Digital transformation in the oil and gas industry doesn’t take place in isolation. The reality is digital platforms in the oil and gas industry must fit with numerous other considerations. Whether you’re talking about inspection, integrity, production optimization, emissions monitoring or predictive maintenance, every single platform needs to work with a variety of different vendors. Equipment manufacturers, inspection service providers, engineering contractors, software vendors, cloud providers, and analytics tool vendors all play a role in how data is generated, processed, and used.
Vendors bring necessary expertise and technology; over-dependence on their tools can quietly undermine long-term digital drives. Many platforms work well in the proof-of-concept phase, but struggle to scale, adapt or support advanced analytics and AI once multiple commercial off-the-shelf (COTS) products are introduced. Designing platforms that can survive this reality requires thoughtful architectural thinking, not just technology selection.
The Ecosystem Reality: Why Vendors are Unavoidable
In oil and gas, vendor ecosystems are a reality, not a choice. Safety requirements, regulatory standards, and asset complexity make third-party systems essential. Inspection data comes from external service providers, equipment telemetry comes from OEM platforms, and simulation tools are proprietary commercial products.
Major operators openly acknowledge this interdependency. For example, bp has moved its operational decisions across complex assets through a multi-year partnership with Palantir. This effort highlights an all-important industry shift, the goal is no longer to build everything in-house, but to manage how external AI and analytics are orchestrated across the organization.
Vendor Ecosystems Are Unavoidable in Oil and Gas
Unlike sectors that are digital first, the primary objective for oil and gas companies is not to build their own products in house. Most of the requirements vary based on the Regulatory requirements, safety certifications, and operational complexity make vendor participation necessary. Inspection data often comes from third parties. Equipment data comes from OEM systems and engineering calculations may be embedded inside proprietary software. Based on my experience, many products have their own proprietary software and algorithms to read the sensor data.
The problem is not the presence of vendors; it is what happens when the digital platform becomes shaped around them. When each vendor brings its own data model, workflow, analytics engine, and AI roadmap, the platform can quickly turn into a patchwork of disconnected tools rather than a coherent system which can be used enterprise wide.
The Architecture of Independence: Data Decoupling and OSDU
The primary cause of vendor lock-in is the “bundled” architecture, where data, logic, and the user interface are inseparable. When data lives exclusively inside a vendor’s product, organizations lose the ability to pivot. Historical data becomes difficult to access without specific licenses, and analytics logic becomes non-transferable.
To fight this, leading operators are adopting a strategy of decoupling. In the software layer, this means utilizing the OSDU™ Technical Standard. Companies like Equinor have built data hubs (such as its Omnia platform) that separate the data from the application.
This philosophy is now extending to the plant floor through the Open Process Automation (OPA) initiative. Spearheaded by ExxonMobil, the move toward the O-PAS™ standard aims to break the “black box” of proprietary control systems. By pushing for interoperability, ExxonMobil has made sure that hardware and software from different vendors can just slot in and work, no fuss. Instead of having a massive, unmovable system, this approach lets operators swap out bits without having to rip up the whole thing, just like “plug-and-play.” This treats the platform as a modular ecosystem rather than a monolithic trap, allowing operators to swap components without tearing out the entire foundation.
The AI Scaling Wall: Platform Capability Versus Vendor Feature
Analytics and AI expose platform weaknesses faster than any other technology. While most COTS products now offer embedded AI, these features often work in silos. The challenge surfaces when a company needs to run analytics across different sites, assets or disciplines.
The recent expansion of Chevron’s engineering hub in India underscores this need. Its investment in real-time modeling and digital twins depends on unifying data from dozens of disparate systems. In practice, AI models trained in one vendor’s system cannot easily be reused in another.
To overcome this, AI must be treated as platform capability rather than a tool feature. By using enterprise AI platforms, which are utilized by operators for several supermajors, they can create a unified “intelligence layer.” This allows for predictive maintenance and performance analytics that see the whole operation, not just the data a single vendor provides.
Owning the “Operational Story,” Not Just the Data
Successful platforms focus on owning the operational story of the asset: What happened, when it happened, and why it matters rather than simply storing raw data. Vendor tools may generate measurements, alerts or predictions, but the platform must connect these signals into a consistent narrative that supports decisions.
This approach is particularly important for AI. Machine learning models are only as good as the context in which they operate. When AI is embedded inside individual vendor products, it often lacks visibility into broader operational conditions. Platform level context enables more trustworthy insights and avoids conflicting recommendations from different tools.
Designing for Change, Not Stability
In oil and gas, vendor change is inevitable. Contracts are re-bid, technologies evolve, and business priorities shift. Digital platforms must be designed with this reality in mind. Rather than assuming that your vendors will stick around in the long term, you need to build your platform with the ability to adapt to change without causing too much of a headache.
What that means is making sure your data isn’t locked up in case one of your vendors disappears; i.e., it’s still accessible, analytics can keep chugging along even if you start using new tools, and your AI models can get retrained or replaced without having to start from scratch and rebuild everything.
Governance Without Slowing Innovation
Vendor ecosystems throw up all sorts of governance headaches when it comes to getting access to data, keeping things secure, ensuring you’re compliant, and being able to audit what’s going on. And, when you’ve got a ton of tools and stakeholders in the mix, those manual governance processes can start to clog up and become the slowest part of the operation. You really want your platform to be able to handle governance as a natural part of its design, so that vendors can keep on integrating and innovating, but still stay within some decent boundaries.
This balance is crucial, especially when you’re dealing with analytics or AI where regulators and internal risk teams are starting to get a lot more scrutiny focused on transparency and being able to track everything back to its source.
Keeping the Long View in Mind
Vendor ecosystems are a permanent feature of the oil and gas digital landscape. The real question is whether digital platforms are designed to work with vendors or to depend on them. Platforms that survive over the long term are those that retain control over data, analytics, and decision logic, while allowing vendors to contribute specialized capabilities.
As analytics and AI become central to operational decision making, the importance of platform centric design only increases. Organizations that treat AI as a vendor feature will struggle to scale insights across assets. Those that treat AI as a platform capability will be far better positioned to adapt, innovate, and compete in an increasing digital energy industry.
In oil and gas, digital success is measured over decades, not quarters. Platforms that survive vendor ecosystems are the ones built with that long view in mind.
Shivaprasad Sankesha Narayana is a senior cloud and solution architect with over two decades of experience driving large scale digital transformation, AI adoption, and cloud modernization across the global oil and gas industry. His work spans advanced edge cloud integration, real time telemetry platforms, and AI/ML solutions that strengthen asset reliability, process safety, and operational efficiency for major energy institutions. A senior member of IEEE and a multi-conference reviewer, Narayana has authored influential publications on cloud computing, digital twins, and industrial AI, with several works featured on leading technical platforms. He has served in critical architectural leadership roles as consultant at bp, ExxonMobil, Capgemini, Infosys, and Axiom Medical, shaping enterprise standards and next generation digital ecosystems. Recognized internationally for his expertise, Narayana continues to contribute cutting edge research and thought leadership to advance the future of intelligent energy systems.
