understand the newer, shorter value chain, and how it makes the enterprise more competitive and relevant. Understanding this new world will also assist the CEO to be the best judge of digital transformation in the organization, rather than completely relying on business consultants.

Unfortunately, a lot of businesses are doing the opposite of what is needed. They are creating layers between the business and CEO by creating titles that serve as the “channel” to the CEO or the ear of the CEO. While this worked before digital disruption started creating havoc, innovation can now come from any part of the organization, and the CEO needs to be connected to that part.

Indeed, the CEO should ideally be the driver of digital transformation. Therefore, if the CEO does not understand Twitter, he/she can’t proactively suggest that as a channel to the HR team. If the CEO does not understand mobility, he/she can never outthink disruption or reimagine the business model. Of course, the CEO would have business leaders driving such change, (e.g., the HR head or CTO), but without the CEO’s proactive involvement and understanding of these fundamentally disruptive models, the enterprise won’t be able to derive business value.

The new kind of CEO needed in the age of digital transformation

What enterprises really need is a new CEO – a Chief Everything Officer. This is a C-level executive who understands everything in the digital landscape…the market, competition, customer, and, of course, technology. This CEO directly understands how digital disruption can impact different parts of the organization in order to create a vision for the enterprise that cannot be obtained from reliance on other C-level executives. Sure, the CEO would have a lot more bandwidth if other C-level executives were driving digital adoption. But that bandwidth would be valueless as the organization would be set up to fail. The reality is that digital transformation is not a project, but rather a business in and of itself. And the CEO must drive it in order to create meaningful value.

Is your company’s CEO the “Everything” he or she needs to be to enable the enterprise to compete and thrive in the midst of digital disruption?

AI: Revisiting Future Shock | Sherpas in Blue Shirts

In his 1970 book “Future Shock,” author and futurist Alvin Toffler made the argument that the modern world disorients people as it creates so many overwhelming changes that we are unable to handle them. Almost 50 years after the book was published, I was struck by Toffler’s argument during a recent client engagement in which we were helping an enterprise identify virtual agents/chatbots for its customer-facing processes. All of the bots contained a healthy dose of artificial intelligence (AI), and each one was trying to push the envelope.

Is AI starting to overwhelm people to the point that they may get frustrated with developments they cannot fathom or use?

Every day we see and read about new use cases that “wow” us. We are amazed and bedazzled by advances in AI. And some are becoming increasingly commonplace in the consumer arena…just think smart homes.

On the flip side, there have been instances in which consumers have found dealing with these omnipresent home devices scary and frustrating. Humans have already strongly voiced that they don’t need bots to shop. And, feeling the need for peace in their home, they have switched off many of their home assistance devices.

Some may argue that the technology industry, driven by the high intoxication from the ivory towers of Silicon Valley, is getting way ahead of the people who are expected to be the eventual consumers of these technologies. The amount of new AI research and products coming every day out of these factories is mind numbing. A significant number of such products may not have any immediate utility, but they do indeed demonstrate the far-reaching power of such advanced systems.

There is an unending scare around AI, cognitive, and other advanced systems taking away jobs from human beings. In the case of virtual reality, people are entranced by engaging with virtual objects as if they are real. It’s fun, until they realize the negative impacts it can have on their day-to-day lives. And, instead of assuaging such fears, the technology industry continues to create use cases to replace human tasks with robots.

From an enterprise perspective, organizations need to proactively create an AI adoption strategy for their business. Though most now have some vision around using AI technologies, frighteningly few are preparing for the massive change management aspect. Their employees must be comforted around the impact AI can and will have on their lives. Indeed, the significant disruption AI technologies can create within a business context may require a very different approach than other technology adoption we have ever witnessed. Technology vendors need to focus on how AI-enabled systems are assisting or helping human beings. The use cases need to be very precise, clear, and friendly, not overwhelming and complex, which they currently are.

The problem is not AI technology. The problem is the way it is being introduced, and the hyperbole around it that may end up overwhelming a significant portion of the human race, leading to eventual burnout. We are humans, and should create technology for humans. If the very technology we create results in alienating a large percentage of us, we will have failed as a human race. AI systems need to be leveraged for enhancing human lives, not for creating technology marvels that overwhelm people and create the future shock.

