IFS research shows industrial AI cutting emissions across heavy industry

New research from IFS and PwC UK suggests that industrial artificial intelligence is already reducing emissions across some of the world’s most carbon-intensive sectors. Heavy industries including steel, cement, chemicals, oil and gas, aviation, shipping, and trucking account for around 40 percent of global direct emissions, yet many of their assets are expected to remain in operation well into mid-century.

Rather than waiting for large-scale deployment of new fuels or breakthrough technologies, the research argues that the fastest emissions reductions over the next decade will come from operating existing assets more intelligently.

 

Operational Intelligence Moves Decarbonisation Forward

 

According to the joint white paper, Industrial AI builds on established automation and process control by adding real-time data, machine learning, and advanced optimisation. This allows systems to continuously adjust production, maintenance, logistics, and planning decisions in response to changing conditions.

Evidence cited in the research shows that AI-driven planning, scheduling, and optimisation can reduce field service travel distances by more than 37 percent. These efficiency gains translate directly into lower fuel consumption, reduced Scope 3 emissions, and improved workforce productivity. In energy-intensive operations, carbon-aware scheduling has been shown to cut Scope 2 emissions by up to 47.6 percent by aligning production with periods of lower grid carbon intensity.

The report emphasises that these reductions are not the result of single transformational changes, but of many incremental improvements that compound across fleets, plants, and networks.

 

From Efficiency Gains to Verifiable Carbon Outcomes

 

Beyond operational efficiency, the research highlights the growing importance of traceability and governance. As regulatory scrutiny increases and sustainability disclosures become more demanding, companies must demonstrate not only emissions reductions, but also how those outcomes were achieved.

Industrial AI systems generate digital records linking operational decisions with emissions and financial performance. This creates auditable data trails that strengthen confidence in sustainability reporting and reduce reliance on manual data collection. PwC’s trust-based economic modelling referenced in the paper suggests that combining responsible AI deployment with credible decarbonisation could support net economic growth of around 37 percent by 2035, even as industries manage the risk of stranded assets.

 

Read more: NYU Abu Dhabi Researchers Apply AI to Track Water Quality and Climate Impacts

 

Real-World Applications Across Utilities and Services

 

Case studies included in the research illustrate how Industrial AI is being applied today. Endeavour Energy, which manages a multibillion-dollar electricity distribution network in Australia, uses AI-supported investment planning to assess reliability, safety, environmental impact, and cost simultaneously. The utility reports that AI has helped it quantify environmental impacts alongside customer and operational outcomes.

In field services, Konica Minolta deployed AI-driven scheduling across multiple national operations. Within 18 months, the company reported higher productivity, reduced travel time, and a return on investment exceeding four times the cost of implementation, driven in part by lower fuel use and faster job resolution.

 

Scaling AI Brings New Risks

 

The report also addresses the trade-offs associated with wider adoption of Industrial AI. Increased reliance on data and compute raises concerns about energy use, water consumption, cybersecurity, and workforce readiness. Estimates from the International Energy Agency indicate that global data centre electricity demand could approach the scale of Japan’s total power consumption by 2035 if current trends continue.

To manage these risks, the authors stress the need for strong governance frameworks, renewable-powered infrastructure, carbon-aware computing, and large-scale workforce reskilling. Survey data cited in the paper shows that most organisations expect significant retraining requirements, with many anticipating that more than half of their workforce will need new skills to fully embed AI into daily operations.

 

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Industrial AI Becomes Core to Transition Strategy

 

The central conclusion of the research is that Industrial AI is no longer a peripheral experiment. It is becoming part of the operating foundation of heavy industry, delivering measurable emissions reductions today while building the data backbone needed for credible reporting and long-term transition planning.

The paper argues that delaying adoption in pursuit of perfect future solutions risks slowing progress. Companies that integrate Industrial AI now can reduce costs and emissions simultaneously, strengthen resilience to energy volatility, and establish more credible pathways toward sustainable industrial operations.

 

 

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