OVERVIEW OF THE SEVEN CORE TECHNOLOGIES DRIVING … · SEVEN CORE TECHNOLOGIES DRIVING DIGITAL TRANSFORMATION . 8/9/2016. FUTURUM PREMIUM REPORT | 2. TABLE OF CONTENTS. Executive

2016.2020

Published:TBD 2016

DANIEL NEWMANPrincipal Analyst

SHELLY KRAMER Principal Analyst

OLIVIER BLANCHARD Senior Analyst

OVERVIEW OF THE

SEVEN CORE TECHNOLOGIES DRIVING DIGITAL TRANSFORMATION

8/9/2016

FUTURUM PREMIUM REPORT | 2

TABLE OF CONTENTS

Executive Summary

3

4

5

11

14

17

23

27

34

37

40

Executive Summary

Introduction

Section 1: Big Data

Section 2: Cloud Computing

Section 3: Cognitive Computing (AI)

Section 4: Mobility

Section 5: The Internet of Things (IoT)

Section 6: Virtualization

Section 7: 3D Printing

Report summary, key takeaways, and recommendations

Resources

Copyright © 2016 Futurum Research, LLC All Rights Reserved


Futurum Research provides research, insights and analysis to the market that help tie leading and emerging technology solutions to strategic business needs. The purpose behind each of our reports is to help business executives and decision-makers gain a better understanding of the technologies driving digital transformation, connect the dots between the practical business requirements of digital transformation and the forces that impact employees, customers, markets and experiences, and take appro-priate action regarding critical digital transformation opportunities.

This report focuses on the seven core technol-ogies currently driving digital transformation today. They are: Big Data, Cloud Computing, Cognitive Computing (Artificial Intelligence), Mobility, the Internet of Things (IoT), Virtual-

ization, and 3D Printing. This report provides qualitative and quantitative insight into each technology, their rate of adoption, their val-ue to business ecosystems, and their present and future capabilities.

EXECUTIVE SUMMARY AND FUTURUM RESEARCH OVERVIEW


Tomorrow’s technology adoption challenges are, at their core, the same whether organizations belong to B2B, B2C, or B2G sectors. These sev-en technologies are already driving innovation, business systems evolution, and new catego-ries of commerce across every vertical. They are the technological building blocks of tomorrow’s economy the way that personal computers, the web, and traditional IT transformed business a generation ago. This disruptive new wave of innovation and tech-nologies is why Digital Transformation has become the most vital strategic imperative for businesses

for at least the next decade. Digital Transforma-tion is business transformation. It is the accelera-tion of business processes, models, practices, and experiences all with a view toward fully leverag-ing the capabilities and opportunities technology presents to improve performance across the en-terprise in a strategic manner. Although each of these technologies is, by itself, a game-changer in its own right, it is important to understand how the combined, layered integration of these tech-nologies makes them exponentially more disrup-tive and powerful than a traditional piecemeal or siloed approach to their adoption.

Tomorrow’s technology adoption challenges are, at their core, the same whether organizations belong to B2B, B2C, or B2G sectors. These sev-en technologies are already driving innovation, business systems evolution, and new catego-ries of commerce across every vertical. They are the technological building blocks of tomorrow’s

economy the way that personal computers, the web, and traditional IT transformed business a generation ago. This disruptive new wave of innovation and tech-nologies is why Digital Transformation has be-come the most vital strategic imperative for busi-nesses for at least the next decade.

INTRODUCTION

BIG DATA MOBILITY

THE INTERNET

OF THINGS

NEW PROTOTYPING

AND RAPID PRODUCTION

TECHNOLOGIES (mostly 3D

printers)

VIRTUALIZATIONwhich encompasses several

sub-categories, including but not limited to the

following: Virtual Reality, or VR for

shortAugmented Realityand Mixed Reality

Industrial virtualization

CLOUD COMPUTING

COGNITIVE COMPUTING (also referred to as Artificial

Intelligence, or AI for short)


SECTION 1:BIG DATA

Overview of the seven core technologies driving the next wave of Digital Transformation

What it is and why it matters

As consumers, employees, devices and systems around us continue to generate massive amounts of data, the ability to capture, store, access, process, analyze, convert, and act on these data becomes a greater challenge. Legacy IT systems are not equipped to handle data in such volumes, or at the necessary speeds. Technologies that focus on Big Data are designed to handle the load and scale of growing data demands for organizations that older technologies cannot.

Big Data is the core technological bridge between the information management and computing technologies of the 20th century and those of the 21st century; It is also a bridge between the way that organizations used to make critical day-to-day decisions, and the far more efficient data-driven decision-making that market leaders enjoy today.

From financial forecasting and logistics to retail design and advertising, Big Data is the foundation upon which smart businesses build their operations.

Big Data empowers organizations of every size and every vertical to better understand their markets and improve the efficiency of their infrastructure. It also helps accelerate research and design and model the impact of a decision before making it. Big Data helps deliver better, more tailored customer experiences, and measure business outcomes in real time. It is also the engine behind the scalable automation of tasks that once required massive amounts of manpower to manage. Big Data makes every other technology in this report possible.

What you need to know about Big Data

Big Data can be difficult to integrate into the enterprise. The more complex and diverse the organization, the bigger the challenge seems and that is daunting to companies. There are two major obstacles when it comes to the adoption of Big Data. The first is reluctance by CTOs to abandon legacy IT infrastructure that appear to still fulfill the basic needs of organizations. This “good enough” mentality is still prevalent in many organizations whose success over the last two decades was sustained by in-house IT investments. It isn’t so


much that the need to adopt Big Data solutions isn’t recognized by decision-makers. It just isn’t seen as a priority.

The second obstacle is the complexity of integrating Big Data into a business model, especially when starting from scratch. The larger the organization, the more insurmountable this obstacle may seem. It is critical to understand that Big Data adoption is not as simple as adopting one piece of technology, like virtualization or collaborative networks. Big Data adoption ultimately requires a complete redesign of an organization’s IT infrastructure. That includes interconnected layers of technology, and entirely new types of skills, roles, methodologies, and processes. All of this has to be seamlessly integrated at scale, by hardware, software, and people so that data can be properly captured, stored, filtered, queried, analyzed, and visualized.

Big Data Obstacle Solutions

Big Data adoption requires a true commitment from an organization’s leadership to put data and data-driven decisions at the heart of the business’ technology investments. It cannot be treated as a digital add-on, or a mere building block of an otherwise business-disconnected digital strategy.

Let’s address both obstacles in turn.

1. Obstacle Number One: The reluctance to abandon legacy IT infrastructure that appears to still be “good enough” for the organization.

At no time in history has an organization of any significance ever benefited from rejecting innovation, technological advances, and progress. Business graveyards the world over are littered with the graves and ghosts of companies that stubbornly refused to adapt to change. Business leaders who run a “Good enough” company are rarely successful. Nor are the companies they lead.

To illustrate the degree to which market leaders approach the importance of Big Data adoption, note that while only 52% of organizations around the world currently see Big Data as an opportunity for their business, a whopping 96.3% of companies considered as leaders in their industries view

Big Data an opportunity for their business. Also note that 70% of companies with 1,000 or more employees regard Big Data as an opportunity for their business.

Our conclusion is that the acknowledgment of the massive opportunity offered by Big Data is a hallmark of market leadership. This combination of awareness about innovation and focus on technology adoption is a trait common among market leaders, and one which generally helps fuel their success. Good bets ultimately pay off.

Our other conclusions from these points of data:

The larger the organization, the more likely it is to understand the value of Big Data to their business.

The value of Big Data may still not have been clearly articulated to the SMB (small-to-medium sized business) community.

Access to Big Data services and capabilities may still be (or seem) cost-prohibitive and /or physically limited by the SMB community.

Obstacle Number Two: Big Data adoption is too complex for organizations of a certain age and size.

There is a difference between something being very complex and something being too complex. Digital transformation and Big Data adoption are complex endeavors to be sure, but with the right plan and proper pacing, both can be achieved with minimal friction, resistance, and hurdles. The trick to integrating Big Data into any business model, no matter how large and layered, is simply to do it one piece at a time, and one milestone after the other. It does not have to - and should not - happen overnight.

This is why most large organization incorporating Big Data into their IT infrastructure and LoBs (Lines of Businesses) today are doing so in piecemeal fashion: Rather than upending their legacy investments, they are turning to hybrid IT models which combine legacy IT investments and new Big Data solutions. (Similar parallels exist with


Cloud Computing, which we will cover in the next section of this report.) In short, adopting hybrid IT models allows organizations to adapt to Big Data capabilities at their own pace without upending their technology infrastructure overnight. The biggest challenge posed by this mode of adoption comes when mission-critical systems are at play. It is one thing to bring entirely new capabilities into an IT environment. It is another to replace mission-critical systems altogether. Doing so requires time, testing, new processes, audits and a large degree of risk management.

What is important to keep in mind is that one organization’s hurdle is another’s opportunity: Organizations that can learn to adapt quickly and well to these types of changes -agile ones - will be able to evolve faster than companies that struggle with technology adoption. In other words, we now tend to look at Big Data adoption as a window into

an organization’s operational agility and adaptability. For instance, because of their size, SMBs willing to invest in Big Data capabilities and services should enjoy agility and velocity advantages over larger, more complex organizations.

Building Internal Competencies

One of the ways that some organizations are outpacing their competitors in this technology adoption race, aside from prioritizing Big Data adoption and Digital Transformation, is by gradually building, then scaling internal competencies. The most successful and agile among them do so not just by focusing on their IT departments but on their organizations as a whole, particularly at the LoB level. For them, Big Data adoption is more of a business integration play than a technology investment play. The more Big Data capabilities that can


find a home in specific business processes that yield measurable results, the faster and more smoothly Big Data can become integrated at scale. Notable examples are Amazon, Disney, Capital One, and Starbucks.

This buildup of internal competencies can be in great part driven by partnering with vendors, which can provide education, support and insights into both IT and LoB integration. The idea is to gradually create a Big Data reliant ecosystem that partners with IT and technology vendors rather than treating the process as an IT-centric enterprise-wide rollout of Big Data capabilities whose value hasn’t yet been properly defined.

Let’s cast this observation against the window of opportunity available to businesses today:

In spite of the mission-critical importance of Big Data, only 23.5% of global enterprises have a clearly-defined Big Data strategy today.

Looking at each region individually, Asia and Europe are slightly above that average with 24.9% and 23.9% respectively, while North America and Latin America are below that average at 19.5% and 16.9% respectively.

