【会议演讲PPT】Gartner+生成式+AI+现实：可量化业务成果的主动方法

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Webinars

Gartnerdeliversactionable,objective

insight,guidanceandtoolstoenable

strongerperformanceonyour

organization’smissioncriticalpriorities

ConnectwithGartner

GenerativeAIRealities:Proactive

ApproachesforQuantifiable

BusinessResults

FrancesKaramouzis

DistinguishedVPAnalyst

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Experimenting

Investing

Learning

~30%ofOrganizations

“WearetestingGenAIinfivekeyareasanditeratingaswego…”

CHRO,FinancialServicesCompany

~10%ofOrganizations

“We’reseeinggainsfromapplyingitinourfrontlineandwe’relookingtoexpand…”

D&ALeader,SoftwareCompany

September2023Update:GenerativeAI

~60%ofOrganizations

“Wehaveformedacentralcommitteethatisinvestigatingwherewecanuseitandtheassociatedrisks…”

CIO,FederalGovernmentAgency

Protecting

AllOrganizations

“WeareseekingtoprotectourdataassetsandIPfornow…”CFO,InformationServicesCompany

Source:Gartner

Note:PercentageoforganizationsderivedfromGartnerclientconversations,GartnerclientreadershipofGenAIcontent,andvarioussurveysconductedin2023withGartnerclients.

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MostPrevalentUses

developmentoftext,images,videoetc.MarketingTeamsare

generatedby2026.

increasingspeed,productivityforcontentgenerationfor

GGeneratingContent-“Creative”content

personalization&demographicsegmentation.—Europol

E

N

EngagingStakeholders–Customers,prospects,suppliers,thirdpartiesofall

types.Theusegenerativetopersonalizetheinteraction,offer24/7contactoraccess,addressvolume,reducecostandcreateadifferentcustomerexperiences.Engagewithother

“machinesascustomers”isalsobeingexplored.Goalwouldbetoadddigitalcustomervalueatzeroormorelikelylimitedmarginalcost.

NettingitOut-thefocusisonCLARITYratherthancreativity.Afocusonanalyticsand

decisionintelligencethroughthecombinationandsynthesisofdata,allinserviceofnew

approachestodatadrivendecisionmaking.Theoverallfocusistoreducethelatencytimeofenterpriseaction.

Technologies–Softwaredevelopment,testing,infrastructureandoperationsareallareaswheregenerativeAIisbeingapplied

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SCurvesExplained

Time

Disruptions

Transformation

Optimization

Trigger

Disruption

Time

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Disruptionfrequency

Time

Disruptions

1

Activity

Time

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More

1

Activity

FrequentDisruptions

Time

Disruptions

Time

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EnterpriseAdoptionLags

Time

Disruptions

BELOWInnovationAvailable

1

Activity

!

Enterprise

AdoptionCurve

Time

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<15%

Activity

Enterprise

SmallSliceofEnterprisesWillBe

Time

Disruptions

FullySuccessfulEarlyAdopters

AdoptionLevel

Time

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DisruptionandUncertaintyincreases

Businessasusual(wait&see)approachis

riskierthaninvesting

Organizationsthatleadthroughturbulencewithintentionwillbethemostsuccessful

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Gen

AI

SpectrumofGenerativeAIIntiatives

“Hybrid"

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Buy

Gen

AI

RangeofApplications

Targeted&EmbeddedOptions

Horizontal

CustomerService

Mktg

Sales

Procurement

Finance

HR

KnowledgeMgt

IT

Media

Vertical

CPG

LifeSciences

FS

Insurance

Healthcare

Retail

12

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"Build"

Gen

e

AI

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Category1

Category3

Category4

Category5

Targeted

Situational

Build

Horizontal

&Vertical

MarketMaker

Build

Risk

SpectrumofGenAIintheEnterprise

Category2

Embedded

$$

$$$$

$$$$$

$$$$$$$$

Cost

+++++++

14

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Category1:TargetedTaskSpecific

Category1

Targeted

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Options

OpenAI

ChatGPT

OpenAIGPTPlayground

Microsoft

BingAI

Chat

GoogleBardAI

ClaudeAI

PerplexityAI

YouChat

PoebyQuora

Chatsonic

~$20PerUserPerMonth

X12Months

X40Users

=$9,600Annually

+Training/CoP

+Governance/Security

Category2

Embedded

RangeofPotentialSpendofUplift*

$50K~$500K

*Upliftreferstoadditionalspendaboveandbeyondthe

licensinginitiallyspentfortheproduct(systemofrecord)

Category2:Embedded/ProcessSpecific

IllustrativeList

Salesforce

Workday

SAP

Oracle

ServiceNow

By2026,morethan80%ofindependentsoftwarevendors

(ISVs)willhaveembeddedgenerativeAIcapabilitiesintheirenterpriseapplications,upfromlessthan1%today.

