IBM -人工智能时代的开采与矿业：矿业的下一个生产力前沿Extraction and mining in the AI era Mining's next productivity frontier

IBMInstituteforBusinessValue|ResearchBrief

Extractionand

miningintheAIera

Mining’snextproductivityfrontier

Contents

Foreword 4

Part1

MiningleadersarebettingonAItopower

thenextgenerationofminingperformance 5

Part2

HowAIisdeliveringvaluetoday 7

Part3

Fromoptimizationtoorchestration 9

Part4

ScalingAIacrossminingoperations

isthenextmajortest 11

Part5

Earnautonomy 14

Actionguide 18

ExtractionandminingintheAIera2

ExtractionandminingintheAIera3

Keytakeaways

AI-coordinated

operationsarekey

tothefutureofmining.

AIisalreadyhelpingminingandmineralorganizationsimprove

throughput,recovery,cost

efficiency,andsafety.Thenext

waveofvaluewillcomefrom

orchestratingtheminingsystemasawholeratherthanoptimizingindividualactivitiesinisolation.

Theindustry’sambitionsaremovingbeyond

optimization.

Miningandmineralleaders

increasinglyexpectAItomanageoperationaltrade-offs,respondtodisruptions,andenablemoreautonomousexecutionacross

thevaluechain.

Trust,governance,and

operatingmodelchangewilldeterminewhoscalesAIsuccessfully.

AsAIexpandsfrominsightgenerationtoexecution,competitiveadvantagewilldependlessonalgorithmsand

moreonthetrustandgovernance

neededtoactonAI-drivendecisions.

Foreword

Partone

Parttwo

Partthree

Partfour

|

Action

|Partfive

guide

Foreword

ZahidHabib

VicePresident,GlobalIndustrialSectorLeader

GlobalEnergyandResourcesIndustryLeader

IBMConsulting

Fordecades,miningorganizationsimprovedperformancebyoptimizingextraction,processing,maintenance,andplanning.Thatapproachdeliveredmeaningfulgainsbecauseperformancecouldlargelybemanagedwithinindividualfunctions,sites,andassets.

Mining'snextproductivityfrontierliesbeyondoptimization.Thenextwaveofvaluewillcomefrommanagingtheminingsystemasanintegratedwhole.

AIisbeginningtomakethatpossible.OrganizationsarealreadyusingAItoimproveproductivity,reliability,andsafety.ThequestionisnolongerwhetherAIworks,butwhetherorganizationscanscaleitsimpactbeyondindividualusecases.

RealizingthatopportunityrequiresmorethandeployingnewAIsolutions.Itrequiresthedata,

governance,workforcecapabilities,andoperatingmodelsneededtoscaleAIacrosstheenterprise.TheorganizationsthatsucceedwilluseAItoorchestrateperformance

acrosstheminingsystemasawhole.

ExtractionandminingintheAIera4

ForewordPartoneParttwoPartthreePartfour

Partone

Miningleadersare

bettingonAItopower

thenextgenerationofminingperformance

Partfve

Actionguide

Figure1

StrongexecutiveconvictioninAIasadriverofresilienceandcompetitiveadvantage

Productivityinminingwillnotcomefromoptimizingindividualprocesses.Itwillcomefromoperatingtheminingvaluechainasaconnectedsystem.

AIisbeginningtomakethatpossible.Whilemostorganizationsarestillfocusedonnear-termoperationalgains,leadersincreasinglyrecognizethatthelargeropportunityliesinsystem-levelperformance.

Thisshiftinmindsetisalreadyvisibleamongindustryleaders.Mining

executivesoverwhelminglyviewAIasessentialtofuturecompetitivenessandresilience,signalingbroadconvictionthatAIwillplayanincreasinglycentralroleinhowoperationsaremanaged(seeFigure1).

95%

ofminingandmineralsexecutivessayAIwillsignificantlyenhance

theirabilitytorespondquicklytomarketdisruptionsandshiftsinconsumerpreferences.

and

92%

ofminingandminerals

executivessayinvestinginAIwilldeliverclearandmeasurablecompetitiveadvantagesby09.

Source:Q1.1,Q1.8.Towhatextentdoyouagreewiththefollowingstatements

abouttheuseofAIacrossyourorganization?

