Mike Palladino: Adapt, Adopt and Thrive: The Robot Revolution, Agile and their Impact on Your Profession
Global Online PMDay 2022 Summer
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1. UA Project Management Day 2022
1
Adapt Adopt and Thrive:
The Robot Revolution, Agile and the Impact on
Your Profession
Mike Palladino, PMP, CSM
Ø Director, Enterprise Agility, Bristol Myers Squibb
Ø International Keynote Speaker | Webinar Presenter
Ø Adjunct Professor, Villanova University
Ø Author, Data Management University
Ø Past President, PMI-DVC chapter
5. UA Project Management Day 2022
5
Adapt Adopt and Thrive:
The Robot Revolution, Agile and the Impact on
Your Profession
Mike Palladino, PMP, CSM
Ø Director, Enterprise Agility, Bristol Myers Squibb
Ø International Keynote Speaker | Webinar Presenter
Ø Adjunct Professor, Villanova University
Ø Author, Data Management University
Ø Past President, PMI-DVC chapter
13. Value of Humans?
• Adapt to environment
• Adopt new change
• Thrive in the future
Some time in the
future
Humans
are Free
Human are
Batteries
Stop
Humans adapt,
adopt and thrive
X
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14. No Humans, AI is Still Pretty Stupid
“Our most advanced AI
systems are dumber
than a rat”
AI
Humans
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15. Recent Automation at West Coast Ports
“West coast ports added
automation 10 years ago.
There are now more jobs
than before automation.”
“How to balance a higher
demand with a labor
shortage.”
From a conversation with
Anthony Chiarello, former CEO
of TOTE Maritime, May 6, 2022
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17. AI Doesn’t Think the Same Way
Increase Speed
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• Don’t lose level 2
• Don’t lose at all
• Don’t get killed
18. Additional Articles - Concerns
“Labor vs machines. An employment puzzle”
“A revolutionary decade in machinery emphasizes anew the discarding of
men displaced in history”
“President ranks automation first as job challenge. Burden of
finding work for youths and those displaced by machines”
“Automation report sees vast job loss”
“In concrete constructing, building materials are mixed, like dough,
in a machine and literally poured into place without the touch of a
human hand”
Jun 1,
1930
Feb 15,
1962
Feb 26,
1928
New York Times articles
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20. Horses in New York City - 1900
A Lot of Horses
• 200 000 horses in New York City
• 7 - 16 kg of manure per day
• 1.4 – 3.2 million kg of manure per day
• 1500 – 3500 tonness per day
• In 1880, 15 000 dead horses removed
New Jobs Created
1908 Cars started to arrive – Panic. What will all these people do?
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21. Job Loss vs Job Gain
New
Technology
Expand Lower Prices
Lost
Jobs
New
Jobs
New
Jobs
New
Jobs
Tech
Suppliers
New
Industries
We Buy
More
We Buy Other
Things
Higher
Productivity
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23. Quick Math - Driverless Car
• Ukraine: 9 100 000 cars on the road
• Replacement rate: 1% per year
Ø 82 000 vehicles per year
• How long to convert?
Ø 110 years
• Real challenge: 110 years with both human and
automated drivers
Question: What about Motorcycles?
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24. Sewing Machines
First commercial models 1844-1851
Women spent time sewing clothes,
or hiring a seamstress
Time to
Create
Before Sewing
Machines
After Sewing Machines
Shirt 14 hours 1 hour 15 minutes
Dress 10 hours 1 hour
Pants 3 hours 38 minutes
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25. The Great Sewing Machine Riots of 1830
1830, Barthelemy Thimonnier had a factory
with over 80 machines
Factory was destroyed by a riotous group of
French tailors
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26. Problem with Predictions
• Missing the context
• May not include the bigger picture
• ”Those who don’t know history are
doomed to repeat it” – Edmund Burke
• Example: What can happen if we
use data from only the past few
months
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27. The Great Sunlight Leakage Crisis
• Started in July
• Ukraine is loosing about 2 minutes of daylight per day
• AI model predicts total darkness by June
• Affects the entire Northern Hemisphere
• Daylight is leaking to the Southern Hemisphere
• They are gaining about 2 minutes of daylight per day
• We must “Do Something”!!!!!!
• Give me money, I might be able to reverse the trend by
December
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28. Time Savers - Prediction In Progress
40 years ago - Paperless Society
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“I’ve had it with this
kitchen!”
“I don’t think I’m quite ready for
society to go totally paper-less!”
“Ding. You’ve
got mail”
“436 unread
emails”
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50 years ago - Laborless Kitchens
29. Poor Track Record for Predictions
Professional stock pickers
Monkeys throw darts to pick stocks
Results?
“How are those revised
projections coming along?”
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30. Poor Track Record for Predictions
American football
• Each division has 4 teams
• Eliminate the obvious bad choice
• Chances of picking the correct team: 33%
• Accuracy of Professional Football Analysts?
36%
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31. Predicting the End of the World
100 AD 2000 2022
The latest Predictions
- 2022: Nostradamus - large meteorite or asteroid
- 2026: Asteroid collisions or over population
- 2030: Mass extinction
- 2017: to 2113: Several predictions about Asteroids
- 2280: The world will simply end, no reason given
- 2525: Either human race is extinct, or may take another 7,475 years
1000
Hundreds
Accuracy?
0%
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32. Padding Predictions With Extra Time
Predictions are made far into the future
Nov 8, 2017 - Stephen
Hawking: “…less than
600 years until Earth
becomes a sizzling
fireball”
“ NASA - Galaxies will collide
in 4 billion years”
“ The END is Thursday.
