Professional Diploma in
Artificial Intelligence
The Digital Transformation
1. Introduction to Artificial Intelligence
Course Outline
2. The Digital Transformation
3. Process Automation
4. Cognitive Insight
5. Cognitive Engagement
6. Industry Developments
7. Capability Strategy
8. Strategy and Implementation
9. The People-Process-Technology Balance
10. Societal and Legal Implications of Artificial
Intelligence
11. Ethical Dimensions of Artificial Intelligence
12. The Workforce of the Future
Professional Diploma in Learning Outcomes
Artificial Intelligence
• Explain the core concepts, terminology, and
role of Artificial Intelligence in the Digital
Transformation
• Examine the capabilities of the three common
types of Artificial Intelligence and how they
can be used to drive greater organisational
efficiency and effectiveness
• Analyse how to link the business and
technical capabilities of the organisation to
influence strategy and build competitive
advantage
Professional Diploma in Learning Outcomes
Artificial Intelligence
• Evaluate the People-Process-Technology
balance implications in leveraging Artificial
Intelligence
• Assess the ethical, societal, legal, and
governance considerations of Artificial
Intelligence, and the trends that will influence
and shape the workforce of the future
2. The Digital In This Unit
Transformation
Title Timings
Review previous week’s learning 5
Introduction 15
The Digital Transformation 45
The Hype Cycle 25
Commoditisation and AI 30
Demystifying AI 25
The Plan 15
Final Thoughts 5
Introduction
Topic 1: The Digital
Transformation
Video: What is Digital Transformation?
NASA Chief Technologist – Dr. Douglas Terrier
Video Link: https://vimeo.com/857069100/5ea2dbb355?share=copy
Digital Transformation
Discuss
• Put it into your own words – What does
Digital Transformation mean to you?
• What is the connection between Digital
Transformation and AI?
On Digital Transformation:
‘To succeed in digital transformation, leading companies
focus on two complementary activities: reshaping
customer value propositions and transforming their
operations using digital technologies for greater customer
interaction and collaboration’
– Saul Berman, Corporate Consultant
Industrial Revolution – Disruptive
Technology
• First industrial revolution
• Industrialisation of production began in the
late 1700’s
• Cause: Steam power combined with
mechanical production
• Second industrial revolution
• Mass production began in the mid-1800’s
• Cause: Electricity and assembly lines
Industrial Revolution – Disruptive
Technology
• Third industrial revolution
• Industrialisation greatly accelerated,
beginning around the 1970’s
• Cause: Electronics and information
technology combined with globalisation
• Fourth industrial revolution
• Began around 2010
• Next generation automation
• Digital Transformation of all processes
• Accelerating due to advancements in
Artificial Intelligence
Managing Knowledge in a Digital Society
• Tacit and explicit forms of knowledge
• Democratisation of knowledge
• Reduced information asymmetry
• New meaning and insights with Big Data
• New processes and ways of thinking
• Generative AI
Activity
Breakout room
‘Without a more fundamental business
transformation, digitization on its own is
a road to nowhere’
• Read the article – ‘Digitizing Isn't the
Same as Digital Transformation - HBR’
• Discuss the article and reflect on your
own professional experiences
Topic 2: The Hype Cycle
The Hype Cycle
• Hype Cycles provide a graphic
representation of the maturity and adoption
of technologies and applications, and how
they are potentially relevant to solving real
business problems and exploiting new
opportunities
• The Hype Cycle methodology offers a view
of how a technology or application will
evolve over time
• Provides a source of insight to manage its
deployment within the context of specific
business goals
Five phases of the Hype Cycle
• Innovation Trigger
• A potential technology breakthrough kicks
things off
• Early proof-of-concept stories and media
interest trigger significant publicity
• Often no usable products exist and
commercial viability is unproven
• Peak of Inflated Expectations
• Early publicity produces a number of
success stories — often accompanied by
scores of failures
• Some companies take action; many do not
Five phases of the Hype Cycle
• Trough of Disillusionment
• Interest wanes as experiments and
implementations fail to deliver
• Producers of the technology leave or fail
• Investments continue only if the surviving
providers improve their products to the
satisfaction of early adopters
Five phases of the Hype Cycle
• Slope of Enlightenment
• More instances of how the technology can
benefit the enterprise start to crystallise and
become more widely understood
• Second and third-generation products appear
from technology providers
• More enterprises fund pilots; conservative
companies remain cautious
• Plateau of Productivity
• Mainstream adoption starts to take off
• Criteria for assessing provider viability are more
clearly defined
• The technology’s broad market applicability and
relevance are paying off
Let’s review four years of AI Hype Cycle developments
2019
2020
2021
The Importance of Being Aware of the
Hype Cycle for New Technology
• Look back in history - Big Data is a great
example – you see hyper hype
• Big data was real; big data was and will
continue to be important
• However, organisations that didn’t spend
huge money on their own data systems,
but instead went straight to the cloud,
saved a lot of money
With all technology, look for the value -
be realistic!
Topic 3: Exploring the
Commoditisation of AI
Commoditisation of AI
• AI tech is now highly accessible and low
cost - standard capabilities that every
business can use
• This trend has implications for the competitive
landscape
Discuss
1. How does more accessibility of AI tech
impact the competitive dynamics in your
industry?
