AI:

Augmented Intelligence

CEO White Paper

Mark J. Barrenechea
OpenText CEO and CTO

AI: Augmented Intelligence

Contents

Introduction

2

The Information Age

4

How AI Works

7

What Is AI-Augmented Intelligence?

12

Obstacles to AI-Augmented Intelligence

19

AI-Powered Solutions

22

AI Is the Future31

AI for the Good34

1

Introduction
Big data is here. Along with small data. And dark data. Structured data. Unstructured
data. Machine data. Raw data. Recent data. Open data. Private data.
The data tsunami is unfathomably huge. And it is exponential.
As we entered 2020, humanity had accumulated around 44 zettabytes (44 trillion
gigabytes) of data.1 To put this into context: if we were able to digitize and store all
human speech ever spoken since the beginning of time, it would still only be 42
zettabytes.2 Our current 44 zettabytes amounts to 40 times more bytes in our digital
universe than there are stars in the observable universe. Ninety percent of that data
has been created in just the last two years.3
Every day, we are generating more information than we ever have before… and lessthan we ever will again.
In 2025—fewer than five years from now—the total amount of data in the world isforecasted to hit 175 zettabytes.4 This is not only an unprecedented volume but astaggering speed, considering it took decades to create the first 44 zettabytes.
180

175 ZB

160
140
120
100
80
60
40
20
0

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Information volume, data transmission speed, computing power, technologicaladvancement—it is all going exponential.
Industry 4.0 technologies, like artificial intelligence (AI), APIs, machine-to-machine(M2M) communication and the Internet of Things (IoT), are at the heart of this new
information paradigm. They create vast amounts of data. Likewise, they are fueledby data.

2

Figure 1:

Exponential Growth of Information5

AI: Augmented Intelligence

Consider the IoT. Today’s 22 billion devices will almost double, to 41.6 billion, by2025.6 7 Running on 5G networks, these devices—sensors, robots, autonomousvehicles, wearables, appliances, machinery and many others—will be able to transmit
and receive information with accelerating density, speed and efficiency. Everyone
and everything will be connected. Smart homes, factories and cities will seem to run
effortlessly, while a torrent of information flows behind the scenes.
45000

Figure 2:

40000

IoT Will Change the World
within Five Years

35000

Millions

30000
25000
20000
15000
10000
5000
0
2007

2009

2011

2013

2015

2017

2019

2021

2023

2025

Enterprise IoT

Information is rewriting all of the rules. It is powering new business models, new paths
to understanding the world and new ways of being human. But the sheer volume
of information is already far beyond human capacity to grasp, synthesize or makesense of.
This is the Information Age—and it has only just begun.
Figure 3:

Shift Happens:
It’s an exciting time

Technological Progress—
The Information Age

We arehere

Graphene
Information

MainframesCentralized

Client server

Distributed

Internet

Connected

DigitalExtremeAutomation

AI &Learning

BeyondSilicon3

The Information Age
In the Information Age, information is the new capital.
It is driving widescale disruption and creating significant opportunities in every
industry. Banking is disrupted by FinTech’s convenience and simplicity. Retail is
disrupted by ecommerce’s frictionless customer journey and personalization
algorithms (plus, new ways of shopping thanks to COVID-19). Traditional agriculture
is disrupted by new insights in digital farming. Education is disrupted by customized
e-learning platforms. The list goes on.
Figure 4:

FinTechEcommerce

DigitalFarminge-Learning

TravelWebsites

Sharing
Economy
Apps

Banks

RetailAgriculture

Education

TravelAgentsHotels &Transportation

Digital Disruption

Streaming
Services

Movies,TV, Music, etc.

Data is changing the world and the mantra is get on board or get left behind. Nearly
every industry is either experiencing or vulnerable to digital disruption.
Figure 5:

CURRENT LEVEL OF DISRUPTION

0.80

0.70

VIABILITY(18% of companies)
Comms & Media

0.60

0.50

Software & Platforms
2018 Weighted Average
0.51Life Sciences

0.40

0.30

0.20

0.54
2018 Weighted Average

Disruptability Index Industry Sector Matrix - 2018 results
0-1 scale (1 = most susceptible/disrupted)

High-Tech

ConsumerGoods &
Retail Services

TravelHealthChemicals
EnergyDURABILITY(11% of companies)

0.20

0.30

0.40

0.50

Infrastructure &Transportation Services

4

Insurance

Banking
Automotive
Industrial Equipment & Machinery
Capital Markets

Natural Resources
Utilities

0.60

SUSCEPTIBILITY TO FUTURE DISRUPTION
Source: Accenture Research Disruptability Index 2.0.

VOLATILITY(33% of companies)

VULNERABILITY(38% of companies)

0.70

0.80

Digital Disruption Index by Industry8

AI: Augmented IntelligenceThis new world requires a new mindset. Information is the lifeblood of everyorganization—or at least, those that plan to be around in five years. It flows frompeople, businesses, processes, applications and endpoints both inside and outside
the organization. It comes in new volumes, new formats and from new sources. It
encompasses everything from corporate records, to customer and partner data, toendpoint statistics, to intellectual property.
Figure 6:
Agreements
Drawings Invoices

RemoteOffices

PlantsSuppliers

Field
Workers

Product Information
Internal Documents

Institutions

Financial Documents
Legal Documents
Corporate
Offices

Client Documents HR Documents
Policies and Procedures

Email

Warehouses

3rd Parties

Partner Agreements
Client Information

Drop SharePartners

Quotes Orders Invoices

OfficeWorkers

PhysicalInformation Is
the Organization’s Lifeblood

Customers

Fax

Each company needs information-driven processes, people, technologies andbusiness models—an end-to-end transformation. Information is not a by-productof doing business. It is critical to, and can often be, the product, the service and themethod of doing business.
To achieve this transformation, however, companies must be able to engage with
their data in meaningful ways. As the volume of information rises, so too does thepressure to do more with it. It is not enough to simply collect masses of data—it mustbe leveraged for success.
Most of the data that organizations generate—over 80%—is unstructured.9
Conventional programs cannot read it. But neither can humans fathom it. Which iswhy anywhere from 55% to 99.5% goes unused.10 11 And as our information sits inthe dark, so do we. Employees do not have the information they need to do their jobs
effectively. Automated systems are blind to a vast lake of valuable data. Leaders lackthe insights to make better, data-driven decisions.

Limited Insights

Figure 7:

Data in Use

Process Advantage

Data Enrichment

Automation

The Untapped Potential of Information

Human-to-human Collaboration

Enterprise Resource Planning

Unused Data

Deeper Insights

Information Advantage Potential

Exceptional Customer,
Employee and Partner Experiences

Augmented Human Intelligence
New Business Models

Operational Efficiency

Enhanced Security and Threat Detection
Advanced Risk Management
Data-driven Predictions

5

This unused data is an enormous resource just waiting to be tapped. And once it istapped, abundance overflows.
Information has a truly exceptional and powerful ability. It functions as a two-fold
exponential feedback system: 1) data begets data, and 2) the more data is used, themore valuable it becomes. Each interaction produces more data, which is fed backinto the cycle to produce even more sophisticated context and insights.
It boils down to this: more information means more possibilities, more connections
and a greater advantage.
Figure 8:

Metcalfe’s Law12—The Exponential Powerof Data Abundance

2 = 1 connection

5 = 10 connections

10 = 66 connections

Organizations must find ways to harness their information. Collect it. Manage it.
Synthesize it. Analyze it. Get insights. Win. Repeat.
Humans cannot do this alone. It takes machines to generate this volume andcomplexity of data—and it will take machines to parse it.
Businesses need machine learning and artificial intelligence technology. AI canachieve information processing feats impossible for humans. It can formulate insights
we could not have imagined. And, as we augment human intelligence with artificial
intelligence, the horizons of what is possible expand every day.

6

AI: Augmented Intelligence

How AI Works
Not so long ago, the notion of machines thinking and acting on their own was the
stuff of science fiction. Think HAL, C-3PO and the Terminator. And while AI is indeed
defined as computer systems that are able to perform tasks requiring what we would
typically think of as human intelligence, today’s AI is a far cry from its depiction in
the movies.
AI is not a single technology; it comprises several solutions that allow machines tomanage information, automate tasks and predict the future. It works through machinelearning, which uses complex algorithms to learn from data, without being explicitly
programmed to do so, and to find patterns in large datasets. From those patterns, AIpowered solutions generate insights to enable machine-assisted decision making.
Most people already rely on AI, perhaps without even realizing it. It is in the app thatfigures out the best commute route, Netflix’s suggestions on what to binge-watch
next and the fraud detection systems protecting everyone’s bank accounts. Eightyfive percent of Americans are using artificial intelligence, often interacting with it
multiple times every day.13 So much so, in fact, that many people communicate with AI
more than with their own spouse!14With so many powerful capabilities, it is no wonder that AI-driven solutions are
becoming increasingly ubiquitous. It is the next big technology that will change theworld, with a global market expected to be worth almost $60 billion by 2025.15
Although interacting with AI may seem simple from the user’s perspective, it involves
many sophisticated technologies working together behind the scenes, each withpowerful capabilities.
Unpacking AI
Figure 9:
Artificial Intelligence

Artificial Intelligence

Business Intelligence
MachineLearning

Natural Language
Processing

Big Data
Deep
Learning
Data

Predictive Analytics

Cognitive
ComputingStatistics

7

Machine Learning

Machine learning is a branch of AI in which machines learn, without requiring explicit
programming to do so, by processing data, including text and images. A machinelearning algorithm acts as a series of instructions for decision making. Two of the major
phases for machine learning are learning and prediction.
In the learning phase, the system processes historical data—typically in the form ofstructured data—and applies algorithms to identify trends. For instance, the system
might be fed data on tigers with descriptors like “black and orange,” “stripes”
and “feline.”
In the prediction phase, the system uses what it has learned to process new data. It
finds patterns and trends in any novel data, matching them to those established in
the historical data. For example, if the system encounters an image in an ad for a local
zoo, it could classify the animal depicted as a tiger based on the stripes, orange andblack color, and other pre-established characteristics.
This iterative learning process is refined over time as the system continues to ingest
more information, making the AI more intelligent and adaptive.
Deep learning—sometimes also called “deep neural learning” or “neural network”—isa subset of machine learning that is modeled on the human brain. The system has
many networks of algorithms stacked on several layers (and is, therefore, “deep”) thatallow it to make sense of vast quantities of diverse, unstructured and complex data. It
learns from experience and can acquire new skills without deliberate human guidance.
Looking at our tiger example again, a deep learning system would not need to be told
explicitly about features like “stripes” and “black and orange.” Through processing
enough data, it would come up with its own markers to identify tigers from non-tigers.
Figure 10:

Machine Learning

Machine Learning versus Deep Learning16
Tiger
Not Tiger

Input

Feature extraction

Learning

OutputDeep Learning
Tiger
Not Tiger
Input

8Feature extraction + Learning

OutputAI: Augmented Intelligence

Predictive Modeling and PlanningWith predictive modeling, machine learning models are trained using known resultsfrom historical datasets. They can then forecast outcomes and relationships likely
to occur in future datasets and recommend specific actions. Predictive modelingis particularly useful for detecting areas of potential risk or reward. It can answer
questions such as:• How likely is it that an existing customer will purchase a specific product?
• How will a marketing campaign impact engagement or sales for a targetedcustomer group or region?
• Which claims are most likely to require additional review from fraud investigators?
• How likely is a patient to show up for a scheduled medical appointment?
• What components are likely to fail and when?
• Which customers are most likely to cancel their subscriptions?

