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by Seven Peaks on Nov 18, 2025 9:33:12 PM
The conversation around artificial intelligence in the C-suite is evolving at record speed. Already, the focus has already shifted from generative AI to agentic AI: a new category of autonomous systems that can make independent decisions and execute complex, multi-step tasks, promising a future of autopilot operations.
For the oil and gas (O&G) industry, agentic AI promises a future of autopilot operations, complete with systems that can optimize drilling schedules, fine-tune production parameters, or manage logistics networks with minimal human input.
This technology promises to unlock a new service-as-a-software model, where organizations pay for tangible outcomes (like reducing equipment downtime by 15% or improving logistics efficiency by 5%) rather than per-user software licenses. Analysts estimate it could add between $2.6 and $4.4 trillion in annual value to the global economy.
But beneath the excitement lies a sobering truth: most digital transformation efforts are still stalling long before they reach scale. For O&G companies, the dream of autonomous, AI-driven operations will remain a dream until their underlying data foundations are fixed.
Executive summary
Despite rapid advances in AI, most oil and gas (O&G) operators remain stuck in pilot purgatory. More than half have active digital initiatives, yet only 13% have scaled them successfully. The reason isn't because of failed algorithms. It’s unreliable, siloed, and unstructured data.
This article explores how O&G leaders can build the 90% foundation required for the 10% AI payoff, using lessons from BP, Shell, ExxonMobil, and Aramco.
Key takeaways
- AI success starts with data readiness: Transform static, legacy engineering files into structured, interoperable data assets before deploying AI tools.
- Build ecosystems, not silos: Establish a unified, vendor-agnostic data platform so different tools and AI agents can work from a shared, trusted source.
- Treat data as a living asset: Continuous data governance is essential to keep digital twins and AI systems accurate.
- Adopt a phased approach: Start with high-value assets or units; each phase should deliver validated data and measurable ROI within 6–12 months.
- Fix the 90% problem first: The future of Agentic AI will be built on a foundation of reliable, AI-ready engineering data.
AI success starts with data readiness
Build ecosystems, not silos
Treat data as a living asset
Adopt a phased approach
Fix the 90% problem first
Why digital initiatives stall
For all the talk of an autonomous frontier, the ground-level reality for most O&G operators is one of digital frustration. Across the industry, digital transformation initiatives are stalling at an alarming rate.
The proof-of-concept graveyard is real: promising pilot projects that deliver impressive slide decks but are quietly shelved. 12 months later, never making it to production. Research conducted for Oracle shows a painful gap between ambition and execution. While more than half of industrial companies have active digital initiatives, as few as 13% have successfully scaled them beyond the pilot stage.
Why do large projects fail? Not because the AI models are flawed or the 3D visualizations are unconvincing. They fail because they're built on a foundation of digital sand.
In a 2020 survey by Accenture of over 1,500 industrial executives (including oil and gas), a staggering 75% reported that their departments compete rather than collaborate on digitization efforts, creating data silos that hinder digital transformation. Companies are discovering that their ambitious AI and digital twin projects are grinding to a halt because the underlying data is scattered, unstructured, untrustworthy, or trapped in proprietary formats and decades-old PDFs.
This represents the practical, daily friction that every engineer and operator knows. Maintenance teams don't trust the data because it's three years old. Engineers hunt across three different servers to find the right P&ID, only to discover it's an unsearchable scan of a hand-marked drawing. The planning department works from a spreadsheet that conflicts with the data in the ERP.
You cannot build an autopilot future on a foundation you cannot trust. The most valuable AI case studies, when you look closely, are not about AI at all. They are about data.
What BP can teach oil & gas companies about AI data value
BP is a prime example of AI success. The company uses AI to analyze seismic data, generating 3D models of subterranean structures. Its multimodal approach combines geological, geophysical, and historical data to identify favorable drilling sites and orchestrate drilling equipment settings for optimal outcomes that have resulted in a 20% cut in exploration costs and a 15% increase in successful drilling.
