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The Impact of AI on the UK Workforce

Summary Notes of Event 

Event Description:

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

Prof. Veronica Hope-Hailey - Introduction:

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

Ravin Jesuthasan - Optimising Automation and Human Labour:

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 

Juani Swart - A knowledge-based perspective:

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.

Dionysios Kola: 


  • 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. 

Cihan Kurt - AI and Customer Service:

(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 

Vaggelis Giannikas - 

AI in Industrial Operations: 

  • 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.­­­

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