OpenText

OpenText wanted to find a solution to help retain customers and support the
renewals process. We realized we had a great opportunity to: proactively identify
contracts at risk of cancellation, allowing us to resolve issues earlier and reducecancellations to record lows; prioritize work to maximize efficiency and strive
for operational excellence; and, improve insights to continually serve ourcustomers better.
To that end, the OpenText AI & Advanced Analytics team partnered with theCustomer Support (CS) Enterprise Renewals team to determine how to best use
our OpenText Magellan AI to uncover deep insights about customer contract
renewals, with a goal of reaching record-high customer satisfaction levels.
The teams used Magellan Data Discovery to profile customer cancellations,apply a cancellation risk level from these profiles, and segment customers byrisk level. Today, the renewals team has new Magellan BI & Reporting dashboards
that show cancellation risk with a visual heat map, allowing for quick prioritizationof their work and early intervention. Details of the risk are given, helping renewal
representatives flag issues that they may not have seen.

9This work, in combination with programs like Customer Support Outreach andallowing customers to opt-in to auto-renewing, allows the renewals team to focus
on retention and drive on-time renewals, while maintaining a high level of data
quality for forecasting and communicating next actions for at-risk renewals
and reporting.
OpenText runs on OpenText.

Text Mining and Natural Language Processing

Text mining makes sense of large volumes of unstructured, textual content, such as
emails, documents and social media. Through natural language processing (NLP),
text mining software extracts key metadata, identifies entities such as people, places,
things and events, and can even provide summary or context for analysis. Beyond
simply extracting this data, NLP can also conduct sentiment analysis to determine
the author’s tone (positive, negative or neutral), emotional analysis to understand theauthor’s feelings (happy, sad, angry, etc.) and intention analysis to understand themeaning behind a message (to buy, to sell, etc.). Just a few use-cases for NLP include
differentiating facts from opinions, understanding content so it can be automaticallyclassified and understanding the voice of customers.
Text mining can also gather additional data to supply back into the system fordeep learning.
Other Branches

AI has many branches, all overlapping in intricate ways with each other as well as withother disciplines.
One such branch is expert systems: AI trained by human experts for complex problem
solving, decision making and planning that emulates human ability in very specialized
disciplines, such as healthcare or engineering. Expert systems are currently being
used to help make medical diagnoses, troubleshoot equipment, schedule flights, planmanufacturing processes and much more.17
Meanwhile, other critical branches focus on perceiving, sensing and acting. This
includes AI that is able to see (e.g. machine vision), hear (e.g. speech-to-text), and,intersecting with robotics, feel and manipulate the world around it.

10AI: Augmented Intelligence

Text Mining

Natural Language Processing

Figure 11:

Sentiment Analysis

Key Branches of AI

Content Extraction
Machine Learning

Data
Classification

Deep Learning
Supervised
Unsupervised

Artificial Intelligence

Expert Systems
Planning
Machine Vision

MachineAudition

Robotics
Speech-to-Text

Image Recognition

Facial and Expression
Recognition

Augmenting Human Intelligence
Despite its remarkable capabilities, AI is not all-knowing—at least, not yet. For
instance, although predictive modeling is a practical solution for anticipating buying
trends, it still requires oversight from someone with market knowledge and experience.
Human intervention on the front end must properly frame the problem for the AI and
ensure that is uses high quality data sources. On the back end, humans must verify
that AI solutions provide results that are free of ambiguity and bias. Of course, AI also
lacks the empathy, common sense, intuition, ethics and natural curiosity humans have.
Fortunately, AI and humans are perfectly suited to play to one another’s strengths.
AI is only truly effective when humans work alongside it. It augments human
intelligence by rapidly processing huge pools of data and extracting valuable
insights—a task too tedious and time-consuming for any human. And, humans act
as the gatekeepers of these insights by ensuring ample, diverse data is provided,
monitoring for biases and looking for trends that could suggest predictive models
are becoming stale. When implemented strategically, AI empowers people to become
more informed and efficient while still maintaining a necessary human touch.
With this approach, humans learn from AI systems and vice versa, and both
improve from the resulting partnership. This is the crux of the notion of
AI-augmented intelligence.

11

What Is AI-Augmented Intelligence?
AI-augmented intelligence is the act of enhancing human comprehension andknowledge through the use of AI capabilities, including machine learning andpredictive modeling. It leverages algorithms to detect patterns in datasets invisible toeven the most astute human statistician, solve the thorny problems that stump us andmake better predictions about the future.
That said, AI-augmented intelligence does not aim to replace humans. Instead, it frees
humans from the drudgery of manual, tedious tasks to allow us to spend more time
innovating, strategizing and being creative. It then goes a step further, supportingthese higher-level cognitive functions by breaking complex data down into accessible
and valuable insights.

Human

AI

Abstract ideas

Speed

Non-linear thinking

VolumeHighly adaptive

Accuracy

Broad learning andcomprehension

Specialized
learning andcomprehension

With the wealth of data available to it today, AI has the ability to illuminate new
relationships between elements of our world and reveal uncharted avenuesof understanding.
Why AI-Augmented Intelligence?
AI separates leaders from laggards. It helps us to make smarter decisions. To bebolder. To leverage the tsunami of information, understand more and get closer to thetruth. To unleash the full magnitude of human intelligence.
Organizations are increasingly finding practical ways to implement AI to solve theirgreatest challenges and accelerate business. An estimated 37% of companies are
already using it, with implementations growing an impressive 270% in recent years.18
This trend is set to continue. Within a year, 80% of emerging technologies will havefoundations in AI.19 From optimizing the performance of high-value equipment inindustries such as oil and gas to helping retailers better understand their customers’
preferences, there are countless business applications for AI.
AI empowers organizations to extract every drop of value from their data. It effectively
deconstructs big data, including both structured data and unstructured text or visualcontent, and transforms it into actionable information. This is especially important
for today’s businesses, contending with data-driven competitors who are using
information to disrupt and dominate. Fear of disruption is the motivating factor for 75%
of organizations to ramp up their big data and AI investments with greater urgency.20

12Figure 12:

Human Intelligence + Artificial IntelligenceAI: Augmented Intelligence

Across sectors, AI is the key to success in the information age.
Figure 13:

AI Has a Vast Potential to Create Value21

The Primary Advantages of AI-Augmented Intelligence
When deployed strategically, AI-augmented intelligence offers tremendous benefits.
Figure 14:
Top Benefits of AI
Eliminates Need for
Manual Processing

Generates Shareable
Insights

Automates Processes

Supports Compliance

Improves Security

Increases
Competitiveness& Performance13

Eliminates Need for Manual Data Processing

Processing numerical data is a challenge in itself, but unstructured content isnotoriously unwieldy. Collecting all incoming information and getting it to the rightplace in a timely manner is a significant burden for companies as the quantity of dataswells. Manual data tagging and sorting is laborious, costly and an impractical use of
employees’ time and effort. AI solutions provide one unified system for classifying,
managing and extracting insights from data, eliminating the need to sift throughmultiple content systems and painstakingly process and route data.

North Star BlueScope SteelA wholly owned subsidiary of Australia-based BlueScope, North Star BlueScope
Steel produces and supplies hot-rolled steel bands for coil processors, cold-rolled
strip producers, pipe and tubers, equipment manufacturers and steel service
centers. The company is the largest scrap steel recycler in Ohio, recycling nearly
1.5 million tons of scrap steel every year.
North Star BlueScope needed a new tool to assist in an area of business that was
lagging due to outdated technology. At the time, costing was still being managed
through Microsoft® Excel®, a time-consuming process that saw disparate areas ofthe enterprise manually collecting data in a file so large and inefficient that it could
take up to 10 minutes just to open. North Star BlueScope needed a more efficient
tool to help it more accurately understand its costing data and workflow, so thecompany could use it to engage with customers, conduct market-based analysis
and build purchasing breakdowns.
The company decided to partner with OpenText for our Magellan BI & Reporting
solution, a component of OpenText Magellan Analytics Suite, which they utilized asa business intelligence platform to visualize production and financial data throughreports and dashboards. With an advanced approach to analytics, this tool isdesigned to automatically access, blend, explore and analyze data. It also allows
the company to compare data month-to-month and analyze how events such as
plant delays and bottlenecking might affect company profitability. This allowed
North Star BlueScope to have a better understanding of customer trends andneeds, for a more informed sales team.
Building on its success, North Star BlueScope is also exploring the possibilities ofusing OpenText’s Magellan Data Discovery tool to get better insight into their data.
Potential uses include identifying customers who are likely to switch products anddetermining future needs to meet market challenges.

14AI: Augmented Intelligence

Generates Shareable Insights

Most organizations struggle with internal information silos. Opportunities for crossfunctional collaboration are thwarted without the ability to share valuable data-derived
insights across the entire business. AI helps to connect the organization, catering todata scientists, knowledge workers and everyone in between to break down internalsilos and curb information sprawl. When insights become easy to digest and share,
multiple teams—both inside the organization and out—can collaborate on impactful,organization-wide initiatives that support the overall business strategy.
Automates Processes

AI enhances human knowledge, but there are also many tasks it can automate entirely.
Automation can replace nearly half of all activities in current jobs, with five percent
being fully automatable.22
Automation Potential
Accomodation and food services
Manufacturing
Agriculture
Transportation and warehousing
Retail trade
MiningOther services
Construction
Utilities
Wholesale trade
Finance and insurance
Arts, entertainment and recreation
Real estate
Administrative
Health care and social assistances
Information
Professionals
Management
Educational services

73%
60
58
57

Figure 15:
Automation Will Replace Tasks
in All Sectors23

53
51
49
47
44
44
43
41
40
39
36
36
35
35
27

Taking the human element out of these rote tasks eliminates the potential for humanerror (which grows higher as humans become fatigued with repetition) and enables
companies to repurpose their workforce to higher order, more impactful activities.24
Collecting and processing data is only one type of task that AI is positioned toautomate. It is also annexing retail checkouts, front-line customer support andrepetitive manufacturing tasks. And as it becomes better at reasoning and decision
making, AI is taking over fields like insurance claims, bookkeeping, investment
portfolio management, basic HR and even legal research.

15

Pillsbury Winthrop
Shaw Pittman LLP
Pillsbury Winthrop Shaw Pittman (Pillsbury Law), who specializes in advising andcounseling the world’s largest companies across their litigation portfolios, isthe first law firm to deploy OpenText Magellan, OpenText’s AI-enabled analytics
platform. Pillsbury will leverage Magellan to deliver expanded automation andartificial intelligence enhancements to the firm’s global legal practice.
According to Pillsbury litigation partner, David L. Stanton, “the Magellan AI suite
gives us flexible machine learning capabilities and an adaptive suite of text-mining
and visualization tools, which will change how we approach big data—driving
faster and more in-depth legal analytics and permitting us to leverage custom
data models and taxonomies to streamline labor-intensive tasks. We are building
intelligent tools with Magellan to make our legal teams even more efficientand effective, with automated document summaries and chronologies, instantsentiment analysis and AI-enabled privilege detection already on the way. Our
overriding goal at Pillsbury is exceptional client service, and this investment will
help us deliver on that promise while keeping our clients’ costs down.”
Magellan complements the firm’s existing OpenText Axcelerate Clouddeployment and will deliver extended machine learning abilities beyond traditional
eDiscovery—enabling Pillsbury’s attorneys to interact with large data collections
in new ways that will make it easier to understand and present legal case narratives
and to unearth investigatory facts. By leveraging AI for the heavy lifting, Pillsbury
also enables their highly skilled attorneys to focus on the key documents that will
shape legal strategy.
Supports Compliance

Regulations, security and governance are all converging around data requirements
to comply with information privacy laws like the General Data Protection Regulation(GDPR) in Europe, the Personal Information Protection and Electronic Documents
Act (PIPEDA) in Canada, and the Health Insurance Portability and Accountability
Act of 1996 (HIPAA), the Children’s Online Privacy Protection Act of 1998
(COPPA) and California Consumer Protection Act (CCPA) in the U.S. To keep up
with these regulatory pressures, businesses need adaptable and defensible
governance practices in place. AI with NLP capabilities can automatically classify
sensitive information and apply governance actions to it, minimizing the riskof non-compliance.

