In this talk, we will help you to identify and deliver with confidence on opportunities to intelligently empower your business, customers, team members and other stakeholders, sharing our approach for building the right organisational capabilities.
3. AMAZING OPPORTUNITY
There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
The business plans of the next 10,000 start-ups
are easy to forecast: take X and add AI.
Kevin Kelly – The Inevitable
“
“
5. NO WONDER AI GENERATES
INTEREST FROM LEADERS
But the big change from last
year is that 81% cited
artificial intelligence and
machine learning as either
very important or extremely
important to their company’s
future, up from just 54% in
2016.
Fortune.com Fortune 500 CEO survey 2017
81% “
“
6. There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
Kevin Kelly – The Inevitable
“ “
THE HYPE PROBLEM
So how do we think about opportunities?
AI CAN DO ANYTHING
WE WANT (IT’S MAGIC)
7. There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
Kevin Kelly – The Inevitable
“
AND THE KERNEL OF THE TRUTH
“
AND ABOUT
PEOPLE
AND WE CAN
BUILD
ITERATIVELY
AI IS NARROW
(BUT COMPOSABLE)
There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
Kevin Kelly – The Inevitable
There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
Kevin Kelly – The Inevitable
There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
Kevin Kelly – The Inevitable
11. THE INTELLIGENT EMPOWERMENT CHALLENGE
High volume
judgement tasks are
high cost
or variable quality
High quality human
judgement is hard to
deliver at speed or
scale
Empower all your
people with the
judgement of your
best performers
Leadership
Lenses
BUSINESS
CUSTOMERS
EMPLOYEES
Outperform with insight
Experience driven by AI
Your people at their best
13. PROBLEM EXAMPLE
PREDICT A NUMBER
● How much will this house sell for?
● How many shoes will sell this week, based on recent sales?
BINARY PREDICTION
● Will this customer churn this week?
● Is this transaction fraudulent?
PREDICT A CATEGORY
● Is this customer NEW, LOYAL, FICKLE, …?
● Is the t-shirt in this image CREW, V-NECK, POLO, …?
UNDERSTANDING
NATURAL LANGUAGE
● What is the sentiment of this text?
● What action and parameters is a customer requesting?
RECOMMENDATION
● Given your purchases, what else might you buy?
● Given your contacts, who else might you know?
WHAT TYPES OF PROBLEMS?
17. THE NEW NEW PRODUCT
DEVELOPMENT GAME
PRODUCT
RULES
+ Software architecture
CODE
Research
& Analyse
Code
Deploy
Test
Validate
CUSTOMER OBJECTIVES
18. THE NEW NEW NEW PRODUCT
DEVELOPMENT GAME
Software Engineering Machine Learning
DATA SET
+ Network architecture
MODEL
Research
& Analyse
Code
Deploy
Test Test
Validate
Curate &
Transport
Train
Deploy
RULES
+ Software architecture
CODE
PRODUCT
CUSTOMER OBJECTIVES
19. THE NEW NEW NEW PRODUCT
DEVELOPMENT GAME
Software Engineering Machine Learning
Validate
RULES
GENERATE
DATA
USAGE
GENERATES
DATA
RULES
+ Software architecture
CODE
PRODUCT
CUSTOMER OBJECTIVES
DATA SET
+ Network architecture
MODEL
22. PoC IDEA
TOO MANY PoCs?
MAKE IT
SIMPLER
TEST IN
LAB?
DEPLOY TO
PROD
COLLECT,
EVALUATE
MODEL
ITERATION
DARK
LAUNCH
OR A/B
TEST IN
LAB
ADD VALUE
UNCERTAIN
HOW TO ADD
VALUE
PoC IDEA
CLEAR VALUE
ADD
REPEAT! REPEAT!
24. ML Principals
Product Squads
Scale Partners
ML PRODUCT SQUADS
Self-service data platform
Consumer
responsibilities
Producer
responsibilities
Organisational agility
and learning culture
HCD+ML Diverse Teams
30. We Can Build
Iteratively
AI is Narrow
It’s About People
CONTINUOUS
INTELLIGENCE
COMPOSABLE
INTELLIGENCE
INTELLIGENT
EMPOWERMENT
THE NATURE OF SOLUTIONS
31. WHERE TO FIND OPPORTUNITY
BUSINESS
CUSTOMERS
EMPLOYEES
VOLUME QUALITY SCALE
32. VERY FIRST STEPS
! Introduce a step for Machine Learning
in your data pipeline (input == output)
! Look for a binary Yes/No suggestion or a
suggested delta to existing results
! Then iterate