Every major mining, energy, and aerospace organization I talk to has the same problem. The ERP is live and the data is “in the system”, yet the people who run day-to-day operations are still working out of spreadsheets, shared drives, and institutional memory that leaves when someone retires. Technology was never the issue. The gap was always between what enterprise systems were designed to know and what actually has to happen on site.
The Gap Between Enterprise Systems and the Real Work
Picture the ERP as a large box at the center of the organization. Platforms like SAP, ServiceNow, and Oracle create a common process backbone across countries, sites, and functions. They are essential, but they are also, by design, vanilla.
The reality of work in the field is different. Teams build the workflows they actually need in Excel, in temporary databases, in shared drives, outside the ERP. These workflows are business critical.
ERP vendors don’t understand them deeply enough to support them, and there is limited commercial incentive for them to try. The result: thousands of pilots and proofs of concept that never convert into value, because the engineering knowledge and the workflow context aren’t there.
According to IDC, Poor data quality costs organizations an average of $12.9 million per year — a burden that falls on asset-intensive industries where operational decisions depend on accurate, accessible engineering data.
Here is what that looks like in practice;
At one major global miner, a team of 40 people manually processes data for an operational process every day. The ERP knows the assets exist, but it has no idea how that workflow runs. Taking that 40-person manual process and turning it into an integrated, automated workflow the ERP can execute, that is the value that has been uncaptured. That is what the Engineering Intelligence Platform (EIP) is built to unlock.
The Pressure Is Real
Operations leaders across heavy-asset industries are being asked to do more with less. The pressures are not new, but they are compounding. Fragmented operational and engineering data reduces visibility and slows every decision downstream. Throughput and recovery expectations keep rising without proportional headcount to meet them. Shutdowns, maintenance, and asset management demand tighter integration than most organizations currently have. And regulatory, closure, and divestment obligations now require levels of lifecycle auditability that manual processes simply cannot support.
Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.Get the deployment right with AI-ready data, and the AI works providing the uncaptured value.
What is needed is an integrated operational intelligence layered on top of existing ERP — connecting plant operations, maintenance, engineering, and enterprise systems into one coherent workflow.
Engineering Intelligence Platform (EIP): Built on Human Expertise, Domain Knowledge, Augmented by AI
Cyient has worked at the intersection of engineering, operations, and enterprise technology for more than 35 years. The Engineering Intelligence Platform is the distillation of that experience, built not to replace the ERP, but to capture and operationalize the workflows that are between enterprise systems and the field.
The approach starts with human expertise and deep domain understanding, then applies AI where it adds the most value. Most big-tech offerings lead with the AI and expect the domain knowledge to follow. It rarely does. The EIP leads with deep engineering understanding of how work actually gets done in a refinery, in an open-cut mine, in an MRO hangar and then applies AI where it creates the most value. Workflow automation, AI ontologies, and intelligent process orchestration are all in the platform and they are built around the engineering workflow.
This is Lifecycle Engineering in practice: managing assets, workflows, and engineering knowledge across the full operational life of a site. It does not replace engineering judgment, but it makes that judgment faster, better-informed, and accessible to the whole team.
The EIP is also platform-agnostic. Heavy-asset organizations already run complex technology ecosystems - SAP, ServiceNow, Oracle, Microsoft, AWS, and a range of specialist operational tools and the EIP integrates into those environments. Once a workflow is proven, it deploys into the customer’s enterprise platform of choice. The engineering IP stays with the operator, ready to scale.
What This Looks Like When It Works
The best way to understand the EIP is to see the pattern across industries. Mining buyers learn from energy operators. Energy operators learn from aerospace. The underlying problem is the same everywhere: organizations have the operational knowledge that is effectively unreachable, and the EIP making that knowledge a working part of the operating model.
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Global Mining Operator: From Fragmented Archives to Regulator-Ready
Millions of legacy documents, environmental, geotechnical, and operational documents stored across physical archives, shared drives, and disconnected systems. There was no single source of truth. Every time a regulator asked a question, teams had to manually search multiple systems to piece together an answer.
Cyient will deploy the EIP to digitize legacy records at scale with layered ontology-driven AI classification, deduplication, and engineering entity extraction across the full corpus. The structured output is integrated into the operator’s enterprise platforms and operational workflows.
The Result: Millions of fragmented records became a structured, searchable, AI-enabled knowledge platform, improving compliance visibility, document retrieval speed, regulatory traceability, and lifecycle decision-making across operations.
Along with the ability to ingest data within timeframes, and at a cost that is commercially viable.
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Global Refining & Energy Operator: A Million Documents, Finally Accessible
More than a million documents — P&IDs, engineering drawings, inspection reports, and maintenance records, were stored in physical archives across global refinery operations. Asset tags were misaligned between SAP PM and the engineering systems, creating real operational risk: maintenance could be executed against the wrong asset without anything in the workflow flagging it.
The EIP delivered structured digitization at scale, followed by AI-powered metadata extraction and engineering document auto-recognition using NLP and semantic understanding. The output was integrated end-to-end with SAP PM and wrapped with an auto-operating layer that monitors overdue inspections and maintenance signals across the asset base.
The Result:A structured, SAP-linked, searchable operational knowledge platform with full lifecycle traceability, improving retrieval speed, maintenance workflows, compliance, and engineering decision-making across global operations.
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Global Aerospace & MRO Organization: Getting the Right Answer to the Right Person
Engineering and maintenance teams were navigating a crisis hidden in plain sight. Service bulletins, inspection reports, engineering manuals, and operational documentation lived across incompatible systems. Critical engineering interpretation depended on a small number of subject-matter experts - a single point of failure with real consequences: turnaround-time overruns, aircraft-on-ground events, missed service commitments, and rising regulatory exposure.
Cyient used the EIP to ingest engineering manuals, maintenance records, service bulletins, and historical defect data into a unified semantic knowledge layer. Automated ontology generation, engineering entity extraction, and cross-document correlation turned fragmented data into structured operational intelligence. Engineering co-pilots, intelligent search, and root-cause assistance delivered the right information at the point of operational need.
The Result: Engineering and maintenance teams now access, correlate, and operationalize complex technical knowledge in real time, supporting faster troubleshooting, more reliable maintenance execution, and better decision-making across the fleet.
The Future Belongs to Connected Operations
Across mining, energy, and aerospace, the pattern is consistent. The value is not in another standalone tool. It is in connecting the workflows that sit between enterprise systems and the field and treating engineering knowledge inside those workflows as something worth owning, structuring, and scaling.
AI and advanced analytics hold transformative potential for the mining industry, unlocking significant value through smarter operations, predictive maintenance, and data-driven decision-making. The organizations capturing that value are the ones that moved from pilot to at-scale deployment and did it by starting with the engineering problem.
Engineering Intelligence is an operating-model story. The organizations that win the next decade will bring their engineering IP, their workflows, and their enterprise platforms into a single, intelligent source of truth and treat that source of truth as a strategic asset.