Tech Trends 2020

Tech Trends 2020

Trending the trends: Eleven years of research

CORE RISK B USI NE SS O F T E C H N O L OG Y CLOU D & COG NITIVE & DIGITAL EXPERIENCE
BLOCK CHA IN ANA LYTICS & DIG ITAL RE A LITY

Ethical Finance & Human

technology Horizon the future Architecture Digital experience
2020

& trust next of IT awakens twins platforms

DevSecOps Beyond Connectivity NoOps in a AI-fueled Intelligent Beyond

2019

& the cyber the digital of tomorrow serverless organizations interfaces marketing
imperative frontier world

The new Risk Exponentials CIO survey: No-collar Reengineering API Blockchain to Enterprise Digital
2018

core implications watch list Manifesting workforce technology imperative blockchains data reality
legacy sovereignty

Risk Exponentials CIO survey: IT Inevitable Everything- Trust Dark Machine Mixed
2017

implications watch list Navigating unbounded architecture as-a-service economy analytics intelligence reality
legacy

Reimagining Social CIO survey:

2016

core Risk impact of Creating Right- Autonomic Democratized Industrialized Internet AR & VR
systems implications exponentials legacy speed IT platforms trust analytics of Things go to work

CIO as chief IT worker Software-

2015

Core Risk Exponentials API Amplified Ambient Dimensional

integration of the defined
renaissance implications economy intelligence computing marketing
officer future everything

Technical In-memory Cyber Exponentials CIO as Real-time Cloud Cognitive Wearables Digital Social Industrial
2014

debt revolution security venture DevOps orchestration analytics engagement activation crowdsourcing
reversal capitalist

Reinventing No such CIO as IPv6 (and Finding the Gamification

2013

Design as Business Mobile only— Social

the ERP thing as postdigital this time face of goes to
a discipline of IT and beyond reengineering
engine hacker-proof catalyst we mean it) your data work

Big data
2012

Outside-in Digital Measured Hyper-hybrid Geospatial Enterprise User Social Gamification

architecture identities innovation clouds goes to visualization mobility empowerment business
work

Almost- The end of

2011

Cyber CIOs as Capability Real Visualization Applied User Social

enterprise the death
intelligence revolutionaries clouds analytics mobility engagement computing
applications of ERP

Best-of-breed CIO Value-driven

2010

Services Cyber Virtual- Cloud Information Information Wireless User Asset

enterprise operational application
thinking security ization revolution automation management & mobility engagement intelligence
applications excellence management

2
 Introduction

Introduction

I
N 2020, THE next stage of digital’s evolution welcomes us with the promise of emotionally intelligent
interfaces and hyperintuitive cognitive capabilities that will transform business in unpredictable ways. Yet
as we prepare for the coming decade of disruptive change, we would be wise to remember an important
point about yesteryear’s leading-edge innovations: Architects of the 1980s designed mainframe systems that
continue to run and generate business value today. Sure, they’re outmoded by today’s standards, but how
many of us will build systems that run for decades? And how’s that for a legacy?

Architecting for longevity and adaptability requires a deep understanding of both today’s realities and
tomorrow’s possibilities. It requires an appreciation for the technology and market forces driving change.
And finally, it requires a long-term commitment to focused and incremental progress.

Against this backdrop, we present Tech Trends 2020, Deloitte’s 11th annual examination of the emerging
technology trends that will affect your organization over the next 18 to 24 months. Several of this year’s
trends are responses to persistent IT challenges. Others represent technology-specific dimensions of larger
enterprise opportunities. All are poised to drive significant change.

We begin Tech Trends 2020 with a timely update on the nine macro technology forces we examined in
last year’s report. These forces—digital experience, analytics, cloud, core modernization, risk, the business
of technology, digital reality, cognitive, and blockchain—form the technology foundation upon which
organizations will build the future. This year’s update takes a fresh look at enterprise adoption of these
macro forces and how they’re shaping the trends that we predict will disrupt businesses over the next 18
to 24 months. We also look at three technologies that will likely become macro forces in their own right:
ambient experience, exponential intelligence, and quantum.

In subsequent chapters, we discuss trends that, though grounded in today’s realities, will inform the way
we work tomorrow. Our chapter on ethical technology and trust takes an in-depth look at how every aspect
of an organization that is disrupted by technology becomes an opportunity to lose—or earn—the trust of
customers, employees, and stakeholders. We follow with a discussion of human experience platforms that
will enable tomorrow’s systems to understand context and sense human emotion to respond appropriately.
Pioneering organizations are already exploring ways in which these platforms can meet the very human need
for connection.

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Tech Trends 2020

Trends evolve in unexpected ways. And often, the most interesting opportunities happen at the places where
they intersect. Several of this year’s trends represent fascinating combinations of macro forces and other
technology advances. For instance, digital twins represents the culmination of modernized cores, advanced
cognitive models, embedded sensors, and more—a recipe that is in itself a trend, even as it builds on evolving
individual technologies.

We hope Tech Trends 2020 offers the insights and inspiration you will need for the digital journey ahead.
The road from today’s realities to tomorrow’s possibilities will be long and full of surprises, so dream big and
architect accordingly.

Scott Buchholz Bill Briggs

Emerging Technology research director Global chief technology officer
and Government & Public Services Deloitte Consulting LLP
chief technology officer wbriggs@deloitte.com
Deloitte Consulting LLP Twitter: @wdbthree
sbuchholz@deloitte.com

4
 TREND SUMMARY

Leveraging next-generation digital twin capabilities

to design, optimize, and transform the enterprise.

PHYSIC AL DI G I TAL
TW IN TWI N
AG
E S GR
SS

INTEGRATION
E EG
OC AT
PR IO
N

S DA
S OR TA
S EN
Simulation
Visualization
Data sources

ANALYSIS
ACTIONS

WHAT ’S NEW

Instrumentation
Interoperability
AC Platform
TU
AT
OR AI
S
INTEGRATION

DE
CI TS
SI
ON I GH
S I NS

HUMANS

Simulated outcomes
strengthen decision-making
 Digital twins: Bridging the physical and digital

DEFINITION

di-jә-tәl / twin Digital twins

A digital simulation of physical
systems, assets, or processes.
Often paired with IoT
Bridging the physical and digital
technology to instrument
simulated systems. Twins are
supported by data science and
machine learning, and supply
optimizations and insights for
physical world action.

BY THE NUMBERS

I
MAGINE THAT YOU had a perfect digital copy of the physical
world: a digital twin. This twin would enable you to collaborate
virtually, intake sensor data and simulate conditions

38% quickly, understand what-if scenarios clearly, predict results

more accurately, and output instructions to manipulate the
physical world.

