2026从智能协同助手到企业全域转型：AI 重塑供应链发展新要求洞察报告

Executive

Perspectives

FromCopilotstoEnterprise

Transformation:AI’sNewMandateinSupplyChains

SupplyChain

May2026

Introduction

ThestoryforAIandGenAIinsupplychainsin2026isnolongeraboutpotentialbutinsteadexecution.

Overthepast18monthssincethepreviousBCGExecutivePerspectiveonsupplychains,AI

capabilitieshaveevolvedmateriallyfromcopilotsandchatbotstosystemsthatcanreasonandexecutemultistepworkflows.Atthesametime,supplychainshaveincreasinglybecomeaboard-leveltopic,withhighergeopoliticalvolatility,marginpressure,andaneedforsharperworking-capitaldiscipline.

Yetdespiteinvestment,manyorganizationsarestrugglingtotranslateAIintoimpact:

•Lessthan25%ofcompaniesdemonstrateAImaturityatscaleinsupplychain

•Mostremainfocusedonmasterdata,forecasting,andlimitedautomation

•Onlyabout30%reportmeasurableAIvalueinplanningusecases

ThiscreatesanewmandateforchiefsupplychainofficersandCOOs.Intheshortterm,itrequiresleveragingAItoimprovehigh-impact,high-frequencydecisionsthatareconstrained

byanalyticalcapacityandtoreorganizeworkflowstobreakdownorganizationalsilos.

Inthemediumterm,itrequireslayingthefoundationforahuman-agenticsupplychain

operatingmodel;onewhereagentsautonomouslyorchestrateandbalancerevenue,cost,andservice,whilehumanteamsmanageexceptionsandcontinuouslyimprovethesystem.

ThisBCGExecutivePerspectiveprovidesaguideforCEOsandoperationsleadersonhowto:

1FocusonkeydecisionstomovebeyondLLM-on-topexperimentation

2Setupanagenticsupplychainplatformwiththerightfoundationstoscaleandevolve

3Fundthejourneythroughproductivityinparalleltoelevatingdecisionquality

4Incrementallybuildtowardanagent-enabledsupplychainofthefuture

Note:LLM=largelanguagemodel.

InthisBCG

ExecutivePerspective,weprovidean

updatedvisionofthefutureofsupplychain

withAI

1

Executivesummary|AgenticAIispositionedtodrivenear-termtop-

andbottom-lineimpactbytransformingsupplychaindecisions

•AIcapabilitieshaveshiftedmateriallyoverthepast18months,fromconversationalmodelstofullsystemsthatcanreason,usetools,andexecutemultistepworkflowswithlimitedinput

•However,insupplychains,mostdeploymentsarestillstuckusingcopilotsandfocusedautomation,withonly30%ofcompaniesreportingmeasurableAIvalueevenasinvestmentsscaleandexpectationsfromboardsandinvestorsaccelerate

Ahuman-agentic

operatingmodelis

emergingasthe

futureframeworkforsupplychain

Supplychainshave

realizedlittlevalue

afteradoptingsimpleGenAIapps

AgenticAIhasthepotentialtobreaktraditionalsupplychainconstraints

•AIagentscaneliminatethehistorictradeoffbetweeneffectivenessandefficiency;supplychainscannowsimultaneouslymakefaster,betterdecisionsatscale,somethinghuman-ledplanningcouldnotconsistentlydeliver

•LeadersareseeingAIexpandtheperformanceenvelopebeyondwhatanyteamcouldreachbeforewithmorefrequent,moregranularoptimizationthatisunlockingnewrevenue,costlevels,andworkingcapital

•ThequestionisnolongerwhetherAIcanexpandtheperformanceenvelope,buthowtosetupanorganizationtocapturethevaluefromitandmovefasterthanthecompetition

Copyright©2026byBostonConsultingGroup.Allrightsreserved.

