By Martin Hubert, Chief Executive Officer at Freightgate
On a cool March morning, a supply chain planner opens an AI-curated daily briefing. Among the weather alerts and commodity updates, a single recommendation stands out: delay a scheduled steel order by two weeks. The large language model (LLM) behind the briefing has synthesised news of a port strike in Asia, forecast local inventory depletion, and factored in the carbon footprint of expedited freight. The planner clicks “approve”. No air freight will be needed; the order will ship by sea. The decision saves money — and avoids 14 tonnes of CO₂ emissions.
For supply chains under mounting pressure to cut greenhouse gases, moments like this hint at a powerful new tool: generative AI. Yet the technology’s own energy appetite raises an uncomfortable question — can it truly help close the carbon gap, or is it quietly widening it?
The Sustainability Imperative
Sustainability is no longer a “nice to have” in logistics — it’s a compliance and competitiveness necessity. Supply chains are the largest slice of most companies’ climate impact, with Scope 3 emissions averaging 11.4 times the size of operational emissions.
Regulation is sharpening this focus. Australia’s mandatory Scope 3 disclosure law took effect in January 2025, while the EU’s Corporate Sustainability Reporting Directive (CSRD) and the US Securities and Exchange Commission’s climate disclosure rules are aligning global expectations. Failure to produce accurate, auditable supply-chain carbon data now carries reputational and financial risk.
At the same time, customers, investors, and NGOs are watching closely. Amazon, for example, cut single-use plastic use by 16% in 2024 after introducing its machine-learning-driven “Package Decision Engine”—a change applauded by environmental groups and touted in investor calls.
Inside the Generative AI Toolbox
Generative AI — the family of models that create new text, images, designs, or simulations — is proving useful for sustainability in five high-impact ways:
- Demand and Inventory Rightsizing LLM-based copilots summarise disruption signals, run scenario simulations, and recommend adjusted order quantities. Manufacturers use this to keep inventories lean, avoiding waste and unnecessary production runs.
- Dynamic Route Optimisation Graph-generating and reinforcement-learning models recompute optimal transport paths in real time, factoring in emissions as well as cost and speed. Pilot projects have achieved fuel and CO₂ reductions of 10–30%.
- Low-Carbon Packaging Design Generative design tools rapidly iterate packaging shapes and materials, testing each against lifecycle-analysis engines. This has cut material use by up to 20% and logistics-phase emissions by 15% for consumer goods brands.
- Scope 3 Emission Estimation Foundation models impute missing supplier data and simulate the impact of recipe or sourcing changes. One ready-meal manufacturer achieved an 18% cut in recipe-level emissions.
- Supplier-Risk and Modal-Shift Modelling Multimodal Gen-AI platforms combine satellite data, market news, and ESG reports to score supplier risk and propose lower-carbon alternatives, as Unilever now does for key commodity sources.
Case Studies from the Field
Amazon’s “Package Decision Engine” selects right-sized, paper-first packaging for each order. It has eliminated 500,000 tonnes of packaging material per year, and the 16% reduction in single-use plastic earned positive headlines.
Nestlé integrates predictive-demand AI with IoT-linked logistics control towers. Forecast error rates dropped by 30%, reducing both product waste and avoidable transport emissions.
Packaging sector leaders have embedded generative design into product development. Executives report packaging development cycles that are 30% faster, with expectations of more than 6% cost savings and 8% revenue growth from sustainable product lines.
Unilever uses multimodal Gen-AI to evaluate suppliers against emissions, labour, and climate-risk metrics — enabling proactive shifts to greener, more resilient partners.
The Double-Edged Sword: AI’s Own Carbon Footprint
Every one of these wins comes with a hidden cost: the compute power behind Gen-AI is carbon-intensive. Training a single large model can emit more than 500 tonnes of CO₂, and each user query consumes electricity and water for cooling. A typical 400-token LLM call generates about 1.14 g of CO₂ and consumes 45 ml of water.
Hyperscale AI data centres, some requesting 300–1,000 MW of power capacity, are already challenging national grids. Without a shift to low-carbon energy sources, AI’s climate cost could erode much of its sustainability benefit. This makes “green AI” — optimising model size, training cycles, and inference energy — a core part of any responsible supply-chain deployment.
Emerging Best Practices
At Freightgate, we’ve seen that the companies making tangible sustainability gains with generative AI share several practices:
- Start with High-Emission Pain Points Focus on projects where avoided emissions clearly outweigh AI’s own footprint — air freight avoidance, waste reduction, and packaging redesign deliver quick wins.
- Integrate Digital Twins Combining generative scenario generation with high-fidelity digital twins improves emission modelling accuracy and reduces costly trial-and-error.
- Audit Model Outputs for Accuracy Inaccurate or hallucinated emissions data can undermine compliance and credibility. Human oversight remains essential.
- Measure AI’s Own Carbon and Water Use The emissions from AI infrastructure must be visible in the same way freight emissions are.
- Build Cross-Functional Capability Sustainability experts and logistics planners must become AI-literate, while AI teams learn the intricacies of Scope 3 accounting.
Risks and Ethical Considerations
Generative AI is not without pitfalls. Optimising purely for carbon might drive production to regions with weaker labour protections. Supplier risk scores may unintentionally favour large incumbents over innovative SMEs. And over-reliance on AI-generated recommendations could erode human situational judgement.
The answer is to embed environmental, social, and governance (ESG) principles into the model objectives and maintain a human-in-the-loop decision process.
What Good Looks Like
When deployed responsibly, generative AI can become a force multiplier for sustainable supply chains. The winning formula:
- Governance First — Clear ownership of data quality, model outputs, and emissions metrics.
- Green Compute — Selecting renewable-powered data centres and right-sized models for the task.
- Evidence Before Expansion — Scaling only after measurable emissions reductions are demonstrated.
With this discipline, generative AI can help supply chains become more carbon-intelligent — making smarter, faster, and greener decisions at scale.
Conclusion
Generative AI’s real promise is in precision: targeting the most carbon-intensive decision points with models tuned for both sustainability and efficiency. The vision is a future where every prompt, simulation, and forecast is measured not just for cost and service but for its climate impact.
At Freightgate, we believe the leaders of tomorrow’s supply chains will be those who wield AI as both a scalpel and a compass — cutting waste, guiding strategic choices, and steering global trade toward a net-zero horizon.
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About the Author
Martin Hubert
Founder & Group Managing Director Logistics Executive
Martin Hubert is the CEO of Freightgate, a global leader in cloud-based logistics solutions. With over two decades of experience driving digital transformation in freight management, Martin is a recognised innovator in applying technology to create more resilient, efficient, and sustainable supply chains. He is a frequent contributor to industry conferences and publications, shaping the dialogue on the future of logistics and global trade.
