Driving the Future of the Process Industry: AI, Sustainability, and Digital Transformation – Insights from Mark Sen Gupta, ARC Advisory Group

Mark Sen Gupta, Director of Research, ARC Advisory Group, USA

How would you describe the current state of the process industry in terms of growth, innovation, and regulatory challenges?

The process industry is experiencing cautious growth, driven by digital transformation initiatives, improvements in automation, and more focused AI adoption. Sectors like pharmaceuticals and biotech are leading with investment in platform technologies, personalized therapies, and global expansion. Innovation is accelerating in areas such as predictive maintenance, modular automation, and integrated supply chains. However, legacy systems and fragmented data models continue to hinder operational performance.

Regulatory pressure is intensifying globally, especially around decarbonization, safety standards, and sustainability. Governments are enforcing stricter emissions policies while offering incentives for low-carbon technologies. Meanwhile, the shortage of qualified safety professionals in most regions and perceived complexity of compliance are slowing progress.

Despite these challenges, strategic integration of technology and regulatory alignment is helping future-ready companies stay competitive.

What role is digital transformation—technologies like AI, IIoT, and automation—playing in improving process efficiency and safety?

Digital transformation technologies—AI, IIoT, and automation—are playing a pivotal role in improving process efficiency and safety across industrial sectors. Over the past three years, AI has evolved from augmenting human decision-making to enabling autonomous operations. It now supports predictive maintenance, process optimization, and robotic inspection, reducing downtime and enhancing reliability. IIoT platforms unify industrial data from edge to cloud, enabling centralized monitoring and contextualized analytics that improve visibility and operational control.

In safety, AI is transforming risk assessments and process hazard analysis (PHA), especially in oil and gas. Rule-based systems are being used to streamline HAZOP, LOPA, and QRA studies, with machine learning models gradually being introduced as data quality improves. AI-enhanced surveillance and digital twins are also helping detect leaks, fires, and equipment degradation before they escalate.

Together, these technologies are driving a shift toward smarter, safer, and more resilient industrial operations, with growing emphasis on standards, interoperability, and responsible deployment.

How is the process industry leveraging data analytics or cloud technologies to enhance traceability, quality control, and decision-making?

The process industries have increasingly leveraged data analytics and cloud technologies to enhance traceability, quality control, and decision making. Cloud-based technologies have enabled real-time data aggregation and sharing across operations, improving visibility and collaboration. Industrial DataOps frameworks now integrate IT and OT data streams, allowing predictive analytics and digital twins to support proactive maintenance and risk assessment.

Traceability has improved through version control and automated alerts embedded in data pipelines, ensuring data integrity and enabling rapid response to anomalies. Quality control benefits from AI-driven inspection systems and contextualized data, which can unify sensor, engineering, and operational data into dynamic digital twins.

Decision-making is increasingly data-driven, supported by MES platforms and unified data fabrics that provide enterprise-wide access to structured, historical, and real-time data. These systems empower operators to make informed choices, optimize asset performance, and comply with evolving standards. As digital transformation deepens, the synergy between cloud infrastructure, analytics, and industrial AI continues to redefine operational excellence in the process industry.

How is the pressure to reduce carbon emissions and meet ESG goals influencing operations and investment decisions in this industry?

In recent years, pressure to reduce carbon emissions and meet ESG goals has significantly reshaped operations and investment strategies in the process industry. Companies are increasingly integrating carbon accounting into business decision making, using various platforms to track emissions across the value chain and identify high-emission areas for targeted reduction. This has improved operational efficiency and transparency, enhancing stakeholder trust and attracting ESG-focused investors.

Regulatory momentum, such as the EU’s Emissions Trading System and the U.S. Inflation Reduction Act, has accelerated investment in clean energy, leak detection, and emissions mitigation technologies. ESG software adoption is also rising, helping firms meet decarbonization targets and streamline sustainability reporting.

Operationally, manufacturers are redesigning supply chains, adopting renewable energy, and implementing carbon offset strategies to meet net-zero goals. These efforts are not just compliance driven. They are increasingly seen as competitive differentiators that reduce risk and align with investor and consumer expectations. The shift toward ESG-centric operations is transforming the industry into a more resilient, transparent, and future-ready ecosystem.

How viable are alternatives like biodegradable packaging (in packaging/food), green hydrogen (in oil & gas), or continuous manufacturing (in pharma) from a commercial standpoint?

Biodegradable packaging is gaining traction in food and consumer goods due to regulatory pressure and consumer demand. Innovations in bio-based plastics and digitized packaging processes are helping manufacturers meet sustainability goals while maintaining quality and traceability. However, cost and scalability remain challenges, especially in regions with limited recycling infrastructure.

Green hydrogen is emerging as a viable decarbonization tool in oil and gas, particularly for hard-to-abate sectors like steel and refining. Major investments—such as Air Liquide’s €400M electrolyzer project and Chevron’s stake in ACES Delta—signal growing confidence in its commercial potential. Yet, high production costs and limited infrastructure still hinder widespread adoption.

Continuous manufacturing in pharma is commercially promising, especially for mRNA and personalized therapies. It accelerates time-to-market, reduces waste, and supports digital twin integration for quality control. Regulatory alignment and upfront investment remain hurdles, but momentum is building across North America and Europe.

What emerging trends or technologies are most likely to significantly shape the future of this industry over the next decade?

Artificial Intelligence (AI) and Machine Learning (ML) are central to predictive maintenance, autonomous operations, and real-time decision making. AI agents and soft sensors are increasingly used to optimize production and address labour shortages.

Digital Twins are transforming lifecycle management by enabling real-time simulation, monitoring, and optimization of assets and processes. They serve as the foundation for autonomous production and the industrial metaverse.

Edge Computing and Cloud Technologies are driving IT/OT convergence, enabling scalable, flexible, and secure data integration across enterprise and operational systems.

Industrial 5G and AR/VR are enhancing connectivity and workforce productivity, especially in remote operations and training.

Sustainability and Energy Transition are influencing investment in green technologies, including simulation-as-a-service models and integrated energy management platforms.

Together, these technologies are enabling smarter, more resilient, and sustainable operations—reshaping how process industries design, operate, and evolve in response to global challenges and market demands.