The END is Near.
‘Amateur’ ”
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33. Excuses
• “Unpredictable factors, such as the weather”
• “No one else could have predicted …”
• “My prediction was right, but my timing was off”
• “Nobody knows the time of doom in a strict
manner”
• “The evidence was not incorrect, but was not
fully predictive of what was going on”
• People’s fears don’t add up
• 80% of people à robots will take over 50% of the jobs
• 80% of people à but not their jobs
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34. Why Are We So Bad at Predictions?
• Strong incentives to make extreme predictions
• Must be original, different, and stand out
• Only need one correct extreme prediction
• What are the penalties for bad predictions?
• None
• Romania to punish bad predictions - 2 years in jail
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35. Perspective – More Complicated
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“Something’s just not right – our air is clean, our water
is pure, we all get plenty of exercise, everything we eat
is organic and free-range, and yet nobody lives past
thirty.”
“Should we pick up something for the folks
who don’t eat red meat?”
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36. We Don’t Know What We Think We Know
Pyramids – 2750 BC Walking on the
Moon – 1969 AD
Cleopatra – 69 BC
Cleopatra lived 700 years closer to
present day than the pyramids
Nationality à Greek!
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37. We Don’t Know What We Think We Know
Population Size
• 7 Billion people fit within Ukraine with 86 sq. meters each
• The United States alone can feed 9 Billion people
• 100 Year land give back
Population Growth
1939 London 8.6 M à 2015 London 8.7 M
1921 Paris 2.9 M à 2009 Paris 2.2 M
1939 Berlin 4.3 M à 2015 Berlin 3.5 M
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38. We Are not Good with Amounts and Sizes
103 1015 1018
106 109 1012 1021 1024
Grains of sand on all beaches
Stars in the visible universe
Insects for every human
Trees on Earth
Stars in the Milky Way Galaxy
Synapses in the brain
Atoms in a molar gram of matter
200 x 106
100 x 109
3 x 1012
125 x 1012
1.0 x 1024
6.02 x 1023
7.5 x 1018
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40. Irrational Decision Making
• People make irrational decisions
• ”Gut” feel
• Emotional appeal
• Perceived value
• Relative decisions easier than
absolute decisions
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“How Marketing
Works”
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41. Picking Magazines
Digital only Version:
₴ 3300 /year
Digital and Paper Version:
₴ 3300 /year
10% 0% 90%
The Economist
Paper only Version:
₴ 1600/year
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42. Picking Magazines
Paper only Version:
₴ 1600 /year
Digital only Version:
₴ 3300 /year
Digital and Paper Version:
₴ 3300 /year
60% 0% 40%
X
X
The buying habits
changed
The Economist
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43. Picking Magazines
Paper only Version:
₴ 1600 /year
Digital only Version:
₴ 3300 /year
Digital and Paper Version:
₴ 3300 /year
10% 0% 90%
The buying habits
reverted to the
original
Even though no one
buys the Digital only
Version
The Economist
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44. Where Does This Leave Us?
• The world is changing. It has always changed
• People cannot reasonably predict the future
• People have always worked together
• And will continue to work together
• How do we…
• Interact better
• Solve problems better
• Communicate better
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45. Continual Learning
Are we…
• Continually learning in our profession?
• Continually learning in our industry?
• Trying new approaches?
• Improving existing techniques?
Or are we ”too busy”
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46. Agile Manifesto
While there is value in the secondary items, we value the primary items more
Individuals and interactions over
processes and tools
Working solution over
comprehensive documentation
Customer collaboration over
contract negotiation
Responding to change over
following a plan
Agile Manifesto
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48. Communicating Clearly
• Summarize complex data
• 80% communicating
• Understand and speak to the audience
• Short and to the point
• Simple, clear words
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49. Warning: Dangerous Chemical!!!!
Dihydrogen monoxide (DHMO)
• Also known as hydroxyl acid, and is a major component of acid rain
• Can cause sever burns
• Contributes to the erosion of our natural landscape
• Accelerates corrosion and rusting of many metals
• May cause electrical failures and decreases effectiveness of automobile brakes
Often used in:
• Industrial solvent
• Nuclear power plants
• Distribution of pesticides. Even after washing, the product remains contaminated by
this chemical
• Additive in certain junk food and other food products
• Has been found in every single household around the world
http://www.dhmo.org
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53. Ban Dihydrogen Monoxide
Who will sign a petition with me to ban Dihydrogen Monoxide?
• Di – hydrogen, Mono - oxide
• 2 Hydrogen, 1 Oxygen
• 2H, O
• H2O
• Water
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57. Status Reporting
• Audience: sponsors, stakeholders and
executive leadership
• What are the key risks and issues
they need to know
• What do I need them to understand?
• What do I need them to do?
• Time spent reading is inversely
proportional to content
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61. Conclusion - Predictions
• So, don’t worry
• Unrecognizable change will occur and has
occurred
• Top 10 jobs didn’t exist 10 years ago
• “We are currently preparing students for jobs
that don’t yet exist…
• Using technologies that haven’t been invented…
• In order to solve problems we don’t even know
are problems yet.” – Fisch and MeLeod
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62. Conclusion - Predictions
• Beware of predictions made by “professionals”
• Predictions ß à Guessing
• Extreme predictions are amplified
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63. Conclusion – Our Benefits and Learning
• Still comes down to how we interact with people
• Build trust
• Work as a team
• Communicate simpler
• Continue learning to stay relevant
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