Today, companies are using digital twin capabilities in a variety of

ways. In the automotive1 and aircraft2 sectors, they are becoming
Projected compound annual
essential tools for optimizing entire manufacturing value chains
growth rate of the digital twins
market, from US$3.8 billion in and innovating new products. In the energy sector, oil field service
2019 to US$35.8 billion by 2025.† operators are capturing and analyzing massive amounts of in-hole
data that they use to build digital models that guide drilling efforts
in real time.3 In health care, cardiovascular researchers are
creating highly accurate digital twins of the human heart for

TREND BREAKDOWN clinical diagnoses, education, and training.4 And in a remarkable

feat of smart-city management, Singapore uses a detailed virtual
model of itself in urban planning, maintenance, and disaster
readiness projects.5

Cognitive Digital twins can simulate any aspect of a physical object or

process. They can represent a new product’s engineering drawings
and dimensions, or represent all the subcomponents and
corresponding lineage in the broader supply chain from the design
Cloud Core
table all the way to the consumer—the “as built” digital twin. They
may also take an “as maintained” form—a physical representation
Digital
reality of equipment on the production floor. The simulation captures how
the equipment operates, how engineers maintain it, or even how
the goods this equipment manufactures relates to customers.
†
“Digital twin market worth
$35.8 billion by 2025,” (press
release), Market and Markets,
July 2019. 59
Tech Trends 2020

Digital twins may take many forms, but they all more detailed and dynamic than ever.10 For
capture and utilize data that represents the physical longtime digital twins users, it is like moving from
world. fuzzy, black-and-white snapshots to colorful,
high-definition digital pictures. The more
Recent MarketsandMarkets research suggests that information they add from digital sources, the more
such efforts are already underway: The digital twins vivid—and revealing—the pictures become.
market—worth US$3.8 billion in 2019—is projected
to reach US$35.8 billion in value by 2025.6
Models + data = insights
What accounts for this kind of growth? And why and real value
now? After all, digital twin capabilities are not new.
Since the early 2000s, pioneering companies have Digital twin capabilities began as a tool of choice in
explored ways to use digital models to improve the engineer’s toolbox because they can streamline
their products and processes. While digital twins’
7
the design process and eliminate many aspects of
potential was clear even then, many other prototype testing. Using 3D simulations and
companies found that the connectivity, computing, human-computer interfaces such as augmented
data storage, and bandwidth required to process reality and virtual reality,11 engineers can determine
massive volumes of data involved in creating digital a product’s specifications, how it will be built
twins were cost-prohibitive.8 and with what materials, and how the design
measures against relevant policies, standards, and
Digital twins are poised regulations. It helps engineers identify potential

to transform the way

manufacturability, quality, and durability issues—
all before the designs are finalized. Thus, traditional

companies perform prototyping accelerates, with products moving into

production more efficiently and at a lower cost.
predictive maintenance
of products and Beyond design, digital twins are poised to
transform the way companies perform predictive
machinery in the field. maintenance of products and machinery in the field.
Sensors embedded in the machines feed
The digital twins trend is gaining momentum performance data into a digital twin in real time,
thanks to rapidly evolving simulation and modeling making it possible not only to identify and address
capabilities, better interoperability and IoT sensors, malfunctions before they happen but to tailor
and more availability of tools and computing service and maintenance plans to better meet
infrastructure. As a result, digital twins’ capabilities unique customer needs. Recently, Royal Dutch
are more accessible to organizations large and Shell launched a two-year digital twin initiative to
small, across industries. IDC projects that by 2022, help oil and gas operators manage offshore assets
40 percent of IoT platform vendors will integrate more effectively, increase worker safety, and
simulation platforms, systems, and capabilities to explore predictive maintenance opportunities.12
create digital twins, with 70 percent of
manufacturers using the technology to conduct Digital twins can help optimize supply chains,
process simulations and scenario evaluations. 9
distribution and fulfillment operations, and even
the individual performance of the workers involved
At the same time, access to larger volumes of data is in each. As an example of this in action, global
making it possible to create simulations that are consumer products manufacturer Unilever has

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 Digital twins: Bridging the physical and digital

launched a digital twin project that aims to create incorporated into digital twin simulations.
virtual models of dozens of its factories. At each Likewise, IoT sensors embedded in machinery
location, IoT sensors embedded in factory or throughout supply chains can feed
machines feed performance data into AI and operational data directly into simulations,
machine learning applications for analysis. The enabling continuous real-time monitoring.
analyzed operational information is to be fed into
the digital twin simulations, which can identify • Interoperability. Over the past decade, the
opportunities for workers to perform predictive ability to integrate digital technology with
maintenance, optimize output, and limit waste the real world has improved dramatically.
from substandard products. 13
Much of this improvement can be
attributed to enhanced industry standards
Smart city initiatives are also using digital twins for for communications between IoT sensors,
applications addressing traffic congestion operational technology hardware, and vendor
remediation, urban planning, and much more. efforts to integrate with diverse platforms.
Singapore’s ambitious Virtual Singapore initiative
enables everything from planning for cell towers • Visualization. The sheer volume of data
and solar cells to simulating traffic patterns and required to create digital twin simulations can
foot traffic. One potential use may be to enable complicate analysis and make efforts to gain
emergency evacuation planning and routing during meaningful insights challenging. Advanced data
the city’s annual street closures for Formula 1 visualization can help meet this challenge by
racing.14 filtering and distilling information in real time.
The latest data visualization tools go far beyond
basic dashboards and standard visualization
What’s new? capabilities to include interactive 3D, VR
and AR-based visualizations, AI-enabled
Over the course of the last decade, deployment of visualizations, and real-time streaming.
digital twin capabilities has accelerated due to a
number of factors: • Instrumentation. IoT sensors, both
embedded and external, are becoming smaller,
• Simulation. The tools for building digital more accurate, cheaper, and more powerful.
twins are growing in power and sophistication. With improvements in networking technology
It is now possible to design complex what-if and security, traditional control systems can be
simulations, backtrack from detected real- leveraged to have more granular, timely, and
world conditions, and perform millions of accurate information on real-world conditions
simulation processes without overwhelming to integrate with the virtual models.
systems. Further, with the number of vendors
increasing, the range of options continues to • Platform. Increased availability of and
grow and expand. Finally, machine learning access to powerful and inexpensive computing
functionality is enhancing the depth and power, network, and storage are key enablers
usefulness of insights. of digital twins. Some software companies are
making significant investments in cloud-based
• New sources of data. Data from real- platforms, IoT, and analytics capabilities that
time asset monitoring technologies such as will enable them to capitalize on the digital
LIDAR (light detection and ranging) and twins trend. Some of these investments are
FLIR (forward-looking infrared) can now be part of an ongoing effort to streamline the

