Manifest 2026: How AI and Robotics Are Rewiring Supply Chain Decision-Making

The ‘New Never Normal’: A Fundamental Paradigm Shift in Supply Chain Operations

The phrase ‘the new never normal’ echoed throughout Manifest 2026, held February 9-11 in Las Vegas, but this was not mere rhetoric—it represented a precise diagnosis of the underlying logic governing global supply chain operations. Over the past decade, the industry habitually categorized events like pandemics, geopolitical conflicts, and extreme weather as ‘black swans’ or ‘gray rhinos,’ hoping to return to ‘normalcy’ after crises passed. Yet reality tells a different story: the 2023 Red Sea crisis caused a 40% drop in Suez Canal transits, 2024 Gulf of Mexico hurricanes pushed average port dwell times on the U.S. East Coast to 11.2 days, and 2025 saw continuously tightening USMCA enforcement details. These are not occasional disruptions but structural stressors embedded in the fabric of globalization. Supply chain leaders no longer ask ‘when will the next disruption arrive’ but instead design systems presupposing that ‘disruption is the default state.’ This cognitive leap means traditional SCM models centered on cost optimization and linear efficiency are rapidly becoming obsolete: ERP systems relying on rolling forecasts based on historical data falter before dynamic tariff adjustments, JIT inventory strategies trigger chain breaks during sudden border inspections, and multi-tier supplier collaboration platforms expose information silos and response lags when political risks escalate.

This paradigm migration produces disruptive impacts on technology investment logic. Enterprises no longer view AI as a ‘nice-to-have’ tool for improving report KPIs but as an operating system for building ‘antifragility.’ American Eagle Outfitters’ practice is highly representative: its AI models no longer merely predict quarterly sales volumes but integrate real-time U.S. Postal ZIP code-level population flow data, local inflation rates, TikTok trending hashtag geographic heat maps, and the latest customs additional tariff product lists to dynamically generate a four-dimensional decision matrix of ‘demand-capacity-logistics cost-compliance risk.’ This means when a state suddenly raises apparel import surcharges, the system can automatically trigger three contingency plans within 72 hours: switching warehouse transfer routes to neighboring tax-exempt states, activating flexible orders from local quick-response factories, or even adjusting online advertising placement geographic weights to guide consumer flows to low-tax regions. This response speed far exceeds the limits of human operations teams, and its value lies not in how many dollars of freight costs are saved but in transforming ‘policy uncertainty’—an uncontrollable variable—into programmable business logic.

Notably, the ‘never normal’ does not manufacture pessimism but catalyzes new professional capabilities. Multiple consulting firms at Manifest pointed out that leading enterprises are establishing ‘Disruption Intelligence Officer’ positions, whose core responsibility is to deeply couple geopolitical briefings, climate model warnings, and labor strike databases with AI prediction engines. For instance, DHL’s ‘Insight 2030’ report reveals that 62% of North American supply chain leaders expect international tensions to continue impacting operations through 2030, prompting enterprises to incorporate non-traditional parameters like diplomatic relations indices and sovereign credit rating volatility into supply chain risk scorecards. For Chinese outbound enterprises, this paradigm shift is both a challenge and a breakthrough point: when European and American brands avoid certain regions due to political sensitivity, Chinese supply chain service providers with geopolitically neutral technical capabilities can become key partners for multinational clients to avoid ‘compliance chain breaks’ through AI-driven multi-source compliance verification platforms.

The AI Deployment Gap: The Life-or-Death Line from Proof of Concept to Scaled Implementation

One of the most glaring data points at Manifest 2026 was: although 73% of decision makers expect robotics to shape future operations, only 44% have actually deployed them; similarly, 65% plan to increase nearshoring investment, yet few enterprises disclose the integration depth between their nearshore networks and AI systems. This exposes the core contradiction in supply chain AI implementation—severe mismatch between technical feasibility and organizational maturity. Many enterprises fall into the ‘POC trap’ (Proof of Concept trap): spending months developing AI models that can accurately predict sales volumes for certain categories but unable to embed them into procurement approval workflows, warehouse scheduling systems, or carrier selection interfaces. Ryder Supply Chain Solutions’ warning at the conference hit the mark: ‘Agentic AI without a decision framework and correct data is like giving a blind person GPS—the hardware is advanced, but navigation instructions never reach the action end.’ The so-called ‘decision framework’ is essentially translating enterprise strategic objectives (such as ‘compress high-tariff product inventory turnover days to within 28 days’) into computable constraints (such as ‘single warehouse SKU cap ≤1200, cross-warehouse transfer cost increase ≤7%’), then having AI perform multi-objective optimization while satisfying all hard constraints. Without this framework, AI-output ‘optimal solutions’ are often infeasible at financial, legal, or operational levels.

Data quality deficiencies constitute another barrier. Scotts Miracle-Gro’s success in reducing year-end inventory by $600 million through AI was not due to how advanced its algorithms were but rather its integration of the full chain of ‘consumer behavior data’: from real-time sales at Walmart POS terminals, Home Depot store heat maps, Instagram gardening topic engagement volumes, to the lag impact coefficients of meteorological rainfall forecasts on lawn fertilizer demand. However, most enterprises remain trapped in ‘data swamps’—static BOM tables in ERP, discrete operation logs in WMS, and transportation delay records in TMS lacking anomaly reason annotations, with no semantic connections among the three. More severely, when enterprises attempt to integrate external data (such as port congestion indices, fuel price futures curves, labor market skill gap reports), they encounter data sovereignty barriers: shipping companies refuse to open raw AIS trajectory data, meteorological agency API call frequencies are limited, and union databases are not externally licensed. This forces leaders to turn to ‘federated learning + trusted execution environment’ (TEE) architectures: each participant’s data stays within its domain, only encrypted gradient parameters are exchanged, enabling joint model training while protecting privacy. For Chinese logistics enterprises, this suggests a key direction: rather than chasing general large models, it’s better to focus on vertical scenario ‘small and refined’ data governance capabilities—for example, a full-cycle data annotation system for China-Europe Railway Express covering ‘customs-transshipment-gauge adaptation-destination clearance’ will become a differentiated moat for serving outbound customers.

The deployment gap also reflects talent structure fractures. Manifest booth data showed that among the main causes of supply chain AI project failures, ‘lack of composite talents who understand both operations research and customs compliance’ accounted for 38%, far exceeding ‘insufficient algorithm accuracy’ (19%) and ‘insufficient computing power’ (12%). Traditional supply chain practitioners are familiar with VMI agreement terms but cannot read LSTM neural network loss functions, while AI engineers are proficient in PyTorch but do not know the legal differences in risk transfer nodes between FOB and DDP. This disconnect leads to severe decoupling between technical solutions and business pain points. Solutions are emerging: Maersk collaborates with MIT to offer a ‘Digital Supply Chain Officer’ micro-degree with courses including ‘Tariff Game Theory Modeling’ and ‘Blockchain Bill of Lading Legal Effect Analysis’; domestic leading freight forwarders have begun recruiting law school graduates with WTO dispute settlement mechanism internship experience, deploying them as ‘Intelligent Fulfillment Coordinators’ at overseas warehouses after six months of AI toolchain training. This indicates that future supply chain competitiveness will increasingly depend on the quantity and quality of ‘translators’—those who can transform geopolitical risks into constraints, encode customs new policies into algorithm parameters, making AI truly a neural synapse connecting strategy and execution.