--- title: "Supply Chain AI: Hype or Real Competitive Advantage?" description: A clear look at what supply chain AI can really do for visibility, forecasting, automation and operational risk reduction. image: https://blog.digtechs.com/hubfs/shutterstock_2666804787.jpg --- [![Digital Technologies Namirial](https://blog.digtechs.com/hubfs/raw_assets/public/digtechs-theme/images/logo-digtechs.png)](https://digtechs.com/en/) - Ai Solutions [Order To Cash Overview](https://digtechs.com/en/order-to-cash/) [Procure To Pay Overview](https://digtechs.com/en/procure-to-pay/) [Supply Chain Compliance Overview](https://digtechs.com/en/supply-chain-compliance/) [Agentic AI](https://digtechs.com/en/agentic-ai/) [Sales Order Management](https://digtechs.com/en/order-to-cash/sales-order-management/) [Electronic Data Interchange](https://digtechs.com/en/order-to-cash/electronic-data-interchange/) [E-Delivery](https://digtechs.com/en/order-to-cash/e-delivery/) [Transportation Management System](https://digtechs.com/en/order-to-cash/transportation-management-system/) [E-Invoicing](https://digtechs.com/en/e-invoicing/) [Document Distribution Service](https://digtechs.com/en/order-to-cash/document-distribution-service/) [Credit Management](https://digtechs.com/en/order-to-cash/credit-management/) [Bill Payment](https://digtechs.com/en/order-to-cash/bill-payment/) [Matching & Reconciliation](https://digtechs.com/en/order-to-cash/matching-reconciliation/) [Customer Portal](https://digtechs.com/en/order-to-cash/customer-portal/) [Purchase Order Management](https://digtechs.com/en/procure-to-pay/purchase-order-management/) [Electronic Data Interchange](https://digtechs.com/en/procure-to-pay/electronic-data-interchange/) [E-Collection](https://digtechs.com/en/procure-to-pay/e-collection/) [Invoice Collection and Registration](https://digtechs.com/en/procure-to-pay/invoice-collection-and-registration/) [E-Invoicing](https://digtechs.com/en/e-invoicing/) [Supplier Portal](https://digtechs.com/en/procure-to-pay/supplier-portal/) [Invoice Trading](https://digtechs.com/en/procure-to-pay/invoice-trading/) [Customs and Shipping Process Automation](https://digtechs.com/en/supply-chain-compliance/customs-shipping-process-automation/) [Transportation Management System](https://digtechs.com/en/supply-chain-compliance/transportation-management-system/) [IoT & Traceability](https://digtechs.com/en/supply-chain-compliance/iot-traceability/) [ESG Compliance & Reporting](https://digtechs.com/en/supply-chain-compliance/esg-compliance-reporting/) ![ai\_agents](https://blog.digtechs.com/hubfs/tpl_resource/ai_agents.png) Webinar AI Agents in P2P, O2C & Supply Chain From strategy to real-world results [Read more](https://digtechs.com/en/ai-agents-in-p2p-o2c-supply-chain-from-strategy-to-real-world-results/) ![ai\_agents](https://blog.digtechs.com/hubfs/tpl_resource/ai_agents.png) Webinar AI Agents in P2P, O2C & Supply Chain From strategy to real-world results [Read more](https://digtechs.com/en/ai-agents-in-p2p-o2c-supply-chain-from-strategy-to-real-world-results/) ![efti\_e-crm](https://blog.digtechs.com/hubfs/tpl_resource/efti_e-crm.png) Webinar eFTI, e-CMR and digital customs Navigating the future of digital logistics [Read more](https://digtechs.com/en/efti-e-cmr-and-digital-customs-navigating-the-future-of-digital-logistics/) ![whitepaper\_ai](https://blog.digtechs.com/hubfs/tpl_resource/whitepaper_ai.png) White Paper AI Agents How to automate finance processes and support CFO decision-making [Scopri di più](https://content.digtechs.com/white-paper-agenti-ai-come-automatizzare-i-processi-finance) ![ai\_agents](https://blog.digtechs.com/hubfs/tpl_resource/ai_agents.png) Webinar AI Agents in P2P, O2C & Supply Chain From strategy to real-world results [Read more](https://digtechs.com/en/ai-agents-in-p2p-o2c-supply-chain-from-strategy-to-real-world-results/) - 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BLOG # Supply Chain AI: Hype or Real Competitive Advantage? ![AI Supply Chain Management](https://blog.digtechs.com/hubfs/shutterstock_2666804787.jpg) 28 October 2026 7 minutes In this article 1. [1.0 Why supply chain AI is at the centre of the debate](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-1) 2. [2.0 Where supply chain AI creates real value](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-2) 3. [3.0 Visibility, forecasting and exception management](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-3) 4. [4.0 The limitations of supply chain AI to consider before investing](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-4) 5. [5.0 What it takes to get tangible results from supply chain AI](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-5) 