AI Bots: Should Brands be Concerned? Yes, Very | Sherpas in Blue Shirts

Today, users can ask Amazon Echo or Google Home to pay their bills or check their account balances with simple requests following their respective “Alexa” or “OK Google” wake words. Outside the home, various banks are piloting Alexa integration to engage with customers.

Flash forward to tomorrow, when users can launch Amazon’s, Google’s, or another company’s artificial intelligence (AI) bot to book a shared ride car. The users will be impressed when the bot tells them “your car is on the way,” “your car will be there in three minutes,” and “your car has arrived.” They will also hold the AI bot accountable for delivery of the service (even though they ideally shouldn’t.)

But notice I didn’t say launch or hear from Didi Chuxing’s, Lyft’s, Ola’s, or Uber’s app. Indeed, here, the interaction is through the AI bot, and not directly with the brand itself.

While brands spend a fortune on creating exceptional experiences for customers at all touch points, this type of scenario, which will happen, eliminates most, if not all, of those touch points.

So, how should the “brand experience” change?

First, brands should not fight a losing battle. They must understand and accept that AI bots are here to stay, and design their user experiences around them. Brands need to consider AI as their new, or at least a potent, customer engagement model. And, even if brands create their own AI bots to engage with customers, they must align themselves to the market leaders, as users will only have an appetite for a limited number.

Second, there must be a consistent engagement experience, whether the user engages with a bot or directly with the brand. Moreover, frustrated customers will typically blame the brand, not the bot, if things go wrong. This is a concerning reality that brands need to accept and effectively strategize.

Third, this involves significant technology investments. The investments need to drive simplicity in the way services are designed and consumed. The bot world runs on simplicity. It runs on a catalog of services that are fungible, developed on open standards, and demonstrate integration fluidity. Technology that does not cater to this requirement will fall short in the new world. Though some brands may believe that closed-door development creates a better user experience, this won’t be the case in the increasingly complex bot world.

I understand that technology disruption that creates havoc with capabilities and initiatives to drive ideal customer experiences can be overwhelming for brands. And years of investments across channels, touch points, and journeys may appear futile if the bot indeed becomes the customer. After all, machines don’t really care much for “experiences.”

However, brands cannot forget that, irrespective of technology evolution and customer engagement model, they are in the business to create an exceptional customer experience. Brands that understand that their primary, or possibly their only, responsibility is to create customer delight will thrive in this mad digital world.

Gazing into Everest Group’s Crystal Ball: Enterprise Technology in 2017 | Sherpas in Blue Shirts

It is that time of the year again when we channel our inner psychic about enterprise technology trends. We expect these secular technology trends to play out over 2017 and into early 2018.