16AI: Augmented Intelligence

FINRA
ISO 27001:2013
Evidence Act
Rule 2210
Basel III Accord
Patriot Act BSI PD5000
Conflict Mineral
IncomeEU NFR Directive
PIPEDA
Tax Act
Disclosure RuleEU PharmacovigilanceSÄHKE2EU MDRUK Bribery
Directive
HIPAA
Act
GDPR
PSD2
Dodd Frank
SOX

AODA

FATCA
GLBA
FDA 21 CFRPart 11KYC/AML
LGPD

SEC 17a-4

FSMA
NARA M-19-21
TSCA
Directive
HSR Act
Section 508(Rehabilitation Act)TFTEA FOIADSCSAFERC 18 CFR
Parts 35 & 284
DoD 5015.2

UK Modern
Slavery Act

GoBD

PDPA

Solvency II Promotion of Accessto Information Act VERS
Anti-Corruption
POPI
Law 12846/2013■ Data Protection & Privacy Rules■ Regulated Records Retention■ eDiscovery Requirements

A Snapshot of the Global
Regulatory Environment

MiFID I & II, MiFIROpen Government
Information Regulations
APPI
Information
Technology Act
E-Commerce Act(Republic Act No. 8792)

PCI DSS

CCPA

Figure 16:

Telecommunications
Amendment Act
Modern Slavery Act

Public Records Act■ Information Integrity & Authenticity■ Reporting Obligations

Improves Security

The cost of cybercrime is crippling. A staggering $2.9 million per minute to be
exact.25 Cybercriminals have the latest technologies—including AI—at their disposal.
Machines are deployed against cyber-defenses, operating at a galactic scale, withvolume, speed and agility humans can scarcely comprehend. They are perpetrating
attacks that become business crises, like ransomware, denial of service, phishing andmore. Consider that two percent of URLs are malicious, with a quarter of those hosted
on trusted domains. In 2019, phishing sites grew by 640%.26 And the most dangerous
phishing sites only live for hours, or even minutes. Meanwhile, companies must protectswelling numbers of endpoints both inside and outside of the business network.
Organizations need to counter machines with machines to have any hope of stymieing
today’s advanced threats.
AI-powered security operations models are called AIOps, and they are tasked withanalyzing big data from across the organization in real time, recognizing patterns,
making predictions and automatically responding to deviations and threats. AIOps
alone can accomplish the feat of monitoring a multi-layered, distributed network andall associated endpoints for threat activity; of reading the entire internet in real timeand mapping the relationships between billions of IPs, URLs, files, and apps to spot
suspicious behavior and predict future attacks; and of providing timely, accurate andpredictive threat intelligence that can proactively protect against never-beforeseen threats.
Businesses are taking note. Eighty-three percent of organizations in the U.S. believethat they will not be able to stop cyberattacks without AI.27 As machine learning
becomes more integrated in cybersecurity defenses, it will become smarter andsmarter in identifying and responding to threats.
It is a machine versus machine world. Today’s threats cannot be fought withyesterday’s strategies.
Increases Competitiveness and Performance

AI-augmented intelligence is a powerful means of solving some of the most complex
challenges businesses face. It acts as a force multiplier, unlocking the hidden value ofdata, generating strategic insights and helping organizations capitalize on them withagility and precision.
17

Figure 17:
AI Boosts Performance
Across Industries28

Most importantly, it can be integrated horizontally at every level where informationis collected, managed and used—from marketing, to R&D, to customer support, to
supply chain management, to manufacturing. A McKinsey report found that AI has thegreatest potential impact when applied to top-line activities or bottom-line operational
functions.29 With so many potential applications, effective use of AI ultimately results insuperior operational efficiency, exceptional customer experiences and rapid innovation.
However, before AI can live up to its immense potential, there are some challenges thatmust be overcome.

18

AI: Augmented Intelligence

Obstacles to AI-Augmented Intelligence
Despite organizations’ eagerness to leverage AI-augmented intelligence, failed AI
projects continue to run rampant. What is it, exactly, that prevents AI projects fromgetting off the ground?
Figure 18:

Siloed Projects &Lack of Buy-in

Insufficient or
Poor-Quality Data

Lack of Capabilities

AI BiasTop Obstacles to a Successful
AI Implementation

Siloed Projects and Lack of Buy-in
For AI projects to be successful, they must be accessible to all different user types
throughout the organization. This includes knowledge workers and data scientists, aswell as general operational business users.
Often, data science projects become siloed. AI implementations require crossfunctional collaboration: line of business knowledge workers must define the problem
to be solved and the data science team must frame it as a machine learning problem.
Involved teams must then continually communicate to ensure the right data is used,
the machine learning algorithms are effective for solving the problem, and the results
are understandable and distributed throughout the organization. A simple way toensure cooperation among those who will be using AI is to select a solution with userfriendly interfaces designed to accommodate each function.
Leaders must also embrace the use of AI and analytics for crucial decisions. Theirbuy-in can be achieved through clear communication and a compelling business case,including how action on AI-provided insights can increase revenue, lower costs or
unlock new business models.
Insufficient or Poor-Quality Data
Impactful AI projects must also start with the right quality and quantity of data. To draw
meaningful insights and make predictions, machine learning models require clean
data—free of extreme outliers, duplicates and NULL values. The common phrase in theindustry is “garbage in, garbage out.”
The data must also be readily available. If there are security factors involved withsensitive data, what are they? Even if a connected data source is accessible now, will
it be in the future? A data pipeline must be created to continuously feed the machinelearning model. Ultimately, the owners of the data, business teams and data scientists
must collaborate to verify the data is workable for machine learning projects.

19Figure 19:
Characteristics of Good Datasets

Size

Quality

DiverseRepresentative

Lack of Capabilities
AI implementations are supported by a number of key capabilities, including data
handling and engineering. While it is entirely possible to build the right team to create amature AI framework, many organizations fall short in this area.
At the bare minimum, every effective AI project requires data scientists, subject matter
experts, data analysts and a project manager. How those teams are distributed isjust as important as sourcing the right talent. Some organizations embed their data
science teams among various lines of business, while others exclusively maintain acentralized team of data scientists.
Most effective is a hybrid approach, with both a hub for data science and spokes
distributed through various business units. The hub is generally responsible foroverseeing AI talent, data governance and collaboration with software vendors. The
spokes support activities surrounding implementation, including training, incentivizing
use and monitoring results. This approach allows data scientists to play a key role insolving the pains of the business, while also ensuring AI is fully leveraged and bestpractices are followed across the whole organization.
AI Bias
Although powered by machines, AI is not free of bias. Its learning is built on existingdatasets. Since humans—who are inherently flawed—are the creators of data, it often
contains historical biases. And, if a model is tainted by biases, it can have serious
implications. For instance, if existing police records are used to train machine learningmodels, the AI would perpetuate past discriminations during future activities, such as
targeting suspects.
Consider too how financial institutions might use AI to recommend who to lend
mortgages to: the model may find that older individuals are more likely to default, andas a result may discriminate against them by requiring higher rates or recommend
lending to them less often. This type of bias in AI would, in many places, be an illegal
form of age discrimination that would also negatively impact the organization.30 This isjust one example of how futures can be influenced—if not entirely shaped—by AI.
While machine learning models must be built on historical data, some factors canbe controlled to prevent bias. First, AI projects should be managed by a range ofindividuals with diverse perspectives. Second, machine learning models must be
regularly audited for biases. If discovered, the models must be adjusted. Finally,organizations must establish a code of ethics for AI that is followed by all users.

20

AI: Augmented Intelligence

Addressing AI biases is not a simple one-and-done activity. It is important toremember that it can arise at any point before, during or after the development of an
AI model. That is why guarding against bias must become a key component of any AI
framework, and be revisited at each stage of a project’s lifecycle.
Discrimination
by Proxy

Privacy

Representative
Data

PLANNING

PurposeFramework for Discovering and Guarding
Against AI Bias31
Optimization
Criteria

BUILDING

DEPLOYIN
G

Abusability

Figure 20:

Explainability

Generalization
Error

Right toContestOvercoming these roadblocks opens the path to a successful deployment of impactful
AI-powered solutions.

21

AI-Powered Solutions
It is clear that AI can help companies increase operational efficiency and productivity,
make better decisions, decrease costs and enhance customer experiences. Its
solutions can be applied to any industry and across different business units, making it
among the most versatile and promising technologies positioned to radically changeorganizations across the globe.
Below are some of the most influential AI-powered solutions businesses are
already leveraging.
Figure 21:
Connect the Entire
Organization

Get More Value from
Unstructured Data &Automate Content-Driven Processes

Make Better Selling
Decisions

Key Applications of AI
Improve Compliance& Governance

Enhance Security

Achieve Social
Listening

Optimize Performanceof High-Value Assets

Connect the Entire Organization
An organization has data scientists, subject matter experts and business users, allof whom excel in their roles but need to collaborate and connect across teams. AIpowered solutions create a system of insight across the organization, giving each user
access to the learnings from artificial intelligence and enabling collaboration on valueadd data science projects. With business intelligence reporting, data discovery tools, adata science notebook and text mining capabilities, AI and analytics platforms provide
all contributors with the environment they are most comfortable working in.
Data scientists can publish and share machine learning models across the businesswithin an AI data science notebook. Business users can explore this information
through dashboards with business intelligence and reporting features that allow themto visualize insights, predictions and recommendations. Moreover, dashboards for
operational users can be embedded anywhere; for example, in manufacturing facilities,
operators can analyze both historical trends and real-time data from IoT sensors
to prevent breakdowns and minimize downtime. Marketing users can use reporting
features to review metrics from past campaigns as well as predictions for which actions
will improve campaign performance moving forward. From a financial perspective,
AI-powered systems simplify decisions for budget allocation and other key spending
initiatives because they can process and analyze massive volumes of data that exceed
the limits of traditional spreadsheet software, and provide recommendations.
Get More Value from Unstructured Data and Automate Content-Driven Processes
Roughly 80% of enterprise data is unstructured: the messy, textual data which, unlike
numerical data, is difficult to wrangle and use effectively.32 Historically, organizations
have not had the means to efficiently process this information. Seventy-one percent
of enterprises say their unstructured data is growing faster than other business data—
and making sense of these expanding data lakes is a pressing challenge.33 That is why
many companies are data rich, but insights poor.
22

