Hosted by the Future of Work
01/04/19 @ the University of Bath School of Management
Prof. Veronica Hope-Hailey, Dean of University of Bath’s School of Management
Ravin Jesuthasan, author of ‘Reinventing Jobs’ (2018)
Juani Swart, University of Bath
Dionysios Kola, Barclays Bank
Cihan Kurt, GSK
Vaggelis Giannikas, University of Bath
Example of the need to future-proof the school buildings:
There will be changes in education, studying, and even existence in the coming decades.
Nature of change from tech and AI will be fast; and will replicate or replace human skills
Need to think of replacement of tasks, not jobs -> people and relationships are the preserve of humans, regardless.
New jobs being created, e.g.:
IT home consultant
Auditors of AI
Drone operations, e.g. centralised lorry drivers
Managing the human transition
What happens to those left behind?
Brexit given as example of what happens when swathes of people don’t enjoy the benefits of globalisation and lash out against the system.
Manage the disruption of 5-10 years of experimentation
Disruption can be costly to business as they adapt.
Surveillance and security issues?
Implication for government regulation on AI’s role in society
Values and ethics
Wisdom and judgement
Education, learning and research
Prof. Veronica Hope-Hailey - Introduction:
Skills as the currency of the job market
‘Legacy’ was an asset and is now a liability ahead of the 4thindustrial revolution.
Tech growth has come about due to:
large amounts of data
power of convergence
speed of adoption
2nd– in the 19th-20th century
Assembly line model
Organise work into jobs
Jobs as careers
Amplification of labour
E.g. General Motors
Great cost, great certainty
Democratisation of information
Tech enablement and use of web
Companies as nexus of contracts
Mobiles, sensors, machine learning
Companies as platforms
Work as activities
Talent on demand
Human work becomes obsolete <-> Human work is constantly reinvented
Need UBI; Humans as consumers <-> Gigs, projects, tasks; workers evolving; reskilling as key to economic status and progress; access to reskilling essential
Employment relationship changing
Moving from selling products to selling subscriptions.
Jobs that were outsourced now being brought back in (e.g. customer service/call centre)
Regulation was fit for 3rd industrial revolution -> needs to be overhauled.
Regulation as reaction
Doesn’t look ahead.
E.g. China 15% of workers are gig workers. Wild West of regulation that is slowly changing and government is reacting by breaking up market concentration
Uber ‘flexible fund’ on top of cost for labour/gig to be used for e.g. pension or re-skilling.
Washington state rolling this out, extending also to New York.
Negotiated from a position of power rather than as a reaction.
Idea to give Facebook, and platforms like it, a license for 20 years of operation before it gets democratised.
It’s then up to them to stay relevant after
Automation is the tail that wags the dog of the organisation.
Example of staff laid off because system that was developed that automated case claims -> ultimately lead to loss of customers and ex-employees were hired back as contractors
Automation will bring new combinations of work, talent, skill requirements, and work relationships.
E.g. no-code AI: don’t require python developer
Enables non-data scientists/developers to make use of machine learning.
Over the next 3 years:
Organisations redesigning roles for those with more skills (45% of organisations) and lower skills (42%)
Free agent work to rise by 50%
Full time employees set to reduce from 83% to 77% globally.
Skills passports will be used to track the skills gained over time.
Universities may increasingly offer re-skilling opportunities for life.
There’s a need for middle managers to orchestrate and coordinate between multiple facets of an organisation:
Need to deconstruct jobs and see where automation fits in. Enablers of automation include:
RPA (Robotic Process Automation) -> routine high volume
Cognitive automation -> non-routine creative
Social Robotics -> routine collaborative
Automation as augmentation.
Deconstruct and thenautomate.
In terms of return on improved performance:
Failing the minimal standards have consequences that can be damaging to the whole organisation
Being a superstar, innovator and creative makes you more productive than being ‘good’
E.g. creative programmer.
Case study of oil and gas.
Replace task of ‘motor hand’ which involves laying pipes on an oil rig
CEO has aim of 100% retention of workers
Solution is to re-skill motor hand worker as a rig technician:
Set up, calibrate, and maintain the machine.
However, people might be resistant to reskilling; unwilling to be schooled/educated.
Perhaps lower skilled jobs taken by those who were originally unhappy with or hostile to the education system.
Shift to other roles
Replaced by new ones
Emerging pivotal skills:
Ravin Jesuthasan - Optimising Automation and Human Labour:
How our jobs evolve with AI/automation.
There is data that jobs will be lost and data that jobs will be replaced.
Social care and education faces labour shortages
AI will impact on jobs, not replace.
Need to re-draw knowledge boundaries
“what do we do?”
“how do we do it?”
On routine jobs
On knowledge-based jobs
Low to no impact -> future of work is here.
1: Everything stays roughly the same but with new tech that is disruptive and augments work.
2: Shift to experience economy, much more disruptive effect of automation/AI.
E.g. health, beauty, travel, care, etc.
Juani Swart - A knowledge-based perspective:
Process understood as activity that transforms an input to an output.
Automation as deploying the digital workforce to do time-consuming and mundane tasks; physical workforce to focus on value added activities.
RPA: configurable software that does the task assigned to it by the user; automate by imitating a humans interaction with an application.
Workflow automation: enables discovery, modelling, rapid change, governance and end-to-end visibility of processes; process visualisation and basic team management, point to point management.
AI/Machine Learning: uses experience to achieve expertise in a domain.
Doing <-----> Thinking
Rule-based <-> jugement-based
Robotics <-> ML/AI
Benefits of implementing automation
Data analysis and insights
E.g. can work 24/7,
Cost benefit savings
Reduction of operational costs
Increase in sales
Shouldn’t replace all workers as this leads to being less competitive
No disruption to underlying legacy systems.
Less prone to manual errors
Increased employee satisfaction
Less menial, repetitive tasks
Uses (case study of banks)
Price-scanning and setting of e.g. bond prices, faster.
Contract comparison: check if contract is standard, freeing up lawyers
Background checks and risk assessment
Monitor and prevent criminal and fraudulent activities
Key take away is to stay relevant and be resilient.
(1) understand your customer
move from return on investment -> return on experience
we’re living in an age of personalisation
Need to consult what the data tells us.
Social media engagement
Descriptive (what’s happening?) àDiagnostic (why is it happening?) àPredictive (what’ll happen next?) àPrescriptive (what do I need to do?)
(2) AI in interactions
Used to target; predict; recommend; personalise.
E.g. Natural Language Generator
Turns a set of data into natural language text that summarises the data in an easy to read way.
(3) AI in sales
Used for analysis; to deconstruct sales channels; marketing
Calculate return on investment
Dynamic real-time pricing
Next best action suggestions
Cihan Kurt - AI and Customer Service:
AI can help in analysis and decisions– sits there mostly
Lots of people work in sense and act
Tech can help in this domain, AI less so
AI understood as decision-making software
A lot of Industrial Ops is not decision-making
AI is not automation, though they do go together.
It’s worth understanding what digital things can and cannot do.
Vaggelis Giannikas -
AI in Industrial Operations: