NUS-ISS_Learning Festival 2025_From Overworked to Overjoyed after AI.pdf

This "think differently" track challenges conventional ideas about productivity, talent, and the role of human skills in an AI-driven world. Learn from our three notable speakers on the "2-Day Work Week", "First Empathy, Then AI for SMEs", and "Skills That Machines Can’t Replace", and wrap up with an insightful panel discussion on how AI reshapes work, ethics, education, and inequality. If you're ready to question old assumptions and reimagine the future of work, this session is for you. Join us to rethink what it means to thrive in an age of automation.

1. Main Title
•Subtitle / Subtopic
•Presenter’s Name(s)

2. Sudhir Tiku
Managing Director
KEENFINITY Group
Vice President for Asia Pacific
and China at Bosch Singapore
The Case for a 2- Day Work
Why the future of work needs a reset

3. Why the future of work needs a reset
The Case for a 2-Day Work

4. 1926

5. •Industrial Revolution: Factories required long, continuous hours.
•Henry Ford’s 5-day week in 1926 boosted productivity and sales.
•Post-WWII boom cemented 5 days as the norm.
•John Maynard Keynes predicted a 15-hour week by 2030 due to tech –
it never happened.
•Cultural inertia and legacy labor laws kept the system in place.
How We Got Here – The 5/6-
Day Work Week

6. •Work is no longer limited by physical machinery hours.
•AI, automation, and digital tools mean output isn’t tied to time spent.
•Burnout and declining mental health in knowledge workers.
•Mismatch between work hours and true productivity.
•Environmental costs of commuting and office energy usage.
Why the 5-Day Week Doesn’t
Fit Today

7. The Digital & AI Shift
•Knowledge work can now be done anytime, anywhere.
•AI tools can handle repetitive and analytical tasks instantly.
•Collaborative software enables asynchronous work.
•Global teams already function across time zones with less overlap.
•Focus is shifting from hours worked to value delivered.

8. Debunking Productivity
Myths
•More hours ≠ more output – studies show diminishing
returns after ~30 hours/week.
•4-day week trials improved output per hour by 25-40%.
•Shorter weeks reduced absenteeism and increased job
satisfaction.
•Creative work thrives with rest and variety.

9. Economic & Social Benefits
•Better work-life balance increases retention.
•Lower stress means lower healthcare costs.
•More free time fuels consumer spending in leisure sectors.
•Potential for job sharing to reduce unemployment.

10. Case Studies &
Experiments
••Microsoft Japan’s 4-day week
increased productivity by 40%.
••Iceland’s trials (2015-19)
showed stable or improved
productivity- 35 hours
••Sweden 30-hour week Trials
had impressive outcomes-zero
absenteeism,better job
satisfaction and productivity.

11. Why Not Go to 2 Days?
•If AI can double or triple
output, humans need less
time for same results.
•Two focused days + AI could
outperform traditional
weeks.
•Rest of the week for lifelong
learning, entrepreneurship,
and civic engagement.
•Could help address inequality
by freeing time for self-
improvement.

12. 01
Implementation Roadmap
02 03 04 05
Audit tasks to
identify AI
automation
potential
Shift to output-
based KPIs
instead of
hours
Pilot 2-day
weeks in
adaptable
teams
Use hybrid
models during
transition
Address policy,
contract, and
wage ,UBI, AI
tax.

13. The Two-Day Work Week —
What Humans Will Do
1.Personal Growth— Skill development, education, creative pursuits,
2.Health & Well-being — Exercise, Mental health, Better diets
3.Family & Community — Parenting, community work, local economies
4.Innovation & Ventures — Entrepreneurship, Grassroots projects
•Biggest Opportunity: Live deliberately with Tech & Social structure

14. The Future of Work
•The 5-day week was built for factories, not the cloud.
•AI gives us the freedom Keynes imagined – if we take it.
•We have the tech. We have the evidence.
•The question is: do we have the will?

15. Thank You!

16. Jatin Khosla
Co-founder and CEO of Konigle
Empathy First, AI Second for SMEs

17. Empathy First, AI Second for SMEs
Jatin Khosla
Co-founder & CEO, Konigle

18. All characters appearing in this talk are
fictitious real. Any resemblance to real
persons or machines, living or dead, is purely
coincidental surely intentional.

21. Almost every thing on the internet is agenda
driven, at least verify from 3 different
places before believing anything.

22. Please verify the previous statement too

23. And whatever I say next

24. Generative AI
The practical definition

29. Empathy
The practical definition

32. Leave something better than you find it.

33. Jimmy
The story of a flower merchant

37. Nico
The story of a restaurant

41. Red Roses
What is the point ?

44. Yes this is powered by LLMs, Agent
frameworks, RAG all of that and more - but
delivered over email.

47. PS
A confession to make

49. Ivan Ng
Chief Technology Officer
City Developments Limited
What's left for Us?
Human Value in the AI Era

50. What's left for Us?
Human Value in the AI Era
Ivan Ng
Chief Technology Officer, CDL

51. … when machines
can do almost
everything?
•AI free us from routine tasks, to solve humanity's
biggest challenges
•But when AI can write, code, design, and analyze,
what skills remain uniquely human?

52. The Shift in Work
Industrial Age
Value = muscle
Information Age
Value = knowledge
AI Era
Value = uniquely human abilities
•Machines replicate both “muscle” and “knowledge”
•Humans must find our unique “value”

53. Don't Compete With Machines
In an AI- first world, our humanity
is our greatest strength.
We cannot compete with machines on their terms.
Instead, we must find ways to
•Focus on skills machines can't replicate
•Use AI to amplify our humanity, not replace it

54. 3 Human Skills
That Matter

55. 1.
Questions
Ability to ask better
questions that challenge
existing assumptions
2.
Judgment
Navigating ambiguity and
making decisions with trade-
offs
3. Meaning
Creating purpose and
challenging assumptions
beyond patterns

56. Asking Better
Questions
AI gives answers.
Humans ask better
questions.
Breakthroughs come from questions
•Einstein: "What if I rode a beam of light?"
•Netflix: "What if DVDs were rented
online?"
Our edge = curiosity + reframing

57. Navigating Ambiguity
AI needs certainty. Humans lead in
uncertainty.
Real leadership happens in the grey zones where data
is incomplete, and stakes are high.
Examples
•CEOs making tough investment choices
•Doctors balancing medical treatments with ethical choices
Judgment, ethical trade-offs, and intuition
remain irreplaceably human.

58. Challenging Assumptions &
Creating Meaning
AI follows patterns. Humans break
them.
AI excels within established patterns, but
only humans can:
•Challenge fundamental assumptions
(Galileo)
•See beyond current paradigms (Kodak)
•Create meaning, values, and purpose
Meaning- making remains our uniquely human domain.

59. Our Call to Action
Embrace Your Human Edge
Cultivate
Curiosity
Ask questions that challenge the
norm to spark innovation
Develop
Judgment
Make decisions in ambiguity with
with ethical clarity
Create Meaning
Build a purpose that transcends
algorithmic thinking
As AI advances, these uniquely human skills will become more valuable, not less.

60. The task is, not so much to see what
no one has yet seen;
but to think what nobody has yet
thought, about that which everybody
sees
- Erwin Schrödinger

61. Thank You
[email protected]