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Tech Trends 2020

development of industry-specific digital twin And with the cost of sensors dropping, how many
use cases. sensors is enough? Balancing the cost/benefit
analysis is critical. Modern aircraft engines can
have thousands or tens of thousands of sensors,
Costs versus benefits generating terabytes of data every second.
Combined with digital twins, machine learning,
The AI and machine learning algorithms that and predictive models, manufacturers are
power digital twins require large volumes of data, providing recommendations to help pilots optimize
and in many cases, data from the sensors on the fuel consumption, help maintenance be proactive,
production floor may have been corrupted, lost, or and help fleets manage costs.15 Most use cases,
simply not collected consistently in the first place. however, require only a modest number of
So teams should begin collecting data now, strategically placed sensors to detect key inputs,
particularly in areas with the largest number of outputs, and stages within the process.
issues and the highest outage costs. Taking steps to
develop the necessary infrastructure and data
management approach now can help shorten your Models beyond
time to benefit.
In the coming years, we expect to see digital twins
deployed broadly across industries for multiple use
Balancing the cost/benefit cases. For logistics, manufacturing, and supply

analysis is critical. Modern chains, digital twins combined with machine

learning and advanced network connectivity such
aircraft engines can have as 5G will increasingly track, monitor, route, and

thousands or tens of optimize the flow of goods throughout factories

and around the world. Real-time visibility into
thousands of sensors, locations and conditions (temperature, humidity,

generating terabytes of etc.) will be taken for granted. And without human
intervention, the “control towers” will be able to
data every second. take corrective actions by directing inventory
transfers, adjusting process steps on an assembly
line, or rerouting containers.
Even in cases where digital twin simulations are
being created for new processes, systems, and Organizations making the transition from selling
devices, it’s not always possible to perfectly products to selling bundled products and services,
instrument the process. For chemical and or selling as-a-service, are pioneering new digital
biological reactions or extreme conditions, it may twin use cases. Connecting a digital twin to
not be possible to directly measure the process embedded sensors and using it for financial
itself; in some cases, it may not be cost-effective or analysis and projections enables better refinement
practical to instrument the physical objects. As a and optimization of projections, pricing, and upsell
result, organizations need to look to proxies (for opportunities.
example, relying on the instrumentation and
sensors in a vehicle rather than putting sensors For example, companies could monitor for higher
into tires) or things that are possible to detect (for wear-and-tear usage and offer additional warranty
example, heat or light coming from chemical or or maintenance options. Or organizations could
biological reactions). sell output or throughput as-a-service in industries

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 Digital twins: Bridging the physical and digital

as varied as farming, transportation, and smart entire ecosystems. Creating a digital simulation of
buildings. As capabilities and sophistication grow, the complete customer life cycle or of a supply
expect to see more companies seeking new chain that includes not only first-tier suppliers but
monetization strategies for products and services, their suppliers, may provide an insight-rich macro
modeled on digital twins. view of operations, but it would also require
incorporating external entities into internal digital
ecosystems. Today, few organizations seem
Modeling the digital future comfortable with external integration beyond
point-to-point connections. Overcoming this
As the digital twins trend accelerates in the coming hesitation could be an ongoing challenge but,
years, more organizations may explore ultimately, one that is worth the effort. In the
opportunities to use digital twins to optimize future, expect to see companies use blockchain to
processes, make data-driven decision in real time, break down information silos, and then validate
and design new products, services, and business and feed that information into digital twin
models. Sectors that have capital-intensive assets simulations. This could free up previously
and processes like manufacturing, utilities, and inaccessible data in volumes sufficient to make
energy are pioneering digital twin use cases already. simulations more detailed, dynamic, and
Others will follow as early adopters demonstrate potentially valuable than ever.
first-mover advantage in their respective sectors.
It’s time to transition your digital organization
Longer term, realizing digital twins’ full promise from black-and-white to color. Are you ready?
may require integrating systems and data across

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Tech Trends 2020

LESSONS FROM THE FRONT LINES

Prepare for takeoff: Airservices Australia enters the future of aviation

A
IRSERVICES AUSTRALIA IS preparing for While still in development, the digital twin project
the aviation industry’s next evolution. As is also serving as a proving ground for enhancing
the continent’s provider of air navigation Airservices’ traditional ways of working. The
services, it expects the volume of conventional company’s heritage is built on safely delivering
flights in its airspace to double over the next two navigation services 24 hours a day, 365 days a year.
decades. Meanwhile, the emergence of unmanned With an unwavering focus on safe, efficient, and
aerial vehicles in low altitude airspace—from reliable service delivery, the increasing airspace
aerial taxis to delivery drones—is accelerating the complexity is driving Airservices to explore new
need for new intelligent systems, compounding an solutions.
already difficult job.
The digital twin project is helping change
Airservices is addressing these challenges by Airservices’ view of what’s possible. The team
launching initiatives that will enable it to shift to piloted an Agile development approach to improve
leveraging the value of data and providing the time to market while preserving the focus on safety.
information management services of the future. The teams are delivering working software at a
One of these initiatives is to explore how a digital faster pace—iterating, testing, and learning in short
twin, combined with IoT and machine learning sprints—and continuing to provide safe, accurate
capabilities, could enhance Airservices’ ability to predictions. And while Airservices people have
manage air traffic today and in the years to come. deep aeronautical expertise, the company also
needed specialized technical knowledge to build
The Service Strategy team, led by Mick Snell,16 and implement advanced analytic capabilities. The
kicked off its digital twin development project in team filled that gap with vendors and advisers who
early 2019 with a practical objective: determine offer highly relevant experience and off-the-shelf
whether a digital twin can enhance Airservices’ technology.
ability to manage its current air traffic network. For
example, could it be used to enhance flight routes, Meanwhile, the team continues to uncover relevant
optimize takeoff times, and reduce delays? use cases for the digital twin. For example, air
traffic controllers currently work in an assigned
The team began by developing a digital twin of airspace regardless of traffic volume. To optimize
Airservices’ air traffic network using historic air the controllers’ workload, the team plans to use the
traffic data. The team has completed four proofs digital twin to assign airspace to controllers based
of concept proving out the original objective and on predicted customer demand rather than fixed
is looking forward to piloting them in parallel with geographic locations.
existing air traffic control systems. The proofs of
concept were able to optimize flight routes based Optimization is an extraordinarily complex issue
on real-time conditions to provide better traffic that requires volumes of real-time data to support
flow management. what-if scenarios on the fly to help air traffic
controllers to make faster, smarter decisions. The
digital twin can also enable Airservices customers

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 Digital twins: Bridging the physical and digital

(pilots) to optimize flights based on what’s most of scenarios for managing the multidimensional
important in the moment. For example, optimizing airspace of the future.
airspace and routing helps increase on-time
arrivals and saves fuel, but a pilot may decide to With the proof-of-concept phase complete, the
trade fuel for additional speed to avoid passengers team is moving into preproduction. Members will
missing their connections. be running trials with current data for several more
months and then move into full-scale production,
Eventually, Airservices plans to use digital twins planned for 2020. Snell reports, “We’ve been able
to develop and test strategies for dealing with to accelerate to an outcome far faster—we’ve come
disruptive innovations likely to affect its airspace. further in the last eight months than in the last
Strategists will be able to quickly test a wide range eight years.”

