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1. Using Artificial Intelligence & Machine
Learning to Transform Digital Experiences
AI Everywhere & Nowhere
GLOBAL ARTIFICIAL INTELLIGENCE LEAD
Dr. Anand S. Rao
www.pwc.com
2. PwC New Services and Emerging Technology – AI Lab
AI: Computer system or agent that can sense, think, and act in an
environment to achieve a purpose
2
AI that can sense…
Hear
See
Speak
Feel
AI that can think…
Understand Reason
PlanLearn
AI that can act…
Physical
Sensors
Digital
Effectors
• Knowledge Rep.
• Reasoning
• Machine Learning
• Deep Learning
• Simulation
• Robotic process
automation
• Deep question &
answering
• Collaborative systems
• Adaptive systems
• Natural language
• Audio & speech
• Machine vision
• Navigation
• Visualization
Statistics Econometrics Optimization
Complexity
Theory
Computer
Science
Game
Theory
FOUNDATION
LAYER
3. PwC New Services and Emerging Technology – AI Lab
Today’s discussion
AI and Digital Experiences
From Consumer to Enterprise Digital Experiences
Opportunities, Risks, and Implications for Enterprises
01
02
03
3
4. PwC New Services and Emerging Technology – AI Lab
AI and Digital Experiences
4
01
5. PwC New Services and Emerging Technology – AI Lab
Sizing the Prize: AI in productivity & consumption gains
5
Are you ready to exploit the opportunities from AI & overcome the challenges?
Global GDP Impact of AI through 2030
GlobalGDPupliftduetoAI
($intrillions)
2030 IMPACT:
$15.7T
Consumption
Contribution:
60%
Source: PwC Analysis;
Productivity
Contribution:
40%
6. PwC New Services and Emerging Technology – AI Lab
AI and Digital Experience in the Consumer World
6
AI as UI (Ubiquitous
Intelligence)
AI is being embedded in devices,
things, people and is becoming
ubiquitous in our daily life
AI as No UI (User
Interface)
Conversational, chat, haptic and
brain-machine interfaces will augment
existing interfaces
AI as AAAAI
AI is being used as automated,
assisted, augmented, and autonomous
intelligence
7. PwC New Services and Emerging Tech
Confidential information for the sole benefit and use of PwC’s client.
7
Google Search:
Auto-completion
(N-gram)
Google Duplex:
Automating
reservations
Four Uses of AI:
Ai is moving
beyond
automating and
assisting humans
in hardwired
situations to
becoming more
adaptive –
augmenting and
becoming more
autonomous
No human in the loopHuman in the loop
Hardwired /
specific
systems
Adaptive
systems
Automated Intelligence
1
Assisted Intelligence
2
Augmented Intelligence
3
Autonomous Intelligence
4
+
AI as AAAAI (Automated-Assisted-Augmented-Autonomous
Intelligence)
8. PwC New Services and Emerging Technology – AI Lab
From Consumer to Enterprise Digital
Experiences
8
02
9. PwC New Services and Emerging Technology – AI Lab
Companies are starting their AI investments in automation, with long-
term thinkers also exploiting augmented/autonomous AI
• AI techniques enhance
the efficiency of activities
across the business value
chain, but machines do
not dynamically adapt to
changing data
Automated
Intelligence
Assisted
Intelligence
Augmented
Intelligence
Autonomous
Intelligence
Degree of Advancement
High risk - Big bets,
transforming business
models
Low risk - Quick
wins happening right
now
• Computational algorithms
begin to adapt to changing
data; machines do not
automatically make
decisions, however they
put humans in the best
place to make decisions
• AI techniques used by
businesses to automate
the decision making
process with the absence
of human intervention
• Automation of repetitive
tasks that include both
manual and cognitive
aspects
10. Fukoku Mutual insurance
company is automating
business processes to
reduce labor
Automation
Assisted
Intelligence
Augmented
Intelligence
Autonomous
Intelligence
Japanese white-collar workers are already being replaced by
artificial intelligence
Robotic Process Automation
Natural-Language Processing
+30%
increase in labor
productivity
110-140M
reduction in
workers by 2025
3x
benefit over
