This document summarizes a presentation by Dr. Bonnie Cheuk on how AI can transform businesses. In 3 sentences: Dr. Cheuk discusses how AI can help gain a better understanding of diseases, identify new drug targets, speed up drug design and development, improve clinical trial design, and enable personalized medicine. Examples are presented where AI and machine learning have been used at AstraZeneca to classify tablets, identify likely prescribers of new drugs, and review patents. In conclusion, Dr. Cheuk emphasizes that AI should be applied carefully with consideration for ethics and unintended consequences, and that humans will continue to play an important role in applying judgment.

1. What AI can do to
transform our business
1 Apr 2021
Dr Bonnie Cheuk
Senior Director, Business & Digital Transformation Leader
Presentation at the Digital Leadership Forum

2. 1 Apr 2021
2
Hi I’m Bonnie Cheuk
• I build future-ready capabilities for
76,000+ employee in AstraZeneca
• I focus on building digital capabilities AND
learning agility (i.e. making continuous
learning happen naturally at work)
Business & Digital Transformation Leader
AstraZeneca

3. Healthcare in a Changing World
1 Apr 2021
3
Artificial Intelligence vs Automation
What AI can do
Q&A
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4

4. Healthcare in a changing world
1 Apr 2021
4
Global economic
shock
Growing & ageing
populations
Increasing burden
of chronic disease
Impact of
COVID-19
Digital & technical
breakthroughs
Global GDP forecast
5.3% below
pre-pandemic
projections1
Many patients
in US, EU and Asia
deferred or cancelled
scheduled treatment
early in the pandemic4
2.1 billion people
will be aged 60+
by 2050
Digital health market to
increase nearly
six times by 20263
42 million people
die from NCDs
each year2

5. 1 Apr 2021
5
Putting patients at the heart of what we do, we continue to transform
our science to create the greatest and swiftest impact on disease and
deliver life-changing medicines
• Cellular and animal models
• Molecular imaging and AI
• Quantitative pharmacology
• Biomarker identification
Better
predicting
clinical
success
Pioneering
new
approaches in
the clinic
Growth through innovation investments
Data Science and AI
• Dynamic multi-omics
• Knowledge graphs
• Epigenetics
• Cell therapy
• PROTACs
• Augmented drug design
• Digital R&D
• Digital technologies
• Digital solutions
Enhancing
our
understanding
of disease
Designing
next
generation
therapeutics

6. March 2021
6
Automation
• Explicit rules/processes
• Accurate output
• Digitalise the process
• Do tasks better/faster
“Dumb” technology
powered by
programmable bots
• Follows rules to handle
straightforward tasks.
• Can't react to new
situations.
Artificial Intelligence
• No need to spell out
exactly how it works
• Give machine a set of
data (as input)
• Give machine a set of
results (as output)
Machine figures out what
goes on in between
• Can be software, a
robot, an algorithm.
What is AI vs Automation?

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What is Digital | Digital Strategy | Digital leadership | Digital Lexicon | Organisation Responsiveness |
Change Readiness | Data | Data Science & AI | Agile | Design Thinking | Lean | Digital Lexicon
Build Future-Ready capabilities for all employees

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8
After two weeks, the testers were able to successfully identify cancerous tissue with 85%
accuracy, which increased to 99% in the top performing group.
Diagnosing Breast Cancer

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Let’s challenge our scientists’ belief: Only human can do certain task?!
Diagnosing Breast Cancer

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10
“AI will not replace drug hunters, but drug hunters
who don’t use AI will be replaced by those who do.”
― Andrew Hopkins, CEO Exscientia

11. What AI
can do…
• Gain a better understanding of the diseases we
want to treat
• Identify new targets for novel medicines
• Speed up the way we design, develop and make
new drugs
• Recruit for and designing better clinical trials
• Drive personalised medicine strategies
1 Apr 2021
11
Accelerate innovative science
In R&D, we are harnessing data and AI to discover and
develop new medicines faster, for the right patients

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4-year old Learning
Show 2 or 3 pictures of a bicycle to a child.
Now ask the child to find all the other
bicycles in this pile of pictures.
He picks up unicycles, tricycles and even
bicycles that are wrapped around trees.
Based on very limited info, the child is able
to generalise much more broadly than it
would seem possible.