6. [6.0 How to tell a worthwhile supply chain AI project from hype](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-6) In this article 1. [1.0 Why supply chain AI is at the centre of the debate](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-1) 2. [2.0 Where supply chain AI creates real value](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-2) 3. [3.0 Visibility, forecasting and exception management](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-3) 4. [4.0 The limitations of supply chain AI to consider before investing](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-4) 5. [5.0 What it takes to get tangible results from supply chain AI](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-5) 6. [6.0 How to tell a worthwhile supply chain AI project from hype](https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage#section-6) Published on 28 October 2026 Reading time 7 minutes Topic [Artificial Intelligence](https://blog.digtechs.com/en/tag/artificial-intelligence) Share post Every time a supply chain comes under strain, the same question resurfaces: **do we really need artificial intelligence**, or are we simply relabelling planning and analytics tools that already exist? **Supply chain AI** has become a central topic for a simple reason: with volatile demand, cost pressures, geopolitical risk and suppliers spread across multiple tiers, **running the supply chain in reactive mode is becoming increasingly expensive**. The more useful question is a different one: **at which operational steps can AI genuinely improve visibility, control and speed of decision-making? That is where the value of a project is measured**. Outside that scope, you risk chasing a vague promise. ## Why supply chain AI is at the centre of the debate The attention around **supply chain AI** comes from two forces converging. On the one hand, companies need to understand more quickly what is happening across their supply chain. On the other, the technology market is pushing hard towards increasingly autonomous platforms. It is no coincidence that on 21 May 2025 Gartner predicted that by 2030, 50% of cross-functional supply chain management solutions will include intelligent agents capable of autonomously executing decisions across the ecosystem ([Gartner, 2025](https://www.gartner.com/en/newsroom/press-releases/2025-05-21-gartner-predicts-half-of-supply-chain-management-solutions-will-include-agentic-ai-capabilities-by-2030)). This momentum, however, sits alongside a far less spectacular reality. In McKinsey's 2025 Supply Chain Risk Survey, 95% of respondents report visibility into tier-1 supplier risks, but only 42% manage to extend it to tier 2 or beyond. At the same time, only 19% say they have deployed AI in production at scale ([McKinsey, 2025](https://www.mckinsey.com/capabilities/operations/our-insights/supply-chain-risk-survey)). That is why the debate is so heated: the need is real, but the gap between experimentation and industrialised value is still wide. ## Where supply chain AI creates real value  AI does not improve the supply chain evenly across the board. **It works well** where it has to read large volumes of signals, recognise patterns that escape the human eye and help people step in before an anomaly turns into a cost. In practice, **supply chain AI** **delivers value when it shortens the time between an event, understanding it and making a decision**.  ## Visibility, forecasting and exception management **The first practical area is supply chain visibility**. A model trained on orders, stock levels, supplier performance, logistics data and external signals can help build a far clearer end-to-end picture than dashboards based solely on historical data. The goal here isn't to produce more charts, but **to spot earlier where a problem is building**: a recurring delay on a particular route, a lead time that keeps stretching, a warehouse nearing capacity, a supplier whose service level is slipping. The same applies to planning. In McKinsey's 2025 survey, the main use cases cited by leaders are demand forecasting, inventory optimisation and supply planning. That's hardly surprising: these are the areas where **supply chain AI** can best connect seemingly unrelated variables, simulate scenarios and flag deviations earlier. It doesn't replace planners and buyers, but it gives them a richer, faster information base to work with. Then there is exception management, which is often the real test. A supply chain doesn't grind to a halt because data is missing altogether; it grinds to a halt when nobody can tell which anomalies need immediate attention and which don't. AI can rank