Founded in 1967 in Cambridge, England, AVEVA began as a pioneering Computer Aided Design (CAD) research center and has since evolved into a global leader in industrial software and digital transformation.. Since 2023, AVEVA has been fully owned by Schneider Electric, the French multinational energy and automation leader, creating a powerful synergy between industrial software and energy management expertise. The company’s portfolio includes the PI System, a platform for real time industrial data, and Connect, a cloud hub for collaboration and insight—together they are critical for helping companies reduce downtime, optimize assets, and accelerate digital transformation
In this interview, Cindy Crow, Oil and Gas Industry Principle at AVEVA, explained how digital transformation plays a critical role in enhancing operational efficiency and performance across the oil and gas sector. Crow brings over 42 years of industry experience, having held engineering, marketing, and leadership roles at Chevron, ExxonMobil, Baker Petrolite, Nalco, and Schlumberger before joining OSIsoft- a U.S. software company best known for creating the PI System- which later became part of AVEVA. She specializes in assisting energy companies with leverage engineering, automation technologies, analytics, and artificial intelligence (AI) to drive business value and operational excellence.
The interview took place at AVEVA World 2026 in Milan, the company’s flagship annual conference, where industry leaders gathered to explore the latest developments regarding digital transformation, AI, cloud, and industrial software solutions across the oil, gas, and energy value chains.
What is the most dynamic shift in oil and gas digital transformation?
The most dynamic shift has been the evolution of the digital twin-essentially a virtual replica of physical assets-which is now powered by artificial intelligence (AI). AI is transforming robotics and reshaping nearly every operational process in the industry. This shift allows engineers to better leverage their specialized technical skills because they are no longer bogged down by the time-consuming task of searching for data. Data scientists spend over 80% of their time simply locating the data they need to analyze and contextualize.
Where is the greatest untapped digital transformation potential across the energy value chain?
The greatest untapped digital transformation potential across the energy value chain lies in operational process optimization, where advanced analytics and machine learning remain underutilized. A clear example comes from TC Energy in Canada, which set out to reduce fuel consumption while cutting carbon emissions. By analyzing operating parameters across its compression systems, the company discovered that several compressors were running outside their optimal design envelopes—some too slowly, others over speeding and generating excessive emissions.
Once these underperforming assets were identified, engineers leveraged the facility’s built in redundancies—double and triple equipment backups—to rebalance operations without investing in new machinery. This targeted use of machine learning and data analytics delivered efficiency gains and emissions reductions, underscoring how much value still lies untapped in optimizing existing processes rather than relying solely on new capital expenditure.
What is the primary barrier to digital adoption in the oil and gas sector?
The primary barrier to digital adoption in the oil and gas sector is the prevalence of unstructured, fragmented data that cannot be automatically consumed. Historical project information is often captured inconsistently across engineering, procurement, and construction phases and dispersed among third party vendors, leaving it without a standardized format. As a result, operators are forced to begin anew with each project rather than building on existing data.
Overcoming this challenge requires unified engineering, a framework that integrates 1D, 2D, and 3D design models (such as piping and instrumentation diagrams, equipment layouts, and structural drawings) with enterprise resource planning (ERP) systems like systems, applications, and products in data processing (SAP). While plant information (PI) systems provide a digital twin of real time asset behavior, they lack critical corporate data such as procurement schedules and maintenance lifecycles, which reside in ERP platforms. Breaking down these silos by merging operational data with ERP histories enables comprehensive analytics, allowing operators to resolve fundamental process bottlenecks—such as improperly sized pipelines—and shift engineering focus from data collection to asset optimization.
Given Egypt’s reliance on brownfield projects, where do you see the greatest opportunities for modernization and digital transformation?”
Robotics on wheels technology offers a breakthrough in rapidly modernizing brownfield assets by enabling simpler, faster laser scanning. Once scans are complete, operators can execute 1D, 2D, and 3D engineering work directly on the captured data. This framework allows companies to integrate new production trains or expand facilities without the capital intensive burden of manually scanning entire sites. Beyond modernization, these digital models provide a foundation for layering advanced analytics and, eventually, artificial intelligence, unlocking further efficiency gains across Egypt’s brownfield portfolio.
