International Explorations of Service Science Conference 2022 (IESS 2.2)

1. IESS 2.2
Closing Remarks – Thank-you all
Jim Spohrer
Retired IBM Executive, UDIP Fellow
Member ISSIP.org
Questions: spohrer@gmail.com
Twitter: @JimSpohrer
LinkedIn: https://www.linkedin.com/in/spohrer/
Slack: https://slack.lfai.foundation
Presentations online at: https://slideshare.net/spohrer
Thanks to Michele Leonard, Monica Dragoicea, Thang le Dinh, Marco
de Marco.Diem Ho for the opportunity Feb 18, 2022 IESS 2.2.
Highly recommend:
Humankind: A Hopeful History
By Dutch Historian, Rutger Bregman
<- Thanks
To Ray Fisk
For suggesting
this book

3. Service Innovations Questions
• What real-world service system studied?
• What interaction process improved? How measured?
• What change process improved? How measured?
• Which stakeholders (responsible entities) benefitted?
How measured?
If you have concise and clear answers to these service innovation questions for your paper,
Then please consider applying for the ISSIP Excellence in Service Innovation Award
Recognition includes an ISSIP Digital Badge and opportunity for great visibility for the work.
Go to ISSIP.org website, recognition menu, second menu item - to learn more, and apply by filling out form

4. What is the most innovative service offering
that you contributed to delivering or
experienced in 2021?
Service innovations lead to more win-win outcomes
for all stakeholders and improve interaction
and change processes in business and society.
For example, infusing AI successfully,
better ways to upskill workers,
or more inclusive business models for
under-served populations;
all these and more qualify as service innovations.

5. URL: https://www.linkedin.com/posts/spohrer_issip-excellence-in-service-innovation-award-activity-6899738967005126656-qOkc
https://issip.org/recognitions/issip-excellence-in-service-innovation-award/

6. Service in the
AI era
Science science Service
dominant (S-D)
logic
Service Dominant
Architecture
(SDA)
Service in the
AI era
revisited
Core
message?
Better automation
and augmentation
improve service
Better science
improve
understanding
Better logics
improve
interactions
Better
architectures
improve adaption
(change)
X+AI requires
investing
wisely to
improve
service
Where are the
better
models?
Technology Disciplines Minds Enterprise Minds + AI
Enterprise + AI
What type of
model?
Technology
System (T)
Quantitative &
Qualitative (I)
Mental Model in
Person (P)
Distributed
Organizational (O)
P+O+I+T
Service in the AI Era: Science, Logic, and Architecture Perspectives
(Spohrer, Maglio, Vargo, Warg – in progress)

7. Two disciplines: Two approaches to the future
Artificial Intelligence is almost seventy-years-old discipline in computer
science that studies automation and builds more capable technological
systems. AI tries to understand the intelligent things that people can do
and then does those things with technology. (https://deepmind.com/about “...
we aim to build advanced AI - sometimes known as Artificial General Intelligence (AGI) - to
expand our knowledge and find new answers. By solving this, we believe we could help
people solve thousands of problems.”)
Service science is an emerging transdiscipline not yet twenty-years- old
that studies transformation and builds smarter and wiser socoi-
technical systems – families, businesses, nations, platforms and other
special types of responsible entities and their win-win interactions that
transform value co-creation and capability co-elevation mechanisms
that build more resilient future versions of themselves – what we call
service systems entities. Service science tries to understand the
evolving ecology of service system entities, their capabilities,
constraints, rights, and responsibilities, and then then seeks to improve
the quality of life of people (present/smarter and future/wiser) in those
service systems.
26-30 July 2015 3rd International Conference on The Human Side of Service Engineering
7
Artificial Intelligence
Automation
Generations of machines
Service Science
Transformation
Generations of people
(responsible entities)
Service systems are dynamic configurations of people,
technology, organizations, and information, connected
internally and externally by value propositions, to other
service system entities. (Maglio et al 2009)