What does this mean?

l Globally, over 75% of companies don’t yet have a clear understanding of how to use, integrate and deploy Big Data.

l Only the remaining 23.5% are currently taking full advantage of this window of opportunity to race ahead and gain a competitive advantage in their respective markets.

l The opportunity to leverage Big Data’s potential ahead of the competition is still slightly greater in North America than it is in Europe and Asia.

The fundamental lack of understanding regarding the value of Big Data to an organization indicates the prevalence of an IT-centric enterprise-wide rollout mentality. On the whole,

IT-centric thinking has grown too narrow for today’s digital landscape, and is increasingly ill adapted to the realities of technology adoption today. The practical value of Big Data can be most easily identified at the LoB level now, which is why it is so vital for companies to experiment with Big Data there before attempting Big Data enterprise-wide rollouts. Internal competencies regarding new technologies and capabilities must be developed and scaled across the organization in order to serve as vehicles of adoption. Limiting the development of internal competencies to the IT department is not conducive to the effective and agile adoption of critical new technologies, especially one as fundamental as Big Data.

Overcoming the absence of one-stop-shop Big Data solutions:

Organizations well into their technology adoption cycle as it pertains to Big Data have, however, hit somewhat of a roadblock in recent years: None of the large Big Data vendors appear to have been able to create a true one-stop-shop solution across the infrastructure, application and analytics layers. As a result, most early adopters have found themselves forced to improvise, and essentially patch together various Big Data solutions that don’t necessarily fulfill the full potential and promise of all that Big Data has to offer.

In spite of the still somewhat disconnected aspect of the Big Data technology ecosystem, investment and innovation in the space look extremely healthy. On the vendor side, the first wave of Big Data companies - HortonWorks, Cloudera, MongoDB, New Relic, and the like – have continued to attract rounds of funding, scaled, and adapted to their successes and failures, and now offer more mature and well-designed solutions. New entrants into the space also attract VC investments, signaling that Big Data still has a tremendous amount of maturing and growth to look forward to. In fact, Big Data startups accumulated $6.64 Billion in venture capital investments in 2015. This amounts to 11% of all VC capital in the tech industry.

Overall, the Big Data vendor space appears healthy, with established giants like Amazon, Microsoft,


Google and IBM showing no signs of weakness even as new players continue to enter the ecosystem. One trend that we are noticing, however, is a lateral shift from the infrastructure layer to the analytics and application layers.

A closer look at the current Big Data window of opportunity:

For a little more insight into the scope of the window of opportunity we see in Big Data adoption, here is what we know about Big Data investments - actualized and intended - over the next two to three years. This is whether or not organizations yet have a Big Data strategy:

Globally, 76% of organizations plan to either increase or maintain their Big Data investments in the next 2-3 years.

l That number jumps to roughly 93% among North American companies, compared with 79.3% for Europe, 74.9% in Latin America, and 71% for Asia.

l Globally, 42.9% of companies intend to maintain their current level of investment in Big Data during that time. Broken down into regions, the numbers look relatively even with 46.9% in Latin America, 45.6% in Asia, 42.5% in Europe, and 40.5% in North America.

l The investment dynamics radically change when we look at the intent to increase investments in Big Data over the next 2-3 years: While only 37.1% of companies in Europe, 28% of companies in Latin America and 25.4% of companies in Asia plan to increase their investments in Big Data, that number jumps to an impressive 52.4% in North America.

l Of the three Big Data submarkets, Software (which includes information management, discovery, analytics, and applications) is growing ahead of that curve with a 26.2% CAGR. Compare this figure to Infrastructure (computing, networking, storage and security) and Services (mostly support), with CAGRs of 21.7% and 22.7% respectively.


The North American region appears to be significantly more competitive in regards to Big Data investments, possibly indicating a higher degree of near-term focus on Digital Transformation than other regions.

The Acceleration of Big Data adoption

As infrastructure and implementation challenges increasingly erode, IT’s focus is turning to Big Data’s application layer. As the application layer drives most of the utility at the LoB level, adoption of Big Data solutions that cater to specific business needs is beginning to pick up speed. A growing ecosystem of Big Data tools is beginning to bring much needed utility to key business functions, from marketing, sales and product management to HR and logistics. Vertical-specific Big Data solutions are also beginning to capture large amounts of beachhead, notably in healthcare, finance, retail, utility management, manufacturing, and government.

The promise of cognitive computing (AI) has also

helped invigorate and drive interest in Big Data as machine learning and cognitive computing are perfectly positioned to help realize the full potential of Big Data investments. If recognizing the value of Big Data and digital transformation can be considered a first adaptive phase, and building an integrated infrastructure capable of capturing, storing, processing and leveraging massive amounts of data is the second phase, the integration of cognitive computing (AI) into this new Big-Data-driven model is certain to be the third phase in Big Data’s evolution in the enterprise.

From advances in healthcare, research and engineering, we are already seeing the impact that a rudimentary integration of AI and Big Data can have on process improvement, innovation and market disruption. In our opinion, AI will transform, drive, and increasingly own the application layer of Big Data in coming years, until AI and Big Data are indistinguishable from one another. To invest in Big Data is ultimately to invest in artificial intelligence. For more insights into this synergy, see our section in this report on Cognitive Computing and AI.


What it is and why it matters:

Cloud Computing is essentially a vast IT ecosystem that, unlike traditional on-premise IT systems, can be accessed remotely from virtually anywhere by way of the internet. The Cloud provides potentially unlimited on-demand, scalable access to every function once performed by traditional IT systems, from data storage and computing capacity to the management and use of software solutions.

Some of the primary advantages of the Cloud are low upfront infrastructure investments; easy implementation and customization; broader functionality and LoB-specific options; significantly reduced licensing, maintenance, and staffing costs; relatively painless hardware and software updates; and helps organizations shift their IT infrastructure from a CAPEX-focused model (heavy on capital expenditures) to an OPEX-focused model (favoring more agile operational expenditures). Cloud computing is elastic in the sense that it can grow and contract at will, following organizations’ needs, while on-premise IT tends to be rigid and monolithic. The disadvantages of the Cloud tend to focus mostly on data security concerns, the potential impact of connectivity outages, and the belief that ownership of IT capabilities and data can, as it relates to certain key business-critical functions, require owned, on-premise IT rather than subscription-based off-premise IT.

This dilemma, coupled with most mature organizations’ need for a progressive adaptation to Cloud capabilities, has led to a broad range of hybridized IT models that combine on-premise legacy IT systems, Cloud solutions, and even private cloud solutions.

What you need to know about the Cloud:

82% of enterprises currently have a hybrid cloud strategy, with private cloud adoption rising from

63% in 2015 to 77% in 2016. Lack of expertise and resources, not security, is now the primary Cloud challenge for organizations, followed by the inversely proportional relationship between growing Cloud cost challenges and stalled Cloud optimization.

Overall, the global cloud computing market grew 28% to $110B in revenues in 2015, with public IaaS/PaaS services (Amazon and Microsoft) realizing the highest growth rate (51%), followed closely by private & hybrid cloud infrastructure services (IBM and Amazon) with 45% growth. Other cloud services like Saas (Salesforce, Microsoft) grew roughly 30%, and U CaaS (Cisco, Citrix) falling just shy of 20 growth%. In Infrastructure, hardware and software, the Public Cloud (Cisco, HPE) saw roughly 30% growth and Private Cloud (HPE, Cisco) didn’t quite break 20% growth. Total spend on infrastructure hardware and software in 2015 was about $60B. About half of that was on private clouds, but public clouds are growing notably faster.

Global public cloud revenue is expected to increase from $80B in 2015 to $167B by 2020.

This is due in part to roughly half of the market now believing that the public cloud is just as secure (if not more so) than private clouds. This shift is good news for cloud adoption as until 2016, security was still the top concern for decision-makers weighing the pros and cons of cloud adoption.

SECTION 2:CLOUD COMPUTING

ESTIMATEDPUBLICCLOUD

REVENUE

2012

2015

2017

2020

$43B$80B

$117B$167B


Other key facts, figures and forecasts:

l Worldwide Public IT Cloud Service Revenue in 2018 is predicted to reach $127B.

l Managed Services are projected to reach $256B by 2018.

Emerging markets are predicted to be 21% of the Worldwide Public IT Cloud Services market by 2018.

l Morgan Stanley predicts that Microsoft cloud products will constitute 30% of the company’s revenue by 2018. Office365, CRM, and Azure will increase from 11% of revenue in 2015 to 30% in 2018.

l In 2015, Amazon Web Services (AWS) generated $7.88B in revenue with Q4 2015, up 69% over 2014.

Takeways

By all accounts, 2016 is seeing the mainstreaming of the Cloud. Although many organizations are still struggling to reinvent their IT infrastructure to take full advantage of the Cloud, the business world is no longer in what can be considered an “early adoption” phase.

Barriers to cloud adoption are eroding, with data security no longer being a primary concern. Still, there is uncertainty about the technology, including how to build agile and functional hybrid IT models, questions as to how much data to keep in-house vs. off-premise, how to offset Cloud costs, and an ongoing reluctance to part with legacy IT infrastructure. These uncertainties are the lion’s share of organizational resistance to fully

integrated Cloud and Hybrid IT models and are not small objections to overcome.

There are two particular obstacles to Cloud adoption that come up time and again:

The restructuring of IT: In most organizations, with a strong centralized legacy IT infrastructure, the IT department still operates as its own silo. For these organizations more than digital native organizations, the transition to new models of collaborative IT, in partnership with LOB units, can be slow and difficult. The biggest challenge for this type of organizational adaptation to the Cloud isn’t so much technical as it is one of adequate change management. Both the role of IT and its relationship at the tactical level have to be re-imagined and redesigned, then deployed and implemented.


In the new world of the Cloud and hybridized IT, the IT department can no longer exist solely as a centralized and monolithic technology gatekeeper. It must rebuild itself as a flat, agile solutions enabler at the LoB level. In order to effectively serve the organizations of tomorrow, it must actively partner with and/or embed developers in key business units, from Customer Service, Marketing and Sales to Logistics, Engineering, and Product Management, and work to fulfill their specific needs rather than expect them to conform to the organization’s existing capabilities.

