Beyond predictive maintenance: unlocking AI value

Artificial intelligence has rapidly moved from research laboratories into operational strategies. In industrial process industries, however, the path to tangible value can appear less defined. While predictive maintenance is often cited as the prime example of AI adoption, its potential extends much further, to optimising production, improving quality, reducing emissions, and increasing energy efficiency.

Data as the foundation

Process industries such as pulp & paper, chemicals, building materials, and metallurgy operate in complex environments that continuously generate vast amounts of data. This includes:

• Production data from ovens, extruders, dryers, and reactors
• Laboratory data from quality tests and compliance checks
• Environmental data from air and wastewater systems
• Energy data from grid usage and local generation

The challenge lies not in the lack of data, but in its fragmentation. Information is often stored in separate systems, limiting cross-functional insights. AI becomes most powerful when these data sources are connected and contextualised, enabling pattern recognition and real-time optimisation that human operators alone cannot achieve.

Process optimisation

AI models can continuously analyse sensor data to recommend optimal process parameters. In practice, this can mean:

• Adjusting furnace or dryer temperatures dynamically to reduce energy use without compromising quality
• Balancing extruder speed and pressure to maintain material consistency
• Stabilising product output even when raw material properties vary

In Norway, where energy markets are increasingly volatile and sustainability goals are closely linked to national policy, such optimisation delivers both economic and environmental value.

Norwegian industry tends to favour gradual, evidence-based innovation rather than rapid disruption

Quality prediction and control

Quality laboratories remain central to production assurance, but their results are often retrospective. By linking laboratory and production data, AI enables proactive quality control:

• Anticipating paper strength or surface quality deviations in pulp & paper
• Adjusting chemical ratios or mixing times before off-spec batches occur

This integration turns the laboratory into a predictive function rather than a reactive one, reducing waste, rework, and customer complaints.

Energy efficiency

Energy-intensive industries face increasing pressure to balance output with emissions targets. AI can support this by:

• Optimising boiler and turbine performance in real time
• Coordinating grid and local renewable energy use to minimise cost and carbon footprint
• Analysing production schedules to avoid simultaneous high-load operations

Norway’s growing emphasis on green industrial transformation has made such digital optimisation a key enabler for competitiveness, particularly as energy markets become more volatile.

Emissions monitoring and compliance

Environmental accountability has evolved from a regulatory requirement to a core business responsibility. AI contributes to compliance and sustainability by:

• Enabling continuous emissions monitoring to detect anomalies early
• Automating data collection and reporting, reducing manual work and errors
• Providing early warnings that prevent costly non-compliance events

These capabilities are especially relevant in industries managing multiple permits and reporting obligations.

This pragmatic approach suits AI integration well: start small, validate gains, and expand once benefits are proven

Empowering the workforce

A common misconception is that AI replaces human expertise. In reality, it enhances it. Process operators and engineers retain responsibility for critical decisions but gain tools that extend their situational awareness and response speed.

AI-powered systems can:

• Alert operators to deviations before they escalate
• Suggest setpoint adjustments with quantifiable trade-offs
• Visualise process interdependencies across departments

This human-AI collaboration aligns well with the Norwegian model of industrial competence, where skilled operators remain central, supported by advanced digital tools.

From data to decision

For many companies, AI adoption begins with pilot projects focused on narrow use cases. The next stage is integration, combining production, energy, and environmental data into unified platforms that support continuous learning and improvement.

Emerging applications include:

• Real-time decision support, where operators receive AI-guided recommendations during live production runs
• Autonomous process control, where closed-loop systems make fine adjustments automatically within safety limits
• Automated sustainability reporting, linking process data directly to ESG and regulatory frameworks

Each of these steps builds on the same foundation: consistent, contextualised, high-quality data.

A Norwegian perspective

Norway’s process industries, like metals, chemicals, energy and building materials,  have long combined resource efficiency with innovation. The country’s strong digital infrastructure, renewable energy base, and emphasis on sustainability position it well to lead in industrial AI adoption.

Yet, cultural factors matter too. Norwegian industry tends to favour gradual, evidence-based innovation rather than rapid disruption. This pragmatic approach suits AI integration well: start small, validate gains, and expand once benefits are proven.

Collaboration also plays a key role. Shared learning between industrial clusters, research institutions, and software providers accelerates progress. By building open, interoperable data frameworks, companies can ensure AI investments deliver value not only for individual plants but across entire value chains.

Conclusion

Artificial intelligence offers far more to process industries than predictive maintenance. It enables smarter production, higher quality, improved energy performance, and stronger environmental accountability.

The technology’s success depends less on algorithms than on the readiness of data and the people who use it. By treating AI as a strategic layer built upon reliable, integrated data platforms, industrial companies can enhance both profitability and sustainability.

In this sense, AI is not an external disruptor but a natural evolution of the industrial tradition by combining human expertise, operational experience, and digital intelligence to shape the next generation of process excellence.

Pic: Strohm (background)

Geir Jåsund (pictured) is CEO at Mikon AS