16

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ExtensiveRangeofPotentialSpend

duetolargespectrumofproducts

~$250Kto$1Mil

+Design,Implement,Manage&Operate

+Governance/Security

Category3

Horizontal

&Vertical

Category3:Horizontal&Vertical

Horizontal

Mktg

Finance

Sales

HR

CustomerService

Procurement

IT

KnowledgeMgt

Media

Vertical

FS

Insurance

Healthcare

Retail

CPG

LifeSciences

Tool:VendorIdentificationfor

GenerativeAITechnologies

17

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ManyExamplesofCategory3

Tool:VendorIdentificationforGenerativeAITechnologies

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OVER500VendorOfferingsinToolkit

Tool:VendorIdentificationforGenerativeAITechnologies

19

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ApplicationPrompting

SystemPrompting

Model

Orchestration

FineTuning

Layers

Examples

“Build”CategoriesofGenerativeAISpectrum

Inputs

UserorProcess

DrivenInputs

Configuration,History,

Context,Prompts

ProgrammaticChainingofModelInputsandOutputs

SpecializedTrainingData

MassTrainingData

Model

SoftwareOptional

Deliverable

Deliverable

Usertextentry,structured,

data,images

Context,persona,

outputexpectations

(format,length,style,tone)

Iteration(Generatesubtasks,thenresponsesforeach),Combination(Speech->Text->Image)

Companyknowledgebase,

SpecializedImagecollection,

Businessdata

Model

Variants

Text:CommonCrawl,

OpenWebText2,Wikipedia,

WordNetImages:LAION,COCO

FoundationModel

SystemPrompting

Model

Orchestration

FineTuning

ExtensiveBuildingBlocks

DecisionIntelligence

Application

Prompting

EnterpriseSpecific

MLOps

ModelDeploy

ModelAPI

VectorDB

Models

FineTuned

Domain

Foundation

Compute

Network

Storage

ExtensiveBuildingBlocks

By2028,morethan50%of

enterprisesthathavebuilttheir

ownlargemodelsfromscratchwillabandontheireffortsduetocosts,complexityandtechnicaldebtin

theirdeployments.

Application

Prompting

System

Prompting

Model

Orchestration

Fine

Tuning

SpectrumAcrosstheEnterprisePortfolio

$10K~

50K

Category1

Targeted

$50K~

$500K

Category2

Embedded

$250K~

$1M

Category3

Horizontal&Vertical

$1M~

$5M

Category4

Situational

$5M~

$100M+

Category5

MarketMaker

Efficiency

&Speed

Embedded

“BUY”

Off-the-Shelf

“Build”

LongShelf-Life

CompetitiveAdvantage

23

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SpectrumofGenerativeAIInitiatives

Gen

AI

“Hybrid"

NotLinear

NotMutuallyExclusive

MultipleConcurrentInitiatives

Talent/Execution/GovernanceDiffers

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Risk/Reward

Category2

Category3

Category4

Category5

Category1

Variable1

Variable2

Variable3

Variable4

Variable5

Variable6

Variable7

Variable8

CreatingaPortfolio

Variables…

•Risk(orRisk/Reward)

•BusinessImpact(magnitudeof

change,targetedareasofbusiness)

•TimetoMarket

•Scalability

•Dexterity(DataLiteracy,DecisionMaking)

•Costofentry

•OperatingCosts

•TCO(multiyearcomparison)

•Minimumscaleneeded

•Skillsneeded

•DegreeofControl(internalversusexternal)

•Maturityofoffering

•Switchability(abilitytoshiftgearsandgowithanotherchoice)-howlong

wouldittaketochange?Howhardwoulditbe?

•R&DInvestment

•Leverage(withinenterpriseor

externalwithpartners,consortiaetc.)

•Multi-tenancyofsolution

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Category2

Embedded

Build

Cost$$$$$$$$$$$$$$$$$$$

Risk

TalentShifts/Dexterity

BusinessValue/ROI/Measurement

TimetoDeliver

Capability/Capacity/AbilityToScale

Governance/Security

CriticalVariables

Category3

Category4

Category5

Category1

Targeted

Situational

Build

Horizontal

&Vertical

MarketMaker

TrustLevel/BehavioralShiftsinDecisionMaking

People

Process

Technology

DataistheNewAdditiontoTraditionalTRIAD

People

Data

ProcessTechnology

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People

Technology

DataistheCostVariableMostCommonlyOmitted

Process

Data

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BusinessCreation/Transformation

DifferentiatingValue

CompetitiveParity

IncrementalBenefits

BusinessValueandRisk

Dynamic

DegreeofComplexity

Predictable

Outcome

Business

BusinessCreation

Ecosystems

CrossFunctionalProcesses

Orchestratedtools

ProcessSpecificonaplatform

Multipletools,

APIs/Metadata

TaskSpecific/I&O

FewerTools/TargetedData/I&O

Point/Specific

Narrow

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Scope

Integrated/End-to-End

Broad

ExplainingProcessOrchestraion

Sales

ProductDesign

Marketing/

Strategy

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Product

Dev.(Mfg)

Distribution

Finance&Accounting

D&AAnalyticsPiercesAllBusinessFunctions

Arrowsrepresent-->Data/Tools/Approach

Sales

ProductDesign

ProductDev.(Mfg)

Marketing/

Strategy

Distribution

Finance&Accounting

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Orchestrated

Efforts

StrategicAlignmentAcrosstheEnterprise

DisparateEfforts

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CaseStudy:

Ericsson

RadicalTransformation

Drivenby

ExponentialTechnologies

Source:Ericsson(EnterpriseAutomation&AITeam)

WWRevenue(2022)-$26+Billion

WWEmployees(2022)–104,000+

Geographies:

.EuropeandLatinAmerica:~47,500

(Sweden:~14,400)

.SoutheastAsia,OceaniaandIndia:~28,000

.NortheastAsia:~13,000

.NorthAmerica:~11,800

.MiddleEast&Africa:~4,500

Ericsson:RadicalTransformationdrivenbyExponentialTechnologies

.RickardWieselfors

.MilapPatel

.ParthasarathyA.KandaKumar

.JohanVallin

EnterpriseAutomation&AITeam

Ericsson

LeadershipTeam

TelefonaktiebolagetLMEricsson(Parent)CommonlyknownasEricsson,founded1876

Ericssonistheworldleaderintherapidlychangingenvironmentofcommunicationstechnology

Develop,deliverandmanage

hardware,software,andservicestoenablethefullvalueofconnectivity

Source:Ericsson(EnterpriseAutomation&AITeam)

ValueDeliveryJourney

RiskAdjustedRepeatability

2016-2017

Ignition&Build

2018-2019

Acceleration&

Operationalization

2020-2021

CreatingImpactatScale

2022-2023

Industrialization

Valueenabled(cumulative)

$1.5Million

$40million

$185Million

$556Million

#Solution

Delivered

(cumulative)

6

109

220

335+

#FTEs

(teamsize)

10

100+

350+

600+

Source:Ericsson(EnterpriseAutomation&AITeam)

MillionsofUS$

-13

-32

-87

-99

Journey:ApplyingExponentialTechnologies

400

350

300

250

200

150

100

50

0

-50

-100

371

Totalannualrun-ratebusinessvalueenabledpertimeperiod

Totalfundingpertimeperiod

145

38

2

2016-2017

Ignition&Build

2020-2021

CreatingImpactatScale

2022-2023

Industrialization

2018-2019

Acceleration&

Operationalization

Source:Ericsson(EnterpriseAutomation&AITeam)

MillionsofUS$

38

2

6

-32

-13

145

-11

Journey:ApplyingExponentialTechnologies

400

350

300

250

200

150

100

50

0

-50

-100

Totalannualrun-ratebusinessvalueenabledpertimeperiod371

272

Totalfundingpertimeperiod

58

-99

-87

2016-2017

Ignition&Build

2020-2021

CreatingImpactatScale

2022-2023

Industrialization

2018-2019

Acceleration&

Operationalization

Source:Ericsson(EnterpriseAutomation&AITeam)

MillionsofUS$

556

40

185

124

224

ValueSurpassed$556Million

Totalcumulativebusinessvalueenabledpertimeperiod

Totalcumulativefundingspentpertimeperiod

5

237

2020-2021

CreatingImpactatScale

2022-2023

Industrialization

2016-20172018-2019

Ignition&BuildAcceleration&

Operationalization

Source:Ericsson(EnterpriseAutomation&AITeam)