That's a fantastic win. But the lesson most executives will take from it is wrong.
While the surface-level lesson is "We should buy AI to find oil," the real lesson is that BP's AI success is merely the 10% tip of the iceberg visible above the water. The 90% that enabled these wins was BP's long-term, strategic investment in its underlying data.
BP's AI generates those 3D subsurface models because it has access to decades of high-quality, structured geological and geophysical data in massive, standardized repositories. Its multimodal analysis works because historical drilling data, seismic surveys, and production records have been meticulously integrated into a common data language. Its equipment orchestration works because real-time operational data from drilling rigs flows through mature, integrated IoT sensor networks.
BP's true competitive advantage is not just having AI but having AI-ready data. The company did the hard, unglamorous 90% of the work first. It turned scattered geological surveys, drilling logs, and equipment data into a queryable, trustworthy asset. Only then could the 10% magic of AI deliver substantial cost reductions and operational improvements.
The same lesson across industry leaders
This pattern repeats across the industry's AI success stories. Shell has deployed AI-powered predictive maintenance to monitor more than 10,000 pieces of equipment globally, processing 20 billion rows of data weekly from over 3 million sensors and generating 15 million daily predictions. But this massive-scale deployment only works because Shell first built the data infrastructure to support it.
ExxonMobil has reduced 4D seismic processing time from months to weeks using its Discovery 6 supercomputer and achieved production uplifts greater than 5% across more than 200 Bakken wells through machine learning optimization. Again, this is built on years of high-quality subsurface data collection and management.
Even Saudi Aramco's impressive 18% reduction in power consumption and 30% reduction in maintenance costs at its Khurais field depended on creating a robust data foundation first.
According to McKinsey research, on average, companies only capture about 30 % of the value they expect from their digital transformation initiatives. The companies that do succeed have several qualities in common, including a focus on rich, verified data. For O&G leaders, the truth is that you must first fix your data problem. Doing so requires three core shifts in how you approach your digital strategy.
Three shifts for an AI-ready O&G business to make
Building the groundwork for AI is not just a technical IT project. It's a strategic overhaul that requires a new way of thinking about your data, your architecture, and your operational workflows.
1. Prioritize data readiness over AI readiness
Your first priority must be to make your data ready for any platform.
For decades, critical asset data like P&IDs (Piping and Instrumentation Diagrams), CAD models, vendor spec sheets, and maintenance histories have been trapped in static, unstructured formats. An AI agent cannot read a 20-year-old scanned P&ID PDF any better than a new human engineer can. To an intelligent system, that data is invisible.
Data readiness means transforming this invisible, unstructured data into structured, validated, and interoperable digital assets. This is data archeology. It means going into the digital trenches, finding the scattered data, and re-engineering it. Converting a drawing of a pump on a P&ID into a pump object in a database, complete with all its associated properties: maintenance history, operational specs, vendor data, links to other assets.
Gartner research found that poor data quality costs organizations an average of $12.9 million annually, with process industries experiencing even higher losses due to the complexity of their asset data.
The challenge is creating that single, structured data asset first, which requires deep engineering domain expertise to understand what a pump specification means, how maintenance intervals relate to operational context, and which data points are critical for decision-making.
This often involves adopting global interoperability standards. One example is DEXPI (Data Exchange in the Process Industry), an open standard that defines how engineering data from P&IDs should be structured and exchanged. Think of it as a universal language that ensures a valve specification from one system can be correctly interpreted by another system, whether that's a digital twin platform, a maintenance scheduler, or an AI analytics tool. Standards like DEXPI ensure that when you transform your static drawings into structured data, that data remains usable across different platforms and vendor tools.
The validated, standardized asset becomes the single source of truth that all other intelligent systems can then plug into.
