AI-Driven Autonomous Process Control for Industrial Decarbonisation

Decarbonisation of energy-intensive industries such as refining, chemicals, cement and metals doesn’t start with new assets – it starts with optimising how existing processes run. Process control is evolving beyond traditional methods focused on condition monitoring towards full AI-driven autonomous control. 

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Plant managers, faced with rising energy costs, pressure to reduce emissions and operational complexity, are increasingly using automated sensors and instrumentation to gather, analyse, and then apply multiple real-time data points to run their industrial operations in the most sustainable and productive way possible.

The role of process control in energy efficiency

We recently conducted a major study of 400 senior energy and chemicals leaders across 12 countries which reaffirms our belief that process control is an under-recognised but powerful lever for efficiency and emissions reduction.

Schneider Electric’s Global Autonomous Maturity Report shows that the global energy industry is ramping up investments in autonomous operations as AI reshapes performance, with the sector racing towards almost 50% full automation by 2030 – and close to a third of operations already fully autonomous. 

This should not come as a surprise: electricity demand is set to nearly double to around 1,000TWh by 2030, pushing industrial plants to run closer to optimal limits – something that only advanced process and multivariable control can reliably deliver. AI power demand, driven mainly by hyperscale cloud and data centre growth, is placing huge pressure on energy systems, intensifying the need for flexible, efficient and resilient operations.

Within this emerging AI energy nexus, 49% of executives identify AI as the single biggest enabler of autonomous acceleration, followed by cybersecurity advancements, cloud and edge computing, digital twins, advanced process control (APC), and open, software-defined automation. 

Refineries, chemicals, cement, and steel production plants are using optimal setpoints to reduce fuel consumption, process inefficiencies, energy losses and maintenance downtime, and real-time adjustments to boost asset utilisation.

The message is clear: against a backdrop of tighter margins and volatile energy systems, less variability equals lower energy waste – every percentage gain in control efficiency translates into significant carbon savings at scale.

From incremental gains to strategic lever 

Process control is no longer merely operational; it is a strategic tool. Advances in AI-driven autonomous process control are helping hard-to-abate industries satisfy decarbonisation KPIs and compliance, hedge against energy price volatility, and address the challenge of ageing industry workforces and skills gaps. 

Process control is no longer merely operational; it is a strategic tool.

This latter issue is now key. Energy and chemicals leaders warn that delaying adoption risks worsening talent shortages (52%) and declining competitiveness (48%), with more than half (59%) also worrying it will drive up operating costs as the sector races to manage inflation and a retiring workforce.

This shift is not about replacing people; it is about empowering the workforce to focus on higher-value work, strengthening safety, and elevating skills. Traditional optimisation has its limits: human-led control cannot manage growing system complexity, emphasising the need for continuous, adaptive optimisation of open, software-defined automation at scale. Energy and chemicals companies that can manage this delicate balancing act and scale their operations now will shape the next era of industrial performance.

The limits of conventional process control and APC

The overarching purpose of APC is to deliver sustainable, measurable benefits by stabilising operations

and produce a consistent yield of quality products, enabled by tools such as distributed control systems that optimise processes so that industrial manufacturing equipment is performing at its full potential.

Across heavy industries, from power generation and oil and gas to manufacturing and mining, traditional control and safety systems were not designed for today’s dynamic and interconnected operating conditions. Operators now face fluctuating inputs from renewable energy, increasingly variable demand driven by electrification and digitalisation, and the need for greater efficiency and sustainability. 

Increasing system complexity requires more dynamic, self-learning systems that optimise performance in real time, reduce costs, emissions and overhauls, and adapt to shifting market conditions. This is where AI begins to reshape process control – and why heavy industry leaders see autonomy as a critical priority.

Organisations that scale AI autonomous control now will define the next era of low-carbon, high efficiency industrial performance and gain a competitive edge in an increasingly complex market.

AI-driven autonomous process control: a step change

Smart, data-driven systems that can predict, adapt and self-optimise with minimal human input are redefining how today’s operators run their industrial facilities. AI-driven autonomous process control is not an incremental improvement – it is fast becoming a new operating model. Energy demand growth driven by AI, data centers and electrification is forcing industry to do more with less energy and carbon intensity. 

