Keynote 20221028 v5.pptx

2022 IEEE MetroXRAINE
Future of AI and IA:
A Service Science Perspective
Jim Spohrer
Retired Executive – IBM and Apple
UIDP Senior 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 Pasquale Arpaia and Cristina Mele for the invitation
to discuss Future of AI and IA
Friday October 28, 2022, 11:00-12:00 Rome Time
Highly recommend:
Humankind: A Hopeful History
By Dutch Historian, Rutger Bregman
<- Thanks
To Ray Fisk
For suggesting
this book, see
My summary here.

LA LUCE DELLA SCIENZA CERCO E ‘L BENEFICIO
10/28/2022 Jim Spohrer (ISSIP.org) 2
I seek the light of science and benefit
National Research Council HQ of Italy
Piazzale Aldo Moro 7, Roma
“The goal of science is to make the wonderful and complex
understandable and simple – but not less wonderful.” – Herb
Simon, The Sciences of the Artificial

Prof. Mariano Alcaniz
XRBB = Extended Reality
Behavioral Biomarkers

Prof. Cristina Mele
Science as a public good.

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.
7
From 2002 - 2009, Jim co-founded
IBM Almaden Service Research (ASR)
ASR mission - advance service science
and people-centered, data-driven
service innovation
Who I am

Who I am: Take 2
The Three Ages of Man (Giorgione)
Thanks to Alan Hartman for kind inspiration (slides) (recording)
Service is an actor applying resources (e.g., knowledge) to benefit another
Service system entities are responsible actors that give and get service
(e.g., people, businesses, universities, nations, etc.)
Service science studies service systems as an evolving ecology
of responsible actors that interact and change.
Service innovations improve win-win interaction and change
in business and society
Service systems are dynamic configurations of four types of resources

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

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.
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)

Overlap
Acknowledgement: E. Noei, S. Brisson, Y. Liu
2010
2019
11
Kelly Lyons

Service Science: Conceptual Framework
10/28/2022 (c) IBM MAP COG .| 12
Service Science

(c) IBM MAP COG .| 13
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)

Why the complex systems trend is impotant
Business and societal systems and supply chains are increasingly complex and interconnected.
Real-world problems do not respect discipline boundaries.
Scalable solutions require many schools of practice working together, and current solutions may have unintended
consequences, short-term or longer-term, especially if perspectives are not invited/considered.
Technological progress improved the scalability of agriculture and manufacturing, and next all types of service will be
made more scalable (and currently, energy intensive) by future AI capabilities and progress.
A small sampling of schools and disciplines below – more exist  - apologies for not adding yours to this summary.
School of practice for
Physical Sciences & Engineering
Technology
Behavioral $ Social Sciences,
Humanities & Arts
People
Managerial Sciences &
Entrepreneurship
Information & Organizations
Comp. Sci./AI
HCI/Robotics
Electrical &
Mech. Eng.
Systems
Engineering
Economics Public Policy
& Law
Design Information
Systems
Operations
Research
Marketing &
Strategy
Read enough of Kline (1995) to understand conceptual foundation of multidisciplinary thinking
and the techno-extension factor and the accelerating soci—technical system design loop concepts.
10/28/2022 National Academy - Service Systems and AI 14

Early Service Science Work
• Revisiting SSME and T-Shaped Skills in the AI Era
• SERVICE SCIENCE DEFINED.—In this section, the term ‘‘service
science’’ means curricula, training, and research programs that
are designed to teach individuals to apply scientific, engineering,
and management disciplines that integrate elements of
computer science, operations research, industrial engineering,
business strategy, management sciences, and social and legal
sciences, in order to encourage innovation in how organizations
create value for customers and shareholders that could not be
achieved through such disciplines working in isolation.” (US 110th
Congress 2007)
• T-SHAPED SKILLS DEFINED. – T-shaped skills is a metaphor for
describing the skills of a person who combines both breadth and
depth, like the shape of the letter T - a combined generalist with
excellent interactional communication skills across business and
technology as well as a specialist in one or more areas with
contributory problem-solving skills, the area(s) of the person’s
earned “bon fides.” (see also IfM and IBM 2008).
Service Systems Engineering in the Human-Centered AI Era 15
Nick Donofrio
IBM Fellow Emeritus
NAE Member

