AI in the Chain

AI Dominance in 2025: How Supply Chains Are Being TransformedSupply chains once relied on manual processes and linear planning. The pandemic and geopolitical shocks exposed their fragility and accelerated digital transformation. By late 2025, artificial intelligence (AI) has become integral to how goods move, forecasts are made, and decisions executed. According to a Liferay article outlining supply-chain trends for 2025, more than half of supply chain organisations now use AI tools, and adoption is expected to accelerate. This article explores why AI has become so dominant, explains the spectrum of predictive, generative, and agentic AI, showcases practical use cases and provides a step-by-step example to help readers start applying AI in their own operations.

Why AI Has Taken Over

Explosive data growth: Modern supply chains generate enormous volumes of data from IoT sensors, ERP systems, weather reports, and social media. Traditional planning tools and spreadsheets cannot process this volume or variability. Supply Chain Shaman founder Lora Cecere argues that many companies still use outdated linear models, resulting in high costs, labour-intensive workflows, and poor user experience. AI algorithms, by contrast, can learn patterns from high-dimensional data and provide real-time insights.

Heightened volatility and risk: Geopolitical tensions, climate events, and cyberattacks make supply chains inherently unpredictable. Gartner identifies agentic AI and intelligent simulation as top trends for 2025, noting that these technologies help organisations anticipate “unknown unknowns” and adapt quickly. Without AI-driven simulation, disruptions can cascade through networks unchecked.

Talent shortages and productivity pressures: Many logistics and warehousing roles face persistent labour shortages. Robots and AI co-pilots automate repetitive tasks, while humans focus on value-added work. Deloitte notes that generative AI can reduce documentation lead times by up to 60% and free employees to focus on higher-value activities.

Competitive differentiation: Early adopters achieve faster order fulfilment, lower inventories, improved customer service, and new revenue streams. Companies ignoring AI risk obsolescence, akin to retailers that failed to embrace e-commerce.

From Predictive Analytics to Generative and Agentic AI

AI in supply chains covers a spectrum:

• Predictive analytics uses machine-learning models to forecast demand, optimise inventory, and schedule production. However, forecasts can be thrown off by unanticipated events. GAINS Systems emphasises that predictive demand forecasting and inventory optimisation remain foundational but acknowledges that inconsistent data patterns limit accuracy.
• Generative AI synthesises information and creates new content. In supply chains, generative models generate shipping documents, labels and RFQ responses, design warehouse layouts, and suggest replenishment orders. Yet generative AI struggles with demand forecasting due to unpredictable events.
• Agentic AI comprises autonomous agents capable of reasoning, planning, and executing tasks. Bluecrux describes how specialized agents work together across supply-chain functions (inventory, lead-time, carbon management), enabling integrated decisions. These agents bridge planning and execution, recommending or carrying out actions while humans supervise.
• Intelligent simulation uses AI and machine learning to create digital twins of supply chains. Rolls-Royce has built simulation twins to forecast how maintenance actions affect engine operations, while ports like Los Angeles use digital twins to optimise container flows.

Practical Use Cases

1. Supplier onboarding and contract management: Traditional onboarding involves checking certificates, credit histories, and ESG compliance across many documents. Generative AI can extract key fields from certificates and auto-populate onboarding forms, while predictive analytics assesses risk. Agentic AI can auto-approve routine suppliers and flag exceptions.
2. Demand planning and inventory replenishment: Predictive models forecast SKU-level demand; generative AI explains demand drivers; agents monitor sales, weather, and social media to adjust safety stock. When inventory drops below thresholds, an agent automatically issues replenishment orders.
3. Transportation routing and warehouse operations: AI-driven route optimisers consider traffic, driver hours, and delivery windows. Robots and drones perform cycle counts and picking tasks, while AI agents schedule labour and assign inventory to optimal locations. MIT Sloan highlights Uber Freight’s algorithmic carrier pricing, reducing empty miles and improving efficiency.
4. Risk management and ESG compliance: AI monitors news and weather to anticipate disruptions; digital twins run scenarios to test contingency plans. Generative AI summarises supplier sustainability reports, while agents flag suppliers that fail to meet ESG criteria. Deloitte notes that AI helps track raw-material origins and assess supplier sustainability practices.