  1. The beginning of scary AI democratization
    2016 seemed like the year Artificial Intelligence (AI), and its subset machine learning, turned the corner. A lot of this has been due to the cloud-enabled ease in advancing computing resources, a pre-requisite for AI/machine learning. Self-learning and “intelligent systems” give rise to a multitude of applications. Technology giants such as Amazon, Facebook, Google, IBM, and Microsoft are leading the way through dedicated groups and high-profile hires for advancing the AI mandate. As the technology looks for sizable scale, we are going see the “democratization of AI” help broaden access to its immense potential. For example, Elon Musk’s Open AI alliance released Universe, a software platform for measuring and training an AI’s general intelligence across games, websites, and other applications. Google’s parent Alphabet is putting its entire DeepMind Lab training environment codebase on GitHub. Microsoft is also taking a multi-pronged approach to help make its AI resources available to anyone via the cloud. Although these moves are aimed at catalyzing innovation, only vendors with real skin in the game and an inherent understanding of AI’s potential stand to benefit, limiting the impact system integrators can have via the typical operating model (which is focused on incorporating the latest buzz words in pitch decks and sales collateral). While this democratization will help people accelerate the time-to-value of their AI-enabled initiatives by avoiding the heavy lifting, it will lead to a certain level of commoditization as well. Since every solution will bear the AI tag, enterprises will find it hard to separate the wheat from the chaff. The AI market resembles the cloud wave from some years ago where there was a clot of “cloud-washing” versus “true cloud” implementations. Although the buzz around AI and machine learning is visible from the profile of its varied use cases – whether it is a hedge fund streamlining most of its operational tasks using algorithmic model from its employees’ brains or a law firm hiring an AI lawyer for its bankruptcy practice. Even outgoing U.S. president Barack Obama weighed in, reflecting the importance of AI in shaping our future.
  2. Conversational analytics to focus on engagement
    While conventional analytics – which sounds quite like an oxymoron – will still be viewed through the lens of reporting-descriptive-predictive-prescriptive, conversational analytics and engagement solutions will continue to receive a tremendous fillip. We have analytics engines where one can almost talk or query in human language. At an overall level, this theme represents an amalgamation of product design, natural language processing (NLP), AI, and the platform economy for the right user experience. In the consumer world, Amazon has made it a reality with the Alexa-powered Echo speaker/personal assistant, while Google is trying to replicate that through its assistant-enabled speakers. One particular use case – chat bot – is increasing exponentially, and is now beyond the realm of fancy to tangible use cases across multiple industries. While Facebook Messenger is the most mainstream (consumer-facing) example of these phenomena, there are various interesting experiments across Google (Allo), Microsoft (Tay, Xiaoice), Salesforce (NLUI Server Demo), etc. New conversational analytics offerings will take inspiration from chat bots, and also try to become context-aware for greater value tasks.
  3. SaaS mess becomes messier
    The enterprise applications market is going to witness more bloodshed as legacy and SaaS natives continue to battle it out for supremacy, further stepping on each others’ toes. While Workday leads its surge beyond the HCM domain to financial management, SAP and Oracle will put their considerable resources behind their cloud push. As these players (and others) continue to eat into each others’ pie, there will be an increase blurring of lines between segments and dilution of a unique value proposition. Also, large independent software vendors have confused the market by masking their hosted offerings as SaaS (read: more cloud-washing), which has increased the competitive intensity. Additionally, the SaaS-ification of everything has left the enterprise application landscape looking very messy. CIOs have a considerable governance and management issue when it comes to their app portfolio, upgrades, patches, visibility, usage, etc. You get a feeling that something’s gotta give, and there will be more consolidation among SaaS vendors in the race for market dominance and scale.
  4. Workplaces to move the needle on productivity versus just engagement
    The emergence of Facebook at Work and Slack, (among others), represents the true vision of what is often called the “consumerization of IT.” This refers to the confluence of consumer imperatives such as seamless user experience (UX), BYOD/CYOD convenience, and boundary-less communication within an enterprise IT framework. Digital technologies are reshaping the workplace of the future, while enterprise applications tend to be stuck in a bygone era. Slack/Facebook and their ilk aim to redefine this paradigm. Microsoft has also bought the bait, and launched Teams aka “Slack-killer” in November 2016 (selling it as a part of the Office 365 subscription suite). The focus of these changes has largely been on improving employee engagement, while focus on productivity has been diluted. We expect enterprises to focus more on productivity through differential strategies. Buyers tend to approach these policies in wide variance depending on their business and culture. Think Amazon, which abhors meetings and has a ruthless focus on execution, or Apple, which actively encourages meetings as ideation pods, coming from a culture which is obsessed with making the best products. Bringing machine learning into the mix can help coalesce people, organizational resources, and data. True that workplace collaboration and productivity seems almost unrecognizable.
  5. The automation tsunami will begin to gather speed
    Automation dominated headlines (and earnings calls) in 2016, as enterprises and their service partners alike realized the widespread implications of this technology wave. We believe that the impact on the jobs market is just starting as automation takes roots in mature people-intensive processes/functions. The short-medium kerfuffle will be focused on job losses and pricing pressures, with more pertinent questions around the fundamental nature of employment going forward. 2017 will continue to see similar headwinds, with the bulk of the bloodshed still a couple of years away. Beyond the immediate hullabaloo, the automation narrative is much more profound. While machines can already perform many forms of manual labor, the workforce needs to be reskilled/upskilled to remain relevant as the machines move toward more cognitive tasks. Contrary to popular belief, any wave of industrialization such as automation almost invariably leads to a redistribution in the profile of work, with employees focusing on higher value processes/functions. We are just getting started with the immediate pain that precedes an upward shift in the workforce. Unlike others waves of hyper industrialization, e.g., when cars were invented, there are less systemic problems for technology to solve. Riding this wave will require massive social changes in terms of organizational design, training programs, incentive structures, STEM education, demographic policies, etc.