Upskilling: As IT moves towards rich ecosystems filled with new layers of specialization (relating to CRM, IoT, mobile, AI, social channels, security, etc.) and recognizes the need to be able to design customized solutions for LoB assets (rather than

the organization as a whole) IT skillsets need to adapt accordingly. That kind of change is difficult, and IT departments need to seriously shift their focus from hardware and software investments to skillset investments. This will require a greater degree of collaboration and partnership between IT and other departments, but also between IT and HR, which is ideally positioned to facilitate the transition from technology-centric IT departments to skills-centric IT departments.

This means that as IT costs shift away from infrastructure investments, and IT ecosystems grow more agile and scalable, IT must realize that its role will increasingly move away from the day-to-day management of technology solutions and instead increasingly shift towards helping manage the overall in-house process of Digital Transformation.


SECTION 3:COGNITIVE COMPUTING (AI)


Cognitive Computing, often also referred to as Artificial Intelligence (AI) is any computerized or virtualized simulation of human thought processes that relies on adaptive, self-learning systems that combine natural language, real-time data analysis, and pattern recognition to emulate or mimic human thought. The idea behind AI isn’t simply to program supercomputers to sort through massive amounts of data, but to apply human-like thinking, logic and learning to the process of big data computing, and furthermore, to do so through a simple, intuitive, natural language interface. In short, if the massive computing capacity of the Cloud cannot yet be connected to a human brain, human-like thinking can instead be built into the Cloud.

Artificial Intelligence was once the realm of science fiction, but we squarely entered into the age of AI in recent years, with significant advances in AI now finding their way into common technologies, from data analysis to task automation. Among the current leaders in the AI space are IBM, whose Watson AI continues to test the boundaries of AI applications in a variety of industries, from healthcare and medical research to crime-fighting and marketing, Google, with its vast ecosystem of libraries and languages, and Microsoft, with its flag firmly planted at the center of the Cloud API world.

The current state of Cognitive Computing offers two distinct non-mutually-exclusive trajectories of opportunity for the technology. The first involves AI-aided human tasks, in which intelligent computers assist humans in making decisions and performing tasks. The second involves intelligent automation, which eliminates the need for human involvement altogether. These two categories of opportunity will be at the heart of this section of our report.

What you need to know about (AI):

Commercial AI applications are still in their infancy, with AI still moving steadily out of its proof of concept and case studies phase. As the market watches and waits, investments in AI technology have not yet scaled. According to CB Insights, however, the acceleration of investments in AI technologies, particularly startups, is notable between 2010 and 2016. The relatively weak investment volume of $56 Million in 2010 grew to a far more optimistic $394 Million in 2014. This acceleration appears to be continuing well into 2016, with healthcare, logistics, retail, utilities, and process manufacturing driving demand for AI solutions.

This trend, along with the tremendous potential for AI, helps put IBM’s own massive investment in its AI solution (Watson), in perspective. Although growth has remained relatively slow as the technology works towards realizing its potential, it is worthy of note that IBM’s Watson division has set for itself a $10 Billion revenue target to be reached by the end of 2023.

Fellow technology solutions giants Microsoft and Google are also actively developing their own AI solutions -Microsoft Azure and Google DeepMind. Google’s DeepMind AlphaGo project recently made headlines for defeating the world’s best Go player, Lee Sedol, in a 4-to-1 match, much as Watson famously beat human contestants in Jeopardy just five years ago.

Beating humans at complex games is not the ultimate goal of AI. These are only demonstrations of AI capabilities. At its core, Cognitive Computing’s purpose is to bring sense to the chaos of data,


especially massive sets of data, and do so both in real time and in the most appropriate context. Whatever a human mind could do if only it had the computing power of the Cloud, AI is tasked with doing. The possibilities for AI applications are virtually endless. Armed with super-intelligent computers, doctors can improve and accelerate patient diagnoses, then prescribe customized treatments based on each patient’s medical history and the degree to which diagnostic systems are aware of the patient’s medical history, genetic information, and lifestyle. Financial analysts can use AI to better understand, model, and even predict market behaviors. Marketers can leverage AI to maximize the ROI of their campaigns and programs by better understanding what makes their customers tick. Urban designers can use AI to help them design better cities and utilities. Every single profession and vertical can potentially benefit from human-AI partnerships, which is why the technology is so exciting.

AI-human partnerships vs. AI automation:

One of the biggest question marks about cognitive computing isn’t so much what it can do but what it will do. Or rather, what impact it will ultimately have on the job market. It is one thing to enhance the capabilities of healthcare professionals and engineers with AI

solutions. It is another entirely to replace humans with AI counterparts. One of the looming challenges of AI integration in our everyday lives, particularly as it pertains to certain tasks and the jobs that are associated with them, is that as AI opens the doors to human-like automation, certain jobs may become obsolete. Self-driving vehicles, for instance, could eliminate the need for commercial truck drivers, bus drivers, airline pilots and cabbies. AI-powered virtual “bots” could also eliminate the need for human Customer Service representatives. Likewise, business strategists, accountants, law clerks, media planners, CEOs, and even technology analysts could someday be replaced by lifelike AI.

Regardless of the moral, cultural, and macroeconomic aspects of this particular discussion, the move towards business automation is already underway. According to Bank of America and Merrill Lynch Global, the robotics and AI market will reach $150 Billion dollars by 2020, promising productivity boosts of as much as 30% in certain industries. Furthermore, where automation and robotics once threatened manual labor roles, AI now presents a similar threat to knowledge workers, whose jobs could not, at least until now, be performed by machines. The report estimates that, as a result, as much as 47% of workers in the US could be displaced by technology in the next twenty years.


AI’s impact on Big Data:

We are seeing a definitive trend towards AI integration in Big Data analytics in recent months, and predict that increasingly, AI and Big Data will find themselves thoroughly intertwined until they become indistinguishable. As AI promises to help analyze massive amounts of data and derive increasingly more accurate insights (real-time and predictive), this inevitable synergy has been a long time coming.

We don’t anticipate AI replacing data scientists any time soon, but we do expect to see an increase in the automation of routine and basic tasks formerly performed by data scientists, resulting in boosts in productivity. As AI and Big Data integration are ultimately inevitable, it is important to note that no discussion of AI should ever exclude Big Data, and, likewise, no discussion of Big Data should ever exclude a corresponding discussion about AI.

The dilemma businesses will have to face in the coming five to ten years will be in striking the right balance between automating tasks that no longer require human intervention or management, and enhancing human-driven roles with AI solutions. Equipping decision-makers, project managers, attorneys, retail clerks and analysts with AI

capabilities may ultimately be a more efficient model than replacing them with machines altogether. AI may also give rise to an industry of independent contractors capable of leveraging virtual teams of AI-driven worker bees to boost their productivity and gain a competitive advantage over their competitors.

On the consumer side, the growth of voice-activated AI should help accelerate t he adoption of the following technologies:

IoT devices like smart refrigerators and smart apparel

IoT ecosystems like smart homes and smart offices

Self-driving vehicles

Mobile AI assistants (next generation Siri-like interfaces)

Home health monitoring and diagnostic tools

Service and companion robots (also called social robots)


SECTION 4:MOBILITY

What it is and why it is important:

One of the most significant forces driving digital disruption in our time (and the foreseeable future) is mobility, and particularly mobile computing. Most of us now carry enough computing power and internet connectivity in our pockets to access every corner of the web, work remotely, manage IT systems, watch movies, download music, book flights and hotels, shop, control smart appliances and drones, and perform hundreds of other tasks that once required large desktop computers.

Just over four decades after the world’s first mobile telephone call from handheld subscriber equipment on 3 April 1973, the state of mobility around the world is staggering in both scale and scope. Here are some numbers to help illustrate how far we have come, and how quickly the mobile ecosystem is evolving:

Globally, there were 7.9 Billion active mobile devices and connections in 2015.

The mobile ecosystem is still generally divided between cellular phones with no or limited internet capabilities and internet-capable smart phones. Though smart phones currently represent only 36-40% of mobile devices globally, they account for nearly 90% of all mobile data traffic.

Data-wise, average smart phone usage grew 43% in 2015, compared to 2014. (We will touch on the role that video plays in this growth later in this section.)

The number of mobile-connected devices per capita will reach 1.5 by 2020. Based on current population forecasts, 1.5 mobile devices for 7.7 Billion people in 2020, amounts to 11.5 Billion active mobile devices around the world This is compared to 7.9 Billion in 2015).

Global mobile data traffic grew nearly 75% in 2015,

with Middle East and Africa averaging roughly 117% growth, Asia Pacific, Latin and America, and Eastern Europe averaging 83%, 73% and 71% growth respectively. The more mature mobile markets of North America and Western Europe still managed to grow by 55% and 52% respectively. Overall, this shows strong global growth with no significant signs of slowdown.

Global data traffic and usage are also growing at an astounding rate, with an expected increase from 3.7 exabytes per month in 2015 to a mind-boggling 30.6 exabytes per month by 2020. This is an eightfold increase in just five years.

In 2015, 55% of all mobile traffic was video. By 2020, video will represent roughly 75% of all mobile traffic.

These data points help illustrate the degree to which mobility will remain a primary vehicle for change, innovation and disruption for years to come.

What you need to know about the state of Mobility:

This section will focus on the four emerging opportunities in mobility for which we currently see the most promise. They are: Micro-location (a subset of location-based services, or LBS), VR integration, AR integration, and AI integration.

Microlocation:

While standard LBS uses a combination of GPS and accelerometer data to place a mobile user in a particular location and capture both their speed and direction of travel, microlocation uses short-


range Wi-Fi and bluetooth beacons and sensors to help place the same mobile user in a specific aisle or department inside a store.

The technology is fairly simple: When a user’s phone comes within range of a Wi-Fi signal, it sends back a signal to the beacon, telling it that it is within range. A network of beacons and sensors spread out around a store can then map, with a high degree of specificity, where every phone searching for a Wi-Fi signal is inside the store. This data can be captured, mapped, and either analyzed in real time or analyzed along a timeline to identify recurring patterns.

This kind of traffic mapping can be used in several ways:

Traffic Analysis: By plotting customer traffic on a virtual floor plan of the store, customer foot traffic patterns can be analyzed, giving store managers valuable insight into what parts of the store see the most traffic and least traffic. This data can help retailers identify the impact of high traffic areas on sales of particular items, for instance, as well as design floor plans that naturally steer visitor traffic into specific parts of the store.

Visitor Analysis: The same data can indicate to retailers how many visitors are in each of their stores right now, or how many visitors their stores saw an hour ago, or on a specific day, or all month. This data can then be used to spot visitor trends over time Fr example, Is a store’s management seeing an incremental drop in visits quarter over quarter, or an increase in visits?

