As industrial organizations accelerate AI, advanced analytics and digital transformation initiatives, traditional point-to-point data architectures are becoming a major barrier to innovation.

Manufacturers routinely lose margin due to the gap between what is planned, what is scheduled and what actually happens on the plant floor.

Industrial organizations are under increasing pressure to connect data from legacy systems, modern applications and distributed assets to support optimization, resilience and AI-driven operations.

As process manufacturers navigate tighter margins, workforce challenges and increasingly complex assets, integrated reliability strategies are becoming essential for maintaining performance and operational resilience.

Aspen Fidelis helps decision-makers evaluate capital investments, maintenance strategies and operational improvements by forecasting performance, cost and risk across complex industrial systems.

AspenTech CTO Claudio Fayad joins Hydrocarbon Processing's The Main Column podcast to discuss where Industrial AI is headed for refining and petrochemicals.

Developing projects in emerging industries often requires making critical decisions with limited information. Engineering teams must evaluate multiple design alternatives early in the project lifecycle to identify the optimal configuration that maximizes return on investment while minimizing risk.

AspenTech Inmation™ OT Data Fabric unifies OT data into a stateful, persistent enterprise namespace and continuously contextualizes it into trusted, AI-ready intelligence. It connects plants, assets and applications across edge, cloud, and on-premises to establish a consistent, real-time, query-ready operational picture that scales from a single site to the global enterprise.

In this episode of The Main Column Podcast, Nithiya Parameswaran, VP of Product Management at Aspen Technology, joins Lee Nichols, Editor-in-Chief of Hydrocarbon Processing, to explore how Industrial AI is transforming Asset Performance Management.

Industrial organizations are increasingly focused on making operational data accessible across plants, business units and cloud initiatives—yet many data platforms struggle to scale without introducing complexity, cybersecurity risk and high operational overhead.