In this article, we share how, alongside the supply chain and logistics team at one of the world’s most iconic LNG plants, we helped “get ahead of the heat” of managing demand during a complex turnaround. By providing supply chain experts with access to powerful GenAI models, combined with context, data, and insight, they were able to unlock more value through increased cost avoidance opportunities without risking production. From this experience, we explain why we believe the oil and gas supply chain and logistics functions have a significant opportunity to shift from ‘fire fighting’ to ‘fire prevention.’ Embracing this opportunity confidently can transform supply chain and logistics into a data-driven cost avoidance machine, directly connected to projects and maintenance. This transformation is only possible by decoupling data from applications, breaking down silos, and connecting demand with fulfillment as seamlessly as possible, with experts always at the command center.
When executed properly, and in collaboration with strategic partners, this AI-powered supply chain can lead the organisation in demonstrating practical productivity gains. However, trust in data, partners, and context is crucial, as hallucination in safety-critical operations like oil and gas is simply not an option.
The Journey Begins
Our journey with AI began in 2016, inspired by DeepMind AlphaGo‘s success, which demonstrated AI’s potential to surpass even the best human minds. Watching AlphaGo master the complexities of Go reminded us of the challenges in the oil and gas supply chain—a deceptively simple yet unpredictable orchestration requiring deep expertise for flawless execution. AlphaGo inspired us to imagine how AI could support and empower operational experts, not compete against them, and to think in new, innovative ways, just like the famous Move 37—a move no human would have made.
We started with ‘Logistics Summary,’ a simple AI application that helped increase High Cost Rental (HCR) compliance to 99% by notifying experts when an HCR was at risk of not being mobilised on time due to vessel delays or unorganised transport.
Over time, we added to this capability, but there remained a barrier: insight needed to be translated by subject matter experts into action across multiple functions. With the rapid advancement of Generative AI, we have finally breached this barrier, directly connecting supply chain to projects for a self-service supply chain that “gets technology out of the way.” By reducing process friction, capturing context, and linking to demand, Generative AI enables the supply chain function itself, controlled by its experts, to respond more quickly and flexibly, creating a truly adaptive and resilient supply chain.
Today, VOR AI provides real-time, context-aware answers to questions like, “Where is my shipment?” and “Will it arrive on time?” in seconds through tools like Microsoft Teams. This eliminates the need for phone calls and emails, freeing supply chain and logistics teams from repetitive information-hunting tasks. VOR AI has evolved beyond merely providing information—it is now an active partner for both supply chain and operations and maintenance experts, executing workflows, managing tasks, and driving actions to “Get Ahead of the Heat.”
Key Areas of Focus – How to Apply AI for Maximum Impact
Start Simple with Medium-to-High-Value, Repeatable Workflows
By working with operational experts, we identified pain points that required cognitive effort or involved repetitive tasks that could be automated, such as identifying cost leakage, managing container statuses, or sending on-site risk notifications. VOR AI, supervised by operational experts, can now autonomously execute these workflows, enabling experts to focus on strategic decisions and taking meaningful action.
Separating Quality Data from Applications
By moving away from fragmented spreadsheets and adopting an integrated ecosystem approach with key suppliers, operators can interact directly with supply chain data, leading to faster adaptation and informed decision-making. This approach enables consistent access to quality data, ensuring that issues like underperforming suppliers are identified and addressed early, regardless of the system or interface used.
4PT Ecosystem – An Agent-Led API Integration
The benefits of using natural language boil down to making workflow automation easier, a critical friction point with complex technologies that require a masters degree to use. Instead of breaking processes into complex steps requiring technical expertise, agentic-led systems allow nontechnical experts to automate, test and control workflows quickly, using plain language. This broadens access to AI and integrates their expertise seamlessly to allow for solving repeatitive tasks that add little value but take up a lot of time. By taking this approach further and opening these workflows up to the ecosystem, we believe Agent-led API automation will accelerate data integration, reduce manual tasks, and consolidate context, boosting efficiency, data quality, and reliability in supply chain operations.
Customer-Driven Rapid Iteration
During this turnaround, continuous bi-weekly feedback was crucial to align VOR AI capabilities with the real operational needs on the ground. This transparent feedback helped VOR AI evolve quickly, both from measuring value and from getting the improvements in model capabilities into the hands of operational experts. This resulted in providing accurate insights on where issues were going to be and an opportunity to ask why to the right person while demystifying AI for the organisation as they experienced its value firsthand.
