28 January, 2025
Automaatioväylä: The Role and Use Cases of Artificial Intelligence in Process Industries
8-minute read | By Roosa Peippo & Petteri Ormio
The Role and Use Cases of Artificial Intelligence in Process Industries
The rapid and powerful emergence of artificial intelligence (AI) in data management, analytics, and as a new industrial tool has triggered mixed reactions—some even skeptical. On the other hand, AI has become a hot topic, often attached to various contexts to spark interest. In reality, it presents an enticing new opportunity.
This was also the view of many industrial players when Green Factory AI interviewed key individuals from its customer target group between November and February 2025. Founded in 2024, Green Factory AI is a startup developing AI-based software and platforms for production process automation and optimization. Initially focused on the forest industry, the solution is also applicable to chemical and steel sectors. This article is based on those interviews and Green Factory AI’s perspective on the opportunities AI brings to industry.
Changing Operating Environments
The rapid changes in business environments—fluctuations in raw material and energy prices and availability—have spurred new thinking and projects aimed at improving efficiency. AI, in principle, is not new but a highly effective way to analyze massive amounts of data, compute, and predict future outcomes. Most industrial companies have collected large volumes of operational data for years, though often underutilized. Machine learning, a subset of AI, has already been widely piloted and adopted. With the rise of the Industrial Internet (IoT), sensors and measuring equipment have become standard parts of major machinery investments.
AI now plays a significant role in modern industry, especially in automating production and optimizing costs. It has introduced new ways of organizing operations. Despite its potential, large-scale use of AI remains limited in both the EU and Finland. Still, AI is becoming an increasingly critical part of future-oriented industry—so now is the time to act.
Implementing AI
When planning AI implementation, key considerations include the amount and quality of available and collectible data, engaging staff with the benefits of AI, user-friendliness and seamless integration into current systems, and aiming to raise the level of automation. AI software must be easy to use and smoothly integrate into automation systems. A user-friendly system reduces the need for extensive staff training and avoids becoming just “another screen” in the control room. The system should be accessible across organizational units working toward green transition and productivity goals.
By reducing manual work in process optimization, AI frees up personnel to develop new practices, improve current processes, and pursue strategic objectives. It enables higher work quality, better customer satisfaction, and exploration of new business opportunities. Thus, the software not only optimizes production—it helps set the stage for future growth.
Benefits of AI in Industry
AI is already delivering positive global results in predictive maintenance and supply chain management. A key driver for process optimization is the green transition: optimized production reduces raw materials, chemicals, energy, and water use—without compromising quality. This also has major cost implications, improving profitability and competitiveness.
Another major benefit is the ability to analyze large datasets. With years of collected production data, companies may now face a situation where human analysis is no longer feasible. One factory may generate data from 250,000 different points. AI can quickly detect anomalies and patterns, drawing conclusions much faster and more accurately than humans. Even the quality of the data can be improved, simulated, and synthesized to support automation. Therefore, don’t delay AI adoption—even if the data isn’t “perfect.” Over time, errors and issues can be identified and addressed.
Impact on Sustainability
Sustainability has gained significant importance in recent years. Regulations increasingly demand minimized environmental impact, and customers and owners expect tangible green transition results. Production optimization plays a crucial role in reducing energy, chemical, and water overuse, and maximizing raw material efficiency. Sustainability is now a core part of strategy for many, shared across entire organizations.
Challenges and Solutions
Successful AI integration requires high-quality, sufficient data. According to our interviews, data is typically collected at nearly all stages of production, and in most cases, even small investments enable its effective use. Thus, data volume is rarely a barrier. AI systems can also correct missing or faulty data, ensuring performance improves over time as algorithms evolve.
In some cases, data may not have been collected or the organization is unaware of where historical data resides. Although equipment may have built-in data collection, integration into analysis systems may not yet be the norm. New AI tools help accelerate the culture and investments in data collection. Just as earlier digital transformation initiatives spurred better data practices, the same is now happening with AI.
A challenge can also be lack of organizational knowledge or skepticism toward AI. Initial projects may have required costly consulting if internal expertise was missing. Off-the-shelf AI applications with built-in logic lower costs, speed up deployment, and reduce the need for in-house technical AI know-how—allowing the focus to shift to business value.
Examples
A simple example: AI can optimize raw material usage and flow or control correct temperatures in specific process phases. Suboptimal heating or cooling leads to major energy waste. Energy consumption is especially relevant now. Many companies aim to cut energy use by 20–30% by 2030. Achieving this requires optimizing energy use across multiple production stages—especially important in the face of volatile prices. Even small reductions in energy demand can yield substantial financial and profitability gains.
Chemical use in the tissue paper industry is another example. If consumers weren’t used to white paper, bleaching wouldn’t be necessary. But since we demand white paper, AI can optimize chemical dosing, reduce waste, and improve the bleaching process. This is especially useful in complex, environmentally impactful processes where raw materials are expensive and human intuition can’t grasp the full picture.
Conclusions
Based on the market research and interviews conducted, it’s clear that process industries are ready for both AI trials and real-world applications. When launching such projects, they must be properly resourced, with leadership and staff fully committed—especially around data availability. The resources spent on data collection can now be fully capitalized.
“The resources spent on data collection can now be fully capitalized.”
Read the actual article in Finnish here:
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