While most of these themes are already playing out in certain shades, they will gather more wind beneath their sails as we dive into 2017. Although Niels Bohr presciently remarked that “prediction is very difficult, especially if it’s about the future,” we would love to hear what you have to say about enterprise technology in 2017 and beyond.

AI Bots for Strategy-in-a-Box? This Is Not a Google Problem | Sherpas in Blue Shirts

Most, if not all, of us rely on some form of Google search these days to accomplish our tasks. And because of its ease, we tend to be unwilling to say no to questions because we know we can Google to get the answers. By proxy, this has deluded us into believing we are “experts” on everything.

Bots for strategy-in-a-box
How does this relate to Artificial Intelligence (AI) enabled-bots in a business strategy context? AI is disrupting every walk of the life. The likes of Google (Alphabet) DeepMind, Facebook FAIR, IBM Watson, and Microsoft Cognitive Network Technology are demonstrating increasing use cases by the day. All these platforms crunch massive amount of data to become intelligent over a period of time.

As strategic decisions and long-term initiatives require huge amounts of data to be churned, AI bots are ideal candidates to assist, particularly because it’s not humanly possible to keep track of the multitudes of parameters that must be factored into development of a business strategy in today’s environment. Thus, strategic leaders may have to rely on a second “expert” going forward…an AI-enabled machine expert, that is.

Can the data crunching go so far that enterprises won’t need strategic leaders at all anymore and, instead, will be able to pretty much leverage a virtual agent to create their business strategy? Can this data crunching make the bot a strategy “expert” that can design a strategy out of the box?  Let’s step back for a moment.

Who needs a 10-year strategic plan?
I have argued multiple times that the days of creating a long-term enterprise strategy are well over. Given business and technology disruption, it is becoming impossible to see beyond your nose, forget 10 years. Can an enterprise afford to create a 10-year strategic plan? Can any business leader put her hands on her heart and say she believes in that strategy?

I doubt any enterprise can make that kind of long-term commitment. While a long-term vision used to be the differentiator between a great company and its peers, the differentiation point is quickly becoming how nimble the strategy is to incorporate and adapt to the rapidly changing business environment. Such a dynamic strategy needs to be built on massive amounts of data that incorporates parameters, including disruptions from outside a given industry, which the human mind cannot fathom. For this, enterprises need AI.

Back to strategy-in-a-box from a bot
If the above is the case, should an enterprise opt for a strategy-in-a-box rather than a strategic planning exercise? Can a bot create an enterprise’s strategy roadmap sans business leaders or maybe with just a little help from them? This may sound far-fetched, but it is certainly possible.

One can argue that strategic leaders rely on their experience, intuition, and other factors beyond data to make decisions and create an enterprise vision. While this experience and intuition were valuable when the business environment was largely stable and “known,” in the rapidly changing world these “assets” could be counterproductive. Strategy leaders experience will continue to hold value but less than what is generally thought.

If the enterprises do not need or can’t afford a long-term strategy in this rapidly changing world, why would they need the experience and intuition of strategy leaders? How is this experience that was accumulated in the last 30 years relevant for the coming years in such a fast changing technology environment? Moreover, an AI-enabled bot can possibly compensate for some of this experience and intuition through other parameters such as correlating seemingly uncorrelated data.

Adopting bots as a fulcrum of strategy development may go against the general perception that AI-enabled systems will augment, rather than replace, human powers. I, for one, don’t buy that “enhance human” argument entirely. In fact, humans may be assisting machines to make better decisions, rather than vice versa.

I think next-generation AI, automation, deep learning, and cognitive systems will definitely result in job losses, and we should be prepared for it. The argument that technology in the long term helps create more jobs has been sugar coated, and no one talks about the fact that disruption can create havoc in the short term. And people being impacted by this mayhem see no long term.

However, this is a reality from which we cannot escape. Enterprises need to ensure they are using AI as much as possible in their strategic planning. Believing that AI is only suitable for basic tasks will set them up for disaster.