Presence Analysis: The same data can also indicate where customers tend to spend the most and least amount of time during their visit and where they stop (presumably to look at an item), and how long they tend to stay in one place before moving on. The same data can also show retailers where visitors don’t stop at all inside their store, and the impact this may have on the sales of specific items based on where they are in the store.

Sales Analysis: All of the above data can then be overlaid with sales data to determine what impact, if any, visitor data, traffic patterns and presence, has on sales. In regards to visitor data, the insights can

help shed some light on the relationship between traffic volume and (a) total revenue, (b) net transacting customers, (c) number of transactions, and (d) average transaction value. Traffic patterns and presence, on the other hand, can shed some light on how well an item sells based on where it is located in a store.

Customer Experience: While customers spending five to eight minutes in a particular section of the store may result in a high probability of purchases made in that section, presence data can also identify pain points in the customer experience, particularly at checkout, or outside a fitting area. Presence analysis can help retailers identify bottlenecks in the customer experience, which can then be rectified for the benefit of all.

Micro-location is arguably one of the most promising and yet underutilized of all mobile-related technologies available to businesses today, which is why we decided to highlight it in our report.

Note that although uniquely suited for the retail space when paired with smart phones, the same beacon and sensor technology can also be applied to an IoT setting (Internet of Things) for use with specialty wearables. (See the IOT section of this report for that portion of our analysis). This is relevant, for the most part, because IoT continues to find its way into our day to day environments. This is not only in retail environments but in our homes, schools, hospitals, work spaces, public spaces and vehicles. The adoption of beacon and sensor technology will naturally follow the same pattern.

Over the course of the next few years, we expect to see a new layer of functionality added to beacon and sensor technology: Identity. Adding identity to traffic data will allow sensors to detect not only that someone is in a particular section of a store, but who that someone is. By combining that individual’s visits, time in store and traffic patterns with CRM and social data, retailers will be able to better understand and ultimately better serve that customer. This is not purely a transactional sales and revenue play. The potential here is to use aggregate data to create customized shopping


experiences for each visitor that will ultimately increase preference and strengthen loyalty over time, reduce customer erosion, increase positive reviews and customer recommendations, and ultimately increase lifetime customer value.

The principal challenge to the identity layer is that in order to be effective, it cannot feel invasive to consumers. This means that it has to be opt-in, or voluntary. Retailers who are already experimenting with the identity layer have discovered that the key to making it appealing to consumers is to make it not only generally useful but also infuse it with personal relevance; They are customizing the shopping experience for each individual customer.

One example of how this works is McDonald’s Netherlands app designed by VMob. Aside from doing most of the things retail apps do, it combines each user’s personal preferences with their current location, time of day, and proximity to their stores to personalize offers and make useful suggestions. The result: 47% increase in average transaction value for users of the app.

What’s interesting about this trend is that while using information like GPS, Wi-Fi data and cell-ID-based learning systems might seem more intuitive to systems designers, replacing cell-ID with an app-based identity - or sign-in - naturally creates a voluntary opt-in for users.

The same principle can be used with micro-location

and the identity layer: By using a mobile app that combines a shopper’s preferences, either from CRM alone or a combination of CRM and social data, and their current location inside the store, along with their proximity to specific items, retailers can combine mobility, IoT, beacon and sensor technology, big data, the cloud, and cognitive computing to create valuable, seamless, and personally relevant in-store shopping experiences for each and every customer.

VR integration:

One of the most exciting opportunities with mobile looking towards 2020 is the integration of Mobile and Virtual Reality (VR). We are already seeing promising efforts to seamlessly combine both technologies, the most notable entry in this nascent hybridized category being Samsung’s Gear VR, a collaboration between mobile giant Samsung and VR pioneer Oculus. The concept is simply to allow a smart phone to be turned into a VR display by pairing it with a specially-designed headset.

This practical synergy between mobility and VR technology has the potential address two key obstacles to adoption by (a) easing economic access to VR technology and (b) allowing for a greater degree of portability for the technology. Making access to VR technology more affordable and practical should accelerate both mainstream adoption of VR technology and increase mainstream demand for original VR content.


We expect that content producers’ ability to meet market demand for VR-specific content will be the principal driver in VR technology becoming competitive in the ecosystem of first, second and third screens. The “First screen” has traditionally referred to television, while “second-screens” have traditionally referred to laptops, tablets and mobile devices. Once VR technology adoption and VR content offerings find their momentum, we expect to see the start of a reshuffling of the hierarchy of screens, with hybrid VR pushing mobility nearer “first screen” status in key categories, both established (entertainment and gaming) and nascent (virtual training, virtual travel, virtual shopping, collaboration, and design).

This brings us to an observation about media fragmentation, which we believe will be further disrupted by mass adoption of VR technology and VR content. Just as print media was disrupted and ultimately transformed by the mass adoption of digital media, digital media will be disrupted and ultimately transformed by the mass adoption of VR media, particularly as it pertains to VR-Mobile integration. This ushers in a potent game-change

for every category of content producer, and an opportunity for organizations willing to be first-movers in the space to establish themselves as market leaders.

One particularly important indicator informing this shift is mobile video consumption. As stated earlier, 75% of global data traffic by 2020 will be video, but equally important is the tenfold increase in data consumption in just five years). As VR content begins to compete against standard video content, we expect the same forces that continue to make mobile video such a strong content category for consumers around the globe to drive a similar demand for mobile-accessible VR content.

The tradeoff between dedicated VR goggles and the mobile-integration of VR is threefold:

Dedicated VR goggles, are equipped with higher end hardware and chipsets not available on mobile devices, resulting in significantly higher resolution and processing speed than VR-capable phones. The downside is that they must be tethered to a PC.


Dedicated VR goggles promise a far superior VR experience than VR-capable phones, but at a much higher price point. (Currently in the $1K-$4K range for the hardware.) While VR-capable phones do make access to VR content far more economically accessible, the technical requirements of high end VR content and experiences may not be compatible with VR-capable phones.

Mobile networks will not be fast enough in the short term to support the wireless transfer of high end VR content. High end VR content still requires a wired connection between VR goggles and a device, either a PC or a gaming platform, and we anticipate that the transition to wireless VR will take some time.

AR Integration:

For a deeper dive into AR (Augmented Reality), see this report’s section on Virtualization.

In regards to the integration of AR with mobility, the future of AR clearly leans towards AR goggle technology like HoloLens, Magic Leap, and Meta. In its immediate future, however, because mobile devices are the most widespread interfaces in the world, AR developers will continue to leverage the mobile space to seed key verticals and drive mass adoption.

Some of AR’s most promising uses have already been outlined in this report’s section on virtualization, but the general idea is to combine mobile cameras and mobile screens to project virtual objects and content onto live images of real world environments: By turning on their smartphone cameras and looking at their screens, users could be shown layers of virtual content projected onto whatever they point their device at.

This can be applied to any number of applications, from tourism, retail, hospitality, public transportation, vehicle maintenance and repair, healthcare, education and research, advertising, and so on.

One of the companies to watch in this space currently is Google. Its innovative Project Tango combines 3D motion-tracking with depth-sensing to create smart layers of connective tissue between hardware (Android smartphones), real world environments, virtual environments, and virtual objects. Technologies like Tango are the vehicles through which mobility, AR and VR are quickly becoming integrated.

While the use of special goggles and hands-free interfaces will significantly increase the utility of AR technology, we expect to see mobile devices


play an enormous part in the normalization of AR functionality in consumers’ daily lives until such a time as AR goggles and other wearables become mainstream. In the meantime, we expect organizations looking to gain an advantage over their competitors to embrace AR technology and incorporate it into their business models, both in terms of creating enhanced utility for their customers and in terms of improving internal processes involving a range of activities, from collaboration and data visualization to fleet maintenance and employee training.

AI Integration:

To understand the future of AI integration into mobile, one only has to look at Siri, Apple’s voice-activated personal assistant. Although still rudimentary and marginally effective, Apple’s innovative effort clearly shows the technology’s potential for mobile users.

AI’s integration with mobile begins with the notion of embedding a personal assistant into a mobile device. This personal assistant needs o be able to capture and process natural language voice commands, also respond using natural language, and perform basic tasks relating to search, scheduling, note-taking, calculations, navigation, communications, mobile payments, and so on.

More advanced functions of mobile AI would also involve both the voice-activated and intuitive automated management of interconnected systems commonly found in smart homes, smart vehicles and smart workplaces, as well as the management of other categories of IoT-based devices like wearables, cameras, sensors, and robots.

The revolutionary utility of mobile AI’s hands-free universal remote control functionality notwithstanding, the real promise of the technology is the combination of natural language interfaces, machine learning, and ubiquitous portability: Not only will phone AI be able to understand and perform increasingly complex tasks whenever prompted by a user’s verbal commands, it will also be able to anticipate the user’s needs based on habits and preferences, and either make suggestions to the user or automate routine tasks altogether.

Mobile AI applications will initially range from helping users remember to take their medication (and monitor whether or not they do), to helping them shop for the best online deals, monitor their health, and optimize their schedules. Over time, they will evolve into far more complex management and decision-making tasks involving personal medical data, financial data, psychological modeling, and predictive modeling. As more and more devices and systems become connected, mobile AI will be able to both acquire and analyze a greater amount of data on its users, and project itself into an ever growing ecosystem of networks, device and environments to create a seamless layer of AI-supported user-device integration and 360-degree life management.

We currently see the greatest potential for near-term growth focusing on healthcare, retail, enterprise collaboration, financial services, and IoT applications, and are especially encouraged by Google and Facebook’s long-sighted shifts from “mobile first” development to “AI first” development, particularly in regards to mobile. Google CEO Sundar Pichai indicated as much on parent company Alphabet’s Q1 2016 investor call, in which he discussed the opportunity for AI to turn mobile devices into personal assistants, and the subsequent need to deploy machine learning into mobile environments in order to make that happen. Facebook CEO Mark Zuckerberg expressed a similar vision at the company’s F8 developer conference, where he also outlined the potential for AI-based chatbots, which would simplify routine and repeatable tasks for users and businesses alike.


SECTION 5:THE INTERNET OF THINGS (IOT)

What it is and why it is important:

IoT is a vast ecosystem of connected devices (including but not limited to objects, wearables, appliances, vehicles, and buildings) embedded with electronics, software and sensors, that can collect and exchange data over a network. Examples of common IoT devices are smart TVs, smart appliances (from coffee machines to refrigerators), fitness trackers, self-driving cars, and smart thermostats.