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AI/Machine Learning
Give a million pictures of bicycle to a
computer.
Teach the computer by pointing to each
picture and specifying which is a bicycle and
which is not.
After a while, the computer learns which
pictures to label as a bicycle.
4-year old Learning
Show 2 or 3 pictures of a bicycle to a child.
Now ask the child to find all the other
bicycles in this pile of pictures.
He picks up unicycles, tricycles and even
bicycles that are wrapped around trees.
Based on very limited info, the child is able
to generalise much more broadly than it
would seem possible.

14. 1 Apr 2021
14
The deep learning-based approach was able to correctly classify ‘damaged’ and ‘non-damaged’
tablets 95.2% of the time.
Case Study 1: Meeting regulatory requirements:
Description Analysis of drug product

15. 1 Apr 2021
15
We created a machine learning model to describe doctors who ‘have’ and ‘have not’ prescribed Lokelma.
We can find doctors that are likely to prescribe Lokelma but have not done so yet. These critical insights
inform the field sales representatives to prioritise limited resources towards engagement with these doctors.
Case Study 2: Who is likely to prescribe a new drug
to patients?

16. 1 Apr 2021
16
Use NLP to find around twenty potentially highly relevant patents - Legal Attorney team now have the
capability to filter and review the most relevant patents for AstraZeneca’s portfolio and take early action.
Case Study 3: Review of all patents to find the relevant documents
that might limit AstraZeneca’s freedom to market products

17. What AI
can do…
• Clinical Supply Chain: Just in time delivery of
products to the patients
• Commercial: Understand the effectiveness of
digital promotional channels with the HCPs –
inform budget allocation and future planning
• HR: Identify and match the best internal
candidates to open internal roles – find the
most suitable offer for the person and for AZ
• Lab: Predict when equipment is going to fail
and optimise maintenance schedules to
prevent failure
1 Apr 2021
17
Accelerate innovative science

18. What is the business or scientific problem you are
trying to solve?
AI is not magic - it is logic and science
that we can choose to apply
1 Apr 2021
18
Do you have clean, unbiased data?
Are you prepared to work with the answers
(predictions) that you have?
Are you clear about the assumptions you have
made?
Are you clear about the implications of how you
use the AI-driven solutions (predictions)?
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3
4
5

19. Future of work: Humans and machines work together to
create value in a data-driven AI world
• AI can help or hurt: humans
can make choices about
which way to go
• Human judgement, values
and ethics matter
• Learning Agility is critical:
proactively anticipate and
handle unintended
consequences

20. Future of work: Humans and machines work together to
create value in a data-driven AI world
• AI can help or hurt: humans
can make choices about
which way to go
• Human judgement, values
and ethics matter
• Learning Agility is critical:
proactively anticipate and
handle unintended
consequences
Apply 5 work habits to keep learning
Self/Team
Reflection
Innovation &
Growth
Mindset
Psychological
Safety
Inclusive
Meetings &
Collaboration
Learning &
Working as
Networks

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Business and Digital Transformation Leader
Digital Capability & Learning Agility, AstraZeneca
Credit to Dr Brenda Dervin for her
ongoing inspiration and guidance
@bonniecheuk
/bonniecheuk http://www.sense-making.org
Let’s learn together!

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contents of this file is not permitted and may be unlawful. AstraZeneca PLC, 1 Francis Crick Avenue, Cambridge Biomedical Campus,
Cambridge, CB2 0AA, UK, T: +44(0)203 749 5000, www.astrazeneca.com
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