priorities, suggest corrective actions, route tickets, check cost deviations, cross-reference documents and alert the right teams before a mismatch spreads downstream. In procurement, too, [Deloitte](https://www.deloitte.com/us/en/about/press-room/2025-chief-procurement-officer-survey.html) found in 2025 that greater supply chain visibility is one of the three risk mitigation strategies CPOs consider most effective, and that the most advanced organisations achieve above-average returns on their GenAI initiatives. Finally, there is **supply chain automation in the strict sense**. Not the generic kind promised in demos, but the kind that **removes friction from repetitive steps**: standardising orders and documents, managing approval workflows, monitoring loading and unloading slots, automatically reopening a case when the operational evidence doesn't add up. When the process is clear and exceptions have been properly mapped, AI can significantly lighten the operational workload. ## The limitations of supply chain AI to consider before investing The fact that credible use cases exist doesn't mean every project makes sense. **The first sticking point is data quality**. If master data, lead times, stock, orders and logistics signals live in separate, inconsistent systems, **the model doesn't create clarity: it amplifies noise**. In these situations, talking about AI before fixing the basics is almost always an elegant way of kicking the problem down the road. Then there is an organisational issue. Many supply chains still work in silos: procurement, planning, warehousing, transport and customer service each see different pieces of the same story. If the project stays confined to one function, the algorithm may produce interesting insights, but it is unlikely to change the end-to-end outcome. Here, too, the McKinsey data is telling: in 2025 most companies were still at the blueprint or pilot stage, not scaling (McKinsey, 2025). Finally, there is governance. In Europe the direction of travel is clear: the European Commission's AI Act takes a risk-based approach, with a focus on documentation, traceability, data quality and human oversight for high-risk systems ([European Commission, updated 27 January 2026](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai)). Not every supply chain management application falls into the same risk category, but the underlying message still applies: when a system suggests or automates decisions that affect operations, costs, safety or compliance, being fast isn't enough. It also has to be controllable. ## What it takes to get tangible results from supply chain AI Companies that achieve real results usually **don't start with the algorithm. They start with an identifiable bottleneck**. It might be unreliable forecasting for critical product families, exception handling that is too slow, poor visibility into the most exposed suppliers, or logistics controls that only kick in once the damage has already hit the bottom line. **The second condition is integration. A worthwhile project genuinely connects ERP, TMS, WMS, supplier portals and relevant external sources**, rather than adding a reporting layer detached from day-to-day operations. If users still have to chase files, emails and spreadsheets to work out whether the system's recommendation can be trusted, the gains quickly evaporate. Next, you need **a business metric, not just a technical one**. Forecast accuracy, fewer stockouts, average exception handling time, OTIF performance, lower transport surcharges, recovered team productivity: **without an indicator tied to an operational outcome**, a **supply chain AI** project is hard to manage. Lastly, **the design of human intervention matters**. In the supply chain, exceptions don't disappear: they change shape. **AI works well when it can escalate the right case**, with the right context, to the right person. And it works poorly when it tries to close automatically decisions that require negotiation, financial accountability or knowledge of the local context.   ## How to tell a worthwhile supply chain AI project from hype **The most honest test is to ask a few precise questions**. Which decision will become faster or more reliable? Which operational cost are you trying to reduce? What data feeds the model, of what quality and how often? What happens when the system has low confidence in its answer? **Who retains final accountability?