Value
Science
Engineering
Policy
Investing in Skills
for Diverse Systems to
Sustainably Serve
People and Planet
in the AI Era
Management
Service
Science
Management
Engineering
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deep
in
one
sector
Deep
in
one
region/culture
Deep
in
one
discipline
T-Shaped Skills
Depth and Breadth
People-centered
Data-intensive
+Design-Arts-
Public-Policy

Why upskilling with AI trend is important to systems thinking
Talent development is moving from I to T to X (eXtended with AI)
National Academy - Service Systems and AI 17
6 T-shape Skills
Knowledge Areas
To be eXtended
By AI tools:
1. Disciplines
2. Systems
3. Cultures
4. Technologies
5. Practices
6. Mindsets
10/28/2022

APPLE
https://podcasts.apple.com/us/podcast/service-science-and-the-impending-ai-revolution/id1612743401?i=1000583800244
SPOTIFY:
https://open.spotify.com/episode/0n3h9rgX6UYDCwxgTzokoK?si=yVF0mtHsRZSmdfy-aMi8DA
GOOGLE
https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xOTQ5NTE3LnJzcw?sa=X&ved=2ahUKEwiPzL-Zxvv6AhXzjo4IHVbTAuUQ9sEGegQIARAC

Timeline: 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
19
10/28/2022 Jim Spohrer (ISSIP)
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

Timeline: GDP/Employee
10/28/2022 Jim Spohrer (ISSIP) 20
(Source)
Lower compute costs translate into increasing productivity and GDP/employees for nations
Increasing productivity and GDP/employees should translate into wealthier citizens

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
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

10 million minutes of experience
10/28/2022 Understanding Cognitive Systems 22

2 million minutes of experience
10/28/2022 Understanding Cognitive Systems 23

IA Progression – Tool, Assistant, Collaborator, Coach, Mediator
Rouse & Spohrer (2018)
Siddike, Spohrer, Demirkan, Kodha (2018)
Araya (2018)
Spohrer& Siddike (2018)

Intelligence Augmentation (IA) =
Socio-Technical Extension Factor on Capabilities
• Engelbart (1962)
• Spohrer & Engelbart (2002)
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.

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
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)

0 25 50 100 125 150
Automobile
75
Years
50
100
Telephone
Electricity
Radio
Television
VCR
PC
Cellular
%
Adoption
Capability Augmentation and Adoption Rate Increases

Better Building Blocks
10/28/2022 Spohrer (ISSIP) 28
But how long would it take one person to rebuild everything from scratch? Teams of 10 students – future of university?
What goal to work towards? What augmentation goal to work towards?

What can one person know and make?

Brief History of Knowledge and Making: Personbyte and Peoplebyte

The IA Resilience Goal: Rapidly rebuild the machine
that can rebuild all machines from scratch?
Both Turing and von Neumann dreamed of universal machines and constructors

Learning to invest
• Run = Routine Activities
• Transform = Copy Activities
• Innovate =
Invent and Apply Activities
Innovate
Invest in each
type of change

33
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

AI Tools to Experiment with Today
• #1 Magic Eraser
• #2 Craiyon
• #3 Rytr
• #4 Thing Translator
• #5 Autodraw
• #6 Fontjoy
• #7 Talk to Book
• #8 This Person Does Not Exist
• #9 Namelix
• #10 Let's Enhance
Thanks to @TessaRDavis
for compiling this list: “Service providers
will not be
replaced by AI,
but service providers
who do not use AI
will be replaced by
those who do.”
Try at least two
from the list
as soon as possible
What do you think?
And Stable Diffusion
Every person in a role in an organization is a service provider.
10/28/2022

Who I track to learn about new AI tools to use
• Alan D. Thompson
• https://lifearchitect.ai/how-do-
i-talk-to-gpt/
• https://youtu.be/D3tTsoX02d8

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The International Society of Service Innovation Professionals
(ISSIP.org)
36

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(ISSIP.org)
37

10/28/2022
(ISSIP.org)
38

Read Wakefield
(2020)
enough to
understand what a
”digital twin” of
you might be like in
the future decades
with very advanced
AI capabilities.
Also see Rouse
(2018; 2022) ”Life
with a Cognitive
Assistant.”
AI Tools
in coming
decades…
10/28/2022

10/28/2022
(ISSIP.org)
40

10/28/2022
(ISSIP.org)
41

“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?