Gaining traction: Bridgestone’s digital twin drives an innovative

business model

B
RIDGESTONE, THE WORLD’S largest tire chain, with the goal of enhancing profitability,
and rubber manufacturer, is transforming sustaining competitive advantage, reducing
to become a leader in mobility solutions. time-to-market, and delivering leading-edge tire-
The company is reimagining its core business as-a-service offerings.
by developing digital capabilities that will
enable it to revolutionize tire management European fleets are gradually shifting to a price-
services to its portfolio of offerings addressing per-kilometer (PPK) subscription model, a way for
vehicle manufacturers, fleet operators, and fleet operators to optimize cash flow and reduce
individual drivers. total cost of ownership. But while the business
model is simple, setting the appropriate price
While the business model per kilometer is anything but. A tire’s lifespan is
heavily influenced by a myriad of factors, including
is simple, setting the load, speed, road conditions, and driving behavior.

appropriate price per A digital twin can provide insight into how these
interrelated conditions affect tire performance by
kilometer is anything but. simulating various driving conditions. But without
real-world data inputs for the digital twin, setting
Digital twin technology is at the heart of a price that hits the sweet spot at which the PPK is
Bridgestone’s transformational journey. The competitive—and sustainably profitable—is difficult
company has used digital twin simulations if not impossible.
augmented by sensor data as an R&D tool for
several years to improve tire life and performance, Bridgestone took a strategic leap by entering the
but that’s just the beginning. Jerome Boulet, digital PPK market with a product priced to win business
strategy director, and Hans Dorfi, director of from large fleets. The company used this initial
digital engineering,17 together with their teams, are install base to collect performance data that was
developing sophisticated digital twins to eventually then fed into advanced analytics algorithms.
deliver insights across Bridgestone’s entire value

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Tech Trends 2020

According to Dorfi, “Some people ask, ‘Why do tires are being used in real time, enabling the
you need a digital twin if you have big data—why company to help fleets select the appropriate tires
not just run analytics?’ I explain that while for their specific driving conditions and provide
analytics plays a major role, it only augments the customized insights into how they can reduce tire
digital twin. The digital twin is able to capture the wear or avoid breakdowns. As the digital model
multidimensional performance envelope of tires becomes more and more accurate, Bridgestone will
and can also be applied to product in development, address increasingly advanced use cases for its PPK
where no data is yet available.” He sees the digital business model.
twin as a key component of Bridgestone’s digital
infrastructure. Incoming sensor data is augmented, Today, Bridgestone is using digital technologies
cleaned, and processed; then digital simulations to add more value for its fleet customers. Over
and analytics are applied to derive insights that time, the company intends to expand its use of
inform decisions around maintenance, rotations, digital twin technology to connect its entire value
and other factors that can deliver more value for chain, from drivers and fleet managers to retailers,
Bridgestone and its customers. distributors, and manufacturers. Looking ahead,
leaders see opportunities to inform safety protocols
Bridgestone continues to enhance the digital twins. in a world that includes self-driving vehicles.
The 2019 acquisition of WebFleet Solutions and 18
“We’re making sure we have the enablers in place
the development of next-generation sensors will that will take us into the future,” Dorfi says. “And
enable Bridgestone to learn how vehicles and that’s where digital twin technology comes in.”

Takeda pursues end-to-end manufacturing automation with

digital twins

T
AKEDA PHARMACEUTICALS IS constantly Pistek is always looking for ways to accelerate
seeking scientific breakthroughs to deliver experimentation and business processes.
transformative therapies to patients
worldwide. Christoph Pistek19 leads innovation Even in the digital age, pharmaceutical
during the company’s development life cycle, manufacturing processes may contain manual
translating promising research ideas into tangible steps. For example, making biologics, vaccines,
medical products. His team also develops processes and other pharma products derived from living
for how commercial manufacturing partners will organisms involve biochemical reactions, which
actually make the products. can be variable and difficult to measure, making
automation challenging. And no one has yet
Because the industry is tightly regulated with strict perfected a method for automatically progressing
quality control mandates, any process innovation from one manufacturing step to the next. True
must be thoroughly tested in the development end-to-end manufacturing automation has become
lab for compliance before being introduced to the industry’s “holy grail,” Pistek says.
the manufacturing floor. It can take up to 15
years to bring a new medicine to patients, so This is where digital twins come in. They help his
team accelerate experimentation, develop new

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 Digital twins: Bridging the physical and digital

manufacturing approaches, and generate data to over time while still in development.” The end
enable more informed decisions and predictions goal is a digital twin that can control and steer the
that could help automate complex chemical and automation process without human intervention.
biochemical processes.
In Takeda’s development labs, the ecosystem for
To that end, Pistek and his development team this integrated approach is up and running for
build sophisticated virtual representations of the one modality: biologics, which is the company’s
manufacturing processes in their development fastest-growing category and involves one of
labs. The team builds a digital twin for each process pharma’s most complex manufacturing processes.
step and then links all parts via an overall digital The foundational work is complete—the twins
twin that controls and automates the flow from one are operational, the architecture is built, and the
step to another, forming an end-to-end simulation method is in place.
of the manufacturing process.

While modeling chemical processes is complicated,

While modeling chemical
modeling biochemical reactions can be far more processes is complicated,
modeling biochemical
complex and irregular. In many cases, real-time
sensors cannot monitor the desired outputs, and
the output quality remains unknown for hours reactions can be far more
or days. Instead, the development team uses
“soft sensors” or proxy measurements to attempt complex and irregular.
to predict the time required to complete the
biochemical reaction, which is fed into a digital Now the team is refining the process to make
twin that incorporates AI and machine learning. it more robust. Pistek expects to expand this
“The important aspect is that the architecture of automation approach within the development
digital twins allows the system to evolve on its lab across all modalities in the next year. And in
own,” Pistek says. “Every time we do an additional two to three years, he expects to see sophisticated
run and compare the soft sensor results against examples of this automation approach in use on
a true measurement that comes back from the commercial manufacturing floor.
the quality control lab, we’re able to make the
predictions more accurate.” Modeling biology and chemical reactions in a
digital twin is not straightforward and is difficult
Some pharma companies think the key to to recreate. Pistek’s advice to others considering
automation is a matter of better equipment, building digital twins: “Don’t wait, don’t be
sensors, or technology. But Pistek has a intimidated, just do it. It’s a learning process that
different opinion: “The true enabler for pharma takes time. At Takeda, it’s a critical capability for
is the control architecture across and around the job we have to do—find cures for diseases and
the process—and the foundation of that is a provide aid to those who suffer.”
sophisticated digital twin that can mature itself

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Tech Trends 2020

MY TAKE

P
EOPLE RELATE TO the frustration that traffic congestion creates—and are dissatisfied that it often
takes decades to build infrastructure improvements. Our mission is to plan and develop transportation
systems that accommodate San Diego’s growing population and healthy economy, while meeting
government requirements for improving traffic flow, air quality, and greenhouse gas emissions. And of
course, we are working with our various communities to build public support
for our anticipated recommendations. Anything we can do to get projects
underway quickly can shave months or even years off the timeline.