2. Design an ecosystem, not a collection of silos
The service-as-a-software model is both exciting and dangerous. Exciting because it promises best-in-class tools for specific problems. Dangerous because it presents a new version of an old problem: tool fragmentation and data silos.
If you buy a dozen different black box AI agents from a dozen different vendors, you'll create an integration nightmare. What happens when your drilling optimization agent and your predictive maintenance agent can't exchange data? You get conflicting recommendations, operational chaos, and a fragmented architecture. Each agent will hoard its own data, making integration impossible.
Organizations using multiple disconnected digital tools consistently experience more project delays and higher total cost of ownership compared to those with unified data platforms. The challenge compounds with scale, where each additional disconnected tool increases integration complexity exponentially.
This second shift is to design a unified data management ecosystem rather than collecting siloed apps.
Build a central data platform that is tool-agnostic. The platform, built on your AI-ready data from shift 1, becomes the central nervous system for your asset, allowing you to plug in (and unplug) best-in-class apps as needed. You can use one vendor's digital twin visualizer, another vendor's AI analytics engine, and a third vendor's simulation tool, all operating from the same single source of trusted data.
Think of this like building a data hub with standardized connections. The hub holds your master asset data, including all your equipment specifications, operational histories, and engineering documentation in structured, validated form. When a new analytics tool comes along, it connects to the hub through standard interfaces (like APIs following DEXPI or other industry standards), reads the data it needs, and writes back its insights. This means you're never locked into a single vendor's ecosystem.
Companies that have built unified data platforms consistently report faster deployment times and lower total costs when adopting new digital tools, precisely because they've eliminated the integration bottleneck.
This architectural approach future-proofs your digital strategy, giving you the control to adopt new AI tools as they emerge without being held hostage by a single vendor's closed system.
3. Manage data as a living asset, not a one-time project
The third shift is to recognize that your data groundwork is not a one-and-done project. It's a living asset that must be governed and maintained for the entire lifecycle of your facility.
Here's where most digital twin initiatives die. A team spends 12 months building a perfect digital replica. It works. The project is declared a success.
Twelve months later, that digital twin is dangerously out of date.
It doesn't reflect the hundreds of as-built changes, redline markups, and component swaps that have happened in the field. The AI agent making predictive maintenance suggestions is now working from false data. An operator trying to plan a shutdown from the digital twin is looking at a system that no longer exists.
Digital twin accuracy degrades rapidly without proper governance. Within months of deployment, the gap between digital representation and physical reality begins to widen. Each unrecorded change, from a valve replacement or pressure rating adjustment to a piping modification makes the digital twin less trustworthy. Eventually, operators stop consulting it altogether.
A digital twin that doesn't reflect as-built reality is a critical operational liability. An engineer planning a shutdown based on a digital twin with the wrong valve specs faces a clear and present safety risk.
That's why successful operational frameworks implement robust systems for continuous data governance. This is not a technology problem but a workflow and process problem. It involves creating a simple, iron-clad Management of Change (MOC) process that is digital-first, where every single change made in the field is fed back into the central digital asset daily, not stuffed in a filing cabinet to be scanned later.
Making data governance work
The implementation typically involves three components.
1. Field capture tools
Mobile devices or tablets that allow technicians to record changes as they happen, with photo documentation and validation requirements built into the workflow.
2. Automated validation
Rules engines that flag inconsistencies. For instance, if replacing a valve changes the pressure rating, the system flags affected downstream equipment and initiates reviews.
3. Closed-loop verification
Before any change is marked complete in the digital system, a qualified reviewer validates that the digital record matches the physical reality.
Implementing effective data governance requires upfront investment in process redesign and change manage ment, but organizations that have done so report dramatically reduced long-term data maintenance costs. More importantly, they avoid the far larger costs of unplanned downtime, safety incidents, or failed optimization initiatives based on operational decisions made on inaccurate information.