Any object can now be turned into a connected device. An umbrella can be connected to the web and equipped with an LED system that alerts its user if the local weather forecast calls for rain. A pet’s water bowl can be equipped with a system that sends an alert to its user’s phone when the water level starts to get low. Any toy, tool, piece of furniture or toothbrush can be upgraded into an IoT device and transformed into what technologist David Rose refers to as “enchanted objects.” Aside from the endless commercial possibilities of bringing digital functionality to thousands of day to day objects, there are also thousands of business and systems-focused applications, from utilities management (smart grids) and logistics (smart warehouses), to robotics (self-driving harvesters) and healthcare (remote health monitoring). These applications can, among other uses, help boost productivity, accelerate production, streamline logistics and operating costs, improve the utility of common tools, and better connect organizations and teams to vital day-to-day data.

What you need to know:

The Internet of Things is already making headway in a number of key usage categories. They are, in no particular order: healthcare, utility management, home automation, manufacturing, infrastructure management, environmental monitoring, retail and marketing, and transportation.

Healthcare: The most obvious healthcare use for IoT devices is remote health monitoring (RHM) through a combination of wearables and implants. RHM devices can also offer emergency notification capabilities for patients with health risks and chronic medical conditions. These devices can measure blood pressure, body temperature, heart rate, blood sugar levels, the presence of infection, and even a patient’s state of consciousness. The information they collect can then be used to either activate implants (like pacemakers and insulin pumps), notify first medical professionals of an imminent problem, and alert first responders to a medical emergency. IoT sensors installed within living spaces can further help monitor the health and general well-being of patients and senior citizen. We see a strong opportunity for end-to-end health monitoring IoT solutions to help revolutionize health care and remote health management.

Utility Management and Home Automation: On a macro level, the IoT’s potential for utilities can be observed in Smart Grid type systems, which automatically collect energy and power-related data all along the grid in real time, then self-optimize to improve efficiency in both the production and distribution of electricity. By investing in advanced metering infrastructures (AMI), utilities can extend that data collection and energy distribution right down to individual end-user connections.


Concurrently, IoT also enables home-based energy management systems that let consumers optimize their energy consumption. We are already seeing IoT functionality being integrated into thermostats, appliances, home electronics, and smaller energy consuming devices like light bulbs, power switches and power outlets. This functionality can include user-based remote management through mobile devices (real-time manual management), advanced remote scheduling (automated management), and even AI-assisted home energy management (intuitive real-time automated management).

Manufacturing: We see massive potential for IoT technology in manufacturing (a vertical already primed for automation and robotics). From the management of manufacturing equipment and processes, inventories, and assets, industrial IoT applications will continue to move manufacturing out of its manufacturing automation phase and into a realized smart manufacturing phase. As with smart grids and smart homes, smart manufacturing will allow IoT-driven facilities to not only optimize their supply chain, production, and inventories, but accelerate the manufacturing of new products based on near-real time demands. Industry estimates put the potential boosts in manufacturing productivity at 30%.

Smart manufacturing isn’t limited to production optimization, however. It also touches on plant safety and security. It can also be integrated into smart grids to optimize utility requirements. An additional focus of smart manufacturing is maintenance optimization, with the National Science Foundation (NSF) providing research and governance on intelligent maintenance systems (IMS) to help monitor Industrial IoT. The objective is to achieve near-zero breakdown of equipment using IoT-based analytics.

The opportunity for IoT technology in manufacturing is so vast that the term “Industrial Internet of Things (IIOT) is increasingly common when describing this subset of the larger IoT ecosystem in industrial circles. Another term associated with IIOT is Industry 4.0, which references the fourth industrial revolution. In terms of numbers, the economic potential of IIoT is enormous. Conservative estimates put IIOT

spending at $50 Billion in 2012, when IIOT was still in its infancy. That number is expected to reach $500 Billion by 2020.

Infrastructure and Environmental Monitoring: Large scale infrastructure deployments of IoT are beginning to appear around the world, and the focus appears to be on smart cities. The world’s first smart city (Sondo, in South Korea) is nearing completion, while infrastructure IoT deployments in existing cities are in various stages of completion in San Francisco and San Jose in the United Sates, Guangzhou Knowledge City in Singapore, and Santander in Spain. India’s Modi government has announced a plan to transform 100 cities into smart cities, at a cost of $150 Billion. We expect this global trend to continue to grow in the next several decades.

The objective of smart cities is to layer networks of sensor, device, and data to help cities optimize themselves, much like smart grids but on a far more complex and layered scale. Smart cities will be able to, among other things, optimize their energy demands and production, manage their utilities (water, power, broadband, etc.), monitor their own levels of air, soil and water quality, optimize the flow of traffic, better manage maintenance and construction projects, lower crime, improve first responder systems, provide early-warning systems for tsunamis, earthquakes and other disasters, and provide ubiquitous connectivity for residents and visitors.

Retail and Marketing: IoT is increasingly allowing retailers and marketers to monitor and analyze customer behaviors in retail environments. By combining beacons and sensors, mobility, big data, cloud solutions and cognitive computing, retailers and marketers are now able to gain a much deeper understanding of consumer behaviors, and gauge with a much greater degree of accuracy the effectiveness of their marketing tactics and retail design. IoT enabled retail and marketing also allow businesses to model and test the effectiveness of campaigns in virtual environments, leverage predictive analysis to optimize spend, monitor the behaviors of tens of thousands of individual customers in real time, and target them with


customized offers and messaging at optimal times, to drive transactions, loyalty, and recommendations.

Transportation: Aside from smart traffic control systems focused on optimizing public transportation infrastructure (subways, busses, tramways, major rail, etc.) and roadways (streets and highways), IoT is also going to play a major part in the design and management of roadways and urban design features that will help our transportation infrastructure adapt to self-driving vehicles, from commercial trucks and taxis to private and crowd-owned automobiles.

Emerging IoT-driven technologies to keep an eye on:

In addition to IoT proliferation by sector and vertical, we are keeping an eye on several key technical aspects of the IoT ecosystem. These areas of focus are important because they inform the speed at which IoT is able to scale and mature against macro-level predictions.

IoT Security: As more and more objects and devices are absorbed into the IoT ecosystem, IoT-specific security solutions will be needed to protect IoT hardware and software from unauthorized access.

IoT Device Management: At the device level, management systems will need to be able to monitor devices, manage firmware and software updates, and perform routine diagnostics. At the ecosystem level, IoT Device Management will involve doing this at scale (hundreds, thousands, even millions of networked devices).

IoT Operating Systems: IoT applications require their very own operating systems (separate from heavier, traditional operating systems). Although the IoT space continues to grow, it shows no signs of a consolidation of operating systems (as we have seen with more mature computing technologies). A few notable examples: Riot OS, Windriver VxWorks, Google Brillo, and Microsoft Windows 10 for IoT.

IoT Platforms: Typically, IoT Platforms combine

three distinct functions: Device Control and Management, Application Management, and Data Management. As with IoT operating systems, IoT platforms form a rich ecosystem of competing products. Some names to be aware of: EVRYTHNG, Axeda, GroveStreams, Aeris, Ayla, Thethings,iO, 2lemetry, Xively, SiteWhere, and ProSyst.

Wireless Standards: It is also important to be aware of IoT connectivity options, as they will impact range, data rates, power demands, and security. Among the most common IoT connectivity options today are the ubiquitous Cellular, Bluetooth and WiFi technologies everyone with a smartphone is already familiar with, and NFC (Near Field Communication) technology - used in contactless payment transactions. NFMI (Near Field Magnetic Induction) may also increasingly be used in the place of RF-based wireless communications to optimize power consumption and reduce interference in short range M2M (machine to machine) applications. NFMI is very similar to NFC, but the main practical difference relates to range: while NFC range is limited to a few inches, NFMI’s range can be measured in feet, with much more robust data transfer rates. Also be aware of home automation optimized Z-Wave, industrial application friendly Zigbee, and relative newcomers

SMART CITIES

Broadband

Smart buildings

Mass warning systems

Smart traffic lights

Traffic sensors

Intelligent transit

systems

Electric vehicle

charging stations

Integrated utilities

management

Parking sensors

Smartwastemanagement


Thread, Sigfox, Neul, and LoRaWan, which offer promising solutions specific to IoT connectivity in both urban and rural environments.

Takeaways:

Although IoT may seem like a relatively new technology opportunity for most non-tech related businesses, we are seeing a high velocity of adoption and integration in key verticals, especially manufacturing, consumer goods, and specialized industries like healthcare and transportation. Investment in IoT will continue match its perceived potential by decision-makers, from the CEO on down to LoB leads, which is why it is so important for senior management and middle management teams to make IoT integration discussions a priority between now and 2020.

Given IoT’s deep connection to big data, automation, and effective cost/resource management, organizations with a focus on IoT integration will continue to see natural internal synergies form and grow between IT, Data science management, Operations, and Logistics. The evolution of these internal partnerships will provide organizations with an opportunity to revisit their overall departmental architecture, and reorganize for agility, velocity, and common purpose.

As demand for engineers and developers with strong IoT-specific skills continues to accelerate well into 2020, competition for IoT talent will continue to present a challenge for most organizations. In order to effectively negotiate that challenge and turn it into a strategic advantage, technology and LoB leaders should make sure that HR always has a seat at the IoT planning table. Partnering with HR to recruit IoT-specific talent should be seen as an everyday operational integration initiative, and not as a mere headcount-focused afterthought.

Because most consumer and enterprise focused IoT applications and systems will be accessible via mobile interfaces, expect a strong synergy between mobile and IoT developers, but the IoT connectivity landscape isn’t that simple. Specific IoT wireless standards have yet to be established, and different types of applications and environments

may require radically different approaches to IoT connectivity. For instance, network security (including device authentication and authorization) can be a challenge, especially at scale: The potential authentication and management bottleneck of large IoT ecosystems (made up of thousands, even millions of devices) is driving scale towards M2M (machine to machine) models, similar to peer-to-peer networks, but machine-based. These require very different types of infrastructure, protocols, and standards from typical cloud-based models like WiFi and Cellular. This will be especially relevant to IIOT (industrial IoT) environments and smart city environments.

As pressure to eliminate unwanted friction between devices and networks due to signal interference grows, establishing clear wireless connectivity standards for IoT applications will become a priority for vendors and regulators in the next few years. Therefore, it will fall on IoT-focused organizations to not only keep an eye on the evolution of IoT connectivity standards and adapt to any relevant sea changes, but also play a leadership role in the promotion of their preferred standards both inside and outside of their respective industries.