•In2030,weexpectSCMorganizationstolookverydifferent,withahuman-agenticoperatingmodelinplace,whereAI

autonomouslyrunsmanyprocesses,andasmallteamofexpertshandlescriticaldecisionsandcontinuoussystemimprovements

•Togetthereisajourneyofincrementallyupgradingandconnectingdifferentpartsofthesupplychainthroughanevolutionfromtask-specificenhancementstoprocesstransformationsand,finally,automation

Today,leadersare

makingfivestrategicmovestoensure

futuresuccess

•Leadersmustbeginredesigningworkflowsanddecisionrightsnow,astheorganizationsthatmovefirstcannotjustimprovetheirKPIsbutalsooperatewithfundamentallydifferenteconomicsvs.thosewhodon’t

1.Investinarobustdatafoundation

2.Startwheredecisiondensityandvalueintersect

3.RebuildworkflowsaroundAI-ledenterpriseoptimizationvs.functionalnegotiations

4.Adoptahybridbuild-and-buyapproachtoplatformstrategies

5.MakeAIdecisionstransparent,auditable,andexplainable

Note:SCM=supplychainmanagement.2

AIadoptioninsupplychainsiswidespread,butmanycompaniesarenot

findingvalue

ThereissignificantadoptionofAIinsupplychains(~44%)

Yetmanycompaniesareseeinglimitedvalue,withthebiggestgainsinmasterdata(~30%)

AdoptionrateofAIacrossfunctionsDegreeofimpactofAIinsupplychainplanningusecases

fortheorganization

44%

44%

43%

43%

42%

41%

40%

40%

40%

37%

37%

36%

35%

Copyright©2026byBostonConsultingGroup.Allrightsreserved.

Supplychain

Sales Manufacturing CustomerserviceR&DandinnovationLegal

Digitalmarketing Finance MaintenanceCustomerjourneyHR

PricingProcurement

Self-healingmasterdata30 Automationofplanningprocesses28 AgenticAItoassistplanners23 Predictiveanalyticsforforecasting18 AIdecisionlayerontopofAPS17 Geopoliticalandsupplierriskmonitoring12 Laborandworkforceplanning10

0102030Respondents(%)

Note:APS=advancedplanningsystem.Respondentswereaskedtoselectasmanyoptionsaswereapplicabletothem.Percentagesinthebarchartwereroundedtothenearestwholenumber.

Sources:BCGBuildfortheFuture2025GlobalStudy(mergedwithBCG’sDigitalAccelerationIndex),n=1,250;BCGAnnualStateofSupplyChainPlanningSurvey2025(n=181);BCGanalysis.3

EvenwithAI,asupplychaintransformationfacesthesamefundamental

challengesofadoption,functionalsilos,andhavingtherightexpertise

Keychallenges1(respondents,%)2

InsufficientmodelaccuracyandreliabilityLackofaccesstohigh-qualitydata

Datascience

capabilitiestodevelopandimplementalgorithms

Algorithms

10%

Technology

20%

80%78%

65%

67%63%61%

InabilitytoexplainhallucinationsinAI

IntegratingAIwithsystems,tools,andAPIsAI-drivensecurityrisks

Scalableandmodernizedstack thatsupportsbusiness

needs

76%

ComplianceandimplementingresponsibleAIHigh,difficult-to-controlAI-scalingcosts

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Noexpertisetomanageunstructureddata

Siloslimitingcross-functionalcollaborationonAIPeopleresistingchangeandnotusingAIdaily

DifficultyaligningAIinitiativeswithfirmstrategySocialresistanceandlaborconcernslimitingscaleShortageofAItalent

MissingclearAImetricsandROImeasurementLackofleadershipsupportorcommitmentforAI

92%88%86%

People,

organization,andprocesses

70%

78%73%71%71%

59%

Effective processessupportedby talentand changemanagementpractices

1.WhichofthesechallengeshinderadoptionandscaleofAIinyourcompany?;2.Percentageofrespondentswhoagreeandstronglyagree.Note:API=applicationprogramminginterface;ROI=returnoninvestment.

4

Source:BCGBuildfortheFuture2025GlobalStudy(n=1,250).