As Egypt’s oil and gas production ramps up, how can companies be convinced of the return on investment (ROI) in digitalization?
Demonstrating measurable optimization and production gains is key to proving ROI. For instance, tailoring digital solutions for Egypt’s El Hamra Oil Company, such as modeling the most efficient pumping systems, streamlining pipeline configurations, and preempting flow assurance issues, directly maximizes output while safeguarding operational continuity. A clear example comes from Abu Dhabi National Oil Company (ADNOC), where applying AVEVA’s fully autonomous maintenance and optimization solutions delivered a 20% boost in maintenance efficiency and a 50% increase in asset uptime. By combining specialized production optimization software with operational data platforms, the project cut downtime and extended facility runtime. We feel there’s going to be a human in the loop for quite a while because of the dangers and the operating conditions in most of plants and facilities.
How does AVEVA’s Unified Command Center strengthen Egypt’s national energy security and industry operations?
Energy security has become a global priority amid supply route disruptions such as those in the Strait of Hormuz, which have left some nations facing acute fuel and electricity shortages. In this volatile environment, AVEVA’s Unified Command Center gives Egypt the macro level visibility needed to safeguard domestic supply chains while optimizing industry operations. By centralizing data, the platform enables midstream and downstream operators to see exactly where they stand within the wider energy ecosystem.
The system also fosters collaboration by allowing companies to securely share non proprietary data to drive collective efficiency. For example, sharing localized water availability data can improve hydraulic fracturing campaigns, while pooling environmental and safety metrics supports shared sustainability goals. Although operators remain protective of reservoir data for competitive reasons, the industry shows strong willingness to collaborate on safety, logistics, and environmental stewardship. The Unified Command Center harnesses this willingness, transforming fragmented operational information into a unified national asset that strengthens Egypt’s resilience and strategic energy position.
Connect Platform is one of AVEVA’s main products. How do you see it adding value to upstream oil and gas operations?
The primary value of AVEVA Connect lies in its industrial cloud capability to integrate, operationalize, and scale third-party (AI) and advanced analytical tools across enterprise infrastructure. Many energy companies develop internal AI models but struggle to deploy them universally due to data fragmentation. Connect bridges this gap by serving as the central engine that unifies data modeling, Asset Information Management (AIM), Information Standards Management (ISM), and real-time operational data platforms like PI systems into a cohesive architecture.
One example of this operational scalability is a project executed by TC Energy. While their data scientists had developed advanced AI algorithms over several years, they lacked a platform to operationalize the models across their footprint. By deploying Connect to analyze fuel consumption against emissions performance, the company established a protocol where algorithms execute five simulation runs to verify asset optimization before implementation. Over an 18-month period, this framework enabled the deployment of more than 180 distinct operational improvements. The system now continuously evaluates 72 facilities across the United States every 15 minutes, dynamically adjustment compressor operating parameters. This optimization strategy successfully abated 54,000 metric tons of carbon dioxide (CO₂) equivalent—an environmental impact comparable to removing approximately 11,800 vehicles from the road. This case study demonstrates the exact scaling and decarbonization capabilities that upstream operators can capture through unified cloud analytics.
How does SNAM’s dispatching facility benefit from using AVEVA’s HMI SCADA system?
The human-machine interface (HMI) supervisory control and data acquisition (SCADA) network serves as the foundational operational baseline for the facility. Previously branded as Wonderware, the system delivers comprehensive pipeline data management capabilities, including real-time leak detection.
Furthermore, the platform executes advanced pipeline simulations, empowering control room operators to model diverse operational scenarios by dynamically adjusting variables such as pipeline routing, pressure gradients, temperature thresholds, and volumetric flow rates. Centralizing these predictive analytics directly optimizes network throughput and enhances overall system efficiency.