AI: Augmented Intelligence

Fortunately, advancements in AI and analytics have made it possible to overcome
this by integrating NLP, text mining, context processing and AI-based analytics toaccurately process raw text from the moment it is collected through to archiving
and disposal. The system improves its accuracy over time as it ingests more data,
providing increasingly useful findings on which predictive models can be built. Then, as
new insights are generated, they can be distributed across visualizations and reports,
allowing companies to truly operationalize the previously untapped findings within
their textual data.
AI can then automate content assessment, guiding or taking action based on contentderived insights, improving productivity while mitigating risks.
One key application of this technology is AI-augmented capture. Adding to thefoundation of intelligent capture, artificial intelligence enhances the ability to readand understand text within documents, generate insights that improve decisionmaking and automate processes based on those decisions. It digitizes and extracts alldata within content, such as claims, emails, notes, spreadsheets, social media posts
and web activity logs, then applies AI to understand and differentiate documents
contextually and route them to the appropriate workflows or people based on context
and priority.
An organization
receives correspondence

AI classifies the information androutes it to the approprite owner

Correspondence and related
actions and results are recordedAlertcustomer/HR

AI-augmented
captureAckowledge
and notify owner

Figure 22:
AI-Augmented Capture

Records

No actions steps

Consider an incoming document captured in the mailroom—is it an accountapplication or a complaint about an order? If the latter, can the complaint response be
automated, or does it require a human response? If it requires someone to respond,
who is the best person to do so? If it requires manual handling, what process should
be invoked and where should the correspondence and response be archived? Using
natural language elements, such as sentiment, emotion and intention, along withinstitutional knowledge to understand the purpose of content and what steps to takeregarding that content, AI can take care of the entire process. Humans only need to be
involved at the last step to take the required action, if at all.
Organizations can also automate onboarding processes for employees, vendors andcustomers, thereby freeing up teams to work on other projects that demand humancreativity and oversight.
Beyond capturing and routing information, AI can also be applied to categorizing,indexing and classifying it. It can understand meaning and put content in context.
When done manually, categorization is time-consuming, error prone and expensive. It
costs about 63 cents per manually-processed document.34 Put another way, manuallyfinding and organizing incoming information in the public sector alone is costingaround $1 trillion annually.35
23

AI-driven solutions provide quick time-to-value by replacing the inefficiency ofpainstakingly accessing and classifying information with intelligent automation.
Content in any format can be accurately categorized across applications andintegrated into automated and scalable processes.
In automating routing and classification tasks that usually require humanunderstanding, decision making and action, AI can significantly boost efficiency andshrink operational costs, ultimately accelerating business transformation.
Improve Compliance and GovernanceUsing the same capabilities to understand the meaning of content in context andautomate content-driven processes, AI helps reduce risk and proactively avoid
regulatory fines or violations. With a high level of compliance and accuracy, it can
automate processes such as audits, content migration, risk assessment, privacy anddata governance. While important to all organizations, these processes are essential
to healthcare, oil and gas, pharmaceuticals, financial services, government and otherhighly regulated industries.
Regulations like the GDPR now enforce strict requirements for collecting andmanaging personally identifiable information (PII). When companies do not realize they
are storing sensitive information, cannot readily locate it or do not follow proper rules
for handling it, they are at risk for non-compliance. AI can streamline governance by
crawling large volumes of documents to identify this type of sensitive information andautomating compliance actions.
Organizations also store vast amounts of data within their content repositories
which could house countless images with potentially detrimental visuals, suchas alcohol, drugs or violence. This imagery needs to be found, as it could conflictwith organizational policies, compliance and government regulations. Through a
combination of NLP and image analysis capabilities, these materials can be identified
and remediated to mitigate organizational risk.
Machine learning and interactive visualizations can also offer compliance officers
insight into risk levels and make recommendations to safeguard privacy.
Achieve Social Listening
The customer experience is now more multifaceted than ever, with touchpoints acrossonline and brick-and-mortar shopping, apps, emails and everything in between.
The ability to stay competitive lies in providing the best experiences across thesechannels; companies must be able to learn what is working and what is not in orderto improve.
Today’s consumers are vocal with their feedback and expect their wants and needs
to be heard. Positive or negative, feedback must be collected and used to inform
organizational decisions. Yet, with so many platforms for voicing thoughts andopinions—Twitter, customer service chats, surveys, articles and emails, to name afew—getting a complete picture of how customers feel is difficult at best. Moreover,with so many roles involved in the customer experience, from store clerks at physicalstore locations to marketers trying to target the right audiences, managing thecustomer experience can become a fragmented initiative.

24AI: Augmented Intelligence

Organizations are turning to AI to help them listen. AI-driven Voice of the Customer(VoC) platforms tap into customer feedback from every source to determine
sentiment, tone and emotion. They capture the entire customer journey in a single
view, analyzing everything and identifying key engagement and decision drivers
throughout the buying journey—informing organizations as they create better
customer experiences and offer top products and services. AI tracks feedback acrossall channels, enabling fast collection and analysis, pinpointing key issues like shipment
delays, product complaints and similar snags (as well as positive feedback) socompanies can address problems promptly and show their customers that they havebeen heard. It can also help organizations monitor competitors’ brands, as well as
their own.
Ultimately, these powerful systems cultivate a deeper understanding of customers,
helping promote loyalty and overall brand success. And, because visualizations,interactive dashboards and reports can easily be made available across theorganization, cross-functional teams can collaborate on managing customer
experiences and prevent their efforts from being siloed.
Figure 23:

Analyzing customer sentiment andproviding an exceptional experience helps:

Achieve revenue
growth of

5-10%

Increase
upselling andcross-selling rates by

15-20%Creating Exceptional Experiences36 37 38

Prevent

85%

of consumers fromchurning

These benefits are not exclusive to the private sector, either. Governments are using
AI-augmented social listening platforms to hear the collective voice of their citizens
and gain insight into the biggest issues facing them. The technology empowers
government agencies to show their communities that they are listening by responding
with appropriate policies or programs and making transparent decisions that align with
their needs and wants.
For instance, a busy city might use an AI solution to gather feedback from locals
and tourists. With text mining and sentiment analysis, leaders might discover thatcomplaints like “no parking” are popping up on social channels. Based on thisfeedback, they could brainstorm ideas and deploy solutions such as a city-wide
transportation app to help drivers find open parking spaces using their smartphones.

25

Government of Canada

Canada joined government leaders and civil society advocates from aroundthe world to create the Open Government Partnership in 2011. The goal of thispartnership is for governments to become more transparent, accountable, andresponsive to their citizens, thus improving the quality of governance, as well as thequality of services that citizens receive.
Building trust, transparency, and responsiveness requires governments to listen to
their citizens and appreciate their priorities. With recent advances in social, mobile,and artificial intelligence (AI) technologies, governments and other organizations
can listen more closely in order to understand the concerns and urgency of the
public voice. During a recent Open Government Partnership Summit with 2,500
attendees from more than 100 countries, a sentiment tracking and analysis toolfrom OpenText was used to gain insight into attendees’ thoughts, concerns,
and priorities.
OpenText Magellan’s AI-powered Voice of the Customer leverages AI to provide
a comprehensive view of public sentiment. It combines open-source machinelearning with advanced analytics, enterprise-grade BI, and the ability to acquire,
merge, manage, and analyze structured data and unstructured data in a wide rangeof formats. The solution is a powerful tool that quickly unlocks and interprets thevaluable content that can be found in data sources such as social media feeds.
With powerful machine learning and text analytics, the Magellan solution swiftly
sorts through, classifies, and makes sense of tweets, comments, emails, or
documents. The text mining tool processes raw text, extracting mentions ofpeople, places, events, or concepts and then evaluates them for tone andsentiment. At the Open Government Summit, the tool was used to track attendee
sentiment and analyze it according to elements such as source country. Then, thesolution’s reporting module can render the findings as colorful, interactive chartsand visualizations. With just a few clicks, the parameters can be changed to analyze
coverage by numbers, tone, theme, or other elements to reveal unique insights.
The ability to monitor and process thousands of tweets and analyze the discoursearound the Summit’s topics helped the Open Government team gain insight thatfurthers the Partnership’s common goal of honest dialogue and collaboration
between governments and citizens.

26

AI: Augmented Intelligence

Make Better Selling Decisions
Today’s fast-paced business landscape demands agility. Companies must optimize
the customer experience, providing personalized interactions that stand out among
generic offers.
AI-enhanced recommendations harness customer preference and sales data,
and routinely monitor activity to predict decision points and impactful actions,such as targeted offers and cross- or upsell opportunities. In doing so, AI enables
organizations in retail, finance and similar consumer-facing industries to deliver
highly personalized experiences to enhance the customer journey, engagement andretention. By leveraging big data analytics and sentiment analysis, AI can predict withincreasing accuracy whether customers will purchase a particular product or service,and then serve up recommendations aligned with those preferences. Continuously
catering to preferences encourages customers to remain loyal for longer. For example,
using a “you might also like” strategy at each purchase interaction can, over time,
boost revenue 10-50%.39
Retailers in particular are leveraging AI recommendation engines to determine
consumers’ buying propensity or level of readiness to make a specific purchase basedon data like previous purchase history and online habits. Armed with these predictions,retailers can trigger the most relevant offers for the right customers at the right times,
instead of annoying them with repetitive, generic offers.
By capturing each offer (or email, text, sale, etc..) that an individual customer responds
to, as well as those they ignore, AI can increase its understanding of customer
preferences and its ability to predict the best experience. With deeper understandingcomes the ability to adjust pricing to maximize sales or margin; indicate the best
time to contact a customer; determine when to hand off leads to sales for maximum
efficiency; or reduce customer churn by offering specials to customers at riskof defecting.
Enhance Security
Data is the new form of capital, and cybercriminals will go to great lengths to get it. As
of 2019, the cost of ransomware attacks alone was $11.5 billion for businesses andnonprofits, predicted to reach $20 billion by 2021.40 These and other types of data
breaches have serious consequences for organizations, including identity theft, theftof actual assets, disruption to business, regulatory risk, reputational damage andrevenue loss.
Any security solution that requires human intervention is simply inadequate forresponding to the speed and sophistication of today’s cyberattacks. Coupled withthe growing volume of big data, human-dependent solutions cannot keep networks
protected. Moreover, highly skilled hackers know how to trigger attacks that bypass
traditional “castle and moat” cybersecurity systems, leaving security teams unaware of
the breach.
Enter AI-enhanced security. AIOps automatically detects and responds to evolving
threats. These technologies gather all security data, structured and unstructured, andenrich it with network metadata to gain visibility into all network activities. This enables
advanced and flexible detection-and-response models that can discover and quickly
adapt to cyberattacks. These systems safeguard organizations against major data
security breaches while also supporting compliance with data privacy laws, including
GDPR and CCPA.
27

AI-enhanced security also supports fraud detection by identifying trends and patterns
in behavior. Predictive analytics can monitor all activity in real time to spot deviations
indicative of fraud, vulnerabilities or threats. Once these anomalies are found, thesystem can automate preventive action—a particularly important function in thefinancial services industry. Today, fraud detection is expanding as cybersecurity
becomes a major concern.
Finally, AIOps achieves endpoint detection and response. Traditional approaches tosecuring endpoints based on the hardware characteristics of a given device are nomatch for today’s breach attempts. Attackers are combining AI, machine learningand new social engineering techniques to thwart endpoint security controls andgain access to business systems. AI-augmented security can combine data such asprevious behavioral patterns, time of login and many other variables to assign risk
scores that are effective at securing and controlling endpoints.
Figure 24:

The cyber professionals' workload
WITHOUT COGNITIVE TECHNOLOGIESWITH COGNITIVE TECHNOLOGIESTime and talent can be focused on thosetasks requiring human ingenuity.

Cyber professionals spend too much time on
routine and repetitive activities, wastingvaluable time and talent already inshort supply.

!