Measurement&QuantifyingValue

ValueAreaValueMetrics

Efficiency&Productivity

Annualizedemployeehours:

•Automationofhumantask

•Reductionofeffortsduetofirsttimeright

•Productivity/Volume/Efficiencyincrease

Speed&Agility

•Leadtimereductionperactivity

•OverallElapsedtimereduction

•DecisionIntelligence

QualityControl&Insights

•Errorratereduction(i.e.,quality/accuracy)

•ForecastingAccuracyincrease

•Compliancerateincrease

Liquidity,RevenueUplift&Costoptimization

•Workingcapitalimprovement

•RevenueUplift(attributedtoautomatedprocesses)

•Costreduction

CustomerExperience&Sustainability

•UserExperienceimprovements

•Sustainabilityimpact

Source:Ericsson(EnterpriseAutomation&AITeam)

Ideas

Top-DownPotential

Discovery

Committed

Achieve

Results

Ericsson:CollaborationwithBusinessUnits

ValueProcess

Top-downpotential-estimatedbusinesspotentialfromautomation&AI-driveninitiativesacrossan

end-to-endbusinessflowororganizational

structures/model

Discoveredvalue-estimatedbusinessvalueoftheinitiative.Collaborativeagreementwithbusiness

units.

Committedvalue-agreedandsigned-offbusinessvaluethroughtheinitiative.Fundedbybusiness

unit.

Enabledvalue-valueenabledatthetimeof

solutiongo-live(measuredbyus).Measuredandreportedasstatedabove.

Source:Ericsson(EnterpriseAutomation&AITeam)

Success

•HighLevelofCollaborationwithBusiness

•FundingversusBudgeting

•Measurement(Continual&Consistent)

•RiskAdjustedRepeatability

•LeverageExponentialTech.

•TechnologySkillsDepth

EricssonSuccessStory

StrategicInvestments

22X

increaseof

businessunit

fundingover6yrs

Source:Ericsson(EnterpriseAutomation&AITeam)

•SWdevelopmentlimitationsinproductivityandefficiencyduetocomplextasks,timeandeffort,codingerrors

•Collaborationamongdevelopersareoftenmanual

•Inefficienciessuchassuboptimalcoderefactoring,optimizations,documentationandtesting

GenerativeAIExampleatEricsson

DeployedAIpowereddevelopmentassistantforefficiencyandquality

CHALLENGE

RESULTS

Ericsson’sCodingBuddyusecasedeliveredradical

improvementtoEricsson’ssoftwaredevelopmentby

providingcodegeneration,auto-completion,andsmartsuggestions,significantlyboostingcodingspeedand

efficiency.Itemployedadvancedalgorithmstodetectandfixerrors,ensuringhighercodequality.With

naturallanguageprocessing,thissolutionalso

providedbettercodecomprehension,aiding

developersinunderstandingcomplexcodebases.AI-poweredtoolsautomaterepetitivetasks,streamliningthedevelopmentprocess,enablingdevelopersto

deliverrobustsoftwaresolutionswithincreased

productivityandaccuracy.

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Earlyreturns,estimatedpotential

productivityincreaseof20-30%forapopulationof25,000developers

Immediateeffectinclude:

•HighAdoption

•EnhancedCodeQuality

•IncreasedCodingSpeedandEfficiency

•StreamlinedDevelopmentProcess

•OptimizedCodeRefactoring

TraditionalApproach

Build

Run

Design

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TraditionalApproach

Build

Run

Design

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TraditionalApproachwithApprovalProcess

BusinessCase

Design

Build

Run

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Design

Build

Run

Business

Case

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POV

Pilot

ShifttoNewIterativeApproach

POC

Scale

Execute

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Continuous

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Manage&OperateConcurrentInterconnectedEfforts

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Products/

Services

Identifying,Vetting,Prioritizing,Funding

$

BackOffic

eOperations

Commercia

lCustomer

rd

B

Risk/Rewa

A

E

G

H

C

$$$

D

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Products/

Services

Identifying,Vetting,Prioritizing,Funding

$

BackOffic

eOperations

Commercia

lCustomer

rd

$

$

$

$

Risk/Rewa

$

$

$

$

$$

$$$

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Products/

Services

Identifying,Vetting,Prioritizing,Funding

$

BackOffic

eOperations

Commercia

lCustomer

rd

Risk/Rewa

Threat

C

ompetitive

Parity

CoAd

mpetitive

vantage

$$$

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MostPrevalentUses

developmentoftext,images,videoetc.MarketingTeamsare

generatedby2026.