Therefore, enterprises should not confuse AI-enabled bots as “experts” who rely on Google… “experts” who may not know but still answer. AI-enabled bots will be credible experts who rely on data, massive data, and their own intelligence.

The bottom line is that while your enterprise may not yet be ready for an AI-enabled, bot-developed strategy-in-a-box, you must take baby steps toward that future.

Bots for Enterprise Collaboration: Why We Can’t Say No Anymore | Sherpas in Blue Shirts

Nearly every executive we speak with during digital services research bemoans the issue of enterprise-wide collaboration, and why, despite so many enabling platforms (one proudly counted 29), they struggle to get meaningful information out to the intended user in a reasonable time.

Indeed, enterprises have invested a fortune to ensure knowledge management platforms, collaboration platforms, messaging systems, documentation system, and what not make the required information available to the seekers. They have built extensive enterprise search functionalities across these platforms to help anyone looking for the information. But, it simply does not work. As enterprise search tends to be highly siloed, and CRM, HCM, F&A, Procurement, HR, and other platforms rarely talk to each other. This means information seekers need to query multiple systems, and/or multiple people, to unearth the needed information.

Enter the bots, without the hype. The key agenda of top enterprise collaboration companies such as Microsoft and IBM is to build artificial intelligence (AI) into their collaboration platforms to make it intuitive for users to receive information and jointly solve problems. Smaller but innovative tech companies, such as Slack, are trying to introduce more learning capabilities into enterprise collaboration and search platforms. And with Facebook and Google increasing their focus on this segment – with all players focusing on a massive cross application data crunching engine which can answer queries in a short time – the landscape will rapidly transform.

So, for example, an executive could simply ask an AI bot for the information she requires, rather than sending emails to ten different people. The bot could then crunch the massive data across the enterprise, in all the applications, and get the right answer. But the possibilities extend far beyond this. Think about bots acting not only as servile assistants that answer when the master asks, but also tell executives they are wasting too much time on finding requested information and suggest a remedy to move forward.

Now, think about executives needs that resides outside of the enterprise. Trends such as, IoT are clearly showing that partnership is the lifeline of any digital business. However, this necessarily implies close and fruitful collaboration between the partners. The existing enterprise collaboration and search platforms, which are struggling to meet basic demands, are just not geared to meet this cross application, cross team, and cross companies collaboration. With bots coming into play, there will be tremendous productivity gains for all stakeholders, including faster time to market and better collaboration.

While its true that most technology companies have just made initial plans to enhance enterprise search and embed AI+bots into their collaboration platforms, I still believe we’re not far from a time in which bots helping executives will become a reality in enterprises. Yes, there will be a lot of investments, change management, and user education required; however, this is nothing different than adoption of other technologies.

My sense is enterprises that adopt these technologies sooner than later are bound to reap healthy rewards. Those that embrace bot-enabled collaboration as a “must have,” without which survival will increasingly be difficult, stand to gain significant competitive advantage and innovation.

Google acquires APIGEE – APIs to Overhaul Enterprise Technology | Sherpas in Blue Shirts

APIs have been around for a long time; however, the hyper-connected convergence of digital technologies and the increasing maturity of the digital ecosystem as a business model have made APIs a priority across industries. They have become the keys to unlocking a digital ecosystem (also commonly known as an API economy) that includes digital value chain, digital manufacturing, digital marketplaces, and an ecosystem of connected devices as adoption and advancement of technologies such as cloud, IoT, mobile, and analytics continue to increase.

The chart below explains a basic API value chain

Google acquires APIGEE

API management plays a critical role to help enterprises get maximum value out of their API strategy, hence we are witnessing an increase in activity in the API management space.

As enterprises accelerate their journey to build a digital ecosystem, technology companies like AWS, Dell, IBM, and Microsoft among others are helping enterprises on their digital transformation journey with tools and technology platforms. APIs and API management play a critical role here, with examples of investment that include IBM API Connect, Azure API Management, Oracle API manager, CA API management, and Amazon API Gateway among others. Recently Google announced its plan to acquire APIGEE, an API management company. The deal, for US$17.40 per Apigee share in cash for a total of US$625 million, is subject to shareholder and regulatory approvals. This is a significant acquisition for Google as it looks to enhance its enterprise cloud offerings. Adding APIGEE’s technology to its Cloud Platform provides Google with a more compelling value proposition for enterprise customers looking to move IT to the cloud.

