Industrial Decarbonization: Key Challenges and AI Solutions

Global energy-related CO₂ emissions hit 37.6 gigatons in 2024, the highest ever recorded. Despite steady improvements in efficiency and renewable adoption, heavy industries are still the world’s biggest CO₂ emitters. Cement, steel, and chemicals alone release billions of tons of carbon each year, with progress toward net zero still lagging. According to the World Economic Forum, eight “hard-to-abate” sectors account for nearly 40 percent of global direct CO₂ emissions.

The pressure to decarbonize is mounting. Nations have pledged to stay within a 1.5°C pathway, but reaching that target depends on how quickly industries can modernize their systems and supply chains. Traditional levers like fuel switching or carbon capture can only go so far but not enough to reach the target.

That’s where AI-driven digital intelligence steps in.

It is helping industries see their emissions in real time, predict energy demand, and optimize high-carbon operations with precision that wasn’t possible before. From predictive maintenance in factories to smarter logistics networks, AI is turning sustainability goals into measurable outcomes.

At Omdena, we’ve seen this shift firsthand. In one collaboration, an AI-powered solution helped a global supply chain company cut its emissions by 10 percent while saving $5 million annually. In this article, I’ll share real-world insights on how AI drives industrial decarbonization, exploring its meaning, challenges, methods, and how to scale it globally.

Key Takeaways

• Industrial decarbonization focuses on cutting emissions from heavy sectors like steel, cement, and chemicals, the backbone of global manufacturing.

• AI is transforming this effort by making emissions visible, measurable, and actionable through data-driven insights and automation.

• Traditional levers alone aren’t enough. Fuel switching, renewables, and carbon capture must work alongside digital intelligence to achieve deep decarbonization.

• Omdena’s real-world success shows what’s possible: an AI-powered supply chain solution that reduced emissions by 10% and saved $5 million annually.

• The article breaks down what industrial decarbonization means, the key challenges, 10 proven methods, and how AI is accelerating real progress toward a low-carbon future.

Let’s start by understanding what industrial decarbonization truly means.

What Is Industrial Decarbonization?

Industrial decarbonization means cutting carbon emissions from the world’s most energy intensive sectors. It covers everything from the fuels that power factories to the chemical reactions inside furnaces and kilns. In practice, it targets the full chain of emissions such as direct emissions from production (Scope 1), indirect emissions from purchased energy (Scope 2), and all upstream and downstream emissions across the supply chain (Scope 3). Omdena has already demonstrated this in practice by helping reduce Scope 3 emissions through AI-powered supply chain optimization.

The toughest sectors to decarbonize are often the ones society depends on most. Steel, cement, and chemicals form the backbone of construction, transport, and manufacturing. Yet they rely heavily on fossil fuels and high-temperature processes that are hard to electrify. Even small improvements here can translate into massive global impact.

But knowing what to cut is easier than cutting it. The path to decarbonization is full of technical, financial, and operational hurdles. Let’s look at the biggest challenges industries face in making real progress.

Challenges in Industrial Decarbonization

Every industry knows why decarbonization matters. The real question is how. Turning high-emission operations into low-carbon systems is far more complex than swapping fuels or adding sensors. The barriers run deep, spanning from old infrastructure to fragmented data and limited incentives. These challenges don’t just slow progress; they define where innovation must happen next.

Still, these challenges open the door to innovation. Let’s explore the methods and technologies that make industrial decarbonization achievable.

10 Proven Methods for Industrial Decarbonization

Industrial decarbonization doesn’t rely on one magic solution. It combines proven engineering methods with digital intelligence. Traditional levers focus on cleaner inputs and processes, while AI brings precision and adaptability that legacy systems lack. Together, they create a realistic roadmap toward net zero.

1. Electrification

Replacing fossil-fuel combustion with electric power is one of the fastest ways to cut emissions. High-temperature electric furnaces and heat pumps can substitute for gas-based systems, especially when powered by renewables. The challenge lies in scaling grid capacity and ensuring electricity is clean, stable, and affordable. Steel producers are already building electric arc furnaces and exploring green hydrogen-powered production. These efforts enable deep emissions cuts across steel manufacturing.

2. Renewable Energy

Integrating solar, wind, or geothermal power reduces dependence on coal and gas. Many plants now use hybrid systems that combine renewables with energy storage to manage fluctuations. The more consistent the renewable supply, the easier it becomes to operate energy-intensive industrial processes sustainably.

Omdena’s partnership with NeedEnergy in Sub-Saharan Africa demonstrated this in action. The team developed AI models to forecast solar generation, detect mismatches between energy supply and demand, and design PV sizing tools for off-grid households. This project showed how intelligent forecasting and optimization can accelerate the shift from fossil-based grids to decentralized, solar-powered systems.