Concluding Remarks: Two Books

Better Models (Spohrer, Maglio, Vargo, Warg 2022)
• Increasing complex, interconnected world
• All models are wrong, some are useful
• Better models are needed
• of the world – both physical, social, virtual
(science)
• in people and win-win interactions (logics)
• of organization and win-win change (architecture)
• in technology (AI)
• Better models for better investing
• “We get the future we invest in, so responsible
actors must learn to invest wisely and
systematically in improved win-win interaction
and change.”

From Human-Centered to Humanity-Centered Design (Norman 2023)
• Human-Centered Design
1. Solve the core, root issues, not just the
problem as presented (which is often the
symptom, not the cause).
2. Focus on the people.
3. Take a systems point of view, realizing
that most complications result from the
interdependencies of the multiple parts.
4. Continually test and refine the proposed
designs to ensure they truly meet the
concerns of the people for whom they
are intended.
• Humanity-Centered Design
1. Solve the core, root issues, not just the
problem as presented (which is often the
symptom, not the cause).
2. Focus on the entire ecosystem of people, all
living things, and the physical environment.
3. Take a long-term, systems point of view,
realizing that most complications result from
the interdependencies of the multiple parts
and that many of the most damaging impacts
on society and the ecosystem reveal
themselves only years or even decades later.
4. Continually test and refine the proposed
designs to ensure they truly meet the concerns
of the people and ecosystem for whom they
are intended.
5. Design with the community and as much as
possible support designs by the community.
Professional designers should serve as
enablers, facilitators, and resources, aiding
community members to meet their concerns.

Preamble: On Value
• Service Science
• S-D Logic (Vargo & Lusch 2016)
• Service is the application of resources (e.g.,
knowledge) for the benefit of another
• Value … uniquely … determined by beneficiary
• Improvement processes for service system
innovation and value cocreation
• Learning to invest systematically to…
• Improve win-win interaction
• Improve win-win change
• Businesses as service systems
• Technology platforms, energy, investing, healthcare
• Nations as service systems
• Investments in upskilling people
• Top ranked global universities
• Population (+ capital-technology-wealth)
• A Brief History of Value
• Family (GDP of tribes)
• Cities (GDP of cities)
• Nations (GDP of nations)
• Businesses (GDP of AI-powered platforms that help
people interact, upskill, upenergy, upwealth,
uphealth, etc.)
Value cocreation is accelerated when large numbers of highly skilled people with advanced technology have a safe, ethical, and sustainable environment for win-win interaction and change.
Jim Spohrer (ISSIP) 48
9/2/22

“The best way to predict the future is to inspire the
next generation of students to build it better”
Digital Natives Transportation Water Manufacturing
Energy Construction ICT Retail
Finance Healthcare Education Government

10/28/2022 50
1955 1975 1995 2015 2035 2055
Better Building Blocks

Call to Action
• Responsible actors need to learn to invest wisely in
getting the future service innovations we want with AI
– guided by “Service Innovation Roadmaps (SIRs).”
Read enough of IfM and IBM (2008)
to understand what a “Service Innovation
Roadmap (SIR)” is – and who should be
creating them.
10/28/2022

Discussion
• Are you positive or negative about AI?
• If positive, are you using any specific AI tools today?
• See list of AI tools to try on a previous slide
• How are you investing in upskilling with AI?
• If negative, do you have a specific concern (“ditch to avoid”) – for example…?
• AI will take away my job
• AI will be used primarily by “bad actors” for mischief
• Or used by social media platforms to generate more clicks/attention thru angry
reactions
• AI will try to take over people and planet
• AI will deskill and weaken people over time
• … or other concerns about AI?
• Do you believe responsible actors (e.g., people, businesses,
universities, governments, etc.) are learning to to invest
systematically and wisely in getting the future we want? If not, why
not – what is needed?
• Join ISSIP.org (free for individuals) if you would like to continue the
conversation!
Read enough of pages 45-54 of Spohrer, Maglio, Vargo, Warg (2022) to formulate an
opinion on the topic of “investing wisely to get the future service systems we want.”
10/28/2022

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.
10/28/2022 (c) IBM MAP COG .| 53