Hands-On Example: Automating a Supplier Onboarding Questionnaire

To illustrate how generative AI can simplify supply-chain tasks, try this prompt with a generative AI model (such as ChatGPT):

Prompt: “You are an expert supply-chain manager creating a supplier onboarding questionnaire for a mid-sized manufacturer in the consumer-electronics industry. The goal is to collect key information to assess a new supplier’s capabilities, risk profile, and ESG compliance. Please generate a table with the following columns: Section, Question, and Rationale. Include at least 10 sections such as company overview, financial health, product quality, production capacity, delivery performance, certifications, cybersecurity, ESG policies, and innovation. Each question should be concise, and the rationale should explain why it’s important.”

When you run this prompt, the model should return a table or list of sections with questions and rationales. You can copy these questions into your supplier management system and customise them. For example, the table might include a question under the ESG section like “Describe your company’s greenhouse-gas reduction initiatives,” with the rationale that “Understanding emissions-reduction efforts helps ensure alignment with our sustainability goals.” This exercise shows how generative AI accelerates routine document creation and risk assessment.

Governance, Skills, and Ethics

Adopting AI is not only about technology; it requires organisational change. Lora Cecere warns against layering AI onto outdated processes and urges companies to build unified data foundations, semantic layers, and context engineering. She advocates for reimagined roles where planners become orchestrators—focusing on exception management and strategy while AI handles routine tasks. Training in data literacy, pattern recognition, and cross-functional collaboration is essential. Governance boards should oversee model performance, bias mitigation, and regulatory compliance. McKinsey emphasises that scaling generative AI requires an “AI factory” architecture and careful prioritisation of use cases. Without proper governance, AI initiatives can create more risk than value.

Conclusion

AI’s dominance in 2025 is no accident—it arises from an urgent need to manage complexity, volatility, and competition. Predictive analytics, generative AI, agentic AI, and intelligent simulations work together to make supply chains smarter and more resilient. But technology alone is insufficient. The most successful organisations will pair AI with human judgment, ethical oversight, and continuous learning. By embracing AI strategically—anchored in data discipline and robust governance—supply chains can achieve superior performance and sustainability.

References

• Liferay article on supply-chain trends 2025: Highlights that AI and automation dominate supply-chain trends and that sustainability and cybersecurity are growing priorities.
• GAINS Systems article on AI in supply chains: Discusses generative AI applications (document creation, route planning, warehouse design), predictive forecasting, inventory optimisation, supplier risk management, and highlights limitations of generative AI for demand forecasting.
• Supply Chain Shaman articles by Lora Cecere: “Mistakes and Opportunities” criticises linear planning models and urges redesign and governance; “Native-AI Supply Chain Planning” advocates for unified data foundations, semantic layers, context engineering, and redefined roles; “Pattern Recognition” emphasises cross-functional networking and pattern recognition skills.
• Bluecrux decision intelligence report: Explains how agentic AI involves specialised agents for inventory, lead time, carbon, etc., enabling integrated decision-making.
• MIT Sloan Management Review: Discusses complementarity of machine learning, generative AI, and operations research; features Uber Freight’s algorithmic carrier pricing.
• Woolpert article: Describes projection that 5% of managers will oversee teams of robots and 80% of people will interact with robots daily; includes examples of AI agents and digital twins.
• Bloomberg/CNBC transcript: Highlights logistics cost reductions and AI benefits across levels of optimisation and digital twin implementation.
• McKinsey podcast: Notes generative AI can reduce documentation time by 60%, deliver savings via virtual dispatchers, and emphasises the need for an AI factory architecture.
• Accenture report: Shows that 95% of executives see generative AI as transformative and 43% of supply-chain work hours could be affected.
• Deloitte blog: Discusses generative AI’s role in optimising routes, reducing waste, and verifying supplier sustainability.
• Gartner/Logistics Management: Analyst Christian Titze emphasises agentic AI and intelligent simulation as key technologies for achieving supply-chain goals.