澳大利亚生成式AI机遇-英

Australia’s

GenerativeAI

opportunityJuly

2023This

document

isintendedfor

general

informationalpurposes

only.

ThereportisacollaborationbetweenMicrosoft

andtheTechCouncil

ofAustralia.This

report

isacollaboration

betweenMicrosoft

and

Tech

Council

ofAustralia.Views

andopinions

expressed

in

this

documentarebasedon

thecompanies’

knowledgeandunderstandingof

itsareaofbusiness,

marketsandtechnology.

Thecompanies

donotprovidemedical,legal,

regulatory,

audit,or

taxadvice,

andthis

document

doesnotconstitute

adviceofanynature.

Whiletheinformationin

this

document

has

beenprepared

in

goodfaith,thecompanies

disclaim,tothefullestextentpermittedbyapplicable

law,

anyandallliability

for

theaccuracyandcompleteness

of

theinformationin

this

document

andforanyactsoromissions

madebasedon

suchinformation.Opinionsexpressedhereinaresubjecttochange

withoutnotice.Nopart

ofthis

documentmaybe

reproducedin

anymanner

withoutthewrittenpermission

of

thecompanies.

This

document

maymakereferencesto

thirdpartynames,

trademarks

or

copyrights

thatmaybeownedby

others.Anythird-party

names,

trademarksorcopyrights

containedinthis

documentarethepropertyoftheir

respectiveowners.Executive

Summary

(1/3)Generative

AIisalready

copiloting

workGenerativeAI

(GAI)representsasubstantialeconomicopportunity

for

Australia,

with

thepotential

toaddtensofbillionstotheeconomy

by2030.Toaccount

for

this

uncertainty,

wemodelthreedifferent

scenariosofadoption.In

thescenarioofslow-pacedadoption,

GAIcould

contribute$45Bannually

to

theAustralian

economyby2030.

In

themediumandfast-pacedscenarios,this

figurecould

be$75B

or$115B

respectively.Thisrangeisequivalent

to

2-5%

oftheAustralian

economy.Mostofthiseconomic

value

resultsfromimprovementsto

existing

industries,

with70%fromenhanced

labourproductivity

and

20%fromimprovedquality

ofoutputs.New

products

and

servicesdrive

theremaining

10%ofvalue.In

software

development,GAIcan

translatenaturallanguageinto

code,democratising

coding

skills.It

cansuggest

novel

solutions

tocoding

issues,allowingdeveloperstofocusmoreonstrategisingand

high-valuethinking.GAI,powered

bydeepneural

networkssuch

asLargeLanguageModels(LLMs),

enablesthecreation

ofnovel

content

and

contributes

toautomation,datacomprehension,

andanalysis.

Thisrapidly

evolvingtechnologycan

drive

economic

value

through

twomainchannels:improving

existing

businesses(through

productivity

andquality

gains)and

creatingnew

productsand

services.Forcreatives,GAIcan

streamlineworkloadsbyhandlingtasks

such

asimagegenerationorediting,

and

copygeneration.Thiscan

freethemupto

focusmoreoncreativedirection,

ideation

and

strategy.The

Generative

AIopportunityFirst,GAI

canimproveexisting

businessesbyautomatingrepetitivetasks

and

copiloting

complexprocesses,

leading

toimprovedproductivity

and

workquality.

Research

has

alreadyfound

GAIcodingtoolsreduce

task

timesby56%,1

and

writing

toolsdecreasewritingtimeby37%,

with

improvedquality.2Annualvalue-addedby2030Formarketingandsalesprofessionals,GAI

cancraftpersonalisedsalespitchesbased

oncustomerdataandpreferences,

createinteractive

product

demosand

providereal-timelanguagetranslation,

enhancing

customerengagement.$75B$115B$45BTobetter

understand

howeachoccupation

isimpactedbyGAI,weanalysed

datafromtheOccupational

Information

Network

(O*NET),which

provides

information

onthetasks

undertaken

byeachoccupation

in

theeconomy.Onaverage,

acrosstheeconomy,GAI

canautomate22%oftask-hoursand

augmentanequalshare.Automationoftasks

improvestheproductivity

ofworkers

byallowing

themtoproduce

more

in

anygiven

amountoftime.In

parallel,taskaugmentation,wherebyGAIactsasacopilot

(i.e.anexperthelperto

ausertrying

to

accomplish

acomplextask)

enablesworkers

to

producehigher

quality

outputin

thesameamountoftime.Formanagers,GAIcan

assiststaffcommunication,creating

training

materials,identifying

trendsin

employeesentiment,andanalysing

performancedata.Slow-pacedadoptionMedium-pacedadoptionFast-pacedadoptionForresearchers,GAIcan

helpthink

through

complexproblems

and

developframeworks

to

structure

researchprojects.