Password resets

New insights are identiﬁed,
permitting proactive security.

!

Account lockouts

Firewall alerts!

Automation
handles routine
tasks.

Incident alerts

The typical
workday is ﬁlledwith mundane
tasks while key
insights and events elude
overburdened talent.

!

Machine pairing
augments human talent,
accomplishing tasks
more quickly.

Routine, repetitive tasks

Activities resulting from data-derived insights

Tasks requiring human and machine pairing

Network breach

Source: Deloitte analysis.

Optimize Performance of High-Value Assets
Business leaders responsible for asset performance depend on data to makedecisions about maintenance schedules, parts procurement and retirement ofindustrial assets. Asset optimization is especially critical in industries relying on highvalue machinery, such as oil and gas, manufacturing or transportation. When assets
need to be repaired or replaced, downtime costs $1.3 million per hour, on average.42
AI, combined with the IoT, is being used to acquire, merge, manage and analyze big
data and big content, including data from sensors, information management systems
and external sources, to extend asset lifecycles by 20-40% and maximize equipment
uptime to improve productivity by up to 20%.43
28

AI-Augmented Cybersecurity41

AI: Augmented Intelligence

Figure 25:

How It Works

Consider a steel producer that leverages AI to optimize productivity.

Sheets of steel are pushed
through rollers that flatten
them. Sheets produced too
quickly can moveoff-center, resulting in
crumpled steel. New steel
must be fed into the rollers,causing unplanned
downtime.

A breakout predictor
senses sheet movement
and adjusts production
line speed accordingly to
avoid issues.

With AI, breakout predictor
data can be analyzed to
identify patterns thatindicate potential
problematic conditions.

STEP 2

STEP 3

STEP 1How It Works: Steel Producer

Knowing why problems occur takes a company
from reactively dealing with an unplanned problem
to proactively adjusting production andaddressing conditions.

Harnessing AI, organizations have access to predictive insights derived from a holisticknowledge base, with the ability to improve those insights over time as assets areused, maintained or taken out of service. As more data is collected, the AI could
predict when a part is likely to fail based on factors such as use, external conditions,maintenance history and past performance. The system would automatically flag thepart for maintenance, so the failure never occurs, reducing downtime, avoiding safetyissues and potentially even mitigating unexpected environmental impacts. Becoming
predictive is how companies will reduce risk and optimize their assets.
AI-optimized predictive maintenance schedules empower businesses to intelligently
forecast repairs in order to:• Proactively address problems to ensure smoother operations• Increase uptime for more opportunity to generate revenue• Detect issues before failure to lower costs and improve safety• Improve customer satisfaction by reducing quality defects and delays• Save money and labor while reducing downtime• Reduce capital investment due to longer asset lifetime value29Knorr-Bremse GroupThe Knorr-Bremse Group is the world’s top manufacturer of braking systems forrail and commercial vehicles, such as freight trucks and metro lines. The company
continues its technical innovation and leads the way in Industry 4.0 and Internet ofThings (IoT) solutions to address digital transformation.
Knorr-Bremse has developed its iCOM (Intelligence Conditioned Oriented
Maintenance) platform, providing advanced diagnosis and maintenance for bothlarge and small rail and truck fleet operators. iCOM is an IoT application framework
for innovative fleet management, monitoring and analytics. iCOM uses the data
to enable condition-based, rather than static, scheduled maintenance for KnorrBremse. This means customers of iCOM can identify any issue before it becomes aserious safety problem or needs a more costly repair or replacement.
Knorr-Bremse selected OpenText™ Magellan™ BI & Reporting due to its superior
built-in graphical design studio, dashboard capabilities, speed of development andusability. It was also a natural choice for building on Knorr-Bremse’s knowledge,
developed using Java and BIRT.
Customer reaction to the analytics and reporting available within iCOM has
been extremely positive. With standard widgets delivered as part of their
dashboard, users can quickly make adjustments to display what they need, suchas performance indicators, component failure rates, environmental conditions
and more, with no need to involve IT. In addition, customers can decide what theywish to record across various IoT subsystems and can build their own reports anddashboards as needed. An event message is recorded when a specific condition
is met, such as excessive temperature. iCOM continually records process data,
providing the ability to view subsystem performance over time.
AI-powered asset performance optimization helps asset managers identify key data
points and recommends actions based on the complete picture of the asset, drawing
on its environment and data related to its past, present and future.

30

AI: Augmented Intelligence

AI Is the Future
The future of AI holds enormous promise. This technology, with its ability to skyrocket
our capabilities, will be the defining factor that separates top performing organizations
from those doomed to fizzle out. As AI implementations increase in size and scope,
forward-thinking companies will consistently leverage it to solve their greatest
challenges. It is no surprise that 93% of executives expect AI to bring value totheir enterprise.44
Indeed, we are only seeing the beginning of the ways AI will deliver value toorganizations. AI-driven automation will continue to accelerate. For example, morechatbots will power customer service interactions and other customer-facing
functions, with 80% of businesses using chatbots by the end of 2020, and that
number will only go up.45 These next-gen chatbots will integrate with data analytics topredict customer behavior, and adapt tone and vocabulary to provide a personalized
experience for each customer.46 Advanced AI will give chatbots new capacity torecognize mood, emotion and intent, and respond with apparent awareness andempathy to customers’ needs. Employees, too, will spend more time with chatbots—by 2023, 25% of employee interactions with applications will happen via voice, up
from less than three percent in 2019.47
Figure 26:

S

I

M

P

L

E

Straight-throughIntegration

MobilePersonalized

Liberty

Easy

AI-Driven Chatbotsof the Future: SIMPLE48

Businesses will increasingly leverage AI for categorizing, classifying and routing evergrowing sets of data and content, especially untapped unstructured data. Armed withinformation from every source, AI will automatically assess mountains of data fasterthan any team of humans ever could and enable businesses to gain insight and makestrategic decisions—fast. Human and machine teams will be inseparable, functioning
as one unit that combines EQ and IQ to help organizations thrive.
As AI eliminates many mundane, repetitive aspects of business, job losses areinevitable. Large-scale disruption is at hand. But most evidence suggests that AI
will ultimately spur job creation and growth. It is estimated that AI will create morejobs—around 2.3 million—with many new opportunities predicted to open up in
education, the public sector and healthcare.49 Many of these new occupations will be
unimaginable to us today, as flying an airplane was unimaginable to the farmers of
the 1800s. Demand will rise for roles like big data scientists, machine-human teaming
managers, technology interpreters, quantum machine learning analysts… the list
goes on, and these are just the jobs we can foresee based on the technology
available today.
Even jobs that do not completely disappear will be “shredded,” meaning automation,
robots and AI will take over the mundane aspects of the job, leaving the higher-level
activities to humans. Everyone, from writers to warehouse workers to CEOs, will needto learn to work productively with “cobots” that will do anywhere from 30% to 70%of their previous tasks. By automating routine tasks while simultaneously providing
a clear line of sight into the factors that profoundly influence business, AI will create
unprecedented opportunities to channel creativity and innovation.
31

Figure 27:
AI and Automation Will Free Up
Human TimeIn fact, AI-driven applications may one day commonly function as co-authors forcreative works and services. The next generation of creators are growing up playing
with artificial intelligence—in gaming, on social media and on smartphones—and thesewill inspire their creative endeavors as adults. Experiments have already begun that
use AI to create works of fiction, demonstrating that algorithms have a startling ability
to generate content and understand such difficult-to-code concepts as “plot twist.”50
In the future, such AI tools for content co-generation may be the norm. They will evencontribute ideas for new products. Experimenters recently developed an algorithm togenerate novel scents for candle products.51 Admittedly, some of the scents it createdare better than others (“Coastal Waters Glow” vs. “Lemon Lime Decay” and “BaggyAir”), but R&D teams of the future will rely heavily on more sophisticated versions of AI.
AI in education is also expected to grow by more than 45% from 2017 to 2025.52
Companies are designing smart education platforms that will individualize learningfor each student. AI tutoring applications are already available for disciplines frommathematics to foreign languages.53 Such developments are essential now, as
schools face long periods of distance learning and potentially permanent shifts dueto the COVID-19 pandemic. Under lockdown conditions, AI can help create individual
learning plans for elementary and secondary students, and even provide feedback on
assignments, freeing up teachers to focus on the students and topics that are most in
need of their time.54 Post-secondary institutions are experimenting with AI to respondto students’ questions in real time and remind them of key deadlines.55
Figure 28:

APAC

North
Americas

Fastest-growing
Market by Region(2020-2030)

Largest Market by
Region (2019)2019Market Size

$1.1
Billion32

2030
Market Size

$25.7
BillionMarketGrowth Rate
(2020-2030)

32.9%

Global AI in Education Market56

AI: Augmented Intelligence

As organizations continue to use AI to their advantage, it will stop seeming like afuturistic sci-fi concept and become a powerful practical tool for transforming how welearn, work, live and play.
However, before these exceptional changes can occur, businesses must first execute
meaningful AI projects that work for them—whether it is personalizing the customer
experience, overcoming parking challenges in urban environments or helping save
lives in the healthcare sector. This will require identifying use-cases, building abusiness case, creating cross-functional teams and managing digital platforms tocollaborate. Thanks to the increasing availability of user-friendly AI systems, business
users from across the enterprise will have access to projects powered by machinelearning models. To ensure non-data scientists understand and use these solutions
effectively, more and more organizations will launch enterprise-wide training initiatives.
In fact, Gartner predicts that 80% of companies are already deploying some typeof data literacy training to help the members of their organization extract the mostpossible value from their data.57 And, when all teams have the ability to learn from andcollaborate around the insights that data yields, intelligence becomes
truly augmented.
According to the Flynn Effect, IQ scores increase three points every decade—atleast, that has been the trend since 1930.58 At this rate, average human intelligence
could parallel that of great minds like Stephen Hawkins or Albert Einstein in just 200
years. Yet, AI-augmented intelligence has never been factored into the equation. With
the ability to rapidly process and draw insights from vast stores of knowledge, ourintelligence will only accelerate—likely at a dramatic pace. Especially with the very
real possibility of implanting AI capabilities directly into the human brain throughnew brain-machine interfaces, such as Elon Musk’s Neuralink (still in its early stages
today).59 Achieving a symbiosis with AI, we may see a day when the highest IQs fall notin the upper 100s, but actually exceed 1,000. With that level of intelligence will comethe ability to solve some of the greatest challenges not only in business, but acrossthe globe.