8. Service Science: Conceptual Framework
4/5/2022 (c) IBM MAP COG .| 8
Service Science

9. (c) IBM MAP COG .| 9
Service Science: Transdisciplinary Framework to Study Service Systems
Systems that focus on flows of things Systems that govern
Systems that support people’s activities
transportation &
supply chain water &
waste
food &
products
energy
& electricity
building &
construction
healthcare
& family
retail &
hospitality banking
& finance
ICT &
cloud
education
&work
city
secure
state
scale
nation
laws
social sciences
behavioral sciences
management sciences
political sciences
learning sciences
cognitive sciences
system sciences
information sciences
organization sciences
decision sciences
run professions
transform professions
innovate professions
e.g., econ & law
e.g., marketing
e.g., operations
e.g., public policy
e.g., game theory
and strategy
e.g., psychology
e.g., industrial eng.
e.g., computer sci
e.g., knowledge mgmt
e.g., statistics
e.g., knowledge worker
e.g., consultant
e.g., entrepreneur
stakeholders
Customer
Provider
Authority
Competitors
resources
People
Technology
Information
Organizations
change
History
(Data Analytics)
Future
(Roadmap)
value
Run
Transform
(Copy)
Innovate
(Invent)
Stackholders (As-Is)
Resources (As-Is)
Change (Might-Become)
Value (To-Be)

10. Jim Spohrer, Board of Directors, ISSIP.org
Jim Spohrer serves on the Board of Directors of the International Society of
Service Innovation Professionals, and as a contributor to the Linux Foundation
AI and Data Foundation. He is a retired IBM Executive since July 2021, and
previously directed IBM’s open-source Artificial Intelligence developer
ecosystem effort, was CTO IBM Venture Capital Group, co-founded IBM
Almaden Service Research, and led IBM Global University Programs. After his
MIT BS in Physics, he developed speech recognition systems at Verbex (Exxon)
before receiving his Yale PhD in Computer Science/AI. In the 1990’s, he attained
Apple Computers’ Distinguished Engineer Scientist and Technologist role for
next generation learning platforms. With over ninety publications and nine
patents, he received the Christopher Loverlock Career Contributions to the
Service Discipline award, Gummesson Service Research award, Vargo and Lusch
Service-Dominant Logic award, Daniel Berg Service Systems award, and a
PICMET Fellow for advancing service science. Jim was elected and previously
served as LF AI & Data Technical Advisory Board Chairperson and ONNX Steering
Committee Member (2020-2021), UIDP Senior Fellow for contributions to
industry-university collaborations.
10
From 2002 - 2009, Jim co-founded
(with Paul Maglio) and directed
IBM Almaden Service Research
helping to establish service science,
applying science, technology,
and T-shaped upskilling of people to
business and societal transformation.
Who I am
2021 A big year: (1) hit 65, (2) career award, (3) retired from IBM

11. Who I am: Take 2
The Three Ages of Man (Giorgione)
Thanks to Alan Hartman for kind inspiration (slides) (recording) Service, when responsible entities apply their knowledge for mutual benefits
win-win/non-zero-sum games/value co-creation/capability co-elevation
Service is a central, fundamental concept of the value of systems interacting
(entities-interactions-outcomes)

12. What I study
Service Science and Open Source AI – Trust is key to both
Service
Science
Artificial
Intelligence
Trust:
Value Co-Creation/Collaboration
Responsible Entities Learning to Invest
Transdisciplinary Community
Trust:
Secure, Fair, Explainable
Machine Collaborators
Open Source Communities

13. 4/5/2022 (c) IBM MAP COG .| 13
T-shaped Adaptive Innovator: Deep Problem-Solving and Broad Communication/Collaboration
Advanced Tech: AI to IoT to Quantum, GreenTech, RegTech, etc.
Work Practices: Agile, Service Design, Open Source
Mindset: Growth Mindset, Positive Mindset, Entrepreneurial
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deep
in
one
sector
Deep
in
one
region/culture
Deep
in
one
discipline

14. Upskilling…
T-shaped
(l)earners
Gardner P, Maietta HN
(2020) Advancing Talent
Development: Steps
Toward a T-Model
Infused Undergraduate
Education.
Moghaddam Y, Demirkan
H, Spohrer J (2018) T-
Shaped Professionals:
Adaptive Innovators.

15. “AI won’t replace entrepreneurs, but entrepreneurs
who use AI will replace those who don’t.”
What does it mean to become a digital entrepreneur?

16. Timeline Future of AI: Every 20 years,
compute costs are down by 1000x
• Cost of Digital Workers
• Moore’s Law can be thought of as
lowering costs by a factor of a…
• Thousand times lower
in 20 years
• Million times lower
in 40 years
• Billion times lower
in 60 years
• Smarter Tools (Terascale)
• Terascale (2017) = $3K
• Terascale (2020) = ~$1K
• Narrow Worker (Petascale)
• Recognition (Fast)
• Petascale (2040) = ~$1K
• Broad Worker (Exascale)
• Reasoning (Slow)
• Exascale (2060) = ~$1K
16
2080
2040
2000
1960
$1K
$1M
$1B
$1T
2060
2020
1980
+/- 10 years
$1
Person Average
Annual Salary
(Living Income)
Super Computer
Cost
Mainframe Cost
Smartphone Cost
T
P
E
T P E
AI Progress on Open Leaderboards
Benchmark Roadmap to solve AI/IA
OECD_Alistair Nolan to Everyone: “It has been stated that the number of engineers proclaiming the end of Moore's Law doubles every two years.”
Rouse WB, Spohrer JC. (2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation. 2018 Apr 3;8(1-2):1-21.

