$58 m illion in economic benefit for a $3 b illion petrochemical major
$0.1M Sales analytics & forecasting
$2.5M Feedstock cost analysis
$9.2M Grade market & sequence optimisation
$8M Remote monitoring
$0.2M Lab analytics
$5M Asset performance
$0.1M Smart contracts
$5.5M Logistics optimisation
$2M Optimising economic bene t from spread analysis
$10M Hydrocarbon loss analysis
$5M Real-time optimiser (RTO)
$6.7M Inventory optimisation
$2M Smart turnaround
$2M Pricing analytics
AI and data platform Monitor and predict
Optimise and improve
Procurement
Buisness planning
Production
Maintenance
Distribution
Marketing and retail
Figure 5 AI-powered RTO: $58 million of benefits realised
Strategic advantage of mature AI platforms Bridging this gap requires a fundamental shift: moving from fragmented, point-solution AI deployments to integrated, enterprise-grade platforms engineered for scaled transformation. For oil and gas companies seeking to do a technology transformation and implement AI on a large scale, the choice of an AI platform becomes critical. Established, enterprise-grade AI platforms offer significant advantages over nascent solutions through proven scalability, robust security frameworks, and comprehensive integration capabilities. The advanced AI platforms distinguish themselves through critical capabilities: • Modular analytics engine: Low-code and no- code-based developments combining traditional machine learning, generative AI/large language models (LLM), and automated data analysis for enterprise-scale agentic applications. • Semantic data framework : Ontology and knowledge graph-based integration that contextualises data and grounds AI insights in operational realities. • Unified data architecture : Real-time and batch data ingestion and transformation across relational and NoSQL formats, with comprehensive lakehouse storage for all data types. • Self-service intelligence : Dashboarding and operational reporting with on-demand and scheduled distribution, enabling real-time visibility into critical process variables and AI- driven recommendations. • Enterprise governance, and compliance : Advanced workbench with user administration,
Global oil and gas companies have launched numerous AI pilot projects, yet comprehensive, enterprise-wide AI adoption remains limited. Most AI applications are concentrated in exploration and production, particularly in seismic analytics, drilling optimisation, predictive maintenance, and field-level emissions monitoring. In contrast, midstream and downstream sectors, such as refining, petrochemicals, logistics, and customer-facing “ Despite significant national investments, AI integration across the entire value chain is generally at advanced pilot or early deployment phases rather than full operational maturity ” operations, have seen relatively few large- scale AI deployments. Despite significant national investments, AI integration across the entire value chain is generally at advanced pilot or early deployment phases rather than full operational maturity. This highlights both the promise and the challenges of scaling AI to transform the oil and gas industry comprehensively. This gap stems from challenges such as fragmented data, siloed systems, and few examples of AI enabling true operational autonomy or end-to-end transformation. As a matter of fact, most companies have still tested AI in pockets, and the major value depends on overcoming these barriers and moving from pilots to scaled impact.
Refining India
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