ExtractionandminingintheAIera5

ExtractionandminingintheAIera6

Foreword

Partone

Parttwo

Partthree

Partfour

Partfve

AIisnolongerviewedasoptional.ThesteadyriseofAIspendingasashareofITbudgetsreflectshowminingorganizationsareembeddingAImoredeeplyintoplanning,operations,anddecision-making(seeFigure2),whileexpectingittocontributeanincreasingshareofbusinessoutcomesovertime.

ThepatternofAIinvestmentreflectswhereminingorganizationscan

realizevaluemostquickly.Acrossextractionandprocessing,organizationsareapplyingAItoimprovethroughput,reliability,energyefficiency,andoperationalcoordination—areaswhereperformancegainscanbemeasureddirectlyandtranslatedintobusinessimpact.Atthesametime,these

investmentsarelayingthefoundationforabroadershiftfromlocaloptimizationtosystem-levelperformance.

WhileAIisalreadycreatingmeasurablevalue,mostdeploymentsremainconcentratedinlocalizedusecases.Thebroaderopportunity—extendingintelligenceacrosstheminingsystem—remainslargelyunrealized.

Figure2

AIspendasapercentageofannualITbudgetisrising

2023-202520262029

6%8%10%

Actionguide

AIspendasapercentageofannualITbudget

Source:D6.Whatwas/willbeyourtotalAIspendasapercentageofyourannualITbudget?

ForewordPartoneParttwoPartthreePartfourPartfveActionguide

ParttwoFigure3

OperationalperformanceimprovementsfromAIadoption

operations/processing

HowAIisdeliveringMiningMinerals

valuetoday

Cleaner,safer,moreproductiveoperationsttiput/10%10%

iery/yield5%8%

operations—improvinghoworeisextracted,processed,andconvertedinto

AIisalreadydeliveringmeasurableimpactacrossminingandmineraliin10%6%

saleableproduct(seeFigure3).Thefinancialimpactisbecomingincreasinglymaterial,withtheshareofrevenueattributedtoAIexpectedtoincrease

bytwo-thirdsoverthenextthreeyears.eise5%5%

WhilethebenefitsofAIarebroadlyconsistentacrosstheminingvaluechain,theareasofgreatestimpactreflecttheoperationalrealitiesof

eachstageofproduction.epreeing5%5%

Acrossbothextractionandprocessing/refining,organizationsreportimprovementsinthroughput,reliability,safety,costefficiency,andoperational

performance.ThisconsistencysuggeststhatAIisdeliveringvalueagainstfie15%15%

manyoftheindustry’smostpersistentchallengesregardlessofwhere

organizationsoperateinthevaluechain.

Source:Q5.TowhatextenthasAIimpactedyourorganization’sperformanceinthefollowingareas?

ExtractionandminingintheAIera7

ExtractionandminingintheAIera8

Actionguide

Foreword

Partone

Parttwo

Partthree

Partfour

Partfve

Thedifferencesemergeinareasmostcloselytiedtoeachsubsector’s

operatingenvironment.Inextraction,organizationsreportcomparativelygreatergainsinenergyefficiencyandequipmentutilization,helpingminesoperatemoreefficientlyinoftencomplexandvariableenvironments.Inprocessingandrefining,improvementsinrecovery,processstability,andthroughputenableoperatorstoextractmorevaluefromexistingresourcesandimproveplantperformance.

Inmanyminingregions,energyavailabilityandcostarebecomingincreasinglyimportantoperationalconstraints.AIcanhelporganizationsreduceenergy

intensity,improvefuelutilization,andbettercoordinateenergyconsumption

acrossassetsandprocesses,creatingbotheconomicandoperationalbenefits.

Beyondthesestage-specificbenefits,AIisalsocontributingtobroader

gainsinreliabilityandsafetyacrossminingoperations,reducingunplanneddowntime,andenablingfasterresponsestooperationalrisks.TheemergingAIstoryinminingisthereforelessaboutfundamentallydifferentoutcomesacrossthevaluechainandmoreaboutacommonsetofperformance

improvementsappliedtodifferentoperationalcontexts.