offshoring
Robotic Process Automation (RPA) Capabilities
RPA vendor solutions are dominating the market for
automating processes but have limitations on the extent
and scope of impact they can have
$2M
annual savings
*at $150K maintenance
11. Construction company
used drones and deep
learning to monitor
construction site progress
and track assets
Automation
Assisted
Intelligence
Augmented
Intelligence
Autonomous
Intelligence
Drones and artificial intelligence can empower complex
infrastructure projects
Computer Vision
Machine Learning
Deep Learning
60%
reduction in
operational cost
$3M
annual savings on
a $5M project
700
hours of labor savings
on one project
From a drone aerial picture company was able to produce
segmented output of different objects in that image
Key:
Background Trees
Asphalt
ConcreteCars
Reinforcement
12. A Global Pharmaceutical
company used NLP to
extract adverse drug
interaction from multiple
unstructured data sources
Automation
Assisted
Intelligence
Augmented
Intelligence
Autonomous
Intelligence
NLP architecture and pipeline are critical to automate cognitive
processes and generate insights
Natural-Language Processing
Machine Learning
Deep Learning
+20%
Annual growth of
adverse events
$14-18 M
annual savings on
current base
96%
diagnostic
accuracy
Clinician
notes
Social
media
Medical
literature
Tokenization
Grammar Parsing
Text Normalization
Text Cleaning
Word Disambiguation
Vectorization
SourcesProcess
1 Gathering key
information output,
e.g., patient
sneezes (event)
2 Deep Learning of
Latent
Relationships,
e.g., sneezing and
antihistamine
3
35%-45%
Savings in
processing costs
13. Global airline used predictive
aircraft maintenance
to reduce maintenance related
costs from Delays &
Cancellations
Automation
Assisted
Intelligence
Augmented
Intelligence
Autonomous
Intelligence
Aircraft predictive maintenance
Natural-Language Processing
Machine Learning
15%
reduction in delays
due to maintenance
-$25M
Cost reduction
Provides airline
clients a deep
analysis on aircraft
fault messages and
text analytics on
maintenance logs to
find significant signals
that cause delay and
cancellation events
Diagnostic
Enables reliability
engineers to monitor
fleet health and
identify trends,
chronic aircraft and
parts
Fleet
Reliability
Provides maintenance
controllers indication of
potential failures at the
aircraft component level
that necessarily result in delays
and cancellations (D&Cs) 2-5
days out enabling maintenance
intervention
Alerting
Airline
Predictive
Maintenance
Solution
+0.9%
on time
performance
14. Global auto manufacturer
gamified its strategy to
evaluate go-to-market
scenarios for a new
rideshare and
autonomous vehicle
business
Automation
Assisted
Intelligence
Augmented
Intelligence
Autonomous
Intelligence
Simulations to evaluate go-to-market scenarios
Machine Learning
Deep Learning
200,000
go-to-market
scenarios evaluated
170 M
Miles delivered by
10,000 vehicles
Simulations
Select
Cities
Strategies
Random
Seeds
Market
Condition
Used demographic models
and demand estimator
• Price
• Aggressiveness
• Marketing
• Customer serviceEach strategy repeated
10 times to account for
randomness
Different conditions
of consumer
acceptance
Digital Twins of
consumers…
Socio-
demographics
…were modelled under a set of scenarios
Transport choice
(commute, errand,
weekend)
City topology
$1+ bn
Acquisition of AV
technology start-up
15. Leading companies are
moving from descriptive
and diagnostic analytics to
prescriptive and cognitive
analytics where AI plays a
greater role
Machine Learning
Deep Learning
Describe, summarize
and analyze
historical data
Recommend ‘right’
or optimal actions or
decisions
Monitor, decide, and
act autonomously or
semi-autonomously
Predict future outcomes
based on facts from the
past and simulations
Descriptive
Predictive
Prescriptive
Cognitive
IncreasingBusinessValue
Identify causes of
trends and
outcomes
Diagnostic
Increasing Sophistication of Data & Analytics
(What
happened?)