33

AI for the GoodAI, neural networks and machine learning have tremendous potential to help us solve
the world’s greatest challenges. Challenges like eliminating systemic discrimination.
Reaching zero poverty. Solving the climate crisis. Curing disease. Anticipating andreacting to future global disruptions.
It will not be easy; AI is not inherently good, nor is it inherently bad. It is a tool, whichmust be combined with our capacity for compassion, justice and ethical behavior—ourhumanity, in a nutshell. AI will help herald in a new era of prosperity if—and only if—weprioritize the social impact of technology instead of considering it as an afterthought.
Before we can do this, we must address, again, the elephant in the room: biased AI.
Learning algorithms do not care about morality or preventing discrimination. In the
single-minded pursuit of their tasks, they align themselves with the ideological, racial
and gender biases of their creators and society at large. There are, quite frankly, too
many examples of AI bias in current use-cases. Sexist AI that discriminates against
female job applicants.60 Racist AI that disproportionately targets people of color forpredictive policing and severe sentences within judicial systems.61 62 Homophobic AI
that labels homosexuality as “negative sentiment.”63
Bias in AI entrenches and magnifies existing injustice. This is why any discussion ofusing AI for the good must start with eliminating AI bias and building responsible,
accountable and transparent algorithms.
Figure 29:

Six Ways to Minimize AI Bias64

It will take deliberate human intervention to unshackle machine learning from bias.
Thankfully, both governments and organizations are mobilizing for algorithmic
responsibility. Around the world, pioneering legislation is being introduced, including
the EU’s GDPR and an “Algorithmic Accountability Act” bill proposed in the U.S. lastyear.65 66 Simultaneously, businesses are taking action. With over 100 partners in 13
countries, The Partnership on AI (PAI) is committed to developing best practices
for creating safe, inclusive AI, as well as engaging relevant stakeholders in theconversation. One of PAI’s central goals is to “identify and foster aspirational efforts in
AI for socially benevolent applications.”67 Furthermore, tech giants, including Microsoft
and Google, have dedicated entire divisions to using AI for the good—divisions whose
goals include finding ways to root out and correct algorithmic bias.68 6934

AI: Augmented Intelligence

Unbiased AI tools will open powerful opportunities for equity. For example, traditional
hiring (humans looking at resumes) results in women and minorities being at a 5067% disadvantage.70 AI could be trained to prefer diversity. The first year Unileverused a hiring algorithm that was regularly audited for bias, it hired its “most diverse
class to date.” This included gender parity and a dramatic increase in people of color
among new hires.71 Likewise, Holberton School’s automated admission system for its
esteemed software engineering program admitted 40% women in the typically maledominated field. As with Unilever, the software significantly increased the number ofwomen and visible minorities.72
When used for the social good, AI has the potential to help us become better versionsof ourselves and enable marginalized groups to access better educational, vocationaland economic opportunities.
AI is already being used to empower people living with disabilities. As the technology
gets better at seeing, hearing and understanding, it is able to help people withdisabilities gain more independence and opportunities in their day-to-day lives.
Consider the app that describes what it is seeing to someone with a vision impairment,
turning “the visual world into an audible experience” so they can engage more with
their environment, recognize faces and facial expressions, and hear snippets of text as
it appears in front of the camera.73 Or, speech-to-text—already shown to outperformprofessional lip readers—can help deaf people access spoken language at the samerate as hearing people, from the classroom to the boardroom.74 And autonomousvehicles will provide freedom for people constrained at home by their disabilities.
Freedom to get to their jobs and their social activities independently, combating
loneliness and isolation.75 These are just the beginning of the accessibility possibilities;
with over one billion people with disabilities worldwide, AI applications like these willtransform a lot of lives.76
AI is also being applied to eradicating world hunger and malnutrition. According to theUnited Nations, humanity will need to increase food production by 70% if we want toeliminate hunger by 2050.77 Meanwhile, a staggering one third of all food produced
today is lost or wasted.78 Solving this problem will require more food production andless food waste. And we have the technology to do it. AI is optimizing and reimagining
food production, distribution and consumption processes at every juncture. It is
improving crop yield while using fewer resources.79 It is diagnosing diseased crops
early, preventing spread.80 It is forecasting when and where famines will occur.81 82
It is helping manage supply chains for efficient logistics and greater quality.83 It is
dynamically pricing produce to reduce spoilage.84 It is designing food waste out of
the system.85
The fight against hunger is inextricably tied to a bigger crisis—climate change.86
Without drastic intervention, climate change will make our planet unlivable. Extreme
weather events (wildfires, lethal heat waves, droughts, floods, hurricanes, severe
storms, etc.) will multiply to catastrophic effect. Fresh water will become increasingly
scarce. Farmable land will shrink. And by 2050, there could be as many as one billion
climate refugees.87 Sustainable environmental solutions are urgently needed; the clockis ticking. AI and machine learning will be a powerful tool in the fight for our planet,
helping to tackle some of the most complex and vital problems, including climate
prediction, building energy-efficient buildings and cities, capturing and removing
carbon dioxide, creating low-carbon electricity and greening transportation, among
many others.88

35

Electricity Systems

Enabling low-carbon electricity
Reducing current-system impacts
Ensuring global impact

Buildings & Cities
Optimizing buildings
Urban planning
The future of cities

Farms & Forests

Remote sensing of emissions
Precision agriculture
Monitoring peatlands
Managing forests

Climate Prediction

Uniting data, ML & climate science
Forecasting extreme events

Solar Geoengineering

Understanding & improving aerosols
Engineering a planetary control system
Modeling impacts

Collective Decisions
Modeling social interactions
Informing policy
Designing markets

Finance

Figure 30:

Transportation

Reducing transport activity
Improving vehicle efﬁciency
Alternative fuels & electriﬁcation
Modal shift

Industry

Optimizing supply chains
Improving materials
Production & energy

Carbon Dioxide Removal
Direct air capture

Sequestering CO2

Societal Impacts
Ecology
Infrastructure
Social systems
CrisisIndividual Action

Understanding personal footprint
Facilitating behavior change

Education

Improving access

Personalizing educational tools

Climate investment strategies
Modeling financial impacts

Our health is another field where AI is making a profound difference. It is revolutionizing
healthcare through everything from finding new links between genetic codes tooperating surgery-assisting robots. Medical data is doubling every 73 days—it
simply needs AI and machine learning to unlock its vast potential for breakthroughs,
diagnoses, treatments and cures.90 Using such data, and supported by the insightof human doctors, AI is already better and faster at detecting—or even predicting—diseases.91 This includes diagnoses ranging from heart disease, to cancer, torare conditions doctors are not as familiar with.92 93 On the pharmaceutical side,
AI is helping discover and design new drugs, as well as find new applications forexisting drugs. In just one example, AI-driven research combined with traditional
R&D is accelerating the discovery of treatments for Parkinson’s disease and otherneurological disorders.94 AI is also capable of personalizing the healthcare experience,
improving patient outcomes across the sector. With AI in our corner, we will be living
longer, healthier lives.

36

Top Domains for AI and Machine Learning
to Combat Climate Change89

AI: Augmented Intelligence

Figure 31:

AI-Assisted
Medical Diagnoses
Virtual Nursing
Assistants

AI-Assisted
Robotic Surgery

Medical Data
Security

Automated
Workflow
Assistance

Medical Image
Analysis

Drug
Discovery

10
Applicationsof AI inHealthcare

Medical Risk
Prediction

AI Applications in Healthcare95

Clinical
TrialsFraud
Detection

The value of AI in a healthcare context was truly brought home during the coronavirus
pandemic that swept the world in 2020. AI is (as of this writing) being used in a varietyof high impact applications to address the crisis, from the smallest molecular scale
to largescale societal use-cases. AI is helping us to better understand the molecular
structure of the virus and its impact on the human body, in order to improve diagnosis,
treatment and vaccines.96 It is triaging COVID-19 patients in hospitals faced with staff
and space shortages.97 Supporting medical imaging. Predicting patient outcomesbased on electronic health record data. Tracking the disease, its spread and its
evolution through the population. And, playing a critical role in epidemiologicalresearch modeling and forecasting.98
Beyond even this, an AI designed to look for influenza outbreaks discovered thecoronavirus pandemic when it was in its earliest stages; when it still could have been
contained.99 Next time, AI will predict pandemics—and other crises—before they
happen, giving us even more time to react.100 Such predictive power will be the ultimate
game-changer.
These examples only scratch the surface of the amazing things this technology canachieve. But to reach the best possible future, for all of humanity, we must inject humanvalues into AI.
Artificial intelligence is still a nascent technology. As its capabilities expand, andwith the human oversight to entrench humanitarian means and aims, the question
becomes: is there any limit to what we can accomplish?

37

Endnotes
 eff Desjardins, “How much data is generated each day?” WorldJ
Economic Forum, April 17, 2019, https://www.weforum.org/
agenda/2019/04/how-much-data-is-generated-each-daycf4bddf29f/ (accessed June 2020).

14

 eather Pemberton Levy, “Gartner Predicts a Virtual World of
H
Exponential Change,” Gartner, October 18, 2016, https://www.gartner.
com/smarterwithgartner/gartner-predicts-a-virtual-world-ofexponential-change/ (accessed June 2020).

2

 ark Liberman, “Zettascale Linguistics,” Language Log, November 5,
M
2003, http://itre.cis.upenn.edu/~myl/languagelog/archives/000087.
html (accessed June 2020).

153

 hristo Petrov, “Big Data Statistics 2020,” TechJury, June 16, 2020,
C
https://techjury.net/blog/big-data-statistics (accessed June 2020).

 ick Ismail, “The combination of artificial intelligence and the cloud,”N
Information Age, March 5, 2019, https://www.information-age.com/
combination-artificial-intelligence-cloud-123470999/ (accessed
June 2020).

1“ Volume of data/information created worldwide from 2010 to 2025,”
Statista, December 2018, https://www.statista.com/statistics/871513/
worldwide-data-created/ (accessed June 2020).

4

5

I bid.

” Strategy Analytics: Internet of Things Now Numbers 22 Billion
Devices But Where Is The Revenue?” Business Wire, May 16, 2019,https://www.businesswire.com/news/home/20190516005700/en/
Strategy-Analytics-Internet-of-Things-Now-Numbers-22-BillionDevices-But-Where-Is-The-Revenue (accessed October 2020).

6“ The Growth in Connected IoT Devices Is Expected to Generate
79.4ZB of Data in 2025, According to a New IDC Forecast,” IDC, June18, 2019, https://www.idc.com/getdoc.jsp?containerId=prUS45213219(accessed June 2020).

7

8

9

 mar Abbosh et al., “Breaking through disruption,” Accenture, JuneO26, 2019, https://www.accenture.com/us-en/insights/consulting/business-disruption-innovation (accessed June 2020).
 avid Schubmehl, Holly Muscolino and Marci Maddox, “How AI
D& Machine Learning are Transforming Enterprise Content,” IDC,
August 2019.

10

 plunk, “Companies Collect a Lot of Data, But How Much Do TheyS
Actually Use?” Priceonomics, August 7, 2019, https://priceonomics.
com/companies-collect-a-lot-of-data-but-how-much-do/ (accessed
June 2020).

 anju Bansal, “Big Data: Creating the Power to Move Heaven andM
Earth,” MIT Technology Review, September 4, 2014, https://www.
technologyreview.com/2014/09/02/250240/big-data-creating-thepower-to-move-heaven-and-earth/ (accessed June 2020).

1112

 arl Shapiro and Hal R. Varian, “Information Rules: A Strategic GuideC
to the Network Economy,” Harvard Business Review Press, November
19, 1998.

13

 J Reinhart, “Most Americans Already Using Artificial IntelligenceR
Products,” Gallup, March 6, 2018, https://news.gallup.com/
poll/228497/americans-already-using-artificial-intelligenceproducts.aspx (accessed June 2020).

38“ What is the difference between Deep Learning and Machine
Learning?” QuantDare, January 8, 2019, https://quantdare.com/whatis-the-difference-between-deep-learning-and-machine-learning/(accessed July 2020).

16“ The Applications of Expert Systems,” WTEC Hyper-Librarian,
May 1993, http://www.wtec.org/loyola/kb/c1_s2.htm (accessed
June 2020).

17“ Gartner Survey Shows 37 Percent of Organizations Have
Implemented AI in Some Form,” Gartner, January 21, 2019, https://www.gartner.com/en/newsroom/press-releases/2019-01-21gartner-survey-shows-37-percent-of-organizations-have (accessed
June 2020).