Note:SLM=smalllanguagemodel.5

Whatisanagent?

What

anagent

Anagent

isAIthatusestoolsto

accomplishgoals

IS

>

Memory

Remembersacrosstasksandchangingstates

AImodels

UsesoneormoreAImodels,usuallyanLLMorSLM

Systems

Accessesexternalsystemsonyourbehalf

Copyright©2026byBostonConsultingGroup.Allrightsreserved.

What

>

anagent

Plans

Evaluates

possibleactionsandprioritizestowardagoal

Observes

Collectsand

processesdatafromenvironment

Acts

DOES

Executesby

leveraginginternalorexternaltoolsandsystems

Agentsareexpandingtheperformanceenvelopeinwaysthatadvanced

planningsystemsandhuman-ledprocessescouldnot

AgenticAIhasthepotentialtounlockmassivevaluecreationforsupplychains

AgentsareremovingtheconstraintsthattraditionalAIandtoolscouldnotovercome

Copyright©2026byBostonConsultingGroup.Allrightsreserved.

Agentsenablehighdecisiondensity

Workingcapital

Revenueupside

EBITDA

Granularity

1Highfrequency

Always-onexecutionthatisnotconstrainedbyplanningcyclesoranalystavailability

2Highgranularity

Nolongerforcedtoaggregatedatatomakeproblems

manageableforhumans

Agent-enabledsupplychain

Cross-

functional

optimization

Supplychaindecisionspace

+2%-5%

+2-4pp

15%-30%

Revenueuplift

Profitabilityincrease

Throughput

Inventoryreduction

Costs

Serviceand

satisfaction

+5-10pp

OEEuplift

+5-15pp

Servicerateimprovement

10%-20%

Reductioninmanufacturing,warehousing,and

TraditionalAIandAPS

distributioncosts

Integratedcross-

3

functionaloptimization

CO2emissions

Resilience

Flexibility

20%-50%

Averagenear-termCO2reduction

Divideby10

Timetounderstand

Divideby5

Timeneededtomakeplanandexecute

Onepassacrossprice,

service,cost,andriskversussequentialteamnegotiations

Frequency

upstreamscenariosand

actionsneededvs.suppliers

Note:OEE=overallequipmenteffectiveness.

6

Source:BCGsupplychaincaseexperience.

Copyright©2026byBostonConsultingGroup.Allrightsreserved.

____----------------------⃞

Anagentic-enabledsupplychainoptimizesinventorydailyandatthemost

granularlevel,reducingout-of-stocksandincreasingsales

Inventoryoptimizationagentic-enabledsupply

reimaginedinanchain

Impact

From

To

WeeklyinventoryallocationbasedonDOHtargetsbyproductgroup

Dailyinventoryoptimizationbasedon

SKUeconomicsandforecastconfidence

Illustrative

0123456789101112131415161718192021222324252627282930

Targetinventory(alllocations):

•ASKUs:10DOH

•BSKUs:15DOH

•CSKUs:20DOH

SKU1,location1:Optimalinventory14units

SKU2,location2:Optimalinventory18units

Probability(%)

14

12

10

8

6

4

2

0

95%fillrateDemand

Probability(%)

14

12

10

8

6

4

2

0

lin

0123456789101112131415161718192021222324252627282930

95%fillrateDemand

Note:DOH=daysonhand.DOHisequivalenttoaveragedailysalestimesnumberofdays.

Source:BCGcaseexperienceandbenchmarksofproprietaryandnonproprietaryAIforecastingtools.