It

can

alsoassistin

writing

tasks

such

asoutlinecreation,word

selection,and

proofreading.Drivers

ofvalue10%20%Secondly,

GAIpavesthewayfor

innovative

productsand

services,suchasconversational

virtual

assistantsand

interactive

wearablehealthdevices.

Thisnewwaveofinnovation

can

leadtothecreation

ofnewindustries,

jobs,

and

economic

growth.Importantly,

movingfromthepotential

benefitsofGAI

to

actual,

realisedgainsdependsonarangeoffactors,including

how

useful

it

isforbusinesses,

how

thetechnologyisregulated

andsafelymanaged,andhowworkersaresupported

to

usethetechnology.

In

sizing

theeconomicvalue

ofGAI

to

Australia

by2030,

weneed

to

account

for

significantuncertainty,

particularly

around

howquickly

thetechnologycan

beeffectively

adopted.Productivity

gainsQuality

gains70%Valuefrom

newproductsandservices1.

Kalliamvakou,

E(2022).

Research:quantifying

Github

Copilot’simpact

ondeveloperproductivity

andhappiness.

2.

Noy

andZhang(2023)

Experimental

Evidenceonthe

productivity

effectsof

GenerativeArtificial

Intelligence.

Working

Paper.

MITCopyright

©2023

TechnologyCouncil

ofAustralia.

All

rightsreserved.Executive

Summary

(2/3)Exploring

potential

use

casesof

GAI

highlights

the

significant

valueitcan

unlock

acrossvarioussectors

ofthe

economyThe

second

section

of

thisreport

explorestheopportunity

ofGAIfor

Australia

through

moretangibleexamples.It

identifiesfour

key

sectors

whereAustralia

can

succeedincreating

valuethroughGAI.Theseopportunities

arehealthcare,manufacturing,

retailand

professional

services.IndustryThe

Generative

AIopportunityThesesectorswerechosenfortwokey

reasons.

First,they

arelikely

tocontinue

tobeimportantsectors

foremploymentand

outputin

theAustralian

economyinto

thefuture.

Second,GAI

islikely

tohaveatransformativeeffectonthesesectors.In

thehealthcare

sector,GAI

canenhance

thequality

and

accessibility

ofservices.Byreducingthetimeburden

ofadministrative

tasks,

healthcareprofessionalshavemoretimefor

patient-focused

care.Furthermore,theintegration

ofGAI

into

wearabledevices

canpersonalisehealthcare,enabling

proactivemodelsofcarethrough

earlierdiagnosesatscale.HealthcareAdditionally,

thesesectorsand

theirdiverse

usecasesforGAIillustrate

thebreadthofthetechnology’s

impactontheeconomy.Theselection

process

involved

desktop

research,industry

analysis,consultation

with

experts,

alongwith

engagements

with

executivesand

key

industry

personnelmorebroadly.In

manufacturing,GAI

could

usherin

an

era

ofnew,innovative

capabilities,

contributing

toAustralia's

strategic

focus

onadvanced

manufacturing.

Thistransition

would

strengthenAustralia's

reputationforproducing

high-quality,

technically-advanced

products.ManufacturingRetailImportantly,

thecontentsofthesesector

deepdives

culminates

fromconsultations

with

industry

experts,

and

aRoundtable

discussion

heldin

June

with

leadersfromindustry,

academiaandgovernments.Wethankall

whocontributed

to

thesediscussions.Retailindustries,

alreadyinvesting

in

omnichannel

capabilities

due

tothepandemic,couldintegrateGAIinto

existing

digital

platforms.Thiscan

drive

brand

differentiation

and

allow

greatercustomer

personalisation,all

while

maintaining

cost-competitiveness.Lastly,the

professional

services

industry

could

leverageGAItoautomate

routine

tasks,

freeingupahighly

educated

workforcetofocusonhigher-value

activities.

