Post on 15-Apr-2017
The story so far
AI (1960s)
AI (1990s)
AI (Now)
ENIAC (1946)
The AI “Winters”
Lighthill report, DARPA cuts 5th Gen
fizzle
State of play Co
gni1ve Autom
a1on
Time
Now
Recogni+on intelligence
Cogni+ve Intelligence
General Intelligence (?)
Enablers of work automa1on Robo+c Process Automa+on
Cogni+ve automa+on
Social Robo+cs
TASKS Rou1ne, High-‐volume
Non-‐rou1ne, crea1ve
Rou1ne, collabora1ve
MATURITY
HIGH
EMERGING
MEDIUM
IMPACT MEDIUM HIGH HIGH
The automa1on of jobs
Source: The Future of Employment, by C. Frey and M. Osborne
47% of jobs will be
fully-‐automated in the next 10
years
Source: McKinsey Interim report on automa1on of jobs, Nov. 2015
45% of job ac1vi1es can
be automated +AI = 58%
of job ac1vi1es can be automated
60% of jobs can have
30% of their
ac1vi1es automated
Hello Jane, you look great today! How can I help you?
Automa1ng tasks (not jobs)
5% of jobs will be fully-‐automated
Country and educa1on level variability
9% of jobs will be fully-‐automated
Source: Arntz, M., T. Gregory and U. Zierahn (2016), “The Risk of Automa1on for Jobs in OECD Countries: A Compara1ve Analysis”, OECD Social, Employment and Migra1on Working Papers, No. 189, OECD Publishing, Paris.
Previous impacts: Automa1on means less work…
… but not less jobs 50% increase in total number of employed people Wage rise 2.23% faster than infla1on
The change is on
2nd Industrial Revolu+on
“The assembly
line” Features: § Underpinning for
Coase’s theory of the firm
§ Companies as social ins1tu1ons
§ Organiza1on of work into jobs
§ Jobs as careers
3rd Industrial Revolu+on
“Nikefica1on”
Features: § Technology
enablement and the web
§ Companies as the nexus of contracts
§ Streamlining of jobs to enable outsourcing
4th Industrial Revolu+on
“Uberiza1on”
Features: § Mobile, sensors, AI and
machine learning § Companies as
pla`orms § Disaggrega1on of work
into ac1vi1es § Talent on demand
1900s 1960s-‐1990s 2000s-‐
The 5 Forces of Change
Source: CHREATE Consor1um
Social & Organiza.onal reconfigura.on
A truly connected world
All inclusive, global talent market
Human & machine collabora.on
Exponen.al paCern of technology change
1
2
3
4
5
• Work Automa.on (RPA, CA, Social Robo.cs) • Blockchains • 3D prin.ng • IoT
Technological Empowerment
• Short term • Agile • Skills-‐based • Networks • PlaVorms
Democra.za.on of Work
Possible futures
LOW
Democra+za+
on of W
ork
Technological Empowerment
HIGH
HIGH LOW
Work Reimagined
“UBER” Empowered
Current State
Today turbo-‐charged
1 2
34
Source: CHREATE Consor1um
A shared economy for talent Company
A
Company B
Company C
Company D
Shared talent pla`orm AI-‐enabled
IT
HR
CS
Transformed jobs: A more humane doctor
Proficiency role (now) Pivotal role (future)
Doctor Performance Doctor Performance
Pa1e
nt Sa1
sfac1o
n
Pa1e
nt Sa1
sfac1o
n
AI -‐ Enabled
As cogni1ve automa1on gets beoer with diagnosis human doctors (a “proficiency role”) can spend more 1me with pa1ents, becoming a “pivotal role” in healthcare systems
New jobs created
Data, Talent & AI integrator Virtual Culture Architect Robot Trainer
Cyber Ecosystem Designer AI Ethics Evaluator
A new cyberne1c rela1onship
Second-‐order cyberne1cs in the era of machine intelligence
Humans and machines working together: machines managing complexity, humans providing crea1vity
From knowing what you do not know and searching for it
…to …
…not knowing what you do not know and having “someone” to help you discover it
Cyber-‐physical Systems & Industry 4.0
From hierarchies to networks
CPS-‐based automa+on Field level
Control (PLC) Level
Process Control Level
Plant management Level
ERP Level
Automa+on hierarchy
Zero Latency Enterprise
Company Organisa1on
Enterprise Systems
Enterprise Applica1ons
Enterprise App Integra1on
Data Store
In a real )me, zero latency enterprise, informa)on is delivered to the right place at the right )me for maximum business value.*
*Defini1on of ZLE by Gartner
The Responsive Organisa1on An agile, client-‐facing, innova)ve organiza)on that con)nuously learns and op)mizes talent and technologies in order to deliver superior products and services.
Machine Intelligence Applica1ons
People Networks
Business Systems
Learning & Conversa1ons
Business Applica1ons
Business App Integra1on
Virtual Data Store
People Networks: reinven1ng business organisa1on
• Self-‐organised ad hoc teams • Build-‐in discovery from design to customer service • Scaling Agile • Cross-‐market & Cross-‐exper1se • Collabora1on pla`orms • AI enabled UI/UX • Predic1ve analy1cs
Transforming business with work automa1on
Source: “Lead the Work” by R. Jesuthasan, J. Bourdeau, D. Creelman
Assignment
Organisa1on
Rewards
• Self-‐contained • Unlinked • Exclusive • Stable
• Deconstructed Tasks • Dispersed • Project-‐bound
• Constructed Jobs • Anchored • Employment-‐Bound
• Long-‐Term • Collec1ve and
consistent • Tradi1onal
• Permeable • Interlinked • Collabora1ve • Flexible
• Short-‐term • Individualised and
Differen1ated • Imagina1ve
AI enabled
Geyng there: Scaling Agile organisa1on
Apply agile prac1ce across the organisa1on
hop://crowdmics.com/ hop://crowdmics.com/
INNOVATE
DELIVER
VALIDATE
UNDERSTAND
Geyng there: digital engagement
Apply Next Genera1on Integrated Digital
Engagement Model (IDEM) for the digital
transforma1on of work
Behavioural Modelling
Human-‐machine
conversa1ons AI Interface
Data
Worker experience
Human-‐machine collabora1on
Geyng there: machine intelligence for EX
Build the machine intelligence layer of the responsive organisa1on