Where Are We Now and Where Are We Going?
Timing is everything, and deploying AI that works and is actually useful, at pace, in organisations where safety is the top priority is near impossible without getting the basics right. There is no short circuit for this. It requires doing the heavy lifting and putting in the hard yards. With teams and partners committed, suppliers on board, data and applications decoupled and data quality standards upheld, the foundation for rapidly proving value cases is set, justify bringing the context and information that was once scattered across multiple systems, suppliers and desktops directly under your teams command and control.
Today, VOR AI, integrated with platforms like Microsoft Teams, provides direct answers to questions like, ‘Where is PO12345?’, eliminating the need for multiple searches, emails, or calls. It consolidates data from different systems of record into simple, plain language—whether in Spanish, Australian, or Kazakh—fostering trust and precision across operations, procurement, and maintenance. VOR AI not only answers questions but also helps supply chain and logistics operational experts take proactive intervention and actions to now ask ‘How can we improve?’.
By shifting from reactive to strategic, AI is transforming supply chain execution today, opening it the hard work of bringing together a dependable, resilient ecosystem to the wider organisation and elevating the supply chain function to a visible and critical role in projects and production. This drives accountability across global functions and fosters collaboration with data as the conduit for progress. This is where we are heading—putting supply chain at the center of the organisation to enhance productivity and outcomes while reducing costs and emissions.
And we are calling this Uplift.
VOR Uplift – Unlocking Supply Chain
VOR Uplift is our low-code and no-code tooling framework that leverages the latest reasoning and planning capabilities from reasoning LLM models to help operational experts execute and refine workflows autonomously.
By giving a natural-language prompt, supply chain experts can initiate workflows with high-level plans of tasks, requirements, and conditions. Uplift breaks these into executable subtasks, using the VOR data model to consolidate data and insight from multiple systems, providing context and real-time progress updates. Operational experts remain in control throughout the process, with full visibility into how tasks are being completed, including data sources, suppliers, and insights used to verify outputs. With this context and data secured, organisational data lakes can be continuously improved and leveraged by models across the organisation, where a virtuous circle of continuous improvement is unlocked.
Data quality and context are crucial. Without these basics, LLM technologies can produce unreliable, but convincing “hallucination” results, making trust—vital for operational experts in safety-critical industries like oil and gas—hard to establish. In these high-stakes environments, trust is built through consistent, rigorous effort, the ‘hard yards’.
How Did We Get Here?
In the oil and gas industry, reputation is everything. Our expertise, built through years of hands-on experience, ensures quality, safety, and reliability in our AI technology. Our platform, forged in the heat of the worlds most iconic energy projects, is uniquely tailored to the complex needs of upstream extraction and downstream logistics. By putting this powerful capability directly into the hands of oil and gas people, they can unlock insight by being closer to asset demand and reciprocate by integrating assets (through digital twin) with the complexities of safe, predictable fullfillment.
Alignment, Transparency and Control
Successful technology adoption needs SME, organisational and partner alignment across the ecosystem to simplify interactions and meet real needs, quickly. By tapping into established tools like Microsoft Teams we remove barriers to adoption and increase engagement between internal functional teams, letting experts engage with one another naturally and ensuring functional leaders retain complete control of the process.
Innovation Partnerships for Seamless Ecosystem Integration
Partnership with customers supported by proven industry leaders such as IBM drive innovation. These collaborations help technology companies such as Streamba focus on integrating the capabilities of AI models to meet the accelerating global supply chain needs and manage the change management processes required. Our experience shows that a collaborative, secure, ecosystem driven approach where data flows between applications and external systems through APIs, delivers on the promise that real-time access to data and reducing operational silos brings – a measurable opportunity to improve.
Closing Thoughts
The use cases are clear – reducing response times, minimising cost leakages, decreasing errors, capturing context, unlocking trapped value, consolidating and simplifying contracting. The early signals we see from our experience during this turnaround lend credibility to suggest AI in important industries such as energy will not only change the industry itself, but the industries they serve. With energy at the forefront of powering the AI boom, we are laser focused on ensuring it creates opportunities for oil and gas people to play their ‘Move 78‘.