These are the reasons SANDAG planners and data modelers are developing
a nimble digital twin—or “sketch planning” tool—based on FutureScape™, a
modeling and simulation platform that creates digital replicas of large systems,
RAY TRAYNOR like those in a city or an entire region. We’re using FutureScape to complement
CHIEF PLANNING AND our government-mandated travel demand model, a macro simulation tool we
INNOVATION OFFICER,
refer to as an Activity-Based Model.
SAN DIEGO ASSOCIATION OF
GOVERNMENTS (SANDAG)
Regulators require that we run our proposals through the model to certify that
the proposals meet federal and state government criteria. It’s a deliberate, arduous process that requires
months of calibration and testing and processing times that can take weeks to complete. The new sketch
tool will enable us to quickly evaluate a wider range of traditional and innovative transportation options. The
Activity-Based Model will process the most promising solutions to certify that the proposed transportation
solutions meet regulatory requirements.

For example, one of our goals is to relieve rush-hour congestion between San Diego’s most populated
residential areas and the region’s largest employment center. Widening roads is the traditional go-to solution
in our car-oriented region, but we believe the sketch tool will enable us to compare road-widening with other
options. These options include fast rail lines or light rail systems. Results from the tool should arrive within
hours or days, not weeks.

Clearing the regulatory bar is only one factor, of course. Transportation planners must also wonder, “If we
build it, will they come?” Evaluating different scenarios is key to answering that question. Using the Activity-
Based Model’s historical data, which is largely based on dated commuter surveys and travel diaries, limits
our ability to be dynamic and current in how we measure future utilization and demand. We are working to
incorporate near-time digital data and, eventually, artificial intelligence into the sketch tool to help us better
reflect behavior in response to new transportation options. We also want to consider proposals that include
on-demand transportation options and new trends in mobility, such as ridesharing, electric scooters, and
bikes, with an eye to incorporating driverless vehicles when they become a viable option.

We also use a digital twin to support real-time traffic management. Here, I envision that adding AI
enhancements to the tool will enable proactive decision-making for reducing day-to-day traffic congestion.
The current system works well for reacting to traffic backups, using a microsimulation tool that evaluates
current traffic flows every three minutes. When an incident disrupts normal traffic patterns, the tool can
generate a set of solutions, such as temporarily diverting traffic to another road, which is deployed through

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changeable highway messages. We’re developing an AI-based strategy aimed to sense potential traffic
disruptors in real time. When you’re directing tens of thousands of rush-hour commuters, minutes matter.

By enabling fast, interactive feedback, our sketch planning digital twin will help us quickly develop innovative
solutions to complex transportation problems. At SANDAG, we see data-based tools such as FutureScape
playing key roles in helping us offer appealing—and environmentally beneficial—mass-transit options to
many of our residents accustomed to our car-oriented culture.

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Tech Trends 2020

OUR TAKE

W
HILE A COMPLETE digital twin of the human body is years or even decades away, researchers are
chipping away at understanding the biological processes that transform us from DNA into human
beings. Today’s research is enabled by advances in genetic sequencing and functional genomics,
growing volumes of long-term health data of populations, and increasing capabilities in advanced analytics.
This growing knowledge base will inform digital simulations that could eventually help medical professionals
control or prevent genetic diseases and disorders.

The project is daunting. Within the human body, DNA provides the instructions
for cell growth, which are “expressed” within individual cells to create hundreds
of different cell types, including blood cells, nerve cells, muscle cells, and
immune cells. Different types of cells combine to produce tissues, which are
combined to form organs; for example, there may be more than 10 different
types of cells in the tissues that comprise the liver.

WING WONG As a first step toward creating better virtual models of biological systems, we
PROFESSOR OF STATISTICS AND
are working to understand the “instructions” that influence a cell’s development
OF HEALTH RESEARCH AND
POLICY, STANFORD UNIVERSITY into tissues and organs and, eventually, entire systems, such as the circulatory
system. Our research builds on the development of single-cell genomics. Until
recently, scientists were able to study only groups of cells, because they lacked
the technical capability to extract enough DNA and RNA from a single cell
to support genomic analyses. We’re taking single-cell genomics findings to
the next level, to understand how single cells construct the gene regulatory
systems that underlie the different cell types in tissues and organs.

In Professor Wong’s lab in California, we’re studying the regulation of gene

XUEGONG ZHANG
PROFESSOR OF expression in cells, trying to understand how different genes are expressed
BIOINFORMATICS AND and how those genes affect the cells they eventually create. Using advanced
MACHINE LEARNING, mathematical models, we are studying huge volumes of data to try to better
TSINGHUA UNIVERSITY understand how cells develop into tissues.

After cells and tissues, the next level is organs. In Professor Zhang’s lab in Beijing, we are studying the
heart to understand what types and subtypes of cells make up different parts of that organ. With a deeper
knowledge of how the heart is constructed, we anticipate having a better understanding of how heart
problems arise. By comparing what we’re seeing in the lab with the heart conditions we see in the broad
population, we expect to be able to better predict what conditions lead to which health outcomes—positive
or negative.

We intend to expand beyond studying specific tissues and organs to construct a digital simulation of the
human circulatory system. We’re developing a framework to take in massive amounts of data generated by
electronic health records and large-scale research mapping efforts, such as the Human Cell Atlas project.20
But data sets alone are not very useful, so we’re building a type of digital twin: a multilevel causal network,

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 Digital twins: Bridging the physical and digital

a complex mathematical model to represent the functioning system and the underlying linkages between the
different layers. One day, we hope to be able to connect all the data from the DNA in the genome to health
outcomes in the general population to better understand how cell instructions, cell types, tissues, organs,
and health outcomes are all interconnected.

Within the next three years, our goal is to build out a set of quantitative, layer-by-layer models to help
interpret the genomic system. We expect the day will come when physicians will examine a newborn’s
genome sequence and understand the impact of its variants (that is, its differences from the reference
genome) along with other factors, leading to insights for resolving or preventing disease or disorders. Over
time, researchers may use these findings to create a digital twin of the entire human body to help us better
understand and simulate how disease and other changes may manifest in the body. Meanwhile, we, along
with researchers around the world, have a lot of work ahead.