Key benefits for Google:

  • API management as a differentiator/value-add for its enterprise cloud offerings
  • APIGEE acquisition brings tools that will help Google better manage its own set of APIs
  • Enhance experience for developers on its platforms
  • Enhance Google’s play in the container market, with its planned Integration of Apigee tools with Kubernetes

In the API management space, several specialist API management firms have been acquired by bigger technology firms looking to offer integrated (value add) services to their customers.

  • In June 2016, Red Hat acquired 3Scale, an API management firm
  • In January 2016, Axway acquired Appcelerator, which provides a framework for building and running APIs called Arrow
  • In April 2013, Intel acquired Mashery, a firm specializing in API management. However, Intel sold Mashery to Tibco in August 2015

We wonder what would happen to other API management specialists such as Akana, Cloud-Elements, Pokitdok, and WSO2 among others – will these also be scooped up by larger technology companies?

API management platforms are used by enterprises to manage their burgeoning need to open up their systems and expose functionality to the outside world due to digital transformation and the need to leverage innovation outside enterprise boundaries. Some implications of the increased activity in the API management space are listed below:

  • Consumer technology firms are pivoting their offerings to target the enterprise market
  • Enterprises need to look at API management as a separate stack in their IT systems
  • A key differentiator for enterprises in the age of connected digital ecosystem is the ability to offer high availability, security, and scalability of its APIs
  • Enterprise technology teams with modern infrastructure and strong API culture are able to attract better talent
  • Increased competition among technology firms that promise to play the role of efficient enablers in the enterprise digital transformation journey – leading to a wave of acquisitions and consolidation as technology firms look to become one-stop shops for all the technology requirements
  • Containers take API design, deployment, management, and integration to the next phase
  • Enterprises can accelerate their DevOps journey with APIs – API management embodies core DevOps principles of continuous delivery by modeling and governing the lifecycle of an API and provide developers and administrators with the needed tools and transparency

Are there other implications you believe should be considered? How do you see the API management landscape shaping up?Google acquires APIGEE

Digital Transformation: Design-led, Consulting-led, Technology-led … How about Client-led? | Sherpas in Blue Shirts

As part of Everest Group’s digital services research, we come across all types of digital transformation providers, including design agencies, consulting firms, technology companies, and IT service providers. The key focus is around initiating client conversations in whatever way possible, and the typical approach is “led” by something.

Design-led digital is the most talked about and hyped. Given the massive opportunity, digital service providers want to keep every piece of a client’s budget pie by getting into a sole-source model. Thus, many have invested in design studios to dazzle clients with next generation technologies in an Apple showroom-like experience. The assumption is that clients will come to the studio and fall in love with the digital service provider. Indeed, some providers have become obsessed with their digital studios, thinking they are the end, rather than the means.

Consulting firms and even systems integrators have acquired design companies, assuming that will enable them to deploy “design in technology” concepts. With these new capabilities, they see everything from a design perspective, regardless of what the client is asking for.

And almost all of them are significantly investing in digital talent to drive technology-led conversations with clients, e.g., how enterprises can use mobility, analytics, or social media to impact their business. Strategy consulting firms are taking the lead here, and rapidly improving their technology capabilities. They realize that strategy-led conversations may have limited value for some clients, and that they risk losing business to peers with better technology capabilities.

Each of these digital providers wants to have a one-on-one conversation with prospects, and influence their decisions. And to that end, they are bringing in a plethora of digital services to build credibility, drive prototypes, and develop digital solutions.

But what’s missing in all of these “led” approaches is the client’s input, desires, struggles, and end goals. Digital providers imply that clients do not really understand digital transformation, and that they need a service partner’s assistance to even think about digital disruption, making digital transformation something that is “done to” clients’ business, rather than “with” them.

For digital service providers, everything has become a hammer and every client a nail. But in their quest to be everything to everyone, their fixation on design, consulting, or technology is not valuable for them or for their clients. Their focus should be on hearing their clients and solving their problems, rather than imposing their predisposition around the “correct” way of digital. Digital providers that fail to understand this, and continue to have a “spear” in the market, will not succeed in the long run.

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