Originally published here.
AI is transforming productivity across sectors. But in industrials and manufacturing – where energy use, emissions, and material waste are especially high – it holds massive untapped potential. These are the industries where founders can create solutions that drive both outsized impact and strong financial returns.
AI’s precision and processing power allows us to achieve far more with far less. In heavy industry, that translates directly into meaningful emissions and cost reductions. At Norrsken VC, we conducted a deep dive to map where AI can unlock the biggest efficiency gains. We assessed the most promising industrial use cases and ranked them by their potential to reduce energy use, cut material waste, and lower emissions. Here are the top three areas where AI consistently shows up as a game-changer:
1. Resource efficiency
AI can significantly reduce raw material use by precisely calibrating input needs without compromising output quality. This includes smarter formulations, tailored recipes, and substitution with lower-emission alternatives.
In textiles, AI-driven solutions have great potential to address the high waste rates of 25 percent – higher than any other major sector. For instance by AI-based optimisation of where to place markers on the fabric – reducing the unnecessary cut waste.
In sectors like cement, where clinker is the main emissions driver, solutions like Alcemy are already cutting emissions by up to 65% by optimizing material blends.
The same principles apply across steel, chemicals, batteries and solar PVs where high-embodied carbon materials dominate and small reductions in waste or material input compound into major climate and profitability wins.
Heavy industry is one of the planet’s biggest energy consumers, responsible for 25% of global consumption.
2. Energy and process optimisation
Industrial operations often run on outdated, rule-based systems with a lot of manual touch. AI introduces self-learning, adaptive logic to improve process variables like temperature, pressure, and timing in real time.
AI platforms like Juna.ai can cut energy use by as much as 30 percent through such continuous optimisation of processes in heavy industry.
The highest opportunities lie in sectors where energy represents a major cost driver: cement (30 percent), steel (>20 percent), and chemicals (>10 percent) and textiles (5-10 percent).
3. Predictive maintenance
Breakdowns in heavy industry don’t just cause costly production delays, they also waste vast amounts of energy during restarts for repairs. AI solutions can reduce this friction by anticipating and preventing faults and extending the lifespan of machinery.
AI-enabled predictive maintenance can reduce machine downtime by 30 to 50 percent, increase asset lifetime by 20 to 40 percent and increase productivity by some 30 percent.
This has especially high impact in heavy industry and textiles where machines are energy-intensive and made of large pieces of carbon-heavy materials such as steel.
Scaling these solutions takes more than smart technology. It takes ambition, capital and – most of all – founders who are passionate about solving our biggest problems. If you're building, or thinking about building in AI, this is where you’ll find the white space to meet true lasting demand.
Fabian Erici, Principal