By integrating process control and power management with digital solutions and industrial intelligence, specialist technology providers are now able to deliver integrated software-defined architectures that enable AI-driven digital twins, which function as a bridge between control strategy and decarbonisation. 

The results: reduced energy intensity and emissions and improved plant efficiency and performance. 

Today’s APC solutions are becoming more flexible, easier to integrate and better suited to real-time process demands, making them ideal for modernising conventional plants. In a sector where reliability, safety, and carbon reduction are now non-negotiable, such technologies are emerging as the most effective way for operators to deliver ‘more with less’, and run more resilient, competitive, and sustainable operations.

Industry at a tipping point

Schneider Electric’s Global Autonomous Maturity Report proves the momentum towards AI as a key driver of industry growth and decarbonisation. It shows the energy and chemicals sector at a critical point of transformation as electrification, automation and digitalisation converge.

A third of executives (31.5%) say advancing autonomy is a ‘critical’ priority in the next five years, rising to 44% over a ten-year horizon. Fewer than 5% globally view it as a low priority. Leaders also cited strong commercial pressures and consequences to delaying adoption.

This shift is not about replacing people; it is about empowering the workforce to focus on higher-value work, strengthening safety, and elevating skills.

Perhaps most compellingly, organisations already report operating at around 70% autonomy, with plans to hit 80% by 2030, emphasising that autonomy is rapidly becoming the new industry operating model.

Autonomous control is clearly becoming mainstream as organisations see it as essential for efficiency, resilience, and competitiveness.

Overcoming barriers to adoption

Despite this, business leaders cited high upfront costs (34%), legacy systems (30%), organisational resistance (27%), cybersecurity concerns (26%) and regulatory uncertainty (25%) as key challenges. I would argue that the cost of inaction is higher, and that operators that do not commit to automation will lose competitive advantage. Future-facing organisations stand to benefit at the expense of those that do not adapt their mindset.

Conclusion

In summary, as electrification accelerates and industrial processes evolve, operators across refineries, chemical plants, cement kilns, and steel production facilities must adapt to increasingly dynamic and energy-intensive operations.

This shift presents a valuable opportunity for heavy industry to embrace real-time optimisation and predictive control to transform safety, efficiency, and sustainability. Organisations that scale AI autonomous control now will define the next era of low-carbon, high efficiency industrial performance and gain a competitive edge in an increasingly complex market.

Real-world deployments

AI‑driven autonomous process control solutions are already making a difference to real-world projects.

At Shell’s Scotford Refinery in Canada, Schneider Electric is helping modernise operations through open, software-defined automation, supporting more flexible, autonomous operations. At European Energy’s Kassø Power-to-X facility, the world’s first commercially viable e-methanol plant, Schneider Electric and AVEVA are together enabling AI-supported, self-optimising clean-fuel operations with resilient remote monitoring. 

FAQs

What is AI-driven autonomous process control?

AI-driven autonomous process control uses artificial intelligence advanced analytics and automation to continuously optimise industrial processes in real time with minimal human intervention.

How does AI improve industrial energy efficiency?

AI analyses large volumes of operational data to identify optimal operating conditions reduce process variability minimise energy waste and improve overall plant performance.

Which industries benefit most from autonomous process control?

Energy-intensive sectors such as refining chemicals cement metals mining power generation and oil and gas can achieve significant efficiency and emissions improvements through autonomous process control.

How does autonomous process control support decarbonisation?

By reducing fuel consumption improving asset utilisation minimising process variability and optimising energy use AI helps lower greenhouse gas emissions without requiring major new infrastructure.

Will AI replace industrial operators?

No. AI is designed to support operators by automating routine optimisation tasks allowing engineers to focus on higher-value decision-making safety and operational improvements.

What are the biggest barriers to adopting AI-driven autonomous process control?

Common challenges include integrating legacy systems upfront investment organisational resistance cybersecurity concerns and regulatory compliance.

How do digital twins support autonomous process control?

Digital twins provide virtual models of industrial processes allowing AI to simulate optimise and validate operating strategies before they are applied to the physical plant.

Why is autonomous process control becoming more important?

Growing energy demand rising electricity costs decarbonisation targets workforce shortages and increasing process complexity are making AI-driven autonomous process control a key competitive advantage for heavy industry.