Industrial AI Emerges as a Decarbonisation Catalyst for Hard-to-Abate Sectors, Says New Report
IFS, a global leader in Industrial AI software, and PwC UK have jointly released a landmark whitepaper highlighting Industrial AI as a critical enabler of global decarbonization.
Titled “The Intelligence Behind Sustainability: Industrial AI's Critical Role in Decarbonization”, the report makes a compelling case for deploying AI to cut emissions across eight hard-to-abate sectors that collectively account for 40% of global greenhouse gas emissions.
With aviation, shipping, trucking, steel, cement, aluminum, primary chemicals, and oil & gas facing structural barriers to decarbonization, the authors warn that every new asset built today risks “locking in” carbon emissions for decades.
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Industrial AI, they argue, offers the fastest, most scalable pathway to optimize existing operations while guiding long-term transitions to cleaner business models.
Industrial AI Adoption Accelerates as Sustainability Imperatives Grow
The report draws from IFS’s 2025 global study, The Invisible Revolution, which surveyed more than 1,700 senior executives across manufacturing, energy, construction, and utilities.
Key findings include:
86% of executives believe AI is pivotal to achieving environmental goals—from energy efficiency and emissions reporting to carbon management.
Industrial AI is rapidly shifting from pilot projects to enterprise-wide deployment.
AI Already Delivering Tangible Decarbonization Results
IFS and PwC UK outline several real-world applications where AI is helping industries curb emissions and enhance efficiency:
Field service AI is reducing travel distances by 37%, cutting fuel use and operational emissions.
AI-enabled scheduling can reduce Scope 2 emissions by up to 47.6% by shifting production to greener hours of the energy grid.
Predictive maintenance extends asset life, lowers embodied carbon, and prevents energy waste caused by unexpected equipment failures.
“These improvements show Industrial AI fundamentally reshaping sustainability strategy,” said Sophie Graham, Chief Sustainability Officer at IFS. “From route optimization to smarter production cycles, the gains are measurable: lower emissions, reduced waste, and stronger performance.”
Leigh Bates, Partner at PwC UK, added: “AI offers transformative potential for hard-to-abate sectors, guiding us toward net-zero with precision. But as AI scales, its own energy demand must be responsibly managed. Balancing efficiency with sustainable compute will define the next era of industrial innovation.”
Three Pathways Where AI Accelerates Sustainability Impact
The whitepaper identifies three overarching ways Industrial AI enables organizations to achieve meaningful decarbonization:
Operational Efficiency
Optimizing resource use, reducing waste, and improving asset utilization—enabling industries to “do more with less.”Data-Driven Sustainability
AI unlocks auditable, traceable insights that strengthen reporting, verification, and decision-making across value chains.Business Model Transformation
Through digital twins and AI-driven analytics, companies can explore circular economy models, low-carbon investment portfolios, and climate-scenario planning.
Addressing the AI Energy Paradox
The report acknowledges the rising energy consumption of AI systems but emphasizes the net positive climate impact when deployed responsibly. Research cited shows that advances in AI across energy, transport, and food systems could reduce global emissions by 3.2–5.4 billion tonnes CO₂e annually by 2035.
To ensure AI’s benefits outweigh its energy footprint, the report calls for:
Increased use of renewable energy
Carbon-aware computing
Edge processing to cut data transfer loads
Strong governance over data validation and model accuracy
A standout finding is AI’s ability to produce verifiable, tamper-proof sustainability data, crucial for regulators, investors, and supply-chain partners demanding transparent progress toward net-zero.
Building the AI-Enabled Sustainable Industrial Future
IFS and PwC UK conclude that organizations should begin with high-impact, measurable use cases—predictive maintenance, scheduling optimization, and process control—before scaling toward enterprise-wide AI adoption.
Success, they note, depends on:
Clean, connected data infrastructure
Trusted AI governance frameworks
Sustainable compute strategies
Industrial AI is no longer theoretical—it is already transforming operations. Companies that invest early, the report stresses, will define the next chapter of competitive, low-carbon industry.