Higherrevenuefrombetteralignmentbetweenshort-termforecastandstockdeployment

Lowerout-of-stocksviade-averaged,granular

replenishmentlogic

Inventoryreductionthrough

targetedturn-rateimprovementsenabledbyde-averagedanalytics

Lowerlogisticscostsbyavoidingunnecessaryreplenishments

andtransfers

7

Ahuman-agenticoperatingmodelisemergingasthefuture

supplychainframework

Example

AdvancedE2Eplanningworkflowvision

Meta-agentorchestratessubtasksandcoordinatesdatasourcesandtools

DATAAND

TOOLLANDSCAPE

Providesdataanddeterministiclogic

DATA

AGENTS

Buildsingledatabaseline

SPECIALIZEDSUPPLYCHAINAGENTS

Specialistagentsbyprocess

HUMANSUPPLYCHAINACTORS

Makecriticaldecisionsanddrivesystemimprovement

ORCHESTRATORLAYER

Aggregatesoutputsandeitherdecidesacourseorprovidesrecommendations

Procurementspecialist

<>

ERP

Controltoweragent

Buildsnetwork-widesupply-and-demandoverview

Supplyplanner

Inventoryallocation

<

<

Demandagent

APS

Copyright©2026byBostonConsultingGroup.Allrightsreserved.

Data

collection

Createsdemandforecastandscenarios

Demandplanner

Costvs.

revenuetradeoffs

Logisticsagent

Identifiesdistribution

alternatives,timelines,andcosts

Intranet

Manufacturingexpert

Datavalidationandcleanup

<

Supplyagent

Issue

resolution

WMSandTMS

Createscapacityand

materialsallocationscenarios

Logistics

coordinator

Datahomogenization

Financialreviewagent

Centraldatabase

Quantifiesthefinancialimpactofalternatives

Note:E2E=endtoend;ERP=enterpriseresourceplanning;WMS=warehousemanagementsystem;TMS=transportationmanagementsystem;S&OE=salesandoperationsexecution.8

By2030,SCMorganizationsareexpectedtohaveagentsplanning

andexecutingandhumansmanagingexceptions

Earlyagentadoption;highhumanoversight

Scaledadoption;increasinglyautonomousagents

Limiteddigitalenablement;focusonsystems

Planningactivitytypes

2030

2035

Today

Mosteffortisspentrunningthemachine

Agentsplanandexecute;humansfocusonresolving

Design-led,exception-basedplanning

Design:Improvethesystem

Continuouslyimproveplanningrules,thresholds,andsystems

Copyright©2026byBostonConsultingGroup.Allrightsreserved.

>

Resolve:Manageexceptions

Prioritizeandresolveexceptions

throughcross-functionaltradeoffs

Plannerskeepplans

alivewithlittletimetoimprovethem

Automationreduces

Run:Planandexecute

noise;plannersshifttoproblem-solving

Design:5%

Executeplansthroughroutinesandrule-basedactivities,includingmanualanalysis

Design:20%

Resolve:25%

Resolve:50%

Run:70%

Run:30%

Planningbecomesa

strategiccapabilityfocusedonshapingoutcomes

Design:60%

Resolve:35%

Run:5%

9

Ahuman-agenticsupplychainshiftsfromsiloedoptimizationand

firefightingtoapredictive,unifiedviewthatfacilitatesenterprisedecisions

Maria,vicepresidentofsupplychain

It’sMondaymorningandakeysupplierhasjustmissedacriticaldelivery.Mariaisnowfacinga20%shortfallonherhighest-marginproductlineandneedsananswerassoonaspossibleonwheretogonext.

Maria’sweekwithoutagents

Maria’sMondaywithagents

Monday:Commercialscrambles

Teamfindsoutlate;customercommitmentsalreadyatrisk,notimetoreprioritize

Wednesday:Operationsreallocatesblindly

Capacityshiftedoncostalone,blindtodownstreamrevenueimpact

Nextweek:Financetakesstockoftheimpact

Forecastreviseddownwardonlyafterdecisionsarealreadymade

Mariagetsasuboptimalanswerafterspendingtwotothreeweeksfirefighting

Overnight:Agentflagstheshortfall

Modelscustomerimpactbysegment,flagscommitmentstoprotectbasedonmarginandrelationshipvalue

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6:00AM:Agentevaluatesalloptions