17. Timeline: GDP/Employee
4/5/2022 (c) IBM 2017, Cognitive Opentech Group 17
(Source)
Lower compute costs translate into increasing productivity and GDP/employees for nations
Increasing productivity and GDP/employees should translate into wealthier citizens
AI Progress on Open Leaderboards
Benchmark Roadmap to solve AI/IA

18. Timeline: Leaderboards Framework
AI Progress on Open Leaderboards - Benchmark Roadmap
Perceive World Develop Cognition Build Relationships Fill Roles
Pattern
recognition
Video
understanding
Memory Reasoning Social
interactions
Fluent
conversation
Assistant &
Collaborator
Coach &
Mediator
Speech Actions Declarative Deduction Scripts Speech Acts Tasks Institutions
Chime Thumos SQuAD SAT ROC Story ConvAI
Images Context Episodic Induction Plans Intentions Summarization Values
ImageNet VQA DSTC RALI General-AI
Translation Narration Dynamic Abductive Goals Cultures Debate Negotiation
WMT DeepVideo Alexa Prize ICCMA AT
Learning from Labeled Training Data and Searching (Optimization)
Learning by Watching and Reading (Education)
Learning by Doing and being Responsible (Exploration)
2018 2021 2024 2027 2030 2033 2036 2039
4/5/2022 (c) IBM 2017, Cognitive Opentech Group 18
Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?
Approx.
Year
Human
Level ->
+3
See: https://paperswithcode.com/sota

20. IBM’s Service Journey: A Summary Sketch
4/5/2022 (c) IBM MAP COG .| 20
Spohrer J (2017 ) IBM's service journey: A summary sketch. Industrial Marketing Management. 60:167-172.

23. IfM, IBM (2010)
Succeeding through
service innovation:
a service perspective
for education, research,
business and government.
University of Cambridge
Institute for Manufacturing,
Cambridge UK
2010

32. Intelligence Augmentation (IA) =
Socio-Technical Extension Factor on Capabilities
• Engelbart (1962)
• Spohrer & Engelbart (2002)
4/5/2022 (c) IBM MAP COG .| 32
Dedicated to Douglas E. Engelbart, Inventor
The Mouse (Pointing Device)
The Mother of All Demos
Bootstrapping Practice/Augmentation Theory
Note: Bush (1945) and Licklider (1960) created funding programs that benefitted Engelbart in building working systems.

33. IA as Socio-Technical Extension Factor on Capabilities & Values
IA (human values) is not AI (technology capability)
Difference 1: IA leads to more capable people even when scaffold removed
Difference 2: IA leads to more responsible people to use wisely the capabilities
4/5/2022 (c) IBM MAP COG .| 33
Superminds
Malone (2018)
Things that Make
Us Smart
Norman (1994)
Worldboard
Augmented Perception
Spohrer (1999)
Bicycles for the Mind
Kay & Jobs (1984)
Techno-Extension Factor
Measurement
& Accelerating
Socio-Technical Design Loop
Kline (1996)

34. IA Progression – Tool, Assistant, Collaborator, Coach, Mediator
4/5/2022 (c) IBM MAP COG .| 34
Rouse & Spohrer (2018)
Siddike, Spohrer, Demirkan, Kodha (2018)
Araya (2018)
Spohrer& Siddike (2018)

35. Bigger IA Trend in Human Time Usage & Skills
As smartphone apps grow up and people have 100 digital workers “earning” for them (owners) on platforms
• Hunter Gathers – local sourcing,
generalist
• Agriculture – local sourcing,
generalist – cities specialists
• Manufacturing – outsourcing to
production business, specialists
• Clothing to Shopping
• Service (pre-AI) – outsourcing to
service businesses, specialist
• Cooking to Restaurants
• Service (post-AI &
miniaturization) – insourcing, T-
shapes
• T-shaped (l)earners in platform
society, home again
4/5/2022 (c) IBM MAP COG .| 35
Spohrer & Maglio (2006) SSME, Slide #42
Spohrer (2020) Platform Economy
and Shift in Work