Foreword

Partone

Parttwo

Partthree

Partfour

Partfve

Partthree

Fromoptimizationtoorchestration

Mining’snextperformancefrontier

Thenextphaseofminingperformancewillbedefinedbycoordination,not

optimization.Fornow,investmentremainsanchoredinoperations,withmore

thanhalfofAIspendingdirectedtowardefficiencyandoperationaleffectiveness(seeFigure4).Thisreflectsapragmaticapproach:organizationsarepursuing

measurableoperationalgainsfirstandusingthemasafoundationforbroadertransformation.Butthemixisshifting.By2029,spendingincreasinglyextendstowardinnovation,newproductsandservices,andnewbusinessmodels.

AsAIcapabilitiesmature,leadersaregraduallyexpandingtheirexpectationsfromimprovingoperationstotransforminghowvalueiscreated.

Figure4

AIinvestmentisshiftinggraduallyfromoperationalefficiencytowardinnovationandnewvaluecreation

BreakdownofAIspend

20262029

18%

21%

Businessmodelinnovation

Product/serviceinnovation

Efficiency/operationaleffectiveness

31%

34%

51%

45%

Actionguide

Source:Q2.Howis/doyouexpectyourorganization’sAIspendallocated

acrosseachofthefollowing?

ExtractionandminingintheAIera9

Actionguide

Foreword

Partone

Parttwo

Partthree

Partfour

Partfve

Figure5

Safetyandincident

responsetopthe

outcomesleaderssaywillneedorchestration

Autonomoussafetyriskpreventionacrossmineandplant

47%

Rapidincidentandupset

responseacrossoperations

45%

Integratedenergyandemissionsoptimization

43%

End-to-endore-to-metaloptimization

37%

Themostimportantinsightisnotwhereorganizationsareinvesting.ItiswhattheybelieveAImustultimatelyachieve.Theoutcomesleadersprioritizefor2029increasinglydependonAIcoordinatingdecisions

acrossmultiplefunctions,systems,andoperationaldomainsratherthanoptimizingindividualactivitiesinisolation(seeFigure5).

ross-sitestandardizationofoperatingmodels

37%

Yetdespitetheseambitions,mostminingorganizationsremaininthe

earlystagesofthetransitionfromoptimizationtoorchestration.AIis

Lifecycleoptimizationofcriticalminingandprocessingassets

37%

Fleetandprocessingplantavailabilityoptimization

29%

stillusedprimarilyforrecommendations,analysis,andtightlysupervisedautomation,particularlyinhigher-riskenvironments.Fullyautonomousexecutionremainslimited,reflectingthecomplexity,safetyrequirements,andgovernancedemandsofminingoperations.Thegapbetweentoday’soperationalAIandtomorrow’sorchestratedminingsystemisthereforenotatechnologygapalone.Itisanexecutionchallengethatdepends

Stablethroughputandrecoveryundervariablefeedconditions

26%

ontrust,governance,andorganizationalreadiness.

Source:Q17.By2029,whichoutcomeswillrequireAI-drivenorchestration(notjustanalytics)tobeachieved?

ExtractionandminingintheAIera10

PartoneParttwoPartfourPartfve

Actionguide

Foreword

Partthree

Partfour

ScalingAIacrossminingoperationsisthenext

majortest

AIadoptionacrosstheminingandmineralindustryisalreadyextensive,

spanningawiderangeofoperationalactivitiesacrossbothminingoperations

(extraction)andmineralprocessingandrefining.Organizationsareapplying

AIacrossmonitoring,maintenance,optimization,safety,andcontrolusecases,withagrowingshareofthesemovingfrompilottoearlydeployment.

Acrossbothsubsectors,themostmatureusecasesareconsistentlyconcentratedinassetreliability,operationalvisibility,andproductionoptimization:

–Inextraction,thisappearsthroughreal-timemonitoring,scheduling,dispatchcoordination,inspection,andmaintenanceofmobileandfxedequipment.

–Inprocessingandrefning,thesameprioritiesmanifestthroughpredictivemaintenance,processcontrol,recoveryandyieldoptimization,energy

management,andsafetymonitoring(seeFigure6).