Alternativesupply,productionresequencing,andpartial

shipmentsallassessedwithrevenueandserviceimpact,notjustcost

7:00AM:P&Ltradeoffsquantified

Fullscenariocomparisonwithrecommendation,datasources,andstep-by-steprationale

10

Mariahasanagent-orchestratedresponsethatbalances

revenue,cost,andservicebeforethefirstmeetingisscheduled

Example1

Agenticplanningevolution|ACPGcompanyredesignedreplenishmenttoharmonizecross-functionalprocessesandproactivelymaketradeoffs

Context

GlobalCPGleaderisfacingvolumeandservicepressurefromtopretailers

Goal:StrengthenE2Eplanningcapabilities

Keychallenges:

•Inconsistentcustomerdata

•Reactiveanalyticsapproach

•Fulfilmentseenasexecution,notstrategicenabler

•Limitedteamcapabilities

•Teamspendsmosttimefirefighting

•Limitedcross-functionalinput

Note:CPG=consumerpackagedgoods;DC=distributioncenter.

Execution

Shiftedreplenishmentfromreactivetoproactive

DevelopedGenAI-enabled

agentstodeliverin-stockalertsandsmartreplenishment

recommendations(e.g.,DC-to-storetransfersandexpeditedorders)

Validatedrecommendationsvia

human-in-the-loopfeedback

Builtmodularagentbackendforfutureexpansion

Implementedanenhanced

collaborationworkflowwithretailersfacilitatedbyagenticAI

Businessvaluecaptured

ImpactfromGenAIagent

+2-4%

In-stockpercentagedrivingrevenuelift

+4-10%

Fillrate

40-60%

Administrativesavings

Copyright©2026byBostonConsultingGroup.Allrightsreserved.

11

Example1

Agenticplanningevolution|Thecompany’sagenticplatformprovides

validatedrecommendationswithhumananalystsmakingcriticaldecisions

>

WMS

ERP

APS

TMS

Cloud

computing

Base

systems

platform

>

Customer

Storeorders DCordersForecasts Inventory

Intransit

CPGcompany

Forecasts

自InventoryIntransit

Supplyplans

Data

sources

>

Dataand

monitoringagents

Agenticarchitecturefor

recommendationreliability

AIorchestrationplatform

Subagent1

Datamapandactionflagging

Subagent2

Rationaletracking

Subagent3

Validationandmateriality

…

…

Executingagents

Processandexecuteondriversfortheoutcome

Expandingcapability

platformanchoredina

constantoutcome

Sales

Forecast

reconciliationwiththecustomer

Customerservice

Inventory

replenishment;

predictiveflagging

Logistics

Orderoptimization;backlog

management

S&OP

Allocationfor

constrainedsupply

Triggers

Storeorderand

excessliquidation(inroadmap)

Humanintheloop

Makescriticaldecisionsanddrivesystemimprovement

Target

outcome

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Customerserviceanalyst

12

Improvedcustomerin-stock

Note:S&OP=salesandoperationsplanning.

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Example1

Agenticplanningevolution|ThejourneyconsistedoffundingAIbuilds

throughproductivitygainswhileelevatingdecisionquality

Journeytomake

agenticplanningoperational

1.Agentprocess

capabilityincreases

2.Headcountproductivity

improvesfromautomatingoperationaltasks

Pillar1-Launchpilotsandcaptureproductivity

1.Agentreliability

grows,buildingtrustandautonomy

2.Focusonhigher-qualitydecisionmakingand

competitiveadvantages

Pillar2-Redesignworktoleveragesmarterdecisions

Managementpriority

FundAIbuildswithmorethan50%FTEproductivitysavings

Extractvalue

atscalewith

consistentexecution

Note:FTE=fulltimeequivalent.

Smoothtransition,with

humansretainingmajoritycontrolthroughpilotphase

Agentsstarttakingdirectactioninthesystemviacontinuous

tuningandlearning

Pilotsserveasatemplateforanorganization-widemodelshift

13

Example2

GenAIplanning|Anindustrialgoodsfirmreshapedsupplychainprocesses

withGenAItoolsrunningcomplexscenariosandroot-causeanalysis

Context

LeadingEurope-basedindustrialgoodscompanywasmaking

thousandsofsupplychaindecisionsdaily

Goal:Superchargeexistingsupplychainsimulationcapability

Aimedtooptimizeoperationsandsharpendecisionmakingby:

•Identifyingbottlenecks

•Testingdifferentstrategies

•Runningcomplexscenarios

Source:BCGexperience.