** **A sound project doesn't promise an autonomous supply chain within a few months**. It defines a scope, **focuses on a use case with a clear ROI**, measures the results and then scales up. Hype, on the other hand, can almost always be spotted by three tell-tale signs: it talks about total transformation without naming the processes that need to change, **glosses over data quality and confuses task automation with competitive advantage**. So **supply chain AI** **is neither a bluff nor a shortcut**. It is a serious lever, but only when it is applied where the supply chain loses time, visibility and the ability to respond. If it shortens the time between signal and decision, makes risks easier to read and removes the manual steps that slow down end-to-end control, **it can become a real competitive advantage**. If it remains a generic promise built on fragile data and poorly governed processes, it will be nothing more than a well-told expense.     Topic [Artificial Intelligence](https://blog.digtechs.com/en/tag/artificial-intelligence) Share post ## You might also like [![Digital supply chain and Agentic AI](https://blog.digtechs.com/hubfs/shutterstock_2680956555.jpg) 28 Oct 2026](https://blog.digtechs.com/en/digital-supply-chain-and-agentic-ai-greater-end-to-end-visibility-and-control?hsLang=en) Artificial Intelligence ### [Digital Supply Chain and Agentic AI: Greater End-to-End Visibility and Control](https://blog.digtechs.com/en/digital-supply-chain-and-agentic-ai-greater-end-to-end-visibility-and-control?hsLang=en) [Read More](https://blog.digtechs.com/en/digital-supply-chain-and-agentic-ai-greater-end-to-end-visibility-and-control?hsLang=en) [![eInvoicing in France](https://blog.digtechs.com/hubfs/IMMAGINE%20SELEZIONATA.jpg) 28 Oct 2026](https://blog.digtechs.com/en/e-invoicing-in-france-why-automation-is-the-next-step-after-compliance?hsLang=en) E-Invoicing ### [E-invoicing in France: why automation is the next step after compliance](https://blog.digtechs.com/en/e-invoicing-in-france-why-automation-is-the-next-step-after-compliance?hsLang=en) [Read More](https://blog.digtechs.com/en/e-invoicing-in-france-why-automation-is-the-next-step-after-compliance?hsLang=en) Subscribe to the Newsletter ## Get insights that simplify your business processes Practical content, updates and best practices to streamline your document workflows and stay compliant at all times. ![Digital Technologies Namirial](https://blog.digtechs.com/hubfs/raw_assets/public/digtechs-theme/images/logo-digtechs.png) Milano – Innovation HUB Via Politi 10, Milano, Trezzano sul Naviglio, 20090 Phone: +39 0286882487 Mail: info@digtechs.com Follow us Company [Investor Relations](https://digtechs.com/en/investor-relations/) [Careers](https://digtechs.com/en/careers/) [News](https://digtechs.com/en/category/news/) [Blog](https://blog.digtechs.com/en) Governance & Compliance [Organizational Model](https://digtechs.com/en/organizational-model/) [Privacy Policy](https://digtechs.com/en/privacy-policy/) [Cookie Policy](https://digtechs.com/en/cookie-policy/) [Whistleblowing](https://services.digtechs.com/whistleblowing/welcome) ![Partner 1](https://blog.digtechs.com/hubfs/raw_assets/public/digtechs-theme/images/partner-logo-1.png) ![Partner 2](https://blog.digtechs.com/hubfs/raw_assets/public/digtechs-theme/images/partner-logo-2.png) ![Partner 3](https://blog.digtechs.com/hubfs/raw_assets/public/digtechs-theme/images/partner-logo-3.png) ![Partner 4](https://blog.digtechs.com/hubfs/raw_assets/public/digtechs-theme/images/partner-logo-4.png) ![Partner 5](https://blog.digtechs.com/hubfs/raw_assets/public/digtechs-theme/images/partner-logo-5.png) ![Partner 6](https://blog.digtechs.com/hubfs/raw_assets/public/digtechs-theme/images/partner-logo-6.png) Copyright © 2026 Digital Technologies Srl Benefit Corporation VAT 09720790964 | Company subject to the management and coordination of Ink (BC) Holdco S.p.a. – Tax ID code 14254460968 ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Digital Technologies", "url" : "https://blog.digtechs.com/en/author/digital-technologies" }, "dateModified" : "2026-10-28T11:26:50.089Z", "datePublished" : "2026-10-28T11:26:32.000Z", "headline" : "Supply Chain AI: Hype or Real Competitive Advantage?", "image" : [ "https://blog.digtechs.com/hubfs/shutterstock_2666804787.jpg" ], "mainEntityOfPage" : { "@id" : "https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://blog.digtechs.com/hubfs/02_DIGITAL%20TECHNOLOGIES-namirial-neg.png" }, "name" : "Digital Technologies" } } ``` ```json { "@context" : "https://schema.org", "@type" : "Article", "about" : { "@type" : "Thing", "name" : "Artificial Intelligence" }, "articleBody" : "Every time a supply chain comes under strain, the same question resurfaces: do we really need artificial intelligence, or are we simply relabelling planning and analytics tools that already exist? Supply chain AI has become a central topic for a simple reason: with volatile demand, cost pressures, geopolitical risk and suppliers spread across multiple tiers, running the supply chain in reactive mode is becoming increasingly expensive. The more useful question is a different one: at which operational steps can AI genuinely improve visibility, control and speed of decision-making? That is where the value of a project is measured. Outside that scope, you risk chasing a vague promise. Why supply chain AI is at the centre of the debate The attention around supply chain AI comes from two forces converging. On the one hand, companies need to understand more quickly what is happening across their supply chain. On the other, the technology market is pushing hard towards increasingly autonomous platforms. It is no coincidence that on 21 May 2025 Gartner predicted that by 2030, 50% of cross-functional supply chain management solutions will include intelligent agents capable of autonomously executing decisions across the ecosystem (Gartner, 2025). This momentum, however, sits alongside a far less spectacular reality. In McKinsey's 2025 Supply Chain Risk Survey, 95% of respondents report visibility into tier-1 supplier risks, but only 42% manage to extend it to tier 2 or beyond. At the same time, only 19% say they have deployed AI in production at scale (McKinsey, 2025). That is why the debate is so heated: the need is real, but the gap between experimentation and industrialised value is still wide. Where supply chain AI creates real value AI does not improve the supply chain evenly across the board. It works well where it has to read large volumes of signals, recognise patterns that escape the human eye and help people step in before an anomaly turns into a cost. In practice, supply chain AI delivers value when it shortens the time between an event, understanding it and making a decision. Visibility, forecasting and exception management The first practical area is supply chain visibility. A model trained on orders, stock levels, supplier performance, logistics data and external signals can help build a far clearer end-to-end picture than dashboards based solely on historical data. The goal here isn't to produce more charts, but to spot earlier where a problem is building: a recurring delay on a particular route, a lead time that keeps stretching, a warehouse nearing capacity, a supplier whose service level is slipping. The same applies to planning. In McKinsey's 2025 survey, the main use cases cited by leaders are demand forecasting, inventory optimisation and supply planning. That's hardly surprising: these are the areas where supply chain AI can best connect seemingly unrelated variables, simulate scenarios and flag deviations earlier. It doesn't replace planners and buyers, but it gives them a richer, faster information base to work with. Then there is exception management, which is often the real test. A supply chain doesn't grind to a halt because data is missing altogether; it grinds to a halt when nobody can tell which anomalies need immediate attention and which don't. AI can rank priorities, suggest corrective actions, route tickets, check cost deviations, cross-reference documents and alert the right teams before a mismatch spreads downstream. In procurement, too, Deloitte found in 2025 that greater supply chain visibility is one of the three risk mitigation strategies CPOs consider most effective, and that the most advanced organisations achieve above-average returns on their GenAI initiatives. Finally, there is supply chain automation in the strict sense. Not the generic kind promised in demos, but the kind that removes friction from repetitive steps: standardising orders and documents, managing approval workflows, monitoring loading and unloading slots, automatically reopening a case when the operational evidence doesn't add up. When the process is clear and exceptions have been properly mapped, AI can significantly lighten the operational workload. The limitations of supply chain AI to consider before investing The fact that credible use cases exist doesn't mean every project makes sense. The first sticking point is data quality. If master data, lead times, stock, orders and logistics signals live in separate, inconsistent systems, the model doesn't create clarity: it amplifies noise. In these situations, talking about AI before fixing the basics is almost always an elegant way of kicking the problem down the road. Then there is an organisational issue. Many supply chains still work in silos: procurement, planning, warehousing, transport and customer service each see different pieces of the same story. If the project stays confined to one function, the algorithm may