increasingspeed,productivityforcontentgenerationfor

GGeneratingContent-“Creative”content

personalization&demographicsegmentation.—Europol

E

N

EngagingStakeholders–Customers,prospects,suppliers,thirdpartiesofall

types.Theusegenerativetopersonalizetheinteraction,offer24/7contactoraccess,addressvolume,reducecostandcreateadifferentcustomerexperiences.Engagewithother

“machinesascustomers”isalsobeingexplored.Goalwouldbetoadddigitalcustomervalueatzeroormorelikelylimitedmarginalcost.

NettingitOut-thefocusisonCLARITYratherthancreativity.Afocusonanalyticsand

decisionintelligencethroughthecombinationandsynthesisofdata,allinserviceofnew

approachestodatadrivendecisionmaking.Theoverallfocusistoreducethelatencytimeofenterpriseaction.

Technologies–Softwaredevelopment,testing,infrastructureandoperationsareallareaswheregenerativeAIisbeingapplied

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Appendix

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MandateandScope

Processes:Theoperationsand/orproceduresthatallowthemodeltooperateeffectively.

Communi-cation

Processes

Value

and

Outcomes

Culture:Thenorms,values,systemsofbeliefsandpracticesthatenableorinhibit

accountability,transparency,andother

behaviorsnecessaryforthemodeltooperateeffectively.

DecisionRights

Culture

StructureandRoles

GovernanceOperatingModelPillars

MandateandScope:Theauthorityorsupportfromkeyseniorleadershiptodevelopan

operatingmodeloveraspecificgovernancedomain.

Communication:Thechannelsandcontent

usedtocommunicatekeyinformationbetweenthoseinvolvedinthegovernanceoperating

modelandthebusinessasawholeinaclearandtimelymanner.

DecisionRights:Theframeworkoutliningthedecision-makingremitofeachrole,policies

outliningtheseroles,clearescalationcriteria,andthetoolsandinformationnecessaryforindividualstomakeinformeddecisions.

StructureandRoles:Theroles,responsibilities,skillsandorganizationalstructuresessentialtothesuccessfuloperationofthemodel.

55

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DifferencebetweenGenerative

AIandTraditionalAI

GenerativeAIproducesnewcontent,chatresponses,designs,syntheticdataordeepfakes.

TraditionalAI,ontheotherhand,hasfocusedondetectingpatterns,makingdecisions,honing

analytics,classifyingdataanddetectingfraud.TheprimarydistinctionbetweenpredictiveAIand

generativeAIliesintheircorefunctionalities.

PredictiveAIanalyzesexistingdatatomakepredictions,whilegenerativeAIgeneratesnew

contentbasedonlearnedpatterns

GenerativeAIDefinition

GenerativeAIreferstoartificialintelligencetechniquesthatlearnarepresentationofartifacts

fromdataanduseittogeneratebrand-new,completelyoriginalartifactsatscale,thatpreservea

likenesstooriginaldata.

GenerativeAIenablescomputerstogeneratebrand-new,completelyoriginalvariations

ofcontent(includingimages,video,music,speechandtext).Itcanimproveoralterexisting

content,anditcancreatenewdataelementsandnovelmodelsofreal-worldobjects,suchas

buildings,parts,drugsandmaterials.

56

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ExamplesofRisks

CommonChatGPTOutputRisksFacedbyUsers

1Factualinaccuracies:Partiallytrueoutputsthatarewrongonimportantdetails.1

2Hallucinations:Completelyfabricatedoutputs.Noactual“understanding”ofcontent;itsimplypredictstext.

3Outdatedinformation:ChatGPT’s“knowledge”cutoffisSeptember2021.

4Biasedinformation:Trainingdatabiascanresultinbiasedoutputs.2

5Copyrightviolations:Outputsmayresemble

copyright-protectedwork.3

ExampleofanInaccuracy

HawnandWilliams(2016)doesnotexist4

Source:1

Google’sAIChatbotBardMakesFactualErrorinFirstDemo

,TheVerge;2

ChatGPTWritesJobPosts

,Textio;3

AIArtToolsStableDiffusionandMidjourneyTarget