SECTION 6:VIRTUALIZATION


Virtualization is the process by which a program or system creates a virtual version of something either real or imagined. When we talk about virtualization in regards to technology and digital transformation, we generally refer to virtual hardware, environments and objects. In IT specifically, hardware virtualization allows connected computers to create virtualized versions of themselves on host computers to better manage resources.

There is more to virtualization than hardware virtualization; There is virtualization in regards to engineering and design and used in product/system development and testing and virtualization as it pertains to virtual reality (VR) and augmented reality (AR). Although AR and VR have not traditionally been mentioned alongside virtualization, we feel that as virtual technologies and environments evolve, the connective tissue between them is also evolving.

What you need to know:

Below is a summary of the three virtualization technologies featured in this report.

Virtual Reality: Virtual reality creates a fully or mostly immersive experience for users by creating the illusion, by way of a combination of specialized sensory apparatus, that they are physically present in a virtual environment. Generally, users can interact with this environment by choosing a direction of travel and interacting with virtual objects. The virtual environment can be photorealistic or made to look and feel artificial, depending on its intended use. The most recognizable artifact in the Virtual Reality toolbox is the VR goggle: a face mask similar to large swimming or snorkeling goggles that fit over the eyes, and in some cases the ears, of a

user. The VR goggle either completely or partially obscures the user’s vision, and replaces the actual surroundings with a digital projection of the 360 degree virtual environment.

Currently, Virtual Reality focuses primarily on entertainment uses, such asvideo games and enhancing amusement park rides, but we are already beginning to see Virtual Reality appear in the following areas:

Healthcare – Virtual Reality is being used to treat patients suffering from pain and/or anxiety, improving patient experiences, recovery times from surgeries, and treatment outcomes. Virtual reality is also increasingly being used by surgeons and surgical teams in specialized robot-assisted surgeries both onsite and remotely.

Education and Training – Virtual environments and immersive simulations have helped train aerospace professionals, military personnel and police for well over two decades now, and the latest generation of Virtual Reality technologies naturally replace older ones every four to six years. These technologies help lower training costs, shorten training times, and improve overall trainee performance. They also allow professionals to maintain, hone, and improve their skills with minimal time away from their jobs.

Engineering and Design – Combining industrial virtualization technologies with Virtual Reality is a marriage made in heaven for architects, civil engineers, city designers, and future residents who can now don VR goggles and “walk”, “drive” or glide through the new buildings, parks, sewers and streets that have yet to be physically built or upgraded in the real world. On the one hand, this capability allows for a greater degree of control over the design portion of a project, as well as the degree to which collaboration can occur across project teams that may be separated by only a wall


or by an ocean. On the other hand, this particular use of Virtual Reality can help clearly articulate a project’s prevailing vision to investors, clients, and other stake holders.

Real Estate – Potential buyers and/or tenants can take virtual tours of homes, apartments, offices and commercial properties remotely. This capability speeds up the sales cycle, reduces scheduling conflicts, and expands the geographic scope of a property management or leasing agency.

Tourism – Travelers can plan their vacations more effectively by taking virtual tours of airports, train stations, hotels, cruise ships and even destinations before booking their trips. This eliminates travel-planning anxiety, facilitates decision-making, and accelerates booking. Virtual travel is also an emerging industry, with museums and major cities leading the way. Virtual reality can allow users to “walk through” a gallery, exposition or point of interest virtually, without leaving the comfort of their own homes.

Conferences and Business Travel - Virtual Reality can be leveraged to boost conference

registrations without overloading venues that may be at maximum or near maximum capacity. Partnering with a Virtual Reality service provider, a conference organizer could create a registration option for remote attendees. This already exists, to some extent, with live-streaming, but a far more immersive experience can be delivered around the conference’s content with the use of Virtual Reality technology. A VR Access registration could also be expanded to entertainment, demos, and other activities not normally covered by traditional live-streaming, warranting premium pricing. Overall, this would give some conferences a technological and PR edge over their competition, and would create a vehicle with which to drive additional revenue at scale.

Even onsite, attendees unable to attend key sessions because of a venue’s limitations could be given the option (free or paid) to experience the session virtually from another room. This also opens new opportunities for conferences to partner with technology providers and sponsors.

Entertainment – Movie studios, movie theaters, stage artists, concert venues, theme parks and other


entertainment providers can all leverage Virtual Reality to either enhance attendee experiences. There has already been successful experimentation with Virtual Reality on roller-coasters, where the physical experience of the ride is enhanced by a completely separate visual experience which projects the user into an exciting virtual world of flight and aerial battles. Movie screens could, in some instances, be replaced by VR goggles, or movie-goers could opt to rent VR goggles at a premium to watch the movie that way rather than watch it on the screen.

Venues and content providers could use Virtual Reality to bring their onsite experiences into users’ homes. Virtual ticket sales for concerts and plays could be the most likely winners, followed by home screen programming, which comprises television and new content providers like Netflix, Hulu and Amazon Prime.

Augmented and Mixed Reality: Unlike the mostly immersive Virtual Reality, Augmented Reality simply uses a device, either a screen or specially-designed eyewear, to add virtual layers to a user’s physical surroundings. Reality is thus augmented, not virtualized or transformed. Augmented Reality can also be built into a number of objects and

surfaces including windshields, mirrors and helmet wind screens. Pioneers in this use of technology are Google, Hololens (Microsoft), Magic Leap, and Meta.

On the mobile device side, augmented reality can, for instance, allow a user on the top level of the Eiffel Tower to hold a mobile device over a portion of a view of Paris, and have historical details appear on their screen as they pan over certain landmarks, which then vanish as they pan away. Walking through the streets of London with a phone in hand, a user could, simply aim their phones at certain buildings or landmarks from a certain vantage point and see how they used to look a hundred years ago. On the virtual goggle side, the same effect can be achieved by simply turning towards an object or landmark, and looking at it.

Tourism and Travel - The tourism industry has been quick to jump on this technology since it lends itself so well to enhancing experiences for visitors to major cities and points of historical interest. Museums and exhibitions can also easily benefit from this technology by creating enchanting experiences for visitors with user-friendly, swipe-capable layers of learning built right into the technology.


Retail – One of the most exciting verticals for Augmented Reality is retail, and particularly brick and mortar retail, which has seen online retail and mobile retail progressively eat into its market share in recent years.

One example that perfectly illustrates the potential for AR in retail is digital mirrors, which are increasingly reinventing the fitting room. As digital fitting rooms increasingly become virtual fitting rooms only shoppers can compare. This allows shoppers to see how they would look in an outfit without having to try it on, compare how they look wearing two outfits side by side, or virtually project different colors onto items of clothing to decide which one works best for them. Using this technology, trying on different items in the store should take a fraction of the time it would normally take and improve the customer’s experience, which in turn should increase the likelihood of a purchase.

Another aspect of Augmented Reality being used creatively in retail focuses on product customization: Retailers can leverage Augmented Reality to allow shoppers to select a product, try

it on for size, then customize it in the store before purchasing or ordering it.

Augmented Reality can also be used to provide shoppers with information about a product’s provenance, composition and features without the need for unsightly displays and signage. Panning a phone or device over a product is all a shopper would have to do to access that product’s specifications. This use of AR would be especially valuable in regards to technical purchases. Examples: automotive, technical wear, athletic equipment, and electronics.

There is also potential for food brands and grocery retailers to incorporate augmented reality into their packaging design and/or displays, allowing users to not only browse the nutritional value of a product by panning their device over it (rather than having to pick up the package), but also browse recipe ideas from the aisle, then easily add new items to their shopping list. This particular use of Augmented Reality could help grocers drive up their customers’ average grocery bill by anywhere from three to twenty percent, depending on the frequency of their visits.

Specialty retailers can also leverage Augmented Reality by helping shoppers visualize how a particular color scheme or piece of home décor might work in a room. Furniture and wall paint retailers, for instance can leverage this functionality to not only help shoppers make decisions but

E-COMMERCE SALES AS APERCENTAGE OF RETAIL SALES

March 2010

March 2012

March 2014

March 2016

4.20%

5.10%

6.20%

7.8%


turn their homes into instant virtual show rooms: Combined with mobile shopping functionality, shoppers could thus use Augmented Reality to make purchases relating to their project right from their phones. [size of market]

Education – Because Augmented Reality can create virtual layers of information over images and physical objects, pretty much anything can be transformed into a virtual learning tool. Fitting students with AR goggles can, for instance, allow them to visualize overlapping layers of history, turn a mannequin into an interactive human body, complete with functioning virtual organs, or help them “see” the moving parts inside a gas turbine or a complex piece of industrial machinery.

Design and Engineering –Augmented Reality can also be used to create virtual prototypes for single manufactured parts and complex product assemblies. Rather than being shown a screen shot of a product, it can be experienced virtually, as one might a hologram, by project teams and decision-makers who are continents away from each other. This ability to easily model and “show” prototypes to collaborators in a true 3D context can significantly accelerate ideation, design and approvals, regardless of the organization’s size and geographic challenges.

Business Reporting – The same technology that allows a project team to show physical prototypes of parts and assemblies in a three-dimensional seemingly holographic format can also bring new functionality to business reporting. Instead of conveying data and graphs on flat, two-dimensional surfaces, information can be formatted in dynamic, layers that can be expanded or minimized at will, overlaid with correlating data to prove or disprove causalities, and even set to interactive timelines that can help convey trends and specific market forces. Best of all, these reports and presentations can be experienced together, in a conference room, or continents apart, with people sitting at their own desks.

Systems Management – Looking towards 2018, we expect the use of Augmented Reality in system management to begin slow-to-start but discernible growth. Given that the need for system analysts

and service crews to be able to quickly diagnose and respond to capacity, pipeline and related failures is a vital aspect of systems-driven industries, Augmented Reality will increasingly play a bigger role in inspections and maintenance functions.

Mixed Reality

A subset of Augmented Reality is Mixed Reality. The term “mixed” refers to the hybrid nature of the technology, and how it is used. One of the hallmarks of Mixed Reality is a user’s ability to manipulate virtual objects within the virtual layer of the environment and make them appear to interact with the actual environment the user is a part of. For instance, Mixed Reality would allow a user to create three-dimensional paintings, or painted sculptures that appear to not only occupy space in an actual physical environment but react and conform to it.