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 Tech Trends 2020

EXECUTIVE PERSPECTIVES
Finance Strategy
Risk Finance Strategy
Risk Finance Strategy

STRATEGY FINANCE RISK

While digital twin technologies Digital twins offer increasing As digital twin technology
that simulate the physical world potential to affect the bottom integrates with IoT and AI, its
have been around for years, new line of organizations but aren’t disruptive power grows. In the
advances warrant taking a consistently well understood by current business climate, any
second look at current CFOs and their teams. To many potential technology-driven
capabilities. The combination of in the finance function, disruption has material risk
cheap sensors and IoT, machine traditional digital twin implications for the entire
learning, and the fast, frictionless simulations of manufacturing organization. Digital twin–driven
nature of cloud enable more processes and warehouse process efficiencies might not
sophisticated analyses and real- logistics are black boxes owned increase risk significantly, at least
time simulations. While by manufacturing or engineering. initially. But as reliance on digital
manufacturing scenarios have However, the growing availability twin technology grows,
used these capabilities for years, of high-quality simulations, companies will be aggregating
organizations are increasingly machine learning, and massive stores of data from
exploring ways to deploy digital embedded sensors is changing sensor networks and other
twins for operations, city the art of the possible. Some sources, which may, in turn,
planning, smart infrastructure, organizations that are shifting increase privacy or cyber risk.
and more. Moreover, as from selling products to Likewise, if digital twin systems
companies look to migrate to products-plus-services or as-a- enable a new business model
selling as-a-service business service models are using robust featuring several as-a-service
models, digital twins’ increasingly digital twins. They are tracking offerings, organizations should
sophisticated capabilities are usage with embedded sensors, understand what material impact
worth a closer look. The creating new offerings for usage these new revenue streams may
challenging decisions will then be recommendations, proactive have on finance, technology, and
whether to make small maintenance, or profitability existing business models. If the
investments to create tests and optimization. Working with the IT potential risks are significant,
experiments or larger function to understand digital companies will likely need to
investments to support twins’ uses today and potential develop strategies for measuring
innovation more broadly. uses of tomorrow is becoming and managing them before IT
increasingly important, and the business proceed any
particularly to support new further with the digital twin
product and service design project.
and delivery.

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 Digital twins: Bridging the physical and digital

ARE YOU READY?

LEARN MORE

1
Which of your systems, processes,
products, or outputs would be
strong candidates for inclusion in
a digital twin pilot? EXPECTING DIGITAL TWINS

Learn how leading

companies are using

2
digital twins to increase
If you are moving to as-a-service
efficiency, reduce costs,
models or bundling services with
and build better products.
products, how can a digital twin
reduce your time to market and
reduce overall costs?

3 What infrastructure and technical INDUSTRY 4.0 AND

platforms do you have in place THE DIGITAL TWIN
today to support digital twin
Read on! With a digital
capabilities?
simulation of a manufacturing
process, you can solve
problems more quickly and
build better products.

BOTTOM LINE
In the future, everyone and everything—people, services, INDUSTRY 4.0
global enterprises, and even cities—could have a digital twin. COLLECTIONS PAGE
That scale may not happen in the next 18 to 24 months, but
the digital twins trend will evolve and grow for years to come. Check out this intriguing
Pilots and prototypes can help identify potential areas where collection of articles that
companies can benefit from digital twin capabilities, but the time explores today’s digital
to embrace this next disruptive transformation phase is now. industrial revolution.

73
Tech Trends 2020

Authors

ADAM MUSSOMELI is a principal at Deloitte Consulting LLP with more than 25

years of experience delivering global, highly complex supply chain transformations.
He is the Supply Chain & Network Operations National Offering leader, delivering
substantial income statement and balance sheet benefits to his clients. Mussomeli
cofounded Deloitte’s Digital Supply Networks practice, which is redefining the supply
chain for the digital age.

AARON PARROTT is a managing director with Deloitte Consulting LLP. With more
than 20 years of experience in supply chain and network operations, he focuses on
helping clients complete large-scale transformation in the supply network, developing
analytic solutions to address difficult business issues, and implementing digital
solutions to manage complex supply networks. Parrott’s areas of expertise include
specializing in digital supply networks, IoT solutions, enterprise lean transformation,
and supply network advanced analytics.

BRIAN UMBENHAUER is a principal with Deloitte Consulting LLP. He is a leader in

the Industrial Products & Construction sector, as well as the Global Sourcing and
Procurement practice. In his two decades of experience, Umbenhauer takes a
personalized approach to building consulting practices, collaborating with clients,
driving engagements and, above all, measuring value. He has a proven track
record in global business development, operations/business strategy advisory, and
nontraditional approaches to creating client value.

LANE WARSHAW is a managing director with Deloitte Consulting LLP in the Strategy
& Analytics, Analytics & Cognitive (A&C) market, focusing on enabling better use
of data in the Industrial Products and Construction (IP&C) industry using next-
generation technologies. As the IP&C lead for A&C, he focuses on delivering tangible
value through supply chain and customer analytics programs enabled by data
management, data warehouse, big data, data science, visualization, and cognitive
technologies.

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 Digital twins: Bridging the physical and digital

SENIOR CONTRIBUTORS

Saul Caganoff Markus Stulle

Principal Senior manager
Deloitte Services Pty Ltd Deloitte

Chandra Kiran Reddy Narra Oleg Tyschenko

Managing director Senior manager
Deloitte Consulting LLP Deloitte MCS Limited

Tim Paridaens Anand Ananthapadmanabhan

Director Manager
Deloitte Belgium CVBA Deloitte & Touche LLP

Sandeep Sharma Piotr Kurek

Managing director Manager 3
Deloitte Consulting LLP Deloitte Advisory sp. z o.o. sp. k

Andreas Staffen Paulo Mauricio

Director Manager
Deloitte Deloitte & Associados, SROC S.A.

Jimmy Asher Vikram Tuli

Senior manager Senior consultant
Deloitte Consulting LLP Deloitte MCS Limited

Rick Burke Dan Lu

Specialist leader Consultant
Deloitte Consulting LLP Deloitte MCS Limited

Yang Chu Hermann Tsang

Senior manager Consultant
Deloitte & Touche LLP Deloitte MCS Limited

Eduardo Pereira
Master lead specialist
Deloitte & Associados, SROC S.A.

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Tech Trends 2020

Endnotes

1. Arjen Bongard and Daniela Hoffmann, “Digital twins play a role in all digitalization projects but data
consolidation slows down implementation,” Automotive IT, January 4, 2019.

2. Woodrow Bellamy III, “Boeing CEO talks ‘digital twin’ era of innovation,” Avionics International,
September 14, 2018.

3. Greg Powers, “At Halliburton, real-time data delivers,” Deloitte CIO Journal on the Wall Street Journal, September
25, 2017.

4. Dassault Systèmes, “The Living Heart Project,” accessed January 7, 2020.

5. Felix Todd, “Digital twin examples: Simulating Formula 1, Singapore and wind farms to improve results,”
NS Business, January 22, 2019.