18

 nkush Jain et al., “100 Data and Analytics Predictions ThroughA
2022,” Gartner, May 21, 2018, https://www.gartner.com/en/
documents/3875982/100-data-and-analytics-predictionsthrough-2022 (accessed July 2020).

1920 andy Bean and Thomas H. Davenport, “Companies Are Failing in
R
Their Efforts to Become Data-Driven,” Harvard Business Review,
February 5, 2019, https://hbr.org/2019/02/companies-are-failing-intheir-efforts-to-become-data-driven (accessed July 2020).

 ames Manyika and Kevin Sneader, “AI, automation, and the futureJof work: Ten things to solve for,” McKinsey & Company, June 1,
2018, https://www.mckinsey.com/featured-insights/future-of-work/
ai-automation-and-the-future-of-work-ten-things-to-solve-for(accessed June 2020).

21

22

I bid.

23

 ames Manyika et al., “A Future That Works: Automation, Employment,
J
and Productivity,” McKinsey & Company, January 2017, https://www.
mckinsey.com/~/media/mckinsey/featured%20insights/Digital%20
Disruption/Harnessing%20automation%20for%20a%20future%20
that%20works/MGI-A-future-that-works-Executive-summary.ashx(accessed July 2020).

24

 ian Ai Yeow et al., “Effects of Stress, Repetition, Fatigue and WorkJ
Environment on Human Error in Manufacturing Industries,” Journalof Applied Sciences 14(24):3464-3471, September 2014, https://www.researchgate.net/publication/266025022_Effects_of_Stress_
Repetition_Fatigue_and_Work_Environment_on_Human_Error_in_
Manufacturing_Industries (accessed July 2020).

AI: Augmented Intelligence“ Cybercrime Costs Global Economy $2.9m Per Minute,” Infosecurity
Magazine, July 24, 2019, https://www.infosecurity-magazine.com/
news/cybercrime-costs-global-economy/ (accessed July 2020).

25

“ Threat Report,” Webroot, 2020, https://mypage.webroot.com/rs/557FSI-195/images/2020%20Webroot%20Threat%20Report_US_
FINAL.pdf (accessed July 2020).

 teven MacDonald, “Voice of Customer: How to 10x Your BusinessS
With VOC Data,” SuperOffce, Original October 16, 2019, Updated
June 22, 2020, https://www.superoffice.com/blog/voice-ofcustomer/ (accessed June 2020).

37

26

27

 on Tolido et al., “Reinventing Cybersecurity with Artificial
RIntelligence,” Capgemini Research Institute, July 2019, https://www.
capgemini.com/wp-content/uploads/2019/07/AI-in-Cybersecurity_
Report_20190711_V06.pdf (accessed June 2020).

28

 ichael Chui et al., “Notes from the AI frontier: Applications and valueMof deep learning,” McKinsey & Company, April 17, 2018, https://www.
mckinsey.com/featured-insights/artificial-intelligence/notes-fromthe-ai-frontier-applications-and-value-of-deep-learning (accessed
July 2020).

29

I bid.

30

 ake Silberg and James Manyika, “Tackling bias in artificial
J
intelligence (and in humans),” McKinsey & Company, June 6, 2019,https://www.mckinsey.com/featured-insights/artificial-intelligence/
tackling-bias-in-artificial-intelligence-and-in-humans# (accessed
July 2020).

“ What are AI Blindspots?” AI Blindspot, 2019, https://aiblindspot.
media.mit.edu/ (accessed July 2020).

31“ Tapping into the 80% of unused data with OpenText Magellan,”
OpenText, 2018, https://www.opentext.com/file_source/OpenText/
en_US/PDF/opentext-ebook-magellan-unstructured-en.pdf(accessed July 2020).

32

33

 ick Patience, “Tapping your stores of unstructured data can yieldN
new insights…” OpenText, June 20, 2019, https://blogs.opentext.com/
tapping-your-stores-of-unstructured-data-can-yield-new-insights/(accessed July 2020).

“ Shortcut the paperwork shuffle with AI-augmented content capture,”
OpenText, October 1, 2018, https://blogs.opentext.com/shortcut-thepaperwork-shuffle-with-ai-augmented-content-capture/ (accessed
July 2020).

34

35

36

 em Dilmegani, Bengi Korkmaz, and Martin Lundqvist, “Public-sector
C
digitization: The trillion-dollar challenge,” McKinsey & Company,
December 1, 2014, https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/public-sector-digitization-the-trilliondollar-challenge (accessed August 2020).
 wan Duncan, Harald Fanderl, Nicolas Maechler, and Kevin Neher,
E“Customer experience: Creating value through transforming
customer journeys,” McKinsey & Company, July 1, 2016, https://www.mckinsey.com/business-functions/marketing-and-sales/
our-insights/customer-experience-creating-value-throughtransforming-customer-journeys (accessed July 2020).

38

 ala Afshar, “50 Important Customer Experience Stats for BusinessV
Leaders,” Huffpost, Original October 15, 2015, Updated December
6, 2017, https://www.huffpost.com/entry/50-important-customerexp_b_8295772 (accessed July 2020).

“ Rethinking Retail: Insights from consumers and retailers into anomni-channel shopping experience,” Infosys, 2013, https://www.
infosys.com/newsroom/press-releases/documents/genomeresearch-report.pdf (accessed July 2020).

39

40

 teve Morgan, “Global Ransomware Damage Costs Predicted To
SReach $20 Billion (USD) By 2021,” Cybercrime Magazine, October
21, 2019, https://cybersecurityventures.com/global-ransomwaredamage-costs-predicted-to-reach-20-billion-usd-by-2021/(accessed June 2020).

 eborah Golden and Ted Johnson, “AI-augmented cybersecurity,”
D
Deloitte, June 8, 2017, https://www2.deloitte.com/us/en/insights/
industry/public-sector/addressing-cybersecurity-talent-shortage.
html (accessed July 2020).

41“ AI-powered asset performance optimization,” OpenText, https://www.opentext.com/products-and-solutions/products/ai-andanalytics/opentext-magellan/asset-performance-optimization(accessed July 2020).

42

43

I bid.

44

 il Press, “AI Stats News: 65% Of Companies Have Not Seen
G
Business Gains From Their AI Investments,” Forbes, October 17,
2019, https://www.forbes.com/sites/gilpress/2019/10/17/ai-statsnews-65-of-companies-have-not-seen-business-gains-from-theirai-investments/#18c8806819f4 (accessed June 2020).

45

 om Taulli, “What You Need to Know about Chatbots,” Forbes, AprilT21, 2019, https://www.forbes.com/sites/tomtaulli/2019/04/21/whatyou-need-to-know-about-chatbots/#5d9a4bc4844d (accessed
July 2020).

“ Customer experience of the future,” Ernst & Young, 2018.

46
47

 ichael J. Miller, “Gartner: The Present & Future of Artificial
MIntelligence,” PC Mag, October 29, 2019, https://www.pcmag.com/
news/gartner-the-present-and-future-of-artificial-intelligence(accessed July 2020).

“ Chatbots of the Future: a SIMPLE Framework | Market Insights™,”
Everest Group, March 1, 2018, https://www.everestgrp.com/201803-chatbots-future-simple-framework-market-insights-43863.html/(accessed July 2020).

48

49

 ernard Marr, “5 Important Artificial Intelligence Predictions (ForB
2019) Everyone Should Read,” Forbes, December 3, 2018, https://www.forbes.com/sites/bernardmarr/2018/12/03/5-importantartificial-intelligence-predictions-for-2019-everyone-shouldread/#3f466bb5319f (accessed June 2020).

39 tephen Marche, “I used an algorithm to help me write a short story.
S
Here’s what I learned,” MIT Technology Review, June 17, 2020, https://www.technologyreview.com/2020/06/17/1003665/i-used-analgorithm-to-help-me-write-a-story-heres-what-i-learned (accessed
July 2020).

60

 anelle Shane, “Smells like the future,” AI Weirdness, February 2020,
J
https://aiweirdness.com/post/611219307217797120/what-would-acandle-inspired-by-your-book-smell (accessed July 2020).

61

50

51

” Artificial intelligence (AI) in Education Market : Incredible Possibilities,
Growth With Industry Study, Detailed Analysis and Forecast To
2025,” MarketWatch, August 10, 2020, https://www.marketwatch.
com/press-release/artificial-intelligence-ai-in-educationmarket-incredible-possibilities-growth-with-industry-studydetailed-analysis-and-forecast-to-2025-2020-08-10 (accessed
October 2020).

52

53

 ernard Marr, “How Is AI Used In Education -- Real World Examples
B
Of Today And A Peek Into The Future,” Forbes, July 25, 2018, https://www.forbes.com/sites/bernardmarr/2018/07/25/how-is-ai-usedin-education-real-world-examples-of-today-and-a-peek-into-thefuture (accessed May 2019).

54

 itrankur Majumdar, “How AI Can Help Educators Leap Ahead:M
The Future of Education,” TechRadar, May 11, 2020, https://www.
techradar.com/au/news/how-ai-can-help-educators-leap-ahead(accessed May 2020).

55

 ivic Nation, “Building an Empathy Engine for Higher Education,”C
Forbes, May 11, 2020, https://www.forbes.com/sites/
civicnation/2020/05/11/building-an-empathy-engine-for-highereducation/#4829124d1fa3 (accessed May 2020).

“ AI in Education Market Research Report: By Component (Solution,
Service), Deployment (Cloud, On-Premises), Technology (Natural
Language Processing, Machine Learning), Application (Content
Delivery Systems, Learning Platforms, Virtual Facilitators, Intelligent
Tutoring Systems), End Use (K–12 Education, Higher Education,
Academic Research, Corporate Training) – Global Industry Size,
Share, Trends, Forecast to 2030,” Prescient & Strategic Intelligence,
February 2020, https://www.psmarketresearch.com/market-analysis/
ai-in-education-market/ (accessed July 2020).

56

 hristy Pettey, “Gartner Keynote: Do You Speak Data?” Gartner,
C
March 5, 2018, https://www.gartner.com/smarterwithgartner/gartnerkeynote-do-you-speak-data/ (accessed June 2020).

 eff Larson, Surya Mattu, Lauren Kirchner, and Julia Angwin, “HowJ
We Analyzed the COMPAS Recidivism Algorithm,” ProPublica, May
23, 2016, https://www.propublica.org/article/how-we-analyzed-thecompas-recidivism-algorithm (accessed April 2019).

62

 ascha Eder, “How Can We Eliminate Bias In Our Algorithms?” Forbes,
S
June 27, 2018, https://www.forbes.com/sites/theyec/2018/06/27/
how-can-we-eliminate-bias-in-our-algorithms (accessed April 2019).

63

 ndrew Thompson, “Google’s Sentiment Analyzer Thinks Being Gay
AIs Bad,” Vice, October 25, 2017, https://www.vice.com/en_us/article/
j5jmj8/google-artificial-intelligence-bias (accessed June 2020).

64

 ake Silberg and James Manyika, “Tackling bias in artificial
J
intelligence (and in humans),” McKinsey & Company, June 6, 2019,https://www.mckinsey.com/featured-insights/artificial-intelligence/
tackling-bias-in-artificial-intelligence-and-in-humans (accessed
July 2020).

“ Art. 22 GDPR: Automated individual decision-making, including
profiling,” GDPR, https://gdpr.eu/article-22-automated-individualdecision-making/ (accessed July 2020).

65“ S.1108 - Algorithmic Accountability Act of 2019,” Congress.gov,
https://www.congress.gov/bill/116th-congress/senate-bill/1108/text(accessed July 2020).