Mixed Reality interfaces are typically composed of a hybrid goggle that provide the same type of 360-degree digital projection as VR goggles minus the opacity, and some kind of motion capture technology that translates specific user gestures into commands.

SMART HEALTHCARE

Wearables monitor blood pressure, heart rate, body temperature, state of consciousness, and location monitoring.

Sensors in the walls, furniture, or objects:

Sensors throughout the living space monitor the patient if he falls.

Also act as a one-touch and automated alert device for emergency response.

Smart necklace or ring:

A wearable device allows a patient to test his blood sugar.

Prescription medicine dispensers:

Smart devices manage medication dispensing and dosage for patients.

Bracelet pairedwith smartphone:


Design and Engineering – One of the most exciting uses of Mixed Reality can be found in design and Engineering environments, where prototypes can be created, manipulated and altered virtually and in three-dimensional space rather than on a computer screen. Combined with industrial virtualization software, Mixed Reality can turn design and engineering into a more intuitive and malleable process for three-dimensional thinkers. Using MR can accelerate part and product design, improve contextualization for project teams, and help design and manufacturing engineers intuitively work through three-dimensional problems with their eyes and hands rather than on a screen and via a mouse and keyboard.

Where this capability has the most potential to shine is in collaboration. Equipped with MR apparatus, a project team can collaborate virtually and in real time on three-dimensional virtual objects, whose materials, textures and colors can be altered at will, and whose assemblies can be exploded and reassembled at the flick of a finger. This technology can also be used to provide in-depth demonstrations of prototypes, parts, assemblies and finished products to decision-makers, stakeholders, product managers and even consumers. A tire manufacturer, for instance, could use MR to accelerate the design of a new performance tire and help communicate to consumers some of its most impressive design features and performance characteristics. Because of how easily it pairs itself with industrial design, this application for MR technology will find its way into every product development channel, from golf clubs and performance racing bicycles to wind turbines and high speed rail.

Our opinion is that Mixed Reality is as revolutionary to design engineering as virtualization and modeling software once was. In less than fifty years, we have evolved from two-dimensional blueprints and engineering drawings on paper to flat three-dimensional computer-aided design (CAD) environments on computer screens (like Solidworks), to virtual, interactive, truly three-dimensional models in Mixed Reality.

Healthcare – Mixed Reality is also finding a foothold in medical research. Again, MR capabilities

allow researchers to extract flat three-dimensional models from computer screens and not only project them but manipulate them in 3D. This is particularly helpful in regards to applications that relate to complex systems, and primarily the human body. Some of the more obvious applications so far involve the design and testing of prosthetic limbs and implanted devices (like pacemakers and artificial joints).

Another use for MR in healthcare comes in the form of virtual diagnostics, in which a patient’s scans can be virtualized and modeled, then analyzed and manipulated by a specialist. Example: Scanning an accident victim with possible internal injuries, virtualizing her body scans, then using MR technology to arrive at the most complete and accurate diagnosis possible and decide which medical course of action is most expedient. The same technology can be used to locate, diagnose and treat a range of conditions and injuries, from torn ligaments and slipped discs to blood vessel obstructions and arrhythmic hearts.

Retail Design – Mixed Reality will soon also be used to model the retail spaces of tomorrow. By combining the physical and the virtual, retail designers will not only be able to create layered layouts but manipulate them in space and in real

SMART RETAIL

Smart mirrors help shoppers with on-premise virtual shopping

Smart digital price tags

Personalized touch-screen displays

Mobile payment checkout

In-store pickups for online purchases

In-store 3D Printer for custom products, parts, and accessories.

Wi-Fi beacons


time to test different options. Using MR goggles, a retail space designer will be able to physically walk through an empty retail space or model, and design, alter, and manipulate every aspect of that space in real time. Again, one of the key advantages of this technology, aside from the speed and ease that it brings to the design process, is the layer of intuitive real time collaboration that it adds to the equation by allowing more than one designer to work in the same space at the same time.

Marketing – We are also seeing various degrees of Mixed Reality being used to design virtualized merchandizing models, especially in regards to how products are meant to appear in retail environments. Here, instead of the retail space being designed and manipulated using Mixed Reality, product managers are beginning to look at Mixed Reality to optimize product placement inside retail environments. This effort touches on not only the placement of their products in the store but also other aspects of the shopping experience, like product packaging, line of sight, and other aspects of environment integration. Until recently, this type of testing and shopping experience design could only be done by either by using physical spaces and products, or through the use of screen-based virtualization software. Thanks to MR, photo-realistic modeling in virtualized three-dimensional environments will soon be projected into hybridized virtual environments where adjustments can be made intuitively and on the fly.

Industrial virtualization and modeling software (more commonly known as simply virtualization): Before goggles really entered the technological mainstream, virtualization referred mostly to virtualized 3D modeling experienced via two-dimensional screens. There are essentially two main branches to traditional virtualization: One focuses on business needs, and the other focuses on movie and television special effects (commonly known as CGI). Our focus in this report is in the former: business-focused virtualization. One of the principal players in the space is Dassault Systemes, more commonly known now as 3DS. The company’s rich portfolio of virtualization products includes the following family of products:

SolidWorks®: 3D computer-aided design, used by design engineers around the world.

Catia®: The world’s leading engineering and design software for 3D CAD design.

Simulia®: Virtualized finite element analysis (FEA) and realistic simulation software.

Delmia®: Global industrial operations and logistics software.

3DVIA®: 3D space planning software.

Enovia®: Collaborative Innovation Software.

Geovia®: Natural resources 3D modeling and simulation software.

Biovia®: Virtual biosphere and chemicals. (Chemical research and material science software.)

ExaLead®: Information intelligence, search-based applications and semantic software.

3DExcite®: High end real-time 3D visualization software.

Netvibes®: dashboard intelligence and data systems software.

These specific areas of screen-based 3D virtualization form the core of capabilities that will increasingly find their way into Mixed Reality, Augmented Reality, and Virtual Reality environments. The transition from screen to goggle, and from mouse and keyboard to motion capture and voice command, is still in its infancy, but looking towards 2020, especially given impressive advances from pioneers like Microsoft, Google, Magic Leap, Meta, Samsung, and Dassault Systemes, the pace of that transition is certain to accelerate.


SECTION 7:RAPID PROTOTYPING AND PRODUCTIONTECHNOLOGIES (3D PRINTING)


3D printing technology, also known as additive manufacturing, is a prototype and part-production technology that is generally far more agile, low cost, and portable than traditional production technologies. The 3D printing process typically involves the synthesis of a three-dimensional object through the use of computer modeling, a range of 3D printing technologies, and finishing. 3D printers are adaptable and are increasingly finding their way into various industries from manufacturing and healthcare to foodservice, automotive and retail. Recent examples of 3D printing’s mainstream potential in recent years include 3D-printed hearing aids, prosthetic limbs, housing materials, bone grafts, pills, robots, replacement tools for the International Space Station, and custom athletic footwear.

What you need to know about the state of 3D Printing:

Driven in part by big plays from Hewlett-Packard, 3D Systems, Stratasys and Materialise NV, 3D Printers were all the rage a few years ago, and industry observers may have been quick to jump on the 3D Printer bandwagon without considering the limitations of the technology and practical barriers to adoption. Now that the smoke has cleared, we have a better idea of where 3D printing may be going, and where it may find itself most useful. According to Sculpteo’s 2015 State of 3D Printing report, 68% of respondents reported that they would increase spending on additive manufacturing in 2016. The 68% figure is slightly down from Sculpteo’s 2014 figures which indicated a slightly stronger 71%.

To understand the driving forces behind both the

adoption of 3D printing and the steady investment in the technology, we have to look at organizations’ top priorities as they relate to its use.

At the top of the list, we find that 31% of respondents list “accelerating product development” as their primary drivers between now and 2020. This falls in line with our analysis of (a) accelerated innovation and (b) disruption as a model (DaaM), which are the reasons for the importance of digital transformation: As innovation cycles continue to accelerate, product design and development must accelerate at roughly the same pace in order to keep up with the velocity of potential market disruption. 3D printing enables companies to shorten their prototyping and production cycles, driving the lion’s share of investment in the technology.

Breaking things down a little further, the study reveals that in North America, 3D printing is still mostly used for prototyping (56%) and delivering proofs of concept (43%), while in Europe, those numbers are 63% (higher) and 27% (lower), respectively. The use of 3D Printers for production are roughly even between North American and Europe with an average of 26.5%, (half of their use for prototyping). Creating marketing samples follows production in fourth place with roughly 15% of 3D Printer use.

This indicates that 3D printing has still not reached maturity as a production technology. However, it is notable that >25% of 3D printers already being used in a production capacity indicates real potential for such a young technology.

In terms of adoption by sector, the study limits its findings to the following categories: Consumer Goods, Industrial Goods, High Tech, and Electronics.

Consumer Goods showed the greatest degree of 3D Printing integration, with 15% of respondents self-identifying as a 3D Printing-first company,


and a fairly even split between less advanced levels of integration, resulting in 28% describing their company as having little to no 3D Printing integration.

In contrast, only 5% of Industrial Goods companies self-identified as 3D Printing-first companies, and 47% admitting to little to no 3D Printing integration.

High tech fared only marginally better with only 7% and 30% respectively.

Only 3% of companies in the Electronics sector self-identified as 3D Printing-first companies and 34% admitted to having little to no 3D Printing integration, but 47% did report that 3D Printing does permeate some of their programs.

Our analysis regarding this data is that organizations that are willing to work faster to integrate 3D printing into their design-build model can and will gain a competitive advantage over most of their competitors. This data shows us a window of opportunity for companies looking to edge forward towards potential disruption. Consumer Goods is currently the most competitive among those industries, but with only 85% still somewhere between little to no 3D Printing integration and 3D Printing permeating all of

their programs, the window of opportunity will likely remain open well into 2020.

Other factors in the continued growth of 3D printing are new materials (21.5%), new market applications (17.2%), ease of modeling (17%), and increased efficiency (16.25). Worthy of note, especially in regards to ease of modeling and increased efficiency is the frequent mention by respondents of smartphones being used as 3D capture tools. This portable capability is something that we intend to keep a close eye on, not only in regards to logistics and technology investments, but in regards to how this impacts the possible growth of 3D printing among consumers.

Of particular interest in relation to the proliferation of 3D printing in consumer markets is the fundamental difference in perceptions between European respondents and North American respondents. European respondents felt that 3D printing is the domain of trained specialists whereas their North American counterparts were of the general opinion that 3D printing can and should be accessible to anyone. This suggests that the market for 3D printing technology among consumers may be far more fertile in North America than in Europe, at least through 2020.