With

GAI,Australia

couldfurther

elevateitsstatusforhigh-quality

knowledgeworkers,

notablyin

thebankingand

legalsubsectors.ProfessionalservicesCopyright

©2023

TechnologyCouncil

ofAustralia.

All

rightsreserved.Executive

Summary

(3/3)Key

challengesKey

actionsCapturingtheeconomic

potential

of

GAIrequiresleveragingAustralia’scomparative

advantages

andstrategicactions

byindustryandgovernment.Australia

possesses

severalcomparativeadvantagesthat

canenableit

toseizetheeconomic

potentialofGAI.Theseinclude

ahighly-skilled

workforceproficient

in

datascience,engineering,

andcomputerscience

andarobustresearch

and

developmentsector.Additional

benefits,such

asourstrategic

location

closeto

Asia,astableand

transparentregulatoryenvironment,

and

athriving

start-upecosystem

provide

stronggrounds

forGAIdevelopmentandadoption.••Narrowing

themarginfor

errorFor

adopters:The

scale

of

investmentrequiredtobuildindustry-specificAIorchestrationsTechnologycapability1.

DefinetheGenerative

AI

opportunity2.

Assessreadiness3.

Investandexperimentwith

thetechnology4.

Develop

aResponsibleAI

governance

framework5.

UpskilltheworkforceAlongside

thesekey

strengths,therearealsokeychallenges.

Tocapturetheeconomic

benefitsofGAI,Australia

needsto

addressbarriersaround

technology

capability,

enterprisereadiness,awareness

and

skills,and

responsibleAI.Such

barriersinclude

thesignificant

investmentsrequired

to

build

AI

orchestrationstomeetspecific

industry

contexts,integration

with

existing

systems,

dataprotection,and

workforceupskilling.•••Deciding

toinvestLaunchinginternalAIgovernanceThe

speedof

changeEnterprisereadiness6.

Communicate

tocustomers

andstakeholdersBothindustry

and

governmenthavekey

rolesto

playin

addressingthesechallenges.

Industry

needsto

clearly

define

GAI'sopportunity,assess

readiness,invest

in

and

experimentwith

thetechnology,developprivacy

and

security

guardrails,

upskill

theworkforce,

andmonitorperformance.Meanwhile,

theAustralian

Government'sroleiscrucial

in

settingaclear

vision

forGAIin

Australia,

supportingcollaboration

between

researchinstitutions

andindustry,

providingregulatoryclarity,

incentivising

GAI

adoption,and

investing

in

theright

skills.•••BuildingessentialC-suite

knowledgeBuildingworkforce

digitalliteracyManaging

training

pathwaysFor

regulators

andpolicymakers:Awarenessandskills1.

Definethevisionfor

GenerativeAIinAustralia2.

Support

collaboration

betweenresearchinstitutionsand

industry3.

Provide

regulatory

clarity••••Developing

trust4.

Incentiviseadoption

andinnovationManaging

privacy

anddata

securityRegulatory

certaintyBytakingthesestrategicactionstogether,Australiacan

unlock

thetransformativepotential

ofGAI,

driving

economic

growthand

globalcompetitiveness.ResponsibleAI5.

InvestintherightskillsandsupportworkersthroughthetransitionManaging

intellectualpropertyCopyright

©2023

TechnologyCouncil

ofAustralia.

All

rightsreserved.Contents010203Generative

AIcanpower

economicopportunitiesin

Australiaworthtensofbillion

by2030071623Generative

AIcasestudiesshowcase

thepotential

tounlocksignificantvalueacross

theeconomyBytaking

strategic

action,Australia

can

leveragecomparativeadvantages

and

capture

Generative

AI'spotentialCopyright

©2023

TechnologyCouncil

ofAustralia.

All

rightsreserved.Generative

AIcan

power

economicopportunitiesin

Australia

worthtensofbillionsby2030Copyright

©2023Technology

Council

ofAustralia.

