It’s estimated that over 80% of North American energy companies will have implemented some form of AI in their operations by 2025. Many are starting small, demonstrating value, and looking to aggressively scale. The sector is on the precipice of the most dramatic transformation in how operations are conducted, how decisions are made, and how employees interact with assets. Our keynote panel will discuss:
● Scaling AI PoC’s by championing value to a range of stakeholder to drive greater efficiency, innovation, and value to your organization
● Exploring the anticipated rise in data and AI-related regulations, with 60% of energy leaders expecting new compliance standards within the next five years
● Futureproofing AI projects by increasing data quality and access
● Preparing for workforce transformation: How do you identify – and then develop - the digital skills and capabilities you need?
● Detecting patterns and anomalies, improving reliability and safety in operations and identifying early warning signs of failure before costly breakdowns occur
● AI-powered visual inspections to reduce downtime, enhance asset performance, and optimize maintenance schedules
● Integrating computer vision with existing systems to drive proactive decision-making, minimize risk, and extend the life of critical equipment
● Moving beyond analyzing data from a single source and manual work to identify relevant equipment failure records
● Leverageing GenAI capabilities to automatically extract key failure information that enables the analysis of reliability metrics from multiple sources and sites, including both structured and unstructured data
● Examining how this comprehensive approach will provide enhanced analytics and insights across various sites and businesses within Dow
● Discussing the specific challenges associated with unconventional operations and key causes of downtime
● Applying AI/ML including Liquid Loading prediction and ESP Failure Prediction
● Identifying the data, technology, tools and people needed to successfully realize value from these technologies
● Breaking down data silos and integrating diverse sources to create a unified, scalable data foundation that enhances real-time decision-making
● Best practices for data management and governance to ensure high-quality, reliable inputs that drive accurate digital twin models
● Discovering how an interoperable digital twin ecosystem enables better asset tracking, predictive maintenance, and operational efficiency across multiple systems
● Integrating operational data with regulatory and operational manuals
● Exploring how operators can improve monitoring and supervision of operations to address alerts
● Using Generative AI tools to assist in providing the answers to address the issues
This structured networking session is the ideal opportunity for you to capitalize on time out of the office by speaking to each of our event partners in attendance and learning about the essential solutions available to your specific challenges. How does it work? It’s easy. You spend five minutes at a booth of your choice, and when the bell rings you’ll be directed to the next one to start another round of learning and networking. At the end of the session, you’ll have met a selection of our event partners, and for any you have missed, you can connect with them at the networking cocktail reception at the end of the day.
As AI becomes integral to operations, 68% of organizations express concerns over transparency, fairness, and accountability in AI-driven decisions. In the energy sector, where AI has the potential to revolutionize operations, building trust in these technologies is crucial to mitigating risk and ensuring smooth adoption. This panel of industry operators will discuss the challenges and strategies involved in fostering transparency, managing risk, and building a culture of trust in AI-powered operational transformation.
● Exploring real-world experiences of operators addressing AI-related risks, including data privacy, bias, and decision accountability
● Building transparency into AI systems to gain stakeholder confidence and drive successful AI adoption across operations
● Managing AI governance and ethical considerations in high-risk, high-impact environments like Oil & Gas
● Understanding the role of cross-functional collaboration in developing AI solutions that are both effective and trusted by teams at all levels
● Discovering how AI-powered condition monitoring can predict equipment failures before they happen, reducing downtime and costly repairs
● Learning how to leverage ML models to detect subtle changes in equipment behaviour, enabling proactive maintenance strategies
● Exploring real-time monitoring solutions that provide actionable insights, helping operations teams stay ahead of potential issues
● Understanding how intelligent condition monitoring enhances operational efficiency, extends asset life, and improves overall safety in energy environments
TALK 1: Scaling Time Series Forecasting across the Enterprise
TALK 2: Scaling a R.A.G System
TALK 3: Scaling to Deploy Solutions across Conventional and Unconventional Assets