Additional Resources
• Arthur WB (2019) Foundations of Complexity Economics. Nature Review Physics.
• Dietrich BL, Plachy EC, Norton MF (2014) Analytics Across the Enterprise.
• Donofrio N, DeMarco M (2022) If Nothing Changes, Nothing Changes: The Nick Donofrio.
• Fleming M (2022) Breakthrough: The Growth Revolution (in an Era of Artificial Intelligence and Worker Engagement).
• IfM and IBM (2008) Succeeding through service innovation: A service perspective for education, research, business and government.
• Larson RC (2022) Model Thinking for Everyday Life Working Wonders with a Blank Sheet of Paper. (Coming Soon).
• Lebovitz S, Lifshitz-Assaf H, Levina N (2022) To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis. Organization Science.
• Madhavan G, Poste G, Rouse W (2020) Complex Unifiable System. Editors' Note: Systemic Vistas. Winter 2020. The Bridge.
• Maglio PP, Kieliszewki CA, Spohrer JC (2010) Handbook of Service Science
• Maglio PP, Kieliszewki CA, Spohrer JC, Lyons K, Patrício L, Sawatani Y (2019) Handbook of Service Science, Vol II
• McDermid JA (2022) Safe, Ethical & Sustainable: A Mantra for All Seasons?
• Munn L (2022) The uselessness of AI ethics.
• Norman D (2023) Design for a Better World: Meaningful, Sustainable, Humanity Centered
• Rouse WB (2018) Life with a cognitive assistant. (2022) Emily 2.0..
• Rouse WB, Spohrer JC (2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation.
• Schneiderman (2022) Human-Centered AI.
• Spohrer J (2017) Imagination Challenge: Quantify and graph cost of digital workers and GDP per employee USA from 1960-2080.
• Spohrer J, Maglio, PP (2009) Service Science: Toward a Smarter Planet. In Service Engineering.
• Spohrer J, Maglio PP, Vargo SL, Warg M (2022) Service in the AI Era: Science, Logic, and Architecture Perspectives.
• US 110th Congress (2007) SEC. 1005. STUDY OF SERVICE SCIENCE.
• Vargo SL, Lusch RF (2016) Institutions and Axioms: An Extension and Update of Service-Dominant Logic. JAMS.
• Wakefield J (2022) Why you may have a thinking digital twin within a decade. BBC News Online.
• West S, Meierhofer J, Mangla U (2022) Smart Services Summit: Smart Services Supporting the New Normal.
• West S, Stoll O, Muller-Csernetzky P (2022) A Handbook for Smart Service Design - The design of Smart Services in a world of people, process and things.
• Wladalsky-Berger I (2016) The Continuing Evolution of Service Science. (2019) The Increasing Demand for Hybrid, “T-Shaped” Workers . (2021) The Supply Chain Economy - A New Categorization of the US Economy (2022) A New
Measurement Framework for the Digital Economy. (2022) Foundation Models: AI’s Exciting New Frontier.

ISSIP: Service Innovation and T-Shaped Adaptive Innovators Breadth
Depth
Breadth
Depth
Depth
Depth
Breadth
Breadth
Breadth
Depth
Depth
Depth
Breadth
Breadth
Breadth
Depth
Depth
Depth
Breadth
Breadth
Communications
Problem
Solving
Cultures
Systems
Disciplines
Cultures
Disciplines
Systems
Cultures
Systems
Disciplines
Cultures
Disciplines
Systems
Mindsets
Mindsets
Work
Practices
Advancing
Tech.
Work Practices
Advancing Tech.
Individual Expertise – T1, T3
Collective Expertise – T6
Augmented Expertise – T6
Role
Skills
Identity
Threatened Empowered
Service
Offerings
Service-for-Service
Exchange
Sustaining “mindsets” requires “like-minded”
Sustaining “innovation” requires “depth-diversity-inclusion”
Sustaining above requires “lifelong learning – interaction & change”
Sustaining people requires “rhythmic cycles” – breath, drink & eat, sleep, etc.
T1
T3
T6
I
We
I
We
Me
Me

5/18/22, 6:57 AM
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See Ricardo Martins - https://www.linkedin.com/in/ricardoalexmartins/