(Why it
happened?)
(What could
happen?)
(What should be
done?)
(How do we adapt
to change?)
16. PwC New Services and Emerging Tech
How do enterprises move from a ‘mobile first’
to an ‘AI first’ mindset?
16PwC New Services and Emerging Technology – AI Lab
17. PwC New Services and Emerging Technology – AI Lab
Opportunities, Risks and Implications for
Enterprises
17
03
18. PwC 18
As a result, we focus on addressing the challenges that organizations face, as they
seek to exploit this new technology in their enterprises
Data, Data, Data Everywhere – but not the right kind for AI
Collecting, organizing, storing, safeguarding, labelling and exploiting the data for
enterprise applications
Making AI more human
AI that is more ‘human’, that can interact with humans at the right level, learn from
humans, teach humans, and resolve ethical dilemmas
Acquiring, developing, and retaining the right talent to explore and exploit AI
Ensuring the right mix of business domain expertise, computing experience,
statistical and mathematical knowledge in teams
Building safe & robust AI that is trustworthy
Building AI that can explain itself, is transparent, can be controlled, and is without
bias.
Increased vulnerability and disruption to business
77%
Potential for biases and lack of transparency
76%
Ensuring governance and rules to control AI
73%
Risk to stakeholders’ trust and moral dilemmas
71%
Potential to disrupt society
67%
Lack of adequate regulation
64%
Starting point – From data, automation, or analytics
Aligning AI initiatives across the enterprise emanating from big data, analytics, and
automation initiatives
What’s holding AI back in the enterprise?
Source: PwC CEO Pulse Survey, 2017
Q: Which of the following issues surrounding AI adoption concern you the most
Base: 239
19. PwC New Services and Emerging Technology – AI Lab
Benefiting from AI requires separating myths from facts
19
Myth 1:
Artificial Intelligence is a distinct monolithic
area of study
Fact 1:
Artificial Intelligence is an interdisciplinary
area with many distinct sub-fields
Myth 2:
All types of problems can be solved by a single
AI solution (e.g., ….insert your favorite
solution)
Fact 2:
Different types of problems require
different type of AI techniques and
solutions to be used
Myth 3:
Machine Learning automatically (magically)
learns from data without any human
intervention
Fact 3:
Machine Learning requires a laborious
process of acquiring and cleansing large
amounts of data, selecting, training, and
guiding the algorithm
Searching, Querying &
Conversing
Describing, Classifying,
Understanding &
Visualizing
Diagnosing, Discovering &
Reasoning
Trending, Forecasting,
Projecting &
Predicting
Simulating, Learning,
Optimizing, &
Adapting
Recognizing, Sensing,
and Recommending
AI Uses
20. PwC New Services and Emerging Technology – AI Lab
Start from the business value chain and metrics to be improved to get
better ROI from AI
20
Operations & Development
Product
Development
Service &
Support
Operations
Outbound Logistics
Sales &
Distribution
Customers &
Marketing
Strategy &
Growth
Supply Chain &
Procurement
Finance, HR,
Planning
Inbound Logistics
How will we ensure our
product supply is meeting
demand?
VP, Supply Chain
How can we engage with our
customers to enhance their
experience?
Director, Marketing
How can we grow our market
share and which markets to
enter, exit or expand?
Director, Strategy
How do we innovate and
introduce new products and
services?
Director, Products
How do we increase customer
satisfaction and retain more
customers?
Director, Service
How can we reach more
customers and price our
products to increase sales?
Director, Sales
How can we increase
efficiency and effectiveness of
our operations?
Director, Operations
How can we get a better
return on our talent, capital,
and assets?
