Hosted by the Future of Work 

01/04/19 @ the University of Bath School of Management

Speakers: 

• 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

• Challenges:

  • 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

    • Ethical considerations.

• Answers?

  • Values and ethics

  • Wisdom and judgement

  • Education, learning and research

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:

  • Enabling infrastructure

    • Cloud-based storage

    • large amounts of data

    • processing power

  • power of convergence

  • speed of adoption

• Industrial Revolutions:

  • 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

  • 3rd– 60s-90s 

    • Nikefication

    • Democratisation of information

    • Tech enablement and use of web

    • Enables outsourcing

    • Companies as nexus of contracts

  • 4th– 2000s

    • Uberisation

    • Mobiles, sensors, machine learning

    • Companies as platforms

    • Work as activities 

    • Talent on demand

2 scenarios:

• 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:

  • Agency

  • RPA

  • AI

  • Volunteers

  • Platforms

• 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.

  • Substitute, or

  • Augment, or

  • Transform

• In terms of return on improved performance:

  • Failing the minimal standards have consequences that can be damaging to the whole organisation

    • E.g pilot.

  • 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.

• Activities will:

  • Be centralised

  • Shift to other roles

  • Be augmented

  • Be eliminated

  • Replaced by new ones

• Emerging pivotal skills:

  • Orchestration

  • Curation

  • Enabling 

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?”

​

• Impact:

  • On routine jobs

    • High

  • On knowledge-based jobs

    • Augment

  • Social jobs

    • Low to no impact -> future of work is here.

• 2 scenarios:

  • 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.

Definitions:

• 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

  • Reliability

    • E.g. can work 24/7, 

  • Cost benefit savings

    • Reduction of operational costs

    • Cost avoidance

    • Increase in sales

    • Shouldn’t replace all workers as this leads to being less competitive 

  • Increased productivity

  • Non-invasive tech

    • No disruption to underlying legacy systems.

      • E.g. banks.

  • Accuracy

    • 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

  • Chatbots 

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.

• Data includes:

  • Demographics

  • Transactions

  • Market research

  • 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.

  • Health chatbot

(3) AI in sales

• Used for analysis; to deconstruct sales channels; marketing

  • Optimise strategy

  • Redistribute resources

  • Calculate return on investment

  • Sales forecasting 

  • Dynamic real-time pricing

  • Next best action suggestions

  • Resource optimisation 

• 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.­­­