2. In what ways could your company use AI
to differentiate itself from competitors in
your industry?
Commoditisation of AI
• What is AI fundamentally?
• Hardware and software
• Amazon, Microsoft, Google – the three
major players in this field, particularly in
Machine Learning
• Billions invested in servers
• What can they do with these servers?
• Store data
• Crunch data
• Meet halfway - pre-packaged machine
learning
Commoditisation of AI
• In Natural Language Processing –
Example: Nuance Communications
• Recently acquired by Microsoft
• Known for speech recognition software such
as Dragon NaturallySpeaking
• In Cognitive Robotic Process Automation
– Example: Blue Prism
• Known for automation software –
automating many back office tasks,
particularly in Accounts
Topic 4: Demystifying
Artificial Intelligence
• Neural Networks
• Machine Learning
• Deep Learning
Artificial Intelligence
Artificial Intelligence
Machine Learning
Neural Networks
Deep Learning
Machine Learning
• Next-level analytics
• Machine learning starts with data
• Data quality
• Algorithms detecting patterns in large
volumes of data
• Interpreting meaning and creating
knowledge
Machine Learning
• Self-directed computer system
• Algorithms that improve with experience
• Finds patterns and nuances in datasets
• Three types of Machine Learning
• Supervised learning
• Unsupervised learning
• Reinforcement learning
Artificial Neural Network (ANN)
• Brain-inspired systems intended to
replicate the way that humans learn
• Consist of input and output layers, as
well as a hidden layer (as many as you
want) consisting of units that transform
the input into something that the output
layer can use
• Highly adapted to finding patterns which
are far too complex or numerous for a
human programmer to extract and teach
the machine to recognise
Artificial Neural Network (ANN)
Nature and Capabilities
Correlation
Self-learning
over causation
Doesn’t require
Convoluted
structured
workings
data
Capable of
working with
very large data
Deep Learning
• Subset of machine learning
• Deep learning is at the cutting-edge of AI
• Became prominent in the early 2010s, with
Google having only started using it in its
search engine from 2015
• Allows for processing huge amounts of data
to find relationships and patterns that
humans are often unable to detect
• Progress in Deep Learning has been driven
by huge growth in data, such as from the
Internet, and processing power
Deep Learning
• The “Deep” part refers to the number of
hidden layers in the neural network
• Deep learning has driven impressive
progress of machine learning, with many
different models accomplishing
extremely difficult tasks such as:
• Image and speech classification
• Image and video manipulation/generation
• Digital assistants
• Autonomous driving
Deep Learning Versus Traditional
Machine Learning Flow
Video: How does ChatGPT Work?
Video Link: https://vimeo.com/857069144/572c8ea331?share=copy
Let’s try to visualise this process in a simpler way
Video:
AI Finding its Way Through a Maze by Trial and Error
Video Link: https://vimeo.com/857069174/62a47fd9d5?share=copy
Neural Network Learns to Play Snake
• Each “snake” has a Neural • In each direction it looks for:
Net with: • Distance to Food
• 14 input neurons • Distance to its Tail
• 2x18 Hidden neurons • Distance to the Wall
• 4 output neurons • Each generation has 2000
• The Snake looks in 8 snakes
Directions • In every generation the best
snakes are selected
Video: Neural Network Learns to Play Snake
Link: https://vimeo.com/857069207/5ff6753f7c?share=copy
Video: AlphaGo Zero – AI training itself
Video Link: https://vimeo.com/857069263/1996c2a661?share=copy
The Plan
Discussing how we will Navigate the
Complex Artificial Intelligence Landscape
over the coming three weeks
Navigating the Complex Artificial
Intelligence Landscape
• The Challenge: Convoluted
classification and interaction of AI
technologies
• Generally speaking, there are three key
AI technologies – Cognitive Robotic
Process Automation, Machine Learning,
and Natural Language Processing
• These classifications can be misleading,
and suffer from the same legacy
classification issues that ‘Artificial
Intelligence’ itself does
Navigating the Complex Artificial
Intelligence Landscape
• Davenport and Ronanki (2018) propose
three types of AI for organisations:
1. Process Automation
2. Cognitive Insight
3. Cognitive Engagement
• This is a more useful taxonomy for our
needs because:
a) They focus on the business need rather
than the technology
b) They recognise that AI technologies are
often used in combination
Business Needs
• Businesses across all industries and at
every level need to start thinking about
AI strategically through the lens of
business capability
• Business needs for AI:
• Automating processes
• Finding insights through data analysis
• Engaging with customers and employees.
Summary
• What the Digital Transformation means
in practical terms
• The Hype Cycle
• Commoditisation and AI
• Demystifying AI
• The Plan
• Learning Log Assessments
• Pass/Fail
• 3000 words total +/- 10%
• Business Report
• 100% of the Final grade, if LL is a Pass
• 3,000 words total +/- 10%
Additional Activity if Activity
Needed
• Read the article – ‘Why So Many
High-Profile Digital Transformations
Fail’
• Discuss the article and reflect on your
own experiences of organisational
Digital Transformation
Additional Activity if Activity
Needed
• Read the case study – ‘Inside IKEA’s
Digital Transformation’
• Discuss how Ikea’s experience
compares to how your own
organisation or industry has been
affected by the Digital Transformation,
or the forces influencing future direction