3. Fuel Switching

Substituting traditional fossil fuels with cleaner alternatives such as hydrogen, biofuels, or ammonia can immediately lower carbon intensity. While hydrogen is promising for steel and ammonia production, it requires new pipelines, storage, and safety systems. The cost of clean hydrogen remains a key bottleneck.

Siemens Energy is developing a major green hydrogen electrolyzer project that will supply industries, including steel, with 26,000 metric tons of green hydrogen each year. This project will help reduce about 800,000 tons of CO₂ emissions from industrial operations.

4. Carbon Capture, Utilization, and Storage (CCUS)

CCUS technology traps carbon before it enters the atmosphere and either reuses or stores it underground. It is especially valuable in industries like cement and chemicals, where emissions come from chemical reactions, not just energy use. The main hurdle is high capture cost and limited storage infrastructure.

The Heidelberg Materials plant in Rezzato, Italy is working on a project to capture over 95 % of CO₂ emissions from cement production and store them via a storage hub.

5. Material Substitution

Using low-carbon materials or alternative binders can significantly reduce emissions in construction and manufacturing. For instance, blending cement with industrial by-products like slag or fly ash cuts clinker use. Similar shifts are emerging in steelmaking and packaging through recycled or bio-based materials.

Omdena’s collaboration with HOTOSM advanced this goal through AI-driven material intelligence. The project used computer vision to classify building materials from street-level imagery, helping cities map their urban material footprint. By identifying areas dominated by high-emission materials like concrete or steel, policymakers can target low-carbon substitutions such as recycled composites, earthen blocks, or bamboo.

6. Process Optimization with Digital Twins

Digital twins create real-time virtual models of industrial assets, enabling continuous performance monitoring. Operators can simulate temperature, pressure, and energy variables to fine-tune systems before making physical changes. This reduces energy waste and prevents production disruptions.

In a Limerick city-center digital twin, deep retrofits delivered an 86 percent drop in building operational emissions. Overall emissions fell 67 percent without grid decarbonization and 75 percent with a fully clean grid by 2050.

Carbon emissions to 2050 with grid decarbonisation for Limerick

7. Predictive Maintenance

AI-driven predictive systems analyze sensor data to anticipate equipment failures. By addressing issues early, companies reduce downtime, extend machine life, and optimize energy use. Omdena applied this approach in collaboration with a leading electronics manufacturer. The team developed machine learning models to predict equipment malfunctions, detect quality degradation, and uncover energy inefficiencies. The AI system reduced scrap rates and improved factory throughput. This showed how predictive maintenance directly translates to lower emissions and higher productivity.

8. Material and Chemical Process Optimization

Generative AI and inverse design help researchers discover new low-carbon materials and catalysts faster. These models simulate thousands of combinations digitally before lab testing, cutting both time and emissions. It accelerates innovation in battery materials, green cement, and sustainable polymers.

9. Carbon Intensity Forecasting and Scheduling

AI can forecast grid carbon intensity to help industries plan energy-heavy operations during cleaner hours. Running electrolysis or melting processes when renewable supply peaks can drastically reduce indirect emissions. This approach aligns energy use with the grid’s greenest moments.

10. Supply Chain and Graph Modeling

AI-based graph models visualize emissions across complex supply chains. They reveal interdependencies between suppliers, transport, and materials that traditional spreadsheets can’t. This transparency helps companies target the biggest emission sources and design smarter procurement strategies. Omdena’s supply chain optimization project used similar principles, applying machine learning to map emission hotspots and simulate optimized logistics routes. The result: a 10% reduction in emissions and $5 million in annual savings.

Building the Low-Carbon Future with AI

Industrial decarbonization demands more than generic AI tools. It needs custom tailored solutions built around real data, specific processes, and measurable impact. That is where Omdena stands out.

Omdena partners with organizations to design custom AI solutions that reduce emissions, optimize operations, and accelerate sustainability goals. The workflow starts with data ingestion. It is followed by creating digital twins or sensor-driven models that mirror real industrial systems. From there, our teams develop and deploy machine learning models. These models continuously optimize energy use, resource allocation, and process efficiency.

What makes Omdena different is its collaborative model. Each project brings together global AI engineers, domain experts, and local partners to prototype rapidly and scale effectively.

If your organization aims to achieve measurable decarbonization with AI, partner with Omdena and book an exploration call today. Together, we can build a smarter, cleaner, and more sustainable industrial future. Because every ton of carbon reduced brings us one step closer to a thriving planet.