6. MarketsandMarkets, “Digital twin market worth $35.8 billion by 2025.”

7. Carlos Miskinis, “The history and creation of the digital twin concept,” Challenge Advisory, March 2019.

8. Aaron Parrott and Lane Warshaw, Industry 4.0 and the digital twin: Manufacturing meets its match, Deloitte
Insights, May 12, 2017.

9. Carrie MacGillivray et al., “IDC FutureScape: Worldwide IoT 2019 predictions,” IDC, October 2018.

10. Parrott and Warshaw, Industry 4.0 and the digital twin.

11. Jonathan Lang, “AR and digital twin technologies are a powerful combination,” PTC, July 8, 2019.

12. Elaine Alhadeff, “Serious games using digital twins of offshore oil and gas environments,” Serious Game Market,
November 26, 2018.

13. Jennifer Smith, “Unilever uses virtual factories to tune up its supply chain,” Wall Street Journal, July 15, 2019.

14. National Research Foundation, “Virtual Singapore,” Singapore government, November 7, 2018; William J.
Holstein, “Virtual Singapore,” Compass, November 2015.

15. William Kucinski, “Maintaining the data-rich Pratt & Whitney GTF engine,” SAE International, October 23, 2018.

16. Michael (Mick) Snell, Service Strategy manager, Airservices Australia, interview, September 19, 2019.

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 Digital twins: Bridging the physical and digital

17. Jerome Boulet (digital strategy director at Bridgestone EMEA), and Hans Dorfi, PhD (director of digital
engineering at Bridgestone Americas), phone interview, October 3, 2019.

18. Previously known as TomTom Telematics. Steven Schoefs, “Bye bye TomTom Telematics, welcome WebFleet
Solutions,” GlobalFleet, October 1, 2019.

19. Christoph Pistek (head of Technology Sciences, Pharmaceutical Sciences R&D, Takeda), interview,
November 7, 2019.

20. Human Cell Atlas, accessed December 5, 2019.

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Tech Trends 2020

Executive editors

Bill Briggs
Global chief technology officer
Deloitte Consulting LLP
wbriggs@deloitte.com

Bill Briggs’ 20-plus years with Deloitte have been spent delivering complex transformation programs
for clients in a variety of industries, including financial services, health care, consumer products,
telecommunications, energy, and the public sector. He is a strategist with deep implementation experience,
helping clients anticipate the impact that new and emerging technologies may have on their businesses in
the future—and getting there from the realities of today.

In his role as CTO, Briggs is responsible for research, eminence, and incubation of emerging technologies
affecting clients’ businesses and shaping the future of Deloitte Consulting LLP’s technology-related services
and offerings. He also serves as executive sponsor of Deloitte’s CIO Program, offering CIOs and other
IT executives insights and experiences to navigate the complex challenges they face in business and
technology.

Scott Buchholz
Emerging Technology research director and
Government & Public Services chief technology officer
Deloitte Consulting LLP
sbuchholz@deloitte.com

With more than 25 years of experience in technology innovation and implementation, Scott Buchholz
focuses on helping clients transform the way they deliver their missions and businesses through
technology. He supports organizations across industries by providing advice and insights on how to evolve
their technology and their organizations to improve performance, effectiveness, and efficiency.

In his role as CTO for Deloitte Consulting LLP’s Government and Public Services practice, Buchholz
works with clients to implement innovation across a diverse set of areas, including legacy modernization,
eGovernment and eCommerce solutions, and solution architecture.

As the emerging technologies research director and the sponsor of Tech Trends, he helps identify, research,
and champion the technology trends that are expected to have significant impact on the market and
clients’ businesses in the future.

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 Authors and acknowledgments

Executive perspectives authors

STRATEGY
Benjamin Finzi
US Chief Executive Program leader | Deloitte Consulting LLP

Benjamin Finzi is a managing director with Deloitte Consulting LLP and coleads Deloitte’s Chief Executive
Program. As a founder of New York’s Deloitte Greenhouse® Experience, he has designed and facilitated
hundreds of immersive “lab” experiences for CEOs and their leadership teams, combining principles of
business strategy with behavioral science and design thinking to address clients’ challenges. Finzi has been
focused for more than 20 years on researching and understanding how companies succeed in disruptive
markets.

FINANCE
Ajit Kambil
CFO Program global research director | Deloitte LLP

Ajit Kambil is the global research director of Deloitte LLP’s Chief Financial Officer Program. He oversees
research in areas such as leadership, capital markets, and risk. Kambil created CFO Insights, a biweekly
publication serving more than 38,000 subscribers, and developed Deloitte’s Executive Transition Lab, which
helps CXOs make an efficient and effective transition into their new role. He is widely published in leading
business and technology journals.

Moe Qualander
Principal | Deloitte & Touche LLP

Moe Qualander is a principal with Deloitte & Touche LLP’s Risk & Financial Advisory practice. He has more
than 20 years of experience, specializing in assessing internal controls in financial business operations and
IT. Qualander leads Deloitte’s Chief Financial Officer Program’s Center of Excellence, focusing on creating
and enhancing relationships with clients’ CFOs. As dean of Deloitte’s Next Generation CFO Academy, he
assists future finance executives with enhancing their leadership, influence, and competency skills.

RISK
Deborah Golden
US Cyber Risk Services leader | Deloitte & Touche LLP

Deborah Golden is a principal with Deloitte & Touche LLP and Deloitte’s US Cyber Risk Services leader. She
brings more than 25 years of information technology experience in industries that include government and
public services (GPS), life sciences and health care, and financial services to the role, and previously served
as Deloitte’s GPS cyber leader, as well as GPS Advisory market offering leader. Golden also serves on
Virginia Tech’s Business Information Technology and Masters in Information Technology advisory boards.