Allrights

reserved.Generative

AI

creates

novelcontent

inresponse

touser

promptsand

isbecomingmorepowerful

andaccessible

than

ever

beforeGenerative

AIisastep

changeintheevolutionofAIGenerativeAI

(GAI),thelatestevolution

in

artificialintelligence,

carriesthepotential

forsignificanteconomic

advancement.Theseinclude

thecommand

ofnatural

language,coding

and

mathematics,andthe

ability

to

plan

andproblemsolve​.2Rule-based

systems(1950s-1960s):While

thefull

economic

impactwill

takeyearstorealise,GAI

isalreadyimpacting

arangeofsectorsacrosstheeconomy.Thisreportaimstofocusspecifically

onhowGAI

could

drive

value

fortheAustralian

economy,and

identify

thesteps

needed

toseizethisopportunity.Alongwith

improvedcapability,

innovations

intechnologyhave

alsoled

to

increased

accessibility

ofGAI

by

reducing

costs.ArtificialIntelligence

istheability

for

amachine

toperform

atask

typically

requiring

humanintelligence.Furthermore,thedevelopmentofmoreuser-friendlytoolsand

interfaceshas

madeGAI

more

accessible

toawider

rangeofusers.Forexample,someonline

platformsallowuserstoeasilycreateandmanipulate

GAImodelsusing

drag-and-drop

interfacesor

intuitive

sliders,evenif

theyhavelittle

to

noexperiencein

machine

learning.

Notonly

hasthisdemocratisedGAI,improved

accessibility

createsawider

rangeofuse-cases

forbusinesses

and

workersacrossall

sectors

oftheeconomy.Historically,

thesetasks

havebeenlimited

topattern

recognition

and

processing,withimprovementsin

complexity

and

accuracydeveloping

overtime.Statisticallearning(1970s-1990s)GAI,asubset

ofartificial

intelligence,

usesmachinelearning

togenerate

human-like

content.

It

signifies

aconsiderable

transformationin

theeconomic

prospectsofAI

atlarge,byempoweringmachines

toproducenovel

content

or

data,previously

unseenorunimagined.Deep

Learning(2000s-present):Generative

AIisastepchange

frompreviousevolutionsofAI.Aswellas

recognisingcomplexpatternsand

processingdata,

it

can

create

novelcontentinresponseto

user

prompts.Recentimprovementsin

computing

hardwareandinfrastructure,

along

with

theavailability

oflarge-scaleand

diversetraining

datasets,

havebeen

instrumental

inenabling

thedevelopmentoflarger

and

morepowerfulGAI

modelsthan

ever

before.

One

ofthemostnotableinnovations

in

deeplearning

architectures

camein2017

with

theTransformer

architecture1,

whichfacilitates

parallelprocessingofsequencesandtheuseofattention

mechanismsfortrackinglong-rangewordrelations.This

innovation,

combined

withAdditionally,

modernGAI

modelsarealreadybeingfine-tuned

forspecific

usecases.This

hasmadeit

easierfor

developers,researchers,

and

businessestouseGAIin

their

applications

without

having

tospendtimeandGenerative

AI(2010s-present)Advancements

in

computing

hardware,infrastructure

and,mostnotably,deeplearningarchitectures

such

as

Transformers1

haveenabledthedevelopmentoflarger

and

more

capableGAImodels.LargeLanguageModels(LLMs)

pretrainedonextremelylargedatasetsgenerate

human-likeand

coherent

textand

canbefine-tuned

for

specifictasks.resourcesontraining

theirownmodels​fromscratch.Together,

theimproved

computing

andaccessibility

ofGAI

means

it

isalreadychanging

how

weworkandthewayfirmsoperate.

It

isaugmenting

human

workers

byacting

asacopilot,

increasing

productivity

and

qualityin

various

industries,

aswell

ascreating

newjobs

andbusinesses.advancementsin

optimisationtechniques,

hasfacilitated

thedevelopmentoflarger,faster,

and

moresophisticated

GAImodels.Foundational

largelanguage

models(2018-present)Asmodelsbecome

larger,they

developpowerful‘emergent

capabilities’

that

areonly

possiblewhen

themodelreachesacertain

scale.

No

longerlimited

tocompleting

narrowtasks

basedonanarrowrangeofprompts,

modernGAImodelscannow

perform

more‘generalist’functions.Such

‘foundational

models’provide

aplatformforapplications

tobebuilt

ontop,leading

toevenmoreusecases

and

wider

accessibility.Applicationsof

LLMs(2023+)Copyright

©2023

TechnologyCouncil

ofAustralia.

All

rightsreserved.Notes:

1.Vaswani,Aet

al.

(2017).

Attention

Is

All

You

Need.(Link).

2.Eloundou,

T.Manning,S.

Mishkin,P.

Rock,

D.