66

“About Us,” Partnership on AI, https://www.partnershiponai.org/
about/ (accessed July 2020).

67“ AI for Good,” Microsoft, https://www.microsoft.com/en-us/ai/ai-forgood (accessed July 2020).

68“ Applying AI to some of the world’s biggest challenges,” Google AI,
https://ai.google/social-good/ (accessed July 2020).

69

70

57

“ Is Our Collective IQ Increasing?” Dartmouth Undergraduate
Journal of Science, February 13, 2013, https://sites.dartmouth.edu/
dujs/2013/02/13/is-our-collective-iq-increasing/ (accessed June2020).

58

59

 lizabeth Lopatto, “Elon Musk unveils Neuralink’s plans for brainE
reading ‘threads’ and a robot to insert them,” The Verge, July 16,
2019, https://www.theverge.com/2019/7/16/20697123/elon-muskneuralink-brain-reading-thread-robot (accessed July 2020).

40

 effery Dastin, “Amazon scraps secret AI recruiting tool that showedJbias against women,” Reuters, October 9, 2018, https://www.reuters.
com/article/us-amazon-com-jobs-automation-insight/amazonscraps-secret-ai-recruiting-tool-that-showed-bias-against-womenidUSKCN1MK08G (accessed April 2019).

 ric Rosenbaum, “Silicon Valley is stumped: A.I. cannot alwaysE
remove bias from hiring,” CNBC, May 30, 2018, https://www.cnbc.
com/2018/05/30/silicon-valley-is-stumped-even-a-i-cannotremove-bias-from-hiring.html (accessed July 2020).

 ichard Feloni, “Consumer-goods giant Unilever has been hiring
R
employees using brain games and artificial intelligence — andit’s a huge success,” Business Insider, June 28, 2017, https://www.businessinsider.com/unilever-artificial-intelligence-hiringprocess-2017-6 (accessed July 2020).

71

72

 wapnil Bhartiya, “Can an algorithm remove gender bias?” CIO, AprilS11, 2016, https://www.cio.com/article/3054547/can-an-algorithmremove-gender-bias.html (accessed April 2019).

“ Seeing AI in in new languages,” Microsoft, 2020, https://www.
microsoft.com/en-us/ai/seeing-ai (accessed July 2020).

73

AI: Augmented Intelligence74

 ohn Roach, “AI technology helps students who are deaf learn,”J
Microsoft - The AI Blog, April 5, 2018, https://blogs.microsoft.com/ai/
ai-powered-captioning/ (accessed July 2020).

88 avid Rolnick et al., “Tackling Climate Change with Machine Learning,”
D
arXiv.org, November 5, 2019, https://arxiv.org/pdf/1906.05433.pdf(accessed July 2020).

75

 yle Wiggers, “Here are the ways AI is helping to improveK
accessibility,” Venture Beat, May 17, 2018, https://venturebeat.
com/2018/05/17/here-are-the-ways-ai-is-helping-to-improveaccessibility/ (accessed July 2020).

89

I bid.

90

 oerg Aumueller (interview), “How Artificial Intelligence canJrevolutionise healthcare,” Health Europa, August 13, 2019, https://www.healtheuropa.eu/how-artificial-intelligence-can-revolutionisehealthcare/92824/ (accessed July 2020).

“ Disability Inclusion,” The World Bank, May 15, 2020, https://www.
worldbank.org/en/topic/disability (accessed July 2020).

91“ How to Feed the World in 2050,” Food and Agriculture Organizationof the United Nations, October 12, 2009, http://www.fao.org/
fileadmin/templates/wsfs/docs/expert_paper/How_to_Feed_the_
World_in_2050.pdf (accessed July 2020).

92“ Technical Platform on the Measurement and Reduction of Food Lossand Waste,” Food and Agriculture Organization of the United Nations,
http://www.fao.org/food-loss-and-food-waste/en/ (accessed
July 2020).

 hiara Longoni and Carey K. Morewedge, “AI Can Outperform
C
Doctors. So Why Don’t Patients Trust It?” Harvard Business Review,
October 30, 2019, https://hbr.org/2019/10/ai-can-outperformdoctors-so-why-dont-patients-trust-it (accessed July 2020).

93

 icole Martin, “Artificial Intelligence Is Being Used To Diagnose
N
Disease And Design New Drugs,” Forbes, September 30, 2019,https://www.forbes.com/sites/nicolemartin1/2019/09/30/artificialintelligence-is-being-used-to-diagnose-disease-and-design-newdrugs/#6fbb28a644db (accessed July 2020).

76

77

 am Daley, “32 Examples of AI in Healthcare that Will Make You FeelS
Better About the Future,” Built In, March 25, 2020, https://builtin.com/
artificial-intelligence/artificial-intelligence-healthcare (accessed July2020).

78“ Farmview: CMU Researchers Working to Increase Crop Yield
with Fewer Resources,” Carnegie Mellon University, 2016, https://www.cmu.edu/work-that-matters/farmview/index.html (accessed
July 2020).

79“ About mCROPS,” Makerere University, 2017, http://air.ug/mcrops/(accessed July 2020).

80“ Famine Action Mechanism,” The World Bank, 2018, https://www.
worldbank.org/en/programs/famine-early-action-mechanism(accessed July 2020).

81

I nternational Center for Tropical Agriculture, “Clampdown on hunger
crisis in Africa: cutting-edge technology outlined to stop malnutrition
before it strikes,” Malabo Montpellier Panel, May 31, 2017, https://www.mamopanel.org/news/press-release/2017/may/31/clampdownhunger-crisis-africa-cutting-edge-techno/ (accessed July 2020).

82

83

 achel Murkett, “How Artificial Intelligence Can Help F&B SupplyR
Chain Management,” Food Online, July 19, 2017, https://www.
foodonline.com/doc/how-artificial-intelligence-can-help-f-b-supplychain-management-0001 (accessed July 2020).

“ RapidPricer,” 2018, https://www.rapidpricer.com/ (accessed July2020).

8485

 eter Rejcek, “Food Waste is a Serious Problem. AI is Trying toP
Solve It,” Singularity Hub, November 3, 2019, https://singularityhub.
com/2019/11/03/food-waste-is-a-serious-problem-ai-is-trying-tosolve-it/ (accessed July 2020).

“ UN Warns Climate Change is Driving Global Hunger,” United Nations
Framework Convention on Climate Change, September 12, 2018,https://unfccc.int/news/un-warns-climate-change-is-driving-globalhunger (accessed July 2020).

8687

 aher Kamal, “Climate Migrants Might Reach One Billion by 2050,”
B
Inter Press Service News Agency, August 21, 2017, http://www.
ipsnews.net/2017/08/climate-migrants-might-reach-one-billionby-2050/ (accessed July 2020).

“ BERG Presents Clinical Target Validation Data for the Treatment of
Parkinson’s Disease and Novel Mechanism of Action in Neurological
Disorders at Neuroscience 2018,” BERG, November 7, 2018,https://www.berghealth.com/prnewswire-berg-presents-clinicaltarget-validation-data-for-the-treatment-of-parkinsons-diseaseand-novel-mechanism-of-action-in-neurological-disorders-atneuroscience-2018/ (accessed July 2020).

94

95

 ick Martin, “10 Ways Artificial Intelligence is Transforming
R
Healthcare,” Ignite, November 30, 2018, https://igniteoutsourcing.
com/healthcare/artificial-intelligence-in-healthcare/ (accessed
July 2020).

96

 oseph Bullock et al, “Mapping the landscape of Artificial IntelligenceJapplications against COVID-19,” arXiv.org, April 23, 2020, https://arxiv.
org/pdf/2003.11336.pdf (accessed July 2020).

97

 aren Hao, “Doctors are using AI to triage covid-19 patients. The
K
tools may be here to stay,” MIT Technology Review, April 23, 2020,
https://www.technologyreview.com/2020/04/23/1000410/ai-triagecovid-19-patients-health-care/ (accessed July 2020).

98

 oseph Bullock et al, “Mapping the landscape of Artificial IntelligenceJapplications against COVID-19,” arXiv.org, April 23, 2020, https://arxiv.
org/pdf/2003.11336.pdf (accessed July 2020).

99

 ric Niiler, “AI spotted this pandemic. It could prevent the next one,”E
Experience, April 8, 2020, https://expmag.com/2020/04/ai-spottedthis-pandemic-it-could-prevent-the-next-one/ (accessed July 2020).

100

 ike Gualtieri, “Can AI Predict Global Pandemics Like theM
Coronavirus?” Forrester, March 23, 2020, https://go.forrester.com/
blogs/can-ai-predict-global-pandemics-like-the-coronavirus/(accessed July 2020).

41

Cautionary Note Regarding Forward-Looking Statements
Certain statements in this presentation, including statements about the focus of Open Text Corporation (“OpenText” or “the Company”)
on growth, initiatives, the impact of COVID-19, anticipated benefits of our partnerships and next generation product lines, thestrength of our operating framework and balance sheet flexibility, continued investments in innovation, go-to-market and strategicacquisitions, our capital allocation strategy, creating value through investments in broader Information Management (IM) capabilities,
the Company’s presence in the cloud and in growth markets, expected growth in our revenue lines, total growth from acquisitions,innovation and organic initiatives, improving operational efficiency, its financial condition, scaling OpenText to new levels, and othermatters, may contain words such as “anticipates”, “expects”, “intends”, “plans”, “believes”, “seeks”, “estimates”, “may”, “could”, “would”,“might”, “will” and variations of these words or similar expressions are considered forward-looking statements or information underapplicable securities laws. In addition, any information or statements that refer to expectations, beliefs, plans, projections, objectives,
performance or other characterizations of future events or circumstances, including any underlying assumptions, are forward-looking,and based on our current expectations, forecasts and projections about the operating environment, economies and markets in which
we operate. Forward-looking statements reflect our current estimates, beliefs and assumptions, which are based on management’s
perception of historic trends, current conditions and expected future developments, as well as other factors it believes are appropriate
in the circumstances, such as certain assumptions about the economy, as well as market, financial and operational assumptions.
Management’s estimates, beliefs and assumptions are inherently subject to significant business, economic, competitive and otheruncertainties and contingencies regarding future events and, as such, are subject to change. We can give no assurance that such
estimates, beliefs and assumptions will prove to be correct. Such forward-looking statements involve known and unknown risks,
uncertainties and other factors and assumptions that may cause the actual results, performance or achievements to differ materially.
For additional information with respect to risks and other factors which could occur, see the Company’s Annual Report on Form 10-K,
Quarterly Reports on Form 10-Q and other securities filings with the Securities and Exchange Commission (SEC) and other securities
regulators. Readers are cautioned not to place undue reliance upon any such forward-looking statements, which speak only as of thedate made. Unless otherwise required by applicable securities laws, the Company disclaims any intention or obligation to update orrevise any forward-looking statements, whether as a result of new information, future events or otherwise.

42

AI: Augmented Intelligence

About OpenText
OpenText, The Information Company, enables organizations to gain insight through market leading information management
solutions, on-premises or in the cloud. For more information about OpenText (NASDAQ: OTEX, TSX: OTEX) visit www.opentext.com.

Contact
Sales

Email: sales@opentext.com
Partners

Email: partners@opentext.com
Media Relations

Email: publicrelations@opentext.com

opentext.com/contact

Copyright © 2021 Open Text. All Rights Reserved. Trademarks owned by Open Text.
For more information, visit: https://www.opentext.com/about/copyright-information • 09/21 | SKU18255