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Tech Trends 2020

Chapter authors

MACRO TECHNOLOGY FORCES

Bill Briggs Scott Buchholz Sandeep Sharma, PhD
Global chief Government & Public Deputy chief
technology officer Services chief technology officer technology officer
Deloitte Consulting LLP Deloitte Consulting LLP Deloitte Consulting LLP
wbriggs@deloitte.com sbuchholz@deloitte.com sandeepksharma@deloitte.com

ETHICAL TECHNOLOGY AND TRUST

Catherine Bannister Deborah Golden
Technology Fluency and US Cyber Risk Services leader
Ethics global director Deloitte & Touche LLP
Deloitte Services LP debgolden@deloitte.com
cbannister@deloitte.com

FINANCE AND THE FUTURE OF IT

John Celi Ajit Kambil Khalid Kark
Business Agility US leader CFO Program global US CIO Program
Deloitte Consulting LLP research director research leader
jceli@deloitte.com Deloitte LLP Deloitte Consulting LLP
akambil@deloitte.com kkark@deloitte.com

Jon Smart Zsolt Berend

Business Agility UK leader Business Agility senior manager
Deloitte MCS Limited Deloitte MCS Limited
jonsmart@deloitte.co.uk zsoltberend@deloitte.co.uk

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 Authors and acknowledgments

DIGITAL TWINS: BRIDGING THE PHYSICAL AND DIGITAL

Adam Mussomeli Aaron Parrott Brian Umbenhauer
Supply Chain & Network Supply Chain & Network Industrial Products and
Operations leader Operations managing director Construction leader
Deloitte Consulting LLP Deloitte Consulting LLP Deloitte Consulting LLP
amussomeli@deloitte.com aparrott@deloitte.com bumbenhauer@deloitte.com

Lane Warshaw, PhD

Analytics & Cognitive
managing director
Deloitte Consulting LLP
lwarshaw@deloitte.com

HUMAN EXPERIENCE PLATFORMS

Tamara Cibenko Amelia Dunlop Nelson Kunkel
US Digital Experience lead Deloitte Digital chief Deloitte Digital chief design officer
Deloitte Consulting LLP experience officer Deloitte Consulting LLP
tcibenko@deloitte.com Deloitte Consulting LLP nkunkel@deloitte.com
amdunlop@deloitte.com

ARCHITECTURE AWAKENS
Saul Caganoff Ken Corless Stefan Kircher
Platform Engineering chief Cloud chief technology officer Innovations & Platforms
technology officer Deloitte Consulting LLP chief technology officer
Deloitte Consulting Pty Ltd kcorless@deloitte.com Deloitte Consulting LLP
scaganoff@deloitte.com.au skircher@deloitte.com

HORIZON NEXT: A FUTURE LOOK AT THE TRENDS

Mike Bechtel Bill Briggs Scott Buchholz
Managing director Global chief technology officer Government & Public Services
Deloitte Consulting LLP Deloitte Consulting LLP chief technology officer
mibechtel@deloitte.com wbriggs@deloitte.com Deloitte Consulting LLP
sbuchholz@deloitte.com

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Tech Trends 2020

Contributors

Mukul Ahuja, Zillah Austin, Randall Ball, Sonali Ballal, Tushar Barman, Neal Batra, Jonathan Bauer, Mike
Brinker, Randy Bush, Rachel Charlton, Sandy Cockrell, Allan Cook, Megan Cormier, Amit Desai, Anant
Dinamani, Sean Donnelly, Matt Dortch, Deborshi Dutt, Karen Edelman, Michael Fancher, Frank Farrall,
Jourdan Fenster, Bryan Funkhouser, Andy Garber, Haritha Ghatam, Cedric Goddevrind, Jim Guszcza,
Maleeha Hamidi, Steve Hardy, Blythe Hurley, Lisa Iliff, Siva Kantamneni, Mary-Kate Lamis, Blair Kin,
Kathy Klock, Yadhu Krishnan, Michael Licata, Mark Lillie, Veronica Lim, Mark Lipton, Kathy Lu, Adel
Mamhikoff, Sean McClowry, JB McGinnis, Meghan McNally, Kellie Nuttall, Melissa Oberholster, Arun
Perinkolam, Ajit Prabhu, Aparna Prusty, Mohan Rao, Hannah Rapp, Scott Rosenberger, Mac Segura-Cook,
Preeti Shivpuri, Lisa Smith, Gordon Smith, Tim Smith, David Solis, Alok Soni, Patrick Tabor, Sonya Vasilieff,
Aman Vij, Jerry Wen, Mark White, Drew Wilkins, Abhilash Yarala, Andreas Zachariou, and Jim Zhu.

Research team

LEADS

Cristin Doyle, Chris Hitchcock, Betsy Lukins, Dhruv Patel, Andrea Reiner, and Katrina Rudisel.

TEAM MEMBERS

Stephen Berg, Erica Cappon, Enoch Chang, Tony Chen, Ankush Dongre, Ben Drescher, Ahmed
Elkheshin, Harsha Emani, Jordan Fox, Riya Gandhi, Dave Geyer, Maddie Gleason, April Goya,
Adhor Gupta, Alex Jaime Rodriguez, Morgan Jameson, Solomon Kassa, Pedro Khoury-Diaz, Emeric
Kossou, Dhir Kothari, Shuchun Liu, James McGrath, Hannan Mohammad, Spandana Narasimha
Reddy, Gabby Sanders, Joey Scammerhorn, Kaivalya Shah, Deana Strain, Samuel Tart, Elizabeth
Thompson, Samantha Topper, Kiran Vasudevan, Greg Waldrip, and Katrina Zdanowicz.

128
 Authors and acknowledgments

Special thanks
Mariahna Moore for gracefully accomplishing the impossible year after year, making it look easy, and
ensuring we always follow the rules. Your standards for excellence continue to help Tech Trends live up
to its potential. And your ability to stay cool, keep a firm hand on the tiller, and always have a plan for
navigating the upcoming challenges is unmatched.

Doug McWhirter for consistently developing deft, incisive prose out of copious streams of
consciousness, innumerable interviews, reams of research, and stampedes of SMEs. Your wit, wisdom,
and patience help make Tech Trends 2020 the research opus that it is.

Dana Kublin for your talent of conjuring insightful visuals, intuitive infographics, and fascinating
figures out of thin air and unclear descriptions. Your ability to talk us out of our crazy ideas and then
show us an improved version of what we told you makes all the trends better.

Stefanie Heng for your “gentle persistence” at managing the nonstop, day-to-day activities and always
bringing a smile to everything you do. Your grace under pressure and your unflinching commitment to
the project has enabled us to “get to done.”

Caroline Brown, Tristen Click, and Linda Holland for your depth of craft, inspired creativity, and
immense patience. Whether dealing with infographics, chapters, or interviews, your collective talents,
attention to detail, and willingness to go the extra mile made Tech Trends better.

Kaitlin Crenshaw, Natalie Martella, and Camilo Schrader for a fabulous freshman year. Your
support as part of the Tech Trends family has been invaluable as you helped keep us on track for
interview prep, secondary research, content reviews, designs, graphics, and more.

Mitch Derman, Tracey Parry, and Tiffany Stronsky for continuing to advance our marketing,
communications, and PR game. Your willingness to question, push, and share your ideas have helped
take our program up to eleven. Your efforts to get the right buzz in the right places at the right times
is amazing.

Laura Elias, Martina Jeune, and Faith Shea for an unbelievable impact on your first Tech Trends
report. Thank you for bringing new ideas to the table and helping us push the boundaries of what we
can accomplish.

Amy Bergstrom, Matthew Budman, Sarah Jersild, Anoop K R, Emily Moreano, Joanie Pearson, and
the entire Deloitte Insights team. Your amazing partnership on Tech Trends helps us reach new heights
every year.

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