Director, Finance & HR
• Market Share
• Customer Experience
• Acquisition Rate
• Innovation Rate
• Operational Efficiency
• Customer Satisfaction
• Talent Retention
• Inventory Turn
Over 300+ AI Use Cases Across 8 Sectors – Sizing the Prize
21. PwC New Services and Emerging Technology – AI Lab
Focus on a few key areas of AI to get traction and critical mass of expertise
21
Data Eng./Model Ops
Automated ML Simulation & RL Responsible AIEmbodied AI
▪ Natural Language processing
and text mining
▪ Natural Language generation
▪ Chatbots and discourse
understanding
▪ Sentiment & emotion analysis
▪ Speech-to-text and text-to-
speech
▪ Convolutional Neural Nets
▪ Recursive Neural Nets
▪ Capsule Networks
▪ Generative Adversarial
Networks
▪ Deep reinforcement learning
▪ Hybrid learning models
▪ Regression & classification
▪ Bayesian learning
▪ Probabilistic programming
▪ Anomaly detection
▪ Optimization techniques
▪ Support Vector Machines
▪ Various supervised, semi-
supervised, and unsupervised
techniques
▪ Big data architecture
▪ Big and Fast data
▪ Apache tools
▪ Cloud computing
▪ Cloud ML – AWS, GCP, Azure
▪ Machine Learning deployment
▪ Agent-based simulation
▪ Reinforcement learning
▪ Augmented and synthetic data
generation
▪ System dynamics modeling
▪ ’Digital Twins’
▪ Calibration of models
▪ IoT and Industrial IoT – Edge
computing and Smart sensors
▪ Drone – Autonomy & Image
analytics
▪ Robots – Navigation &
Learning
▪ Brain-Machine Interfaces
▪ Explainable AI
▪ Beneficial AI
▪ ‘Black box’ Interpretability
▪ Maturity models
▪ Ethics and Law
▪ AI Governance
▪ AI Controls framework
▪ Automated data preparation
▪ Automated feature
engineering
▪ Automated algorithm selection
▪ Automated explanation
generation
▪ Meta-model inference
Natural Language Machine Learning Deep Learning
22. PwC 22
Balance the opportunities with the significant risks that need to be assessed,
mitigated and managed
Control
• Risk of AI going ‘rogue’
(e.g., Tay Chatbot)
• Inability to control
malevolent AI
• Swarm drones
Security
• Cyber intrusion risks
• Privacy risks
• Open source software risks
• Digital, Physical, Political security
Societal
• Risk of Autonomous
Weapons proliferation
• Risk of ‘intelligence divide’
Ethical
• ‘Lack of Values’ risk
• Value Alignment risk
• Goal Alignment risk Economic
• Job displacement risks
• ‘Winner-takes-all’ concentration of
power risk
• Liability risk
Performance
• Risk of Errors
• Risk of Bias
• Risk of Opaqueness
• Risk of stability of performance
• Lack of feedback process
Risk
Robust &
Safe AI
Beneficial
AI
Responsible AI
23. PwC New Services and Emerging Technology – AI Lab 23
Start from business
decisions
01
Demonstrate value
through pilots before
scaling
02
Blend intuition and
data-driven insights
03
Fail forward –
test and learn culture
05
Focus on Responsible
AI from the start
06
Address ‘big data’ –
don’t forget ‘lean’ data
04
Six success factors to derive maximum benefits from
artificial intelligence
25. PwC New Services and Emerging Tech
Confidential information for the sole benefit and use of PwC’s client.
25
Google Search:
Auto-completion
(N-gram)
Google Duplex:
Automating
reservations
Man-Machine
combination to create
unique advantage
Large teams of people
watched and tagged
movies and shows (36
page manual)
Created 76,897 micro-
genres
AI as Ubiquitous Intelligence - Netflix
26. PwC New Services and Emerging Tech
Confidential information for the sole benefit and use of PwC’s client.
26
Google Search:
Auto-completion
(N-gram)
Google Duplex:
Automating
reservations
Conversational
Interfaces:
Increasingly
conversational
interfaces will
replace keyboard
input, where
appropriate
AI as No UI – Conversational Interfaces
27. PwC New Services and Emerging Tech
Confidential information for the sole benefit and use of PwC’s client.
27
Google Search:
Auto-completion
(N-gram)
Google Duplex:
Automating
reservations
Brain-Machine
Interfaces:
In our AI Lab we
are experimenting
with EEG &
biometric devices
to control devices
from brain wave
patterns
AI as No UI – Brain-Machine Interfaces
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