What is the Future of Work? AI or Robot? According to a study by McKinsey & Company up to 30% of tasks across more than 60% jobs can be automated with technology available today but there are less than 10% jobs where more than 90% tasks can be automated. Which basically means that technology will impact nearly all the jobs out there at varying degrees and the overall impact is much more on jobs getting modified than the number of jobs getting eliminated or created. So, how can companies prepare for this is impending transition?

1. Future of Work: Human or AI?
11th September, 2019 | Singhal, Abhinav
The AI Summit, Singapore

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Sources: McKinsey Global Institute report: A future that works, World Economic Forum, Boston Consulting Group, fortune.com
62% of occupations have at least 30% of
their activities that are automatable
35% of the skills demanded for jobs across
industries will change by 2020
56% of jobs in ASEAN-5 are at risk of
automation over the next 20 years
85% of jobs at risk due to impact of
automation on developing countries
“Automation could replace
40%of jobs in 15 years”
- AI Expert Kai Fu Lee
All global think tanks talk about risk of job losses in future

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1. Is it Hype or Reality?

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Technology has already changed our personal lives a lot
Morning
Day
Evening
• Grab driver
• Machine learning engineer
• Driverless car engineer
• Drone operator
• YouTube content creator
• Social media manager
And created jobs which
didn’t exist 10 years ago
Source: World Economic Forum, Catalant Consulting

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1. Time spent by US workers on activities that require median or higher levels of human performance for each capability
Source: McKinsey Global Institute analysis based on 800 occupations and 2,000 activities across all occupations; Softbank’s Pepper Humanoid
Most of the tasks at work utilize five types of skills
Sensory
Cognitive
Natural language
processing
Social & emotional
Physical
Sensory perception
Natural language generation
Recognizing patterns/optimization
Information retrieval
Gross motor skills/navigation
Social and emotional sensing
Output articulation/presentation
Natural language understanding
Category Example – Retail Salesperson
• Seeing and greeting customers
• Demonstrate product features
• Make recommendations (e.g., we
don’t have black, but try purple it will
suit you)
• Process sales transactions
• Listen to customers
• Answer questions about products
or services
• Build rapport with customers (using
body language, tone)
• Clean, maintain work area
• Use storeroom, refill shelves
Skills

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Technology is already mature to replace few of these skills
Skills
Sensory perception
Natural language generation
Recognizing patterns/optimization
Information retrieval
Gross motor skills/navigation
Social and emotional sensing
Output articulation/presentation
Natural language understanding
46
35
13
17
41
67
36
15
Impact1 (% of all activities)
Top quartile Median Below median
Maturity of
technology today
Sensory
Cognitive
Natural language
processing
Social & emotional
Physical
Category
1. Time spent by US workers on activities that require median or higher levels of human performance for each capability
Source: McKinsey Global Institute analysis based on 800 occupations and 2,000 activities across all occupations; Softbank’s Pepper Humanoid

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1. Baidu’s new supercomputer
Source: Press search
Software LipNet deciphering lips with 93.4%
accuracy, compared to 52% achieved by an
experienced human lip reader
Recognizing picture library Lip reading
Computers1 able to recognize 97.3% out of 1,000
images, meanwhile humans only recognized 94.9%
Other examples: AlphaGo, DeepStack (poker), DeepCoder (coding), GoogLeNet (cancer)
Or even surpass humans in few skills

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Source: Press research, Tesla
But in some cases, also struggling with over-automation
• Tesla’s Model 3 line was designed to be fully
automated with 1,000+ robots
• Missed 5,000 vehicles a week target in Q1’18
• Had problems in final assembly, which needed
flexibility as Humans could better spot things that
wasn’t right, stop & fix it
• Finally, built a whole new production line hiring 400
employees a week to turnaround

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Source: Deloitte, McKinsey, WEF-BCG Report, Team analysis
So, what is the right solution? #bestofboth
• Displaying empathy and humor
• Flexible, adapting skills to different situations
• Understanding context, picking up cues
• Critical thinking, defining problems and creative
pursuits
• Refine motor skills and dexterity
• Performing repetitive work
• Working in predictable, structured environments
• Processing huge data with speed and accuracy
• Working 24/7 without breaks or distractions
• Gross motor skills, doing dangerous work such as
underwater rescue or bomb disposal
• Care Providers/Counsellors
• Hairstylist
• Creatives
Examples
• Data entry, Accounting
• Warehouse Stockist
• Bomb Disposal
Examples
<20% >80%
Degree of automation
Humans are better in… Machines are better in…

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Source: McKinsey Global Institute report: A future that works, Team analysis
Without impact
of automation
ModifiedEliminated Created After impact
of automation
-9%
>1,500 mn 300-365 mn400-800 mn2,930 mn 2,660 mn
Workforce count projection for 2030 (in mn workers)
Work will be eliminated, created and modified
Overall nearly all jobs will be impacted –
Less than
10% overall
impact

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2. So, what can we do today?

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Our Learnings: Follow a structured approach
• What are the workforce
roles and/or skills needed
to execute on our future
strategy?
• What is the skills gap
between today’s
workforce and the future
workforce?
• How do we utilize hiring and
re-skilling to meet future
requirements?
• What will be the future mix of
permanent and temp
employees?
• How do we release
individuals, or re-deploy?
• What initiatives do we
need to change our
workspace, workforce and
work culture?
Diagnose Design Deliver
Assess current state and
future needs
Design transformation program
Implement, scale,
and sustain change
1 2 3

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Identify initiatives to implement change at scale
Source: IDC Survey, thyssenkrupp
Work space
Work force
Work culture • Borderless, collaborative and innovation
focused
• Foster a ‘life long learning culture’ by making
learning accessible, affordable and
omnipresent
• Mix of physical and virtual co-working spaces
• Enable work-from-anywhere concept
• Flexible configurations, modern design and
naturally interactive technologies
• Attract and retain digital natives (GenZ,
millennials) in the company
• Intelligent machines and humans co-working
together
• Engage freelancers, gig workers
…majority works in data
science, AI, behavioral etc.
Companies in 2030
…only 5 of them are full
time staff
For every 10 employees
…1/3rd will not come to
office
… at least 7 will be mil-
lennials or Generation Z

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‘Future of Work’ is
#bestofboth
Most significant impact is not on number
of jobs but rather on job content
Re-skilling is the biggest enabler to meet both
current and future digitalization needs
Takeaways

15. THANK YOU !
Abhinav Singhal
Chief Strategy Officer
thyssenkrupp, Asia Pacific
https://www.linkedin.com/in/absinghal/
abhinav.singhal@thyssenkrupp.com