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10 mins
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60 mins
- Keynote Address
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30 mins
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90 mins
- Plenary Session
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60 mins
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120 mins
- Technical Session
Session Managers: Izzat Fauzi, Quorom Software; Zayd Umair Zakaria, PETRONAS Digital; Lee Kian Seng, AIEngineer
Building a robust AI Ecosystem requires transformation at the organizational level, beginning with strong change management to align leadership, culture, and processes around AI-driven innovation. To unlock novel AI applications, organizations must first identify key pain points and value levers that clearly define the problems to solve, the measurable outcomes to achieve, and how AI can create tangible business impact. The foundation of this ecosystem lies in a resilient technology stack and scalable infrastructure that supports experimentation and enterprise-wide deployment. Test Automation ensures the continuous validation, reliability, and upgradability of AI models, while robust DataOps and MLOps practices ensure sustenance of data and model lifecycle (from development to monitoring and optimization).
Cybersecurity control must be embedded by design to protect models, pipelines, and sensitive data against evolving threats. Equally critical is a strong regulatory framework that governs model usage, ensures compliance, and addresses data sovereignty requirements. By architecting AI systems with localized data controls, federated learning approaches, and compliant cross-border data strategies, organizations may able to orchestrate & scale AI responsibly while preserving Sovereignty and Trust.
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35 mins
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120 mins
- Technical Session
Session Managers: Tracy Lim, BSP; Syed Amir Zuhaizad, AFED Holdings, Victor Yap, BSP; Anthony Rodrigues, SLB
Share common challenges faced in adopting AI for data-driven analytics and decision-making, such as framing the right use cases, limited data availability and quality, and gaps in problem understanding. Address implementation risks including AI hallucination, model reliability, building stakeholder trust, managing resistance to change, and ensuring that domain expertise remains central to the decision-making loop.
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60 mins
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75 mins
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90 mins
- Panel Session
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15 mins
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120 mins
- Technical Session
Session Managers: Darren Chong, Kongsberg Digital; Mohammad Faizal Che Daud, PETRONAS; Rozaidy Zainul, PETRONAS
As oil and gas companies move from scattered data systems to more connected digital environments, having a strong data foundation is becoming critical to getting real value from AI. This session focuses on three key areas: bringing data together into a unified ecosystem, ensuring data is reliable and trusted for AI and deploying human-centred automation where AI supports but not replacing engineers. Real case studies will show how companies combine domain knowledge and AI to create practical, reliable solutions that improve safety, efficiency and overall operational performance.
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60 mins
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120 mins
- Technical Session
Session Managers: Saito Naoki, Japan Vietnam Petroleum; Tanarat Kanchanachinto, Valeura Energy; Tengku Ezharuddin Tengku Bidin, Faazmiar Technology Sdn Bhd
This session explores AI enabled intelligent automation across upstream operations, spanning subsurface characterisation, drilling and well construction, production operations, surface facilities, and field development planning. We place particular emphasis on Operational Excellence while keeping the scope broad. Submissions may cover human in the loop decision support through closed loop optimisation and autonomous control, including remote operations and real time execution, and the integration of AI driven automation with existing operational systems and data environments. Outcomes can include production uplift, NPT reduction, improved reliability and safety, and lower cost and emissions; quantitative results are welcome. Lessons learned that enable repeatable and scalable adoption are encouraged.
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15 mins
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120 mins
- Technical Session
Session Managers: Daiye Zheng, SLB; Dr. Omar Alfarisi, Dragon Oil
As organisations accelerate their AI adoption, many discover that the journey from promising prototypes to enterprise wide deployment is far more complex than expected. This session brings together bold stories, hard earned lessons, and practical strategies from real world implementations across IoT, software, hardware, and digital ecosystems.We will explore the full lifecycle of scaling AI, beginning with how early prototypes are conceived with scalability in mind—designing for modularity, data readiness, extensibility, and operational fit from day one. Attendees will hear examples spanning industrial IoT solutions, intelligent automation, agent based systems, and integrated hardware software architectures, illustrating what it really takes to move from experimentation to production.
The session also dives into the challenges that often emerge after a pilot succeeds: integration with legacy systems, data quality inconsistencies, MLOps maturity gaps, cybersecurity constraints, multi geography rollout complexity, change management, and the organisational readiness needed to onboard new digital workflows. Through candid reflections on what worked—and what didn’t—you will gain a realistic view of the people, process, and technology shifts required to sustain AI at scale.
Finally, we will discuss a practical framework for scaling digital success: how to design scalable prototypes, how to industrialise AI capabilities, and how to drive adoption across large, diverse enterprise environments. Participants will leave with valuable insights to transform isolated pilot wins into long term, organisation wide impact.
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15 mins