ai and the future of work · linear career path/ experiences based on skills required by job...

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AI and the Future of Work

George Zarkadakis

25 October 2019

Life2019

© 2019 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers

Watson and Willis Towers Watson client use only.


What I will cover today

1. Quick introduction to AI

2. The risks of AI

3. Deep dive: impact of AI on work

4. Impact of AI on business organization and culture

© 2019 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.


Deep

Learning

Machine

Learning

Artificial

Intelligence

Computer

Science

Artificial intelligence (AI)

A general-purpose technology that will define the 4th Industrial Revolution



The 3 things AI can do for you


Automate tasks and

processes

Develop new products

& services

Gain new data insights

01 02 03


$0

$2

$4

$6

$8

$10

$12

$14

$16

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Quality Time Saved Personalization Labor Productivity

The AI Opportunity

AI will add $15 trillion to the World Economy by 2030

AI’s Projected Impact on Global GDP

Trillions of Dollars



The risk of not getting AI right

Companies are likely to fail

if they do not adopt AI quickly enough


Front Runners: +122%

Followers: +10%

Laggards: -23%


AI risks

Only 16% of companies are currently gaining some value from AI.


Risk Description

Regulatory GDPR (algorithmic explainability, data security, right to be forgotten, etc.)

Cybersecurity AI accesses sensitive data – and can be “fooled” by hackers

Reputational & Legal • Algorithmic and data bias may negatively impact CX, EX and brand

• AI failures may result in lawsuits

Data • Inadequate, or inexistent, data strategy

• Lack of effective data architectures

• Lack of adequate data sets, or inability to access the right data sets

Talent • Inability to attract, develop, and retain scarce and critical talent

• Impact of automation on work and skills

Organization & Culture • Leadership not understanding enough the risks and potential of AI

• Not having the right strategy for AI; and the necessary commitment to accept many

failures before achieving success

• Lack of sound AI (and data) governance

• Lack of organizational agility

• Wrong behaviours and mindset


Automation, augmentation, transformation, and the battle for skills

The need for a new operating model to win in the AI economy



Shifting from “jobs” to “work”: the role of AI


▪ Employee

▪ Single job

▪ Linear career path/ experiences based on skills

required by job (demand)

▪ Technology supports people in jobs

Ke

y A

ttri

bu

tes

▪ Internal and external talent

▪ Jobs, work tasks, projects

▪ Variety of experiences based on skills required for

work (demand) and skills and interests of

person (supply)

▪ AI becomes a “work partner” to complete repetitive

cognitive work/tasks

Ke

y A

ttri

bu

tes

Supporting

Technology

New relationship

AI &

Automation

Traditional relationship

Person Job Talent Work


Deconstructing jobs into work tasks: the future of underwriting

Current underwriting work task allocation


Underwriting

Read

Assess

Input Data

Evaluate

Update

Chase

Allocate

RespondMonitor

Approve

Bind/Book

Complex

Judgement

Quote

Interact/

Converse

Negotiate

Fulfil/Issue

Quality

Check

Decide

Identify

Retrieve

Data

Research

Instruct

Estimate Max

Loss

Relationship

Management

Establish

Needs

Select

Illustrative


Deconstructing jobs into tasks: the future of underwriting

Underwriting task allocation with RPA and Artificial Intelligence


Illustrative

Transform

Interactive, Variable

Automate

Repetitive, independent

Augment

Independent, Variable

Read

Assess

Input Data

Evaluate

Update

Chase Allocate

Respond

Monitor

Approve

Bind/Book

Complex

JudgementQuote

Interact/

Converse

Negotiate

Fulfill/Issue

Quality

CheckDecide

Identify

Retrieve

Data

Research

InstructEstimate

Max Loss

Relationship

Management

Establish

Needs

Select

Underwriting Underwriting & RPA Underwriting & RPA & Artificial Intelligence


Matching skills to work with a talent marketplace


I have skills and

interests that extend

beyond my job

requirements – what

are my potential paths?

“

My Skills My Needs(technical skills enabling

skills, experiences,

attributes, etc.)

(interests, ambitions,

circumstances)

+

Da

ta S

ou

rce

s

Résumé

Talent Profile

Assessments

Succession Management System

HRIS

Performance Management System

Career Development System

How do we identify and

optimize the match of

talent to work based on

peoples’ skills and will?

“What’s Required

Da

ta S

ou

rce

s

Learning Management System

Talent Marketplace

Job Postings

Job Requirements

HRIS

Project Postings

Business

INPUT

OUTPUT

Matching

Algorithm

Optimal pathways to close business

and individual gaps

Here is the degree of fit

of various individuals in

meeting the various

business needs

Here are the types of

work, positions, etc.,

that are going to be

most engaging for you

In order for you to get

there, here's what you

need to do (L&D plan)

Individual OutputBusiness Output

Job

Experiences/

projects

Gig Participation

in decision

making/

initiatives

Tasks


Work Architecture – UK FTSE100 Insurer

Solving for attracting, developing, and retaining critical talent


Senior Data Scientist

Accounting

Data Mining

Equity Funds

Derivatives

Excel VBA Market Indices

Fraud

Statistics

Pig

Pricing

Claims

PythonCustomer

Analytics

SQL Fraud

Aladdin

Data Mining

Derivatives

Real Assets

Excel VBACustomer

Analytics

SQL Pricing

Statistics

Data Mining

Pricing

Claims

PythonCustomer

Analytics

SQL Fraud

01Generic role on Workday,

including required Skills

and Domains

02Potential candidates identified on Workday that

have matching Skills & Domains

Skills Domains Skills Domains Skills Domains Skills Domains

Candidate selected for new role based

on significant Skill & Domain overlap03

Skill / Domain

matchKey =


Reskilling the workforce

Using a work architecture to operationalize continuous reskilling


Peter

From oil rig worker to data

dashboard analyst

Automation of offshore oil rigs through AI,

robots and IoT:

Peter had to retrain to read data

dashboards and use his expertise to train

maintenance algorithms and handle

exceptions.

From bank teller to digital factory

product owner

Bank moved to cloud offering mobile only

customer service:

Imani retrained as Scrum Product Owner

and uses her customer service experience

to manage digital product backlogs.

Imani


AI needs organizational agility. So something needs to change…


Chief Idea

Killer Officer

VP

of No

VP

of Status Quo

VP of

Stay the Course

Director of Onerous

Reporting

Director

of Bureaucracy

Director

of Rigidity

Director of

Analysis Paralysis

Manager of New

Growth Ideas (Vacant)


Deconstructing the organization: from silos to platforms and ecosystems


Hierarchies and departmental silosNetwork of loosely coupled full stack

business/software teams (“Micro-businesses”)


Getting AI governance right with cross-disciplinary teams

How to apply systems thinking in reinventing business organization


Business Design

Design Thinking

Tech

From this: To this:

+ +

Tech


AI requires a cultural transformation


AI-empowered workplace Behaviours and mindset

Agile, real-time work from anywhere Best idea wins

Small teams, rapid iterative innovation Extreme customer responsiveness

Extended, Networked Enterprise (Ecosystems) Partners and contingent workforce

embedded in the business

Data-driven decision making Decisions moving to the edge –

team empowerment

AI-driven automated workflows Augmented problem-solving ability

Open platform for collaboration and innovation Security and Privacy embedded in

infrastructure and workflows


Thank you


@zarkadakis

Books on AI and its impact on work by Willis Towers Watson associates



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ai and the future of work · linear career path/ experiences based on skills required by job...

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