Digital transformation has reinforced the success of an iterative and distributed approach to change, yet many organisations have adopted a 'big bang' or centralised mindset for their data strategy. To navigate the Artificial Intelligence (AI) landscape we should look to continuously augment human abilities at scale - in terms of speed, volume or quality - rather than simply look for the next wave of automation.

1. 1
CONTINUOUS
INTELLIGENCE
Mat Kelcey

2. 2
ARTIFICIAL
INTELLIGENCE

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
“
“

4. 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
“
HYPE, BUT A 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

5. PROBLEM EXAMPLE
PREDICT A NUMBER
● How much will this house sell for?
● How many shoes will sell this week, based on recent
sales?
BINARY / CATEGORICAL
PREDICTION
● Is this transaction fraudulent?
● Is this customer NEW, LOYAL, FICKLE, …?
UNDERSTANDING
NATURAL LANGUAGE
● What is the sentiment of this text?
● What action and parameters is a customer requesting?
UNDERSTANDING
IMAGES
● Does this image contain a specific type of object?
● Are these two images similar?
RECOMMENDATION
● Given your purchases, what else might you buy?
● Given your contacts, who else might you know?
WHAT TYPES OF PROBLEMS?

6. 6
INTELLIGENT
EMPOWERMENT

7. AUGMENTATION OVER AUTOMATION

8. AUGMENTATION OVER AUTOMATION
MACHINES
Wider learners
Scalable thinkers
HUMANS
Faster learners
Flexible thinkers
+

9. 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

12. 12
COMPOSABLE
INTELLIGENCE

13. 3
CONSTRUCTION:
Weatherboard
EXIF LOCATION
BEDROOMS
PRICE: $1M
BEDROOMS:
3
PHOTO
CBD DISTANCE:
10km
COMPOSABLE SOLUTIONS

14. CONSTRUCTION:
Weatherboard
EXIF LOCATION
CBD DISTANCE:
10km
BEDROOMS:
3
PHOTO
CONDITION:
Good
PRICE: $1.1M
EVOLVABLE COMPOSABLE SOLUTIONS

15. 15
CONTINUOUS
INTELLIGENCE

16. THE NEW NEW PRODUCT
DEVELOPMENT GAME
PRODUCT
RULES
+ Software architecture
CODE
Research
& Analyse
Code
Deploy
Test
Validate
CUSTOMER OBJECTIVES

17. 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

18. 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

19. DEPLOYMENT
REQUIRES
ENGINEERING
TRANSPORT
REQUIRES
ENGINEERING
THE NEW NEW NEW PRODUCT
DEVELOPMENT GAME
Software Engineering Machine Learning
Validate
RULES
+ Software architecture
CODE
PRODUCT
CUSTOMER OBJECTIVES
DATA SET
+ Network architecture
MODEL

20. Model
Capacity
Data
Estate
PROGRESS
SWEET ZONE
X
RAPID START BUT FRAGILE AND
LIMITED DIFFERENTIATION
(EG MLAAS)
X
SIMPLE TO DO BUT LEAVES
UNREALISED DATA VALUE
(EG SPREADSHEETS)
ITERATIVE SOLUTIONS

21. 21
ML PRODUCT
SQUADS

22. ML Principals
Product Squads
ML PRODUCT SQUADS
Self-service data platform
Consumer
responsibilities
Producer
responsibilities
HCD+ML Diverse Teams

23. 23
ETHICS, RISK
& GOVERNANCE

24. RISKS
Bias
Explainability
Attack Susceptibility
! !
TECH RESPONSES
Baseline Model
Asymmetric Costs
Wide & Deep Networks
Fallback on low Confidence
Distributional expectations on inputs/
outputs
Black/white list inputs/outputs

25. THANKS
Mat Kelcey - mkelcey@thoughtworks.com

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