(2023)

GPTsare

GPTs:AnEarlyLookat

theLabourMarket

ImpactPotential

ofLarge

Language

Models.

(Link)Section1Generative

AI

modelshave

awiderange

ofapplications

and

cancreate

value

throughtheir

distinctive

capabilitiesAnoverviewof

Generative

AIGAI

cancreatenovel

content

in

response

touserprompts.

Thegenerationofthis

content

isprincipally

handled

byfoundational

modelssuchasLargeLanguageModels

(LLMs)

that

aredeepneural

networks.Thesemodelsarebuilt

onrobustcomputing

infrastructure

and

depend

onCloudlargedatasetsfor

training.

Toenhance

theaccessibility

and

usability

ofLLMs,reinforcement

learning

techniques

thatencouragehuman-like

responses

and

intuitiveinterfacesareintegral

componentsoftheGAIframework.Foundationalmodels(e.g.LLMs)Modelfine-tuning(e.g.RLHF1)infrastructureandcomputerhardwareNeural

networks&deeplearningInterfaces

&applicationsTrainingdataComponentsofGenerative

AIAsit

evolves,GAI

promises

to

deliver

immensevalue

acrossseveralfacets.Oneoftheseisautomation,whereGAIcan

expediteprocessesand

minimise

timespentonrepetitiveadministrative

tasks.

In

thecreation

domain,itcan

helpgenerate

new

ideas

in

areas

such

asproduct

design

andcontent

creation.

It

canalsoplayanadvisory

role,acting

asacopilot

guidingworkers

through

complex

issues.Furthermore,GAI

allowstheexploration,interrogation,

andsynthesisoflargedatasets,

leading

to

improveddatacomprehensionand

moreinsightfuldecision-making.Automating

Reducingtheneedfor

humanstoengageintime-consumingadmintasksCreating

Generatingnew

ideasinareas

suchasproduct

designandcontent

creationAdvising

Acting

as

copilots,

guidingworkers

through

complex

problemsValue

deliveredExploration

Enablesexploration,

interrogation

and

synthesis

of

large

datasetsfor

improved

understandingTextCode&dataImageAudio

&voiceVoice

synthesisPatientcareVideo&3DMultimodalmodels(can

processandoutputmultipletypesof

data)Marketing

&salesImagegenerationVideoeditingTypesofmodelsExample

usageCode

generationData

explorationWebsite

buildersandgenerationTheversatility

ofGAIis

underscored

bytherangeofits

models,extending

fromtextandcodegenerationto

thecreation

ofimages,

audio

andvoice,video,

and

3D

content.

Eachofthesemodelsushersin

unique

use-cases,

therebyemphasisingthefar-reaching

applications

andeconomic

benefitsofGAI.Knowledgemanagement3DmodelgenerationDesignSocial

mediacontentCustomersupportGaming&metaverseResearchNotes:

Reinforcement

Learningthrough

Human

Feedback

(RLHF)–process

used

to

further

refineandtrain

models

like

InstructGPTandChatGPT

(Link)Copyright

©2023

TechnologyCouncil

ofAustralia.

All

rightsreserved.Section1Generative

AI

isalready

creating

value

bycopiloting

different

typesof

workFivewaysGenerative

AIisalreadycopiloting

workThedeveloperThecreativeWhile

coding

companionsaren’tnew1,GAI-powered

companionssurpass

existingtechnologyby

being

able

tounderstand

acoder‘saims

andsuggest

an

entirely

newapproachtosolving

aproblem.This

allowscoderstodomore

high-valuethinking,includingcollaborating

withproductmanagers

tothink

carefullyaboutthedesired

end-user

experience.

Additionally,GAI

cantranslatenaturallanguagetocode,making

coding

askillsetavailable

toall.GAI

canhandlerepetitive

andtime-consumingtasks

–allowing

creativestofocusonhigher-leveltasks

such

as

creative

direction,

ideation,

andstrategy.

DALL-E,

anAIsystem

developed

byOpenAI,

cangenerate

high-quality

imagesbasedontextual

descriptions,

whichcanbeuseful

foradvertising,marketing,

andbranding.

AnotherexampleisCopy.ai,

anAI-powered

tool

thatcouldbeused

togenerate

human-like

textforads,productdescriptions,

andsocial

media

posts.Generative

AIassists

workers

intwo

key

ways.First,

byautomating

well-defined

and

highlyrepetitive

tasks,

GAIallows

workers

tospend

moretime

onthemorecomplexaspects

oftheir

jobs.Importantly,

this

islikelytoimprove

jobsatisfaction

for

all

workers.