36. References
• Araya D (2018) Augmented Intelligence: Smart Systems and the Future of Work and Learning. Peter Lang International Academic Publishers; 2018 Sep 28.
• Bush V (1945) As we may think. The Atlantic Monthly. 1945 Jul 1;176(1):101-8.
• Engelbart D (1962) Augmenting human intellect. Summary report AFOSR-3223 under Contract AF. 1962 Oct;49(638):1024.
• Gardner P, Maietta HN (2020) Advancing Talent Development: Steps Toward a T-Model Infused Undergraduate Education. Business Expert Press. URL:
https://www.amazon.com/Advancing-Talent-Development-Undergraduate-Education/dp/1951527062
• Kay A, Jobs S (1984) Wheels for the Mind. Apple Computer.
• Kline SJ (1995) Conceptual foundations for multidisciplinary thinking. Stanford University Press; 1995.
• Licklider JC (1960) . Man-computer symbiosis. IRE transactions on human factors in electronics. 1960 Mar(1):4-11.
• Malone TW (2018) Superminds: The surprising power of people and computers thinking together. Little, Brown Spark; 2018 May 15.
• Norman D (1994) Things that make us smart: Defending human attributes in the age of the machine. Diversion Books; 2014 Dec 2.
• Rouse WB, Spohrer JC (2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation. 2018 Feb 7:1-21.
• Siddike MA, Spohrer J, Demirkan H, Kohda Y (2018) A Framework of Enhanced Performance: People's Interactions With Cognitive Assistants. International Journal
of Systems and Service-Oriented Engineering (IJSSOE). 2018 Jul 1;8(3):1-7.
• Spohrer JC (1998) Information in places. IBM Systems Journal. 1999;38(4):602-28.
• Spohrer JC, Engelbart DC (2004) Converging technologies for enhancing human performance: Science and business perspectives. Annals of the New York Academy
of Sciences. 2004 May;1013(1):50-82.
• Spohrer J, Siddike (2018) The Future of Digital Cognitive Systems: Tool, Assistant, Collaborator, Coach, Mediator. In Ed. Araya D. Augmented Intelligence: Smart
Systems and the Future of Work and Learning. Peter Lang International Academic Publishers; 2018 Sep 28.
• Spohrer J (2020) Online Platform Economy and Gig Workers: A USA Perspective. Presentation.
• Spohrer J & Maglio PP (2006) Service Science Management and Engineering (SSME): An Emerging Discipline. IBM Presentation.
4/5/2022 (c) IBM MAP COG .| 36

37. Upskilling 2030:
Service Innovation Roadmaps and
Responsible Entities Learning
Jim Spohrer (IBM & ISSIP)
International Exploration of Service Science (IESS 2.1)
March 25, 2021

38. IfM, IBM (2010)
Succeeding through
service innovation:
a service perspective
for education, research,
business and government.
University of Cambridge
Institute for Manufacturing,
Cambridge UK
2010

39. What is a SIR?
• Service Innovation Roadmap (SIR) is a kind of Business Model Canvas
(BMC) that responsible entities create for themselves to describe
three types of investments in learning/upskilling activities:
• Run: BMC for optimize activities (e.g., agile improvement method)
• Transform: BMC for copy activities (e.g., find role models)
• Innovate: BMC for invent activities (e.g., research, pilot, prove, monetize)
• Based on March (1991)
• March JG (1991) Exploration and exploitation in organizational learning.
Organization science. 1991 Feb;2(1):71-87.

40. Business
Model
Canvas

42. 42
How responsible entities (service systems) learn and change over time
History and future of Run-Transform-Innovate investment choices
• Diverse Types
• Persons (Individuals)
• Families
• Regional Entities
• Universities
• Hospitals
• Cities
• States/Provinces
• Nations
• Other Enterprises
• Businesses
• Non-profits
• Learning & Change
• Run = use existing knowledge
or standard practices (use)
• Transform = adopt a new best
practice (copy)
• Innovate = create a new best
practice (invent) Innovate
Invest in each
type of change
Spohrer J, Golinelli GM, Piciocchi P, Bassano C (2010) An integrated SS-VSA analysis of changing job roles. Service Science. 2010 Jun;2(1-2):1-20.
March JG (1991) Exploration and exploitation in organizational learning. Organization science. 1991 Feb;2(1):71-87. URL:
exploit
explore

43. 4/5/2022 (c) IBM MAP COG .| 43
Arthur, W.B. Foundations of complexity economics. Nat Rev Phys (2021). https://doi.org/10.1038/s42254-020-00273-3