Future of Service Science
Smarter and Wiser Service Systems:
Entities transform to better future versions of
themselves by inventing win-win games and competing
for collaborators
Past Present Future
Organizational
Units
Family
Local Clan
Family
Business/Nation
Family
Platform Society
Change Individual
Generalist
(Breadth)
Individual
Specialist
(Depth)
Individual
T-shaped
(L)earners
Constant Competing for
collaborators:
win-win games
Competing for
collaborators:
win-win games
Competing for
collaborators:
win-win games
10/28/2022 (c) IBM MAP COG .| 59

60
What is a Responsible Service System Entity?
… customers/practitioners just name <your favorite provider>
… learners just name <your favorite role model>
Economics, Law, & Public Policy
Social & Behavioral Sciences
Design/
Cognitive Science
Systems
Engineering &
Human Factors
Operations
Computer Science/
Artificial Intelligence
Marketing
“a service system is a human-made system
to improve value co-creation interactions;
a dynamic configuration of resources
interconnected by value propositions.”
“service science is
the transdisciplinary study of
the evolving ecology of
responsible service systems entities
& their value-cocreation and
capability co-elevation
interactions.”
What is Service Science?
…researchers just name <your favorite discipline>
…educators just name <your favorite aha moment>

Future of AI
• What is the timeline for solving AI and IA?
• TBD: When can a CEO/anyone buy AI capability <X> for price <Y>?
• Who are the leaders driving AI progress?
• What will the biggest benefits from AI be?
• What are the biggest risks associated with AI, and are they real?
• What other technologies may have a bigger impact than AI?
• What are the implications for stakeholders?
• How should we prepare to get the benefits and avoid the risks?
10/28/2022 (c) IBM 2020, Cognitive Opentech Group 61

Who is winning
https://www.technologyreview.com/s/608112/who-is-winning-the-ai-race/

Robots by Country
• Industrial robots per 10,000 people by country
10/28/2022 IBM #OpenTechAI 63
34

AI Benefits
• Access to expertise
• “Insanely great” labor productivity for trusted service providers
• Digital workers for healthcare, education, finance, etc.
• Better choices
• ”Insanely great” collaborations with others on what matters most
• AI for IA = Augmented Intelligence and higher value co-creation interactions

AI Risks
• Job Loss
• Shorter term bigger risk
= de-skilling
• Super-intelligence
• Shorter term bigger risk
= bad actors

Other Technologies: Bigger impact? Yes.
• Augmented Reality (AR)/
Virtual Reality (VR)
• Game worlds
grow-up
• Blockchain/
Security Systems
• Trust and security
immutable
• Advanced Materials/
Energy Systems
• Manufacturing as cheap,
local recycling service
(utility fog, artificial leaf, etc.)

Predict the Timeline: GDP/Employee
(Source)
Lower compute costs translate into increasing productivity and GDP/employees for nations
Increasing productivity and GDP/employees should translate into wealthier citizens
Alistair Nolan (OECD AI for Science Productivity): “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.
Read Rouse & Spohrer (2018)
enough to understand this slide
including what ”exascale” means
10/28/2022

Write
Speak
Offer
Awards
Volunteers
ISSIP Platform Operations, Leadership Team, Supporters, Sponsors
International Society of Service Innovation Professionals (ISSIP.org)
“service innovations improve win-win interactions and change in business and society”
Join ISSIP to network with service innovation professionals and upcoming students,
while growing your knowledge sharing eminence about service systems/responsible actors,
in a non-profit professional association that promotes lifelong learning and T-shaped upskilling.
“everyone has something to learn, everyone has something to share”
Service – The application of resources (e.g., knowledge) for the benefit of another.
Service Innovations – Improvements in win-win interactions and change in business and society.
Win-Win– Service-for-service exchange, also called value co-creation interactions between actors.
Responsible Actors – individuals, businesses, universities, governments, also called service system entities.
Eminence – Professional reputation that comes from learning to invest more wisely in service innovations.
ISSIP helps members learn to invest more systematically in service innovations that matter.

Discovery Summit:
Future of
Expertise 1

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

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

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.

10/28/2022 (c) IBM MAP COG .| 75
Arthur, W.B. Foundations of complexity economics. Nat Rev Phys (2021). https://doi.org/10.1038/s42254-020-00273-3

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
improves
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)

Service Innovation 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

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