Second,

GAIcanaugment

and

assist

workers

tocompletethesemorecomplextasks.

For

example,theability

tosuggeststep-by-step

problemsolving

instructions

means

GAIisguiding

workers

through

new

skills

and

new

ways

ofapproaching

problems.In2022,14%of

surveyed

creatives

were

already

usingGenerative

AIintheir

work3Users

of

GAI

coding

companions

complete

tasks

in56%less

time

than

non-users2TheresearcherThesalespersonThemanagerGAI

canprovidehyper-personalisedGAI

canhelpmanagers

stayintune

withtheirteamby

supporting

communication,

creatingtrainingmaterials,identifying

trends

inemployeesentiment,

andanalysingperformance

data.Additionally,

GAI

canallow

easieraccesstobusiness

intelligence,analysingcompanydatatoassistmanagers

incompletingrequest

forproposals(RFPs),

understanding

clients,

andassisting

resourceplanning.GAI

canbeavaluable

copilot

forresearchers.Through

interactive

conversations,GAI

modelscanhelpresearchers

thinkthrough

complexproblems

anddevelopframeworks

tostructureresearch

projects.

Furthermore,

GAI

canassistwith

writingtasks,

such

as

generating

outlines,suggesting

wordchoices,

andproofreading.Image-based

modelscanalsoincreasethesizeofdatasets,

by

creatingrealistic

synthetic

images(forexample,

ofbiological

structures)

thatassistresearch.intelligencetoimprove

customer

interactions.Thiscouldincludecrafting

personalisedengagements

based

oncustomer

dataandpreferences,

generating

engaging

marketingmaterials,creatinginteractive

productdemosandproviding

real-timelanguagetranslationduring

salescalls

with

non-nativespeakers.Thisleadstoamorestreamlined

andinteractive

experience

forthe

customer.AlphaFold2

has

predicted

the

3Dcoordinates

of

over375,000protein

structures,

significantly

acceleratingresearch

instructural

biology6GAI

conversational

assistants

help

customer

supportagents

resolve

14%more

issues

per

hour4GAI

tools

havebeen

shown

to

reduce

the

time

ofwriting

tasks

by

37%,withimproved

quality5Notes:

1.Oneofthe

first

Integrateddevelopment

environment

(IDE),Turbo

Pascal,wasreleased

in

1983.2.

Kalliamvakou,E(2022).

Research:

quantifying

GithubCopilot’s

impact

ondeveloper

productivity

andhappiness.

3.Shutterstock

(2022)Whatdo

creators

thinkaboutGenerative

AI.

4.Brynjolfssonet

al(2023)

Generative

AIatwork.

WorkingPaper.

NBER5.Noy

andZhang(2023)ExperimentalEvidenceonthe

productivity

effectsof

Generative

Artificial

Intelligence.

WorkingPaper.

MIT.6.Jones

DT,Thornton

JM.

(2022)

Theimpact

ofAlphaFold2

oneyearon.NatMethods.2022Jan;19(1):15-20Copyright

©2023

TechnologyCouncil

ofAustralia.

All

rightsreserved.10Section1InAustralia,

Generative

AI

can

deliver

economicvalue

byimprovingexisting

industriesandcreating

new

productsandservicesGenerativeAI

hasthepotential

to

deliver

significanteconomic

value

to

theAustralian

economythroughtwomajor

channels:

improvementsto

existingindustries

and

thecreationofnewproducts

andservices.Acrossall

industries,

such

augmentationand

theresulting

gainsin

quality

can

leadtoincreasedcustomer

satisfaction,

loyalty,

andretention,generatingaquality

‘premium’that

drives

value

to

theeconomy.Thetwochannelsof

economic

valuecreated

byGenerative

AITotal

economic

value

fromGenerative

AIImprovements

to

existing

industriesFirstly,

adoption

ofthetechnologyin

existingindustries

drives

higher

productivity

and

quality.Newproducts

and

servicesSecondly,

GAIcan

enablethedevelopmentofnewproducts

andservicesthat

werenot

previouslypossible,such

ashighly

personalisedcontent,conversational

virtual

assistants,andinteractivewearablehealth

devices.

Thiscan

leadt