My slides for the "AI for Executives" program for Gothenburg University Executive Education in January 2024.

1. AI, productivity,
innovation and
sustainability
Dr. Robin Teigland
January 2024

2. Today
• How can AI drive productivity and innovation?
• AI innovation exercise
• AI for sustainability
• Challenges and risks with AI

3. Ungoverned incompetence
Occurs when leaders try to
make the right decisions yet
end up making the wrong
ones due to a lack of
competence (Cebon 2017)

4. The future?
Conway 2003
What we don’t know
we don’t know
What we know
What we know
we don’t know

5. The 30% rule
You only need about 30% fluency in a
few technical topics to develop your
digital mindset
to make sense of and make use of data
and technology and
to enable you to see new possibilities
and chart a path for the future.

6. AI
The frontier of
computational advancements
that references
human intelligence in addressing
ever more complex decision-
making problems.
Berente et al 2021

7. Future
value
creation
Political Economic
Sociological
Technological
Environmental
Legal
Security
PESTELS
Teigland 2024

12. Generative AI

13. Rutschi et al., 2023
Generic Exclusive
General model
Open data
Custom model
Open data
Open model
Confidential data
Custom model
Confidential data

14. Brynjolfsson et al., 2023
GenAI at work
+14%
Productivity

15. Dell’Acqua et al., 2023
GenAI at work
-19%
Productivity
+17-43%
Productivity

16. Henna
Hotel
Japan

17. AI loves three-legged chairs – Space10/IKEA

18. Moving frontier of next
generation computer
advances
SCOPE
PERFORMANCE
Rutschi et al., 2023
Decreased
productivity
Increased
productivity
Increased
productivity
Decreased
productivity

19. AI & building regulations
• Identify code violations
• Correct violations
• Monitoring and
inspection in real time

20. Urban planning with AI
Policy documents
Research
Building permits
Henhouse & Mindlabs, NL

22. The challenge

23. Lack of engagement at work
Costs employers
USD 8.8 trillion, or
9% of global GDP
https://www.gallup.com/workplace/393497/world-trillion-workplace-problem.aspx

26. Improving work design
through GenAI prompts
• Design high-quality work
that enhances positive team
interaction and collaboration
for sales team.
• Design work that is
meaningful to facilitate
employee acceptance of
technological changes in
their work.
Zhang & Parker, 2023

27. Innovation
Designing the
innovation process
and conducting
innovation steps
Designing AI-based
innovation tools to
augment humans or to
be autonomous
As AI tools do more of the creative part of knowledge work,
humans do the integrative sensemaking.
-Verganti et al. 2020

28. Product innovation process
1. Opportunity identification and idea
generation
2. Idea evaluation and selection
3. Concept testing and solution development
4. Commercialization and launch
Rogers 2003

29. Compound
prompting

30. AI innovation exercise
1. Find examples of recent AI innovations within your
industry.
2. Discuss the following:
– What is the innovation’s objective?
– What internal and/or external data were/are needed for this
innovation?
– What challenges are there to successfully implementing this
innovation?
– What did AI do versus humans?

31. A firm (organization) that is
successful at innovation
generally is successful
at Al deployment.
- Ringel et al., 2019

32. Innovation capabilities
Clear top-
management buy-in
Show that ready to redesign business models and end-to-end processes
across whole organization
Cross-functional,
diverse teams
A must-have to ensure the adoption of safe and beneficial technology
Strong feedback
loops
Ensure iterative development process in close connection with relevant
internal and/or external stakeholders
Innovation culture Develop a "succeed fast" approach to innovation that focuses on finding
unmet real needs
Training and hiring
programs with
innovation at core
Create an engaging work environment that enhances employee
experience, incubates ideas and encourages creative thinking
Torre, Engstam, & Teigland, 2020

33. The challenges of AI innovation
Data
Generativity
Responsible AI
X Innovation network
Partnership
Ecosysystem
Alone
or
or
or
Increasing
complexity

34. “Our starting point is not
ocean data but rather
ocean challenges and how
ocean data can address
those challenges. ”
Predict
spread of
invasive
species?

35. University
Public
sector
Industry
Global quadruple helix for data-driven innovation

36. Invasive species Dikerogammarus villosus
Can ML help us predict areas in
the Baltic Sea that would be
suitable for the Killer Shrimp?
Predicting spread of an invasive species
Presence validated

37. Predicting spread of Killer Shrimp
Datasets
1. Port locations in Europe (EMODNET
Human Activities)
2. Ocean surface temperatures and
salinity for Baltic Sea (SMHI) and
North Sea regions (SeaDataNet) as
well as general (Marine Copernicus)
3. Presence data of D. Villosus from
observations ranging from 1928-
2019 (GBIF)
4. Marine data layers (Bio-Oracle)
Features
1. Salinity levels (mean 0 - 2 m)
2. Surface Temperature
3. Depth
4. Substrates
5. Exposure (waves)

38. Our process
• Problem formulation
• Data collection
• Data preparation and
cleaning
• Building and fitting the
models
• Evaluating model
• Interpreting model output
• Continuing until output is
actionable
Time
spent

40. Predicting
harmful algal
blooms
GDPR

41. The challenges of AI innovation
Data
Generativity
Responsible AI
X Innovation network
Partnership
Ecosysystem
Alone
or
or
or
Increasing
complexity

42. AI innovation capability
• Ensure shared understanding of purpose and
motives behind AI innovation
• Enable an adaptable organizing vision
• Align perspectives of AI technology’s potential
• Align and realign AI innovation objective with
capabilities, data, and resources over time
• Ensure responsible AI
Why?
What?
How?

43. AI innovation ecosystem
Innovation
Strategy
Organizational
Structures
AI Skills
Exploit
Which partners fit
your strategy?
How important
are ownership
and control?
What skills do
you want to
build inhouse?
Explore
Closed Open
Inhouse External

44. Twin Transition – Key to Sustainable Development
weforum.org

45. http://www.regenvillages.com/

48. Personalized carbon footprint tracker

49. Blockchain-based
marketplace enabling
companies to purchase
carbon removal certificates
directly from farmers
practicing regenerative
agriculture.

52. AI as enabler and inhibitor of SDGs
Vinuesa et al., 2020
ICTs as % of
Global Energy
Demand
2020 – 1%
2030 – 20%

53. AI can be biased
wherever a human can be biased.
https://www.bloomberg.com/graphics/2023-generative-ai-bias/

54. AIRS - AI/ML Risks and Security
https://ai.wharton.upenn.edu/white-paper/artificial-intelligence-risk-governance/

55. Jail breaking, assisting scamming, data poisoning

58. Risk of a
more
narrow
solution
space
with AI

59. Gary Gensler, Chair of SEC

60. • Guiding AI Operational Capabilities
– Harvesting & analysis of big data in a data
strategy
– AI-driven innovation strategy
– Participation and growth in a business
ecosystem
• Supervising AI Governance Capabilities
– Data management, ethics, & black box
decision-making
– AI cybersecurity
– Participation and leadership in a business
ecosystem
https://www.amazon.se/AI-Leadership-Boards-Corporate-Governance/dp/B08XZ457K1

61. Robin Teigland
www.robinteigland.com
www.slideshare.net/eteigland
robin.teigland@gmail.com
Mafalda de Freitas
Circular Ocean
mafalda.df@gmail.com
www.circularocean.pt
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