ExtractionandminingintheAIera11

ExtractionandminingintheAIera12

Actionguide

Foreword

Partone

Parttwo

Partthree

Partfour

Partfve

Figure6

MiningorganizationsaredeployingAIfirstwhereitimprovesreliability,visibility,andproduction

Piloting

Fullyimplementedorrollingout

Miningoperations/extraction

Real-timemonitoringandoptimizationofmineoperations

Coordinationofhaulage,yard,anddispatchoperations

Mineschedulingandplanningoptimization

Waste,tailings,andemissionstracking

AI-drivenassetinspection

38%

33%

Supplychainandprocurementoptimization

34%

36%

Inventoryforecastingforsparepartsandconsumables

33%

37%

Predictivemaintenanceofmobileandxedminingequipment

30%

37%

Regulatoryandenvironmentaldocumentationautomation

29%

37%

Automatedengineeringandprocesssimulations

21%

44%

Digitaltwinsofminesitesforremoteoperations

13%

31%

Mineralprocessingandrefining

Real-timemonitoringandoptimizationofprocessingplantoperations

Safetymonitoringandincidentpreventioninplantoperations

Predictivemaintenanceofprocessingandreningequipment

Environmentalmonitoringandreportingautomation

AI-assistedprocesscontroloptimization

33%

43%

Energyoptimizationacrossprocessingandrening

32%

43%

Recovery/yield/gradeoptimization

33%

40%

Feedqualitycharacterizationandblendoptimization

27%

4%

Reagent/consumablesoptimization

26%

41%

Digitaltwinsofprocessingplantsforsimulationandoptimization

10%

31%

Source:Q4.Whatisyourorganization’scurrentlevelofAIimplementationineachofthefollowingareas?

ExtractionandminingintheAIera13

Actionguide

Foreword

Partone

Parttwo

Partthree

Partfour

Partfve

Figure7

Useradoptionandcompliancerequirementsarethe

biggestbarrierstoscalingAIacrossminingoperations

Regardlessofwheretheyoperateinthevaluechain,miningorganizationsareusingAItoaddressthesameunderlyingchallenge:reducingvariabilityincapital-intensivesystemswhereperformancedependsonconsistent

flow,uptime,andyield.

1

Useradoptionandchangemanagementchallenges

Alargeshareofusecasesacrossbothsubsectorsisstillconcentratedin

Regulatory/legal/

compliancerequirements

2

pilotandearlydeploymentstages,withfewerfullyscaledimplementations.Organizationshighlightuseradoptionandchangemanagement,regulatoryandcompliancerequirements,workforceskillsandcapacity,andinteroperabilityacrosssystemsaskeybarriers(seeFigure7).Thesechallengesreflectthe

3

Workforceskillsandcapacity

4

Vendorlock-in/ecosysteminteroperability

5

DatafragmentationacrossT,IT,andenterprisesystems

6

Cybersecurityrisks

7

Platformandorchestrationlimitations

8

Dataqualityor

contextualization

realitiesofoperatingincomplex,safety-criticalenvironments,wherescalingAIrequiresnotonlytechnicalintegration,butalsoorganizationalalignment.

Theseconstraintsarereinforcedbydeeperstructurallimitations.Dataremains

fragmentedacrossOT,IT,andenterprisesystems,limitingtheabilitytoconnectinsightsacrossworkflowsandextendsolutionsbeyondindividualsitesorplants.Criticaloperationalcontextcanalsositoutsideformalsystemsinoperating

procedures,shiftandincidenthistories,exceptionhandling,andexperienced

operators’knowledgeofprocessbehaviorandrecoveryactions.Organizations

needtocaptureandvalidatethisknowledgealongsideoperationaldata.PlatformandorchestrationlimitationsfurtherconstrainhowAImodelsaredeployed,

integrated,andmanagedatscale.

Together,thesefoundationalgaps—fragmenteddata,limitedintegration,

andunevenworkforcereadiness—helpexplainwhymanyAIinitiatives

struggletomovebeyondlocalizeddeployments.Yetsolvingthesechallenges

alonewillnotenablethenextphaseofAIadoption.AsAIevolvesfromgeneratinginsightstoactingondecisions,thecentralquestionbecomesoneoftrust:

Governance,decisionrights,andaccountabilitygaps

9

underwhatconditionsareorganizationswillingtoallowAItoinfluenceorexecuteoperationaldecisions?

Source:Q10.Whichfactorsmostlimityourorganization’sabilitytoscaleAIandautonomoussystemsacrossprojectsandtheenterprise?