So what does this mean for businesses?

l 3D printers accelerate product ideation, innovation and design.

l Accelerating product ideation, innovation and design can help radically streamline product development costs. The design trial-and-error process can thus be transformed into: Fail faster, fail cheaper, fail better.

l The ability to shorten design-build cycles allows companies to innovate and get their products to market faster. As innovation cycles keep growing shorter, any technology that can help a company increase its innovation and production velocity brings with it tremendous strategic value.

l The ability to print products on demand with

3D Printers could help companies significantly streamline inventories.

l 3D Printers could help organizations develop new services, product customization and some degree of in-store custom production. Examples: Replacement parts for consumer electronics, custom parts for hobbyists, custom-printed medical devices (hearing aids), and custom prosthetics.

l Although plastics and resins currently represent the lion’s share of 3D Printing materials, we expect to see a continued effort to bring metals into the 3D Printing mainstream. The impact that the widespread 3D Printing of metal parts will have on traditional aspects of manufacturing, such as supply chain, production, inventory remains to be seen, but the potential for widespread disruption of traditional manufacturing models is real.


REPORT SUMMARY, KEY TAKEAWAYS, ANDRECOMMENDATIONS

The seven core technologies driving digital disruption around the world and Digital Transformation in the enterprise are:

Big Data

Cloud Computing

Cognitive Computing (AI)

Mobility

The Internet of Things (IoT)

Virtualization

3D Printing

1) Help drive a balance of deep LoB-driven technical knowledge across the organization with a breadth of common-purpose technology adoption. Although each of these technologies is a game-changer in its own right, the greater opportunity for organizations lies in combining them to maximize their utility and impact. For instance:

l Big Data is stronger when used in conjunction with Cognitive Computing.

l Cognitive Computing is far more useful and accessible to users when deployed through the Cloud.

l Retail-specific IoT systems like sensors and beacons see their functionality enhanced when used in conjunction with the Cloud, Big Data, cognitive computing, and mobility.

l Virtualization in all of its incarnations also requires a strong Cloud and Big Data infrastructure, and

may find its adoption enhanced by mobility and the growth of 5G networks.

Whether you are a CTO, a CDO, a CFO or a CMO, focus on helping your department or LoB acquire a deep vertical knowledge into each of these individual technologies, but also frame that expertise within a broader vision of common purpose and operational integration when it comes to the actual adoption, deployment, and management of these technologies alongside the rest of the organization. It is better to build an interconnected web of technologies across your business, than to invest piecemeal into technological silos.

- Depth: Build deep knowledge centers in and around your organization. Hire and partner with specialists in each of these key technologies.

- Depth and breadth: Invest in internal training programs to improve technology literacy, and facilitate its adoption across all departments.

- Breadth: Make sure that all managers in your organization are well suited to act as change agents, and that they are trained and equipped to incorporate change management into their role.

- Breadth: Empower project teams and individual departments to take the lead when it comes to selecting their technology solutions. Task IT to assist and enable rather than dictate their technology choices.

2) Flatten IT to enable scalability. The days of the centralized IT silo have come to an end. “Hybrid IT” doesn’t only refer to technology solutions. It also applies to IT’s role in the organization as technology migrates into self-managing line of business (LoB) applications. IT departments must learn to either embed capable resources into LoB units, or partner with them in order to be able to assist them in


selecting, integrating, deploying, customizing, and managing these new technology solutions. This doesn’t mean that IT departments should become completely decentralized. One critical function of tomorrow’s IT department will still be to map, coordinate, and manage an increasingly layered and complex technology infrastructure, but expect LoB decision-makers to drive IT investments and demand solutions that cater to their specific needs, and build your IT department with this shift in mind.

3) Make Digital Transformation a full-time change management initiative. While most businesses still look at Digital Transformation as a technology play, organizations already committed to the process are coming to the realization that Chief Technology Officers, Chief Information Officers, and Chief Digital Officers may not have the resources or proper authority to manage the process entirely on their own. As this problem can be compounded in large organizations, we recommend that Digital Transformation initiatives be managed by a full time individual and/or team working alongside the CDO, CTO, CIO, COO, and all relevant LoB teams. This Chief Digital Transformation Officer (or CDTO) will coordinate the effort and work to ensure a seamless deployment and integration of these technologies across the organization. This will free up the rest of the C-level team to advise and assist but without interfering with their day-to-day management duties.

4) Favor velocity over precision. Until these seven technologies mature and become mainstream, speed of adaptation will continue to trump precision and perfection. Risk-averse organizations must accept that in their early phases, innovation and adaptation are always messy. Trial and error is part of the process, and companies that learn to fail faster are the ones that will outpace their more cautious competitors. Learning to fail faster is one of the most critical traits of innovative and disruptive organizations.

As the seven technologies featured in this report drive Digital Transformation, it is vital that companies reward both trial-and-error experimentation and speed of execution. Building operational

agility and incentivizing can-do entrepreneurial experimentation are as important to the process of Digital Transformation as the technologies themselves. Getting every detail right is far less important than the learning process itself. Pursuing perfection is a long term process—it should never be a hurdle or an obstacle to progress.

It is critical for organizations in the next two to three years to increase the velocity of technology adoption, eliminate internal barriers of innovation, and build cultures that both breed and reward entrepreneurial innovation.

5) Shift your thinking. Most organizations are reacting defensively to technology disruption, which is understandable. Digital Transformation is not an easy process and it requires changing everything about business operations, and about the thinking associated with that. Begin by understanding what these disrupting forces are, where they come from, what they mean, and start developing a game plan on how to adapt to them as quickly as possible. To get a better handle on Digital Transformation, try shifting your thinking from threat to opportunity. Look five years into the future, and instead of asking “how do we catch up?” pretend that your organization already has. Now look at your market again, and ask:

How can we use these technologies to create better experiences for our customers and prospects?

How can we use these technologies to disrupt our competitors?

How can we use these technologies to eliminate pain points for our internal teams, for our partners, for our supply chain, as well as for our customers?

How can we use these technologies to automate processes and become more efficient and reduce costs as a result?

How can we use these technologies to do things that we always wanted to do but couldn’t?


This might seem like common sense advice, but most organizations forget to do this. Instead of envisioning a proactive path to technology adoption and market disruption on their terms, they get stuck in an endless cycle of defensive and reactionary adaptation to external disruption.

The easiest way not to fall into this trap is to shift your thinking: Learn to lead every technology adoption and Digital Transformation discussion with a combination of a Five-Years-From-Now (FYFN) outlook, and opportunity-focused Disruption-as-a-Model (DaaM) thinking. Fast-forward five years, put yourself in the driver’s seat, and put together your next five-to ten-year business plan. Then, simply reverse-engineer it with this year as the starting point. This will allow you to create is a clear path to technology adoption and Digital Transformation that will either help you catch up to the rest of your industry and/or get a head start that will quickly differentiate you from your competitors. It will also help your business transition from disrupted to disruptor at some point in the next five years. That transition is the Digital Transformation end goal of every market leader in every industry, and the faster you reach this inflection point, the better your organization’s prospects will be in tomorrow’s technology-driven economy.

Looking Forward: Futurum Research will be building upon this initial report with a series of specific digital transformation focused research pieces to address the specific technology areas referenced here, in conjunction with workplace

behavior, customer experience, employee engagement and other topics that directly tie into the intersection of people and technology. A few examples of future reports that can be expected in the coming months are:

Big Data, Leadership and Decision Making: Exploring the application of big data in the enterprise and its direct impact on executive and management decision making.

IoT + Gamification: Creating More Engaged Employees: With wearables, sensors and connected devices, management has the power to deploy integrated gaming into the workplace in order to increase employee engagement and improve employee productivity.

Mobile Usage: How Gen Z Will Differ From Millennials: Companies have made massive investments to appeal to the millennial generation. However, a new generation of consumers is coming up behind them and they are the most technically savvy yet. What do businesses need to consider to deal with the newest generation of consumers, Gen Z?

Cloud Consumption: Opening Up Shadow IT as a Business Advantage: For many years CIOs have run their department with an Iron Fist. With the rise of Cloud and Rapid Application Deployment businesses are seeing more consumerization of IT and software decisions being made in LOB. How will this trend progress and what must business leaders know.


SOURCES

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SECTION 2RightScale 2016 State of the Cloud Report. (2016, February ). Retrieved May 15, 2016, from RightScale.com, http://assets.rightscale.com/uploads/pdfs/RightScale-2016-State-of-the-Cloud-Report.pdf Synergy Research Group. (2016, January ). 2015 review shows $110 billion cloud market growing at 28% annually. Retrieved May 15, 2016, from srgresearch.com Technology Business Research. (2015, October) Soaring Toward $167B: TBR Projects Key Trends In Cloud. Retrieved May 15, 2016, from tbri.comRhipe Limited. (2015, October). Cloud Licensing Landscape. Retrieved May 20, 2016 from rhipe.com

SECTION 3CB Insights. (2016, February). Deep Interest In AI: New High In Deals To Artificial Intelligence Startups In Q4’15. Retrieved May 20, 2016 from cbinsights.com

Bank of America Merrill Lynch. (2016, December). Robot Revolution – Global Robot & AI Primer. Retrieved May 11, 2016 from bofaml.com

SECTION 4Cisco. (2016, February). Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2015-2020. Retrieved May 18 from cisco.comGeohive. (2016). Population of the entire world, yearly, 1950-2100. Retrieved on June 2, 2016 from geohive.com, http://www.geohive.com/earth/his_history3.aspxVMob.com. (2016).

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Daugherty, P., Banerjee, P., Negm, W., and Alter, A.. (2015). Accenture Technology: Driving Unconventional Growth through the Industrial Internet of Things. Retrieved June 12, 2016 from aaccenture.com

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YCharts. (2016, April ). US e-commerce sales as percent of retail sales. Retrieved June 18, 2016, from ycharts.com, https://ycharts.com/indicators/ecommerce_sales_as_percent_retail_sales In-line

SECTION 7

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Future Research, [email protected]: @futurumxyz

Documents

OVERVIEW OF THE SEVEN CORE TECHNOLOGIES DRIVING … · SEVEN CORE TECHNOLOGIES DRIVING DIGITAL TRANSFORMATION . 8/9/2016. FUTURUM PREMIUM REPORT | 2. TABLE OF CONTENTS. Executive