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Authors Supply Chain &Operations Strategy Lead, Supply Chain &Operations Lead,Americas Supply Chain &Operations Lead, Introduction Companies are striving to build supply chains that areboth efficient and resilient. In a world of persistentinflation, geopolitical tension and volatile demand,they can no longer afford to be one or the other. Asour previous research on autonomous supply chainshas shown, the convergence of AI and autonomous This pragmatic pathway turns AI adoption into a self-funding engine for reinvention. technologies-led journey that leading companies arepursuing to cut costs, fund reinvestment and achievenew levels of end-to-end performance across their this model into practice in links to deeper, companionanalyses of four operational domains—planning,procurement, manufacturing and fulfillment—wheremaking the best decisions around AI and autonomous toward real autonomy. Most remain stuck in low supplychain digital capability and autonomous maturity,averaging just 36% and 21% respectively, leaving vastvalue untapped.1 2Their supply chains continue to relyon fragmented, manual processes that are costlier, self-funding initiatives can reduce operationalexpenditure up to 24%, cut manual interventions byas much as 50% and lower overall supply chain costsby up to 20%.3This sets the foundation for a new eraof continuously improving supply chains that power once, they start gradually, focusing on cost leversthat drive the majority of supply chain spending, usingtechnologies that deliver maximum impact in termsof cost reduction, efficiency and scalability. Theseinitiatives generate immediate savings that fund From pressureto possibility Autonomous systems, from intelligent agents to self-guided robots, are rapidly maturing and are ready forscale. Generative AI (gen AI), agentic architecturesand digital twins now deliver real-time visibility, Companies face mountingpressure to cut costs andcomplexity. Nearly twenty-seven percent of executivesnow rank accelerating costoptimization among their top companies are realizing trapped value and redeployingresources to fund sustained profitability. Intelligenttransportation management using autonomoustechnologies, for example, has the potential to reduce end, supply chains are evolving from data-driven toself-optimizing systems that continuously improveefficiency and performance. The following tableillustrates how this plays out across industries. Finding the high cost share andhigh technology impact opportunities helps companies harness AI to turn efficiency gainsinto this self-reinforcing cycle. quadrant represents the largest share of supply chaincosts and the greatest opportunity for transformation.Here, AI and autonomous technologies can deliversubstantial savings and measurable productivity gains. Categorizing cost components reveals where technologydelivers strategic value. areas delivers rapid, material gains,providing the savings that finance thenext wave of reinvention. categorization framework (Figure 1) that maps costcomponents along two dimensions: their share oftotal cost in their respective domain and the potentialof AI and autonomous technologies to reduce thosecosts, enhance efficiency and improve scalability.This framework helps leaders pinpoint where earlyinvestment will deliver the greatest returns. Each cost levers, companies can shift to low cost, high impactopportunities like spend analytics and forecasting,where AI continues to generate strong returns andincremental savings that reinforce the case forreinvention. Low impact levers, by contrast, may not Once executives identify all relevantcost levers, they can map themto their current capability andautonomous maturity. This clarifiesthe reinvention path—from targetedcost-out moves to scaled autonomous By prioritizing AI-led actions with near-term savingspotential, companies create a self-funding cycle whereearly efficiencies finance the next wave of progress.Even before reaching full end-to-end autonomy, fragmented operations to intelligent, self-fundingsupply chains that build lasting momentum. lens of planning, procurement, manufacturing andfulfillment—four domains where AI and autonomoustechnologies are already driving measurable cost and the ability to take cost out through better tools. Andeach function connects to and is integral to thebroader, end-to-end cost optimization effort (Figure 2). Even as organizations work toward end-to-endcost optimization, autonomous technologiesalready offer functional-level opportunities.The following links provide deeper insight into Realizingend-to-endvalue Building an autonomous supply chain is a progressivejourney—one that begins with targeted actions thatcreate savings across key domains like planning,procurement, manufacturing and fulfillment, and in a connected ecosystem of intelligent capabilities.The breakthrough comes when autonomy extendsacross functions—transforming discrete cost-savinginitiatives into en