ExtractionandminingintheAIera14

ForewordPartfve

Actionguide

Partone

Parttwo

Partthree

Partfour

Partfive

Earnautonomy

StrengthengovernancebeforeexpandingAI’srole

MiningorganizationsarebecomingincreasinglyambitiousabouttheroleAI

willplayinoperations.FewhavedeployedagenticAIsofar—fewerthanone

infiveforanyusecase—butpilotsarewidespreadacrossbothextractionandprocessing/refining.MorethanhalfoforganizationsarepilotingagenticAIin

areassuchassafetyalerts,processupsetresponse,andplant-wideprocess

control(seeFigure8).Thisreflectsabroadershiftinexpectations:AIisno

longerviewedsolelyasatoolforanalysisandoptimization,butincreasinglyasacapabilitythatcanhelpcoordinatedecisions,manageoperationalvariability,andimproveexecutionacrosstheminingvaluechain.

Theexpectedprogressionisevidentacrossbothsubsectors.Inprocessingandrefining,organizationsanticipategreateradoptionofautonomousprocesscontrol,feedoptimization,andplant-widecoordination,reflectingthemoreboundedandcontrollablenatureofplantenvironments.Inextraction,thetransitionisexpectedtobemoregradualbutnolesssignificant,expanding

frommonitoringanddecisionsupporttowardcoordinatedoperationaldecision-makingacrossassets,equipmentfleets,andsites.

ExtractionandminingintheAIera15

ForewordPartfve

Actionguide

Partone

Parttwo

Partthree

Partfour

Figure8

FewminingorganizationshavedeployedagenticAI,butpilotspointtoamoreautonomousfuture

Piloting

Fullimplementedorrollingout

Miningoperations/extraction

Safetyalerts

11%

51%

Emergencyanddisasterresponse

11%

42%

Real-timecontrolsystemsformineoperations

7%

36%

Autonomousdispatchandhaulagecoordination

6%

34%

Coordinationofwarehousingandsitelogistics

5%

35%

Automatedprocurementandinventoryreplenishment

11%

27%

Environmentalandregulatorycomplianceexecution

5%

32%

Predictivemaintenanceorchestration

6%

28%

Source:Q6.Whatisyourorganization’scurrentstageofagenticAIimplementationineachofthefollowingareas?

Mineralprocessingandrefining

Orchestratedresponsetoprocessupsets

16%

57%

Autonomousprocesscontrolacrosstheplant

15%

56%

Safetyriskpreventioninplantoperations

13%

56%

Recoveryyieldoptimizationwithautonomoussetpointchangesandconstraintmanagement

14%

53%

Agenticmaintenanceschedulingacrossprocessingrefiningassets

17%

47%

Environmentalcomplianceexecution

18%

45%

Energyoptimizationorchestration

15%

47%

Cross-unitcoordination

15%

45%

Autonomousfeedblendroutingdecisionstostabilizerecoveryandthroughput

12%

42%

Partone

Parttwo

Partthree

Partfour

Autonomoussystemscanalsoreduceexposuretohazardousoperatingareasand,whereoperationallyfeasible,shiftselectedmonitoring,control,and

planningrolestoremoteoperationscenters,potentiallybroadeningthepoolofpeopleabletoperformthem.

Theindustry’slong-termambitionsareclear.Yetcurrentoperatingrealities

telladifferentstory.NearlyhalfoforganizationscontinuetoviewAIprimarilyasarecommendationengineratherthananautonomousdecision-maker,

andnoneallowittoactonhigh-riskdecisions(seeFigure9).EvenwhereAI

ispermittedtoact,ittypicallydoessowithintightlydefinedboundariesandunderhumansupervision.

Figure9

OverhalfoforganizationsletAIexecutesomeactions,butnonetrustitwithhigh-riskactions

4%

AIprovides

informationonly;humansmakeall

decisionsandtakeallactions

41%

AIrecommends

actionswithhumanapproval

15%

AIautomaticallyexecuteslow-riskactionswithout

humanapproval

40%

AIautomatically

executesmedium-riskactionswith

humanoversight

0%

AIautomatically

executeshigh-riskactionsunderstrictguardrails

ForewordPartfve

Actionguide

AImaturity

Source:Q15.Wheredoesyourorganizationcurrently