Execution

IntegratedtwoBCGXassetsviaanaturallanguageinterface:

•AgentKit:GenAIagenttoolkit(open-sourcecodeframework)

•E2EPlanbyBCGX:

Comprehensiveplanningsuite

LivesolutionstreamlinesS&OPandenablesplannerstoindependently:

•Createsimulationscenarios

•Analyzerootcauses

•SummarizeKPIs

•Runsensitivityanalyses

•Sharekeysimulationsoutputs

Businessvaluecaptured

EBITDAincreaseinyeartwo

ImpactfromunderlyingAIcapability+2ppandafter

ImpactfromGenAIagent

+25Planningprofessionalstrained

3xProcesscycletimereduction

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14

Example3

SmartinventorymanagementIAspecialtymaterialsleaderreshapeditssparepartsstrategybyusingAI’scomparisonandsubstitutioncapabilities

Context

GlobalspecialtymaterialsleaderhaseightmanufacturingsitesacrosstheUSandEurope

Goal:ReduceMROspendingwithout

impactingserviceKeychallenges:

•ExcessiveMROinventory

•Fragmentedsourcingandlocalspare-partsdecisions

•InconsistentMROmasterdata;nooptimizationtools

Note:MRO=maintenance,repair,andoperations.

Execution

AIembeddedthroughoutsourcingtoenablesmarterpurchasing

•DevelopedAIalgorithmtoidentifypartsimilarityusingunstructureddataandcatalogues

•Embeddedintosourcing:checksforsimilarexistingpartsbeforenewpurchases;compares

manufacturersforbestprice

Recalibratedstockparametersinparalleltooptimizeinventory

Businessvaluecaptured

ImpactfromAIandadvancedanalytics

+2%-5%

Savingsinannualspare-partssourcingspending

15%-20%

Inventoryreductionwithminimalimpacttoservice

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15

FivemovesthatleadingsupplychainsaremakingnowtoembedAIand

shiftfromfunctionaloptimizationtoenterprisedecisionorchestration

1

2

3

4

5

Investinarobustdatafoundation

Agentsrequireclean,connecteddata;amoderncloud-nativeplatformdeliveringtrusteddataatbusinessspeedisessential

Startwheredecisiondensityandvalueintersect

Prioritizeplanningand/ortradenegotiations—areaswithfrequenttradeoffsandclearfinancialimpact—ratherthanspreadingAIthin

RebuildworkflowsaroundAI-ledenterpriseoptimizationvs.functionalnegotiations

Copyright©2026byBostonConsultingGroup.Allrightsreserved.

AIgeneratesanE2Eoptimizedplanasthestartingpoint,freeingteamstofocusonstructuraldecisions,nottacticaltradeoffs

Adoptahybridbuild-and-buyapproachtoplatformstrategies

Balancestrategicvalue,customizationneeds,long-termcost,andpaceofinnovation

MakeAIdecisionstransparent,auditable,andexplainable

16

AIplansmustshowdatasources,assumptions,andtradeofflogic;thisisessentialtobuildingsharedtrustacrosscommercial,operations,andfinance

Copyright©2026byBostonConsultingGroup.Allrightsreserved.