produce interesting insights, but it is unlikely to change the end-to-end outcome. Here, too, the McKinsey data is telling: in 2025 most companies were still at the blueprint or pilot stage, not scaling (McKinsey, 2025). Finally, there is governance. In Europe the direction of travel is clear: the European Commission's AI Act takes a risk-based approach, with a focus on documentation, traceability, data quality and human oversight for high-risk systems (European Commission, updated 27 January 2026). Not every supply chain management application falls into the same risk category, but the underlying message still applies: when a system suggests or automates decisions that affect operations, costs, safety or compliance, being fast isn't enough. It also has to be controllable. What it takes to get tangible results from supply chain AI Companies that achieve real results usually don't start with the algorithm. They start with an identifiable bottleneck. It might be unreliable forecasting for critical product families, exception handling that is too slow, poor visibility into the most exposed suppliers, or logistics controls that only kick in once the damage has already hit the bottom line. The second condition is integration. A worthwhile project genuinely connects ERP, TMS, WMS, supplier portals and relevant external sources, rather than adding a reporting layer detached from day-to-day operations. If users still have to chase files, emails and spreadsheets to work out whether the system's recommendation can be trusted, the gains quickly evaporate. Next, you need a business metric, not just a technical one. Forecast accuracy, fewer stockouts, average exception handling time, OTIF performance, lower transport surcharges, recovered team productivity: without an indicator tied to an operational outcome, a supply chain AI project is hard to manage. Lastly, the design of human intervention matters. In the supply chain, exceptions don't disappear: they change shape. AI works well when it can escalate the right case, with the right context, to the right person. And it works poorly when it tries to close automatically decisions that require negotiation, financial accountability or knowledge of the local context. How to tell a worthwhile supply chain AI project from hype The most honest test is to ask a few precise questions. Which decision will become faster or more reliable? Which operational cost are you trying to reduce? What data feeds the model, of what quality and how often? What happens when the system has low confidence in its answer? Who retains final accountability? A sound project doesn't promise an autonomous supply chain within a few months. It defines a scope, focuses on a use case with a clear ROI, measures the results and then scales up. Hype, on the other hand, can almost always be spotted by three tell-tale signs: it talks about total transformation without naming the processes that need to change, glosses over data quality and confuses task automation with competitive advantage. So supply chain AI is neither a bluff nor a shortcut. It is a serious lever, but only when it is applied where the supply chain loses time, visibility and the ability to respond. If it shortens the time between signal and decision, makes risks easier to read and removes the manual steps that slow down end-to-end control, it can become a real competitive advantage. If it remains a generic promise built on fragile data and poorly governed processes, it will be nothing more than a well-told expense.", "author" : { "@type" : "Person", "name" : "Digital Technologies", "sameAs" : [ "https://www.linkedin.com/company/digitaltechnologies/" ], "url" : "https://blog.digtechs.com/en/author/digital-technologies" }, "dateModified" : "1793186810093", "datePublished" : "2026-10-28 11:26:32", "description" : "A clear look at what supply chain AI can really do for visibility, forecasting, automation and operational risk reduction.", "editor" : { "@type" : "Person", "name" : "Digital Technologies" }, "headline" : "Supply Chain AI: Hype or Real Competitive Advantage?", "image" : { "@type" : "ImageObject", "height" : 675, "url" : "https://6476864.fs1.hubspotusercontent-na1.net/hubfs/6476864/shutterstock_2666804787.jpg", "width" : 1200 }, "inLanguage" : "it-IT", "isAccessibleForFree" : true, "mainEntityOfPage" : { "@id" : "https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://content.digtechs.com/hubfs/02_DIGITAL%20TECHNOLOGIES-namirial-neg.png" }, "name" : "Blog", "sameAs" : [ "https://www.linkedin.com/company/digitaltechnologies/" ] }, "url" : "https://blog.digtechs.com/en/supply-chain-ai-hype-or-real-competitive-advantage", "wordCount" : 1368 } ```