-    Reviewing their company’s digital road map and highlighting the barriers to digitalization 
-    Engaging fellow leaders across the organization and communicating the ROI
-    Understanding how initiatives are prioritized 
-    Highlighting the limitations in technology adoption

-    Understanding technology options and where to begin
-    Integrating new technologies into existing processes 
-    Accessing solutions- ‘off the shelf’ and customized in terms of cost, relevance, and pace of delivery

-    Implementing technologies on a low-cost, government-approved budget
-    Moving projects from pilot to full scale at minimal cost 
-    Building adaptable data teams to help collect, maintain, and report data in the correct format
-    Overcoming hurdles of the changing skill paradigm from traditional to digitalization

-    Becoming IoT literate by navigating the IoT Space and its adoption
-    Understanding the technical, legal, social, and regulatory challenges associated with the implementation and deployment of IoT technologies
-    Discussing solutions to specific adoption barriers, including network access, outside engagement, and collaboration

-    Making the best use of the metaverse and its ability to bring together expertise in extended reality systems 
-    Promoting safety, efficiency, and an interconnected energy industry
-    Moving beyond concept to pilot and implementation: repairing devices without disrupting real-world operations
 

-    Designing physical processes, automation, and networks to eliminate cyber risks to public safety
-    Recognizing that NIST CSF and ISO 27001 are blind to engineering approaches to cyber risk
-    Distinguishing engineering-grade from IT-grade designs
-    Defining a new body of knowledge - industrial cybersecurity engineering

- Understanding the techniques Energy Companies have adopted for operating by exception
- Exploring how to deploy exception-based operating procedures in well sites successfully 
- Highlighting best approaches to automation and exception-based operating procedures
 

-    Understanding the next generation of the twin 
-    Reviewing readiness roadmap for business transformation
-    Examples of Digital Twin are being used to drive operations

-    Foundational elements to enable digital transformation
-    Building a “Case for Change”
-    AIM Strategy roadmap, lessons learned & next steps

-    Leveraging digital twin technology to develop energy-efficient assets and operations
-    Building systems to achieve end-to-end operation visibility 
-    Technical use cases of how Digital Twins can drive operational improvement

-    Increasing efficiency and staff safety of operations using the use of drones, wearable technology, robots, big data, and more
-    Building operations with purpose - using ML/AI to map out schedules
-    Using advanced data analytics to make educated field decisions 
 

-    Reviewing the current emission technologies 
-    Carbon tax: answering if technology can help the industry reduce this
-    Examining the modifications in existing protocols and various metrics 

-    Invoice data is the most undermined source of business intelligence
-    Current e-invoicing solutions are resource intensive and provide limited data advantages.
-    Invoices created on paper or using e-invoice processes are greenhouse gas (GHG) intensive 
-    True digital invoicing provides extensive data opportunities, connects buyers and sellers, and all but eliminates the GHG emissions associated with invoicing.
 

-    Building a model to predict elevated extraction recovery deviations for the Kearl oil sands mine
-    Leveraging process surveillance data to isolate our data set to causes of interest only
-    Visualizing Shap explanations over time to shed light on what is driving the model predictions leading up to and during events
-    Evaluating the model has uncovered other potential uses of the approach, not only to predict but also to highlight process control adjustments or post-event root cause analysis
 

-    Successes and learnings in building a Machine Learning product to enable predictive analytics
-    Product Management approach to turn machine learning successes into strategic program opportunity

-    Reducing project management risks and controlling estimated expenditure when implementing technologies 
-    Reviewing the common mistakes companies make when adopting new software 
-    Providing guidance to the latest technology and deployment practices 

•    Proving methodologies executed correctly deliver big gains
•    Developing functional baselines crucial for AI/ML acceleration
•    Mastering data governance using digital twin

-    Providing better quality data and analysis to give an overview of your stock levels and supply chain 
-    Detecting emerging issues and threats and putting preventive measures in place to reduce the negative impact on business
-    Optimizing procurement tasks, including invoice digitization, reconciliation of purchase orders, and invoice prediction

-    Understanding your upstream operations and how they impact profitability
-    Exploring the main reasons for lost productivity and possible ways to capture missing opportunities
-    Evaluating new developments in downhole analytics and machine learning to autonomously identify underperforming wells
-    Operating by exception as a stepping stone to autonomous operations
 

-    Highlighting the talent hurdles within companies and the industry
-    Using information and communications technology and person-to-person knowledge management techniques to facilitate the transfer of tacit
-    Understanding how diversity can help fill the industries talent gap

A discussion on the various paths companies takes to mitigate emissions, lower production costs, and produce more energy in the face of growing global demand.