Strategicmove#1

Investinarobustdatafoundation

LeadersareusingAItobuildthefoundationastheygo

AgenticAIarchitecture

BusinessinterfacesandAPIs

Smartbusinesslayer

Agentic

orchestrationandevaluator

AIlayer-intelligenceandorchestration

Knowledge

Businesslogicandpolicy

BIlayer-enterprisereasoninggraph

Unstructured

data(e.g.,social

listening,proprietary

data,andexternaldata)

Datalayer

Datapreparation

and

transformation

Actionand

workflow

(agentgateway)

Knowledgeaccessandexplanations

Structureddata

(e.g.,ERP,APS,andCRM)

Businessexperiencechannels

AI-enabledbusinessasset

modelingand

structure

Modelgarden

1.StartwithAIonthedataitself

•AIrecommendsdatafixes(e.g.,mergerecordsandfillgaps)

•StewardsreviewAIrecommendationsinsteadofhuntingforproblems

2.Integrateandconnectdataacrosssilosasyougo

•Migrateincrementally,notviamultiyearprograms

•Buildametadatalayerthatenables

cross-systemvisibility(OMS,TMS,APS,andERP)withoutrequiringfullmigrationupfront

3.Letdatagovernanceemergefromusage

•Assigndataownerswithsponsors,butdon’tletgovernancedelaydeployment

•Letagentactivitysurfaceissuesandhighlighthighest-valuegaps

Best-in-classdatastrategiesuseAItobuilditeratively

Note:BI=BusinessIntelligence;CRM=customerrelationshipmanagement;OMS=ordermanagementsystem.

Don’twaitforaperfectdatalake

Pickanareatofocuson

DeployAI

ondata

quality

Exposeand

closegaps

Investincrementallyanduseeachcycletoexposeandclosedatagaps

Ensurethecoredatafoundationimproveswitheverystep

DeployAIagentstoflagandauto-correctmasterandtransactionaldatagaps

17

Expandscope

Strategicmove#2

Focusfirstonimprovinghigh-value,high-densitydecisionsthatareconstrainedbyanalyticalcapacity

High

Value

Low

Highestagentvalue

Whyuseagents

Decisionsubjectivity

Requirereasoningthroughambiguoustradeoffsbeyondlinearrules

Cross-systemorchestration

Spanmultiplesystems;agentsstitchtogetherfragmenteddataandprocesses

Speedatscale

Agentscontinuouslyassessandreassessdecisionsunconstrainedbyhumancapacity

Copyright©2026byBostonConsultingGroup.Allrightsreserved.

Strategic,humanledStarthere

•Networkredesignandcapacityinvestment

•Supplier-qualificationandstrategic-sourcingdecisions

•Capacityscenarioplanning(long-termdemandvs.network)

•S&OPstructuraldecisions(portfolioandchannelmix)

•ResegmentSKUsforinventoryplanning

LowerpriorityAutomatefully

•Lead-timeandMOQpolicyreviews

•Returnsdispositionrules

•Dockandyardmanagementscheduling

•Qualityinspectionsamplingplans

•Warehouselayoutandslottingadjustments

•Demand-supplybalancing(reoptimizeacrossSKUs)

•Inventoryallocationanddeployment(DC-to-storeandcustomer)

•Customerforecastreconciliationandreplenishmenttriggers

•Orderallocationandprioritization(constrainedsupply)

•Supplyanddemandscenariosandrecommendedactions

•Disruptionresponse(supplyshortfallreallocation)

•Productionresequencing

•Supplierrescheduling

•Transportutilizationvs.serviceleveltradeoffmanagement

•Masterdatacorrectionsandparameterupdates

•Freightinvoicevalidation

•Orderstatusinquiriesandstandardresponses

•ReportingcadenceandKPIdistribution

•Carrierschedulingandpickupassignment

•DCworkforcescheduling

QuarterlyandannuallyLowgranularity

Siloedoptimizationbyfunction

Decisiondensity

DailyandcontinuouslyHighgranularity

Integratedoptimizationacrossfunctions

Note:MOQ=minimumorderquantity.

18

Source:BCGanalysis.

Strategicmove#3

RebuildworkflowstobeAIfirstandbreakdownorganizationalsilosacrossfunctions

19

Illustrative

Customernegotiationsreimaginedasanintegratedworkflow

Currently:Commercialand

operationsnegotiatetactically,

From

Siloedoptimization

To

eachpushingtheirowntargets

E2Eintegratednegotiation

AIfirst:Agentsprovide

imparti