This continuous governance maintains the trust and integrity of the entire data foundation, ensuring the 90% groundwork remains solid so the 10% magic of AI can be applied with confidence, year after year.
Addressing common objections
Some leaders might question whether this data-first approach is always necessary. These are valid concerns, but experience shows that skipping the groundwork only delays progress and increases technical debt. The following questions often come up in discussions about AI adoption, and addressing them helps clarify why data must come first.
"Won't this delay AI benefits by years?"
Data transformation doesn't have to be all-or-nothing. A phased approach focusing on high-value assets first can deliver quick AI wins while a broader data transformation continues. The key is ensuring each phase produces validated, structured data that becomes part of your growing data foundation.
Industry evidence shows that organizations attempting to implement AI before data readiness experience significant delay in achieving production deployment.
"What about quick wins from off-the-shelf AI tools?"
Off-the-shelf AI tools absolutely have their place, particularly for well-defined problems with clean, accessible data. The question is: does your organization actually have clean, accessible data? According to a 2024 Gartner survey, 85% of AI projects fail to deliver expected returns. More recently, a 2025 Gartner survey found that 63% of organizations either do not have or are unsure if they have the right data management practices for AI. Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.
For problems like image recognition on drone inspections or document classification where the AI model itself is the main challenge, buying a specialized tool makes sense. But for operational optimization, predictive maintenance, or digital twin applications that require deep integration with your asset data, the data foundation must come first.
"Can't AI help clean up our data?"
Partially, yes. AI and machine learning can assist with data classification, anomaly detection, and identifying patterns. However, AI cannot make domain-specific engineering judgments. It can't tell you whether a hand-written "300#" on a 1987 P&ID refers to a pressure rating, temperature specification, or equipment tag number. That requires engineering expertise.
The most effective approach uses AI as an accelerator for human-validated data transformation, not as a replacement for it. Teams that combine AI-assisted data processing with human domain expertise complete data transformation projects faster and with higher quality than teams using either approach alone.
"Our data is so bad, this seems impossible."
It's not impossible, but it is a significant undertaking. However, the alternative of continuing to build AI and digital initiatives on faulty foundations has an 87% failure rate based on the Oracle data cited earlier. The question isn't whether data transformation is difficult; it's whether you want to pay for digital transformation once (properly) or multiple times (through failed pilots and abandoned platforms).
Every successful digital transformation in O&G has begun with an honest assessment of data quality, followed by a systematic approach to improvement. The companies that have made this investment are now deploying AI at scale. Those that haven't are still stuck in pilot purgatory.
Moving from agentic AI to an actionable data roadmap
The promise of an autopilot O&G facility run by autonomous AI agents is real. The examples, like BP's, prove the value is massive. But this future will not be bought from a software vendor. It will be built on groundwork of clean, structured, and governed engineering data.
The first step is not to hire an AI data scientist or request a demo of an agentic platform.
The first step is to conduct an honest Digital Readiness Assessment. Map your existing engineering data. Perform a data-process audit: Where is critical data created? Who uses it? Where does it get stuck? Where do engineers lose time? Where does your data fail to reflect reality? This assessment allows you to identify where your critical data is trapped in silos and unstructured formats, and build a pragmatic, step-by-step roadmap to transform it into a true digital asset.
Data transformation timelines vary based on asset complexity and scope. Pilot projects focusing on single process units typically require several months. Facility-wide implementations extend to 12-24 months as teams work through multiple asset types and integrate with existing systems. Enterprise-wide rollouts can take 2-4 years, though phased deployments allow organizations to capture value throughout the journey rather than waiting for complete transformation.
The investment is substantial, but organizations that have completed data readiness initiatives consistently report strong returns through improved decision-making, reduced downtime, and successful AI deployment. More importantly, they've built a foundation that enables continuous innovation rather than one-off projects.
If you're ready to move beyond the hype and build the data foundation that makes AI possible, Seven Peaks is here to help. We specialize in turning complex O&G data into operational reality.