Industry
Energy, Resources & Utilities
Rebalance the energy equation
services
Artificial Intelligence
Shape the future with human-first AI
Highlights
- As AI demonstrates immense potential to transform every industry, energy companies explore its use to spur innovation and grow revenue throughout the energy value chain— from exploration, and production to sales and marketing.
- A multidimensional strategy is key to making AI consumable for an enterprise-grade transformation and to deliver sustained performance.
- While various aspects need to be considered during the initial stages of developing an AI model to drive a real-time operational AI effectively, access to a multitier architecture and integration to enterprise systems is essential for energy companies.
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Reimagining every sector
The energy industry is on the cusp of a major transformation, powered by artificial intelligence (AI).
In the last few years, AI has demonstrated enormous potential to reimagine every industry, with a level of disruption to business models similar to earlier breakthrough technologies, such as smartphones a decade ago.
Per TCS’ AI for Business Study, 68% of senior executives surveyed in the energy and resources industry believe AI’s impact on their business model will be greater or equal to that of the internet, and 57% feel optimistic about its potential.
The study also highlights that 89% of energy and resources executives are prioritizing AI to drive innovation at some level. About the same number of executives are focused on using AI to drive productivity.
Energy and resources executives understand the value of AI and how the combination of traditional AI, business intelligence, and generative AI (GenAI) will increase speed and effectiveness of working. The data generation capabilities of GenAI will help improve the quality of decisions and output of various tasks in the industry, accelerating operational transformation.
Innovation as an objective
The energy and resources industry focuses more on using AI to spur innovation and grow revenue than most other industries.
On a 10-point scale between optimization and innovation, energy and resources executives averaged 6.90, compared to an average of 6.71 for all other industries, according to the AI for Business study.
82% of respondents at pacesetter—those organizations with stronger financial performance compared to other energy and resources companies in the survey—energy and resources organizations say they are primarily focused on innovation as an objective for AI implementations.
AI for energy value chains
The industry has been researching the use of AI for exploration, and production for a long time as well as looking at new use cases for transportation, sustainability initiatives, distribution, and marketing and sales.
Exploration
For oil and gas, AI solutions provide many potential benefits to the exploration process. GenAI-assisted well design is one of the leading emerging solutions, which promises more rapid and lower costs in drilling activities. AI tools are assisting geoscientists in complex interpretation tasks, which are, finding more resources in more places.
The current direction is to use AI to automate geoscience interpretation from seismic to well evaluation. Infusion of AI in the energy industry can improve reservoir simulation and modeling. By analyzing voluminous data, AI algorithms can generate accurate models of underground reservoirs, allowing energy professionals to make more informed decisions about drilling and development strategies. AI can enhance subsurface images captured through seismic surveys by creating 3D models and using fewer seismic data scans, avoiding the need for repeated data acquisitions.
Production
Applying AI in upstream operations such as field operations and maintenance, drilling optimization, asset surveillance, and equipment recalibration enables an organization to reduce unplanned outages, increase uptime and minimize revenue loss.
Digital twin-infused AI enables predictive maintenance of on-field assets and equipment and helps ensure employee safety, including that of on-field technicians. In drilling, wells have already been drilled by drilling rigs, without human intervention. The limiting factor for the broader application of AI is the amount of data and energy required for a complete drilling operation using AI.
GenAI/AI can address the challenges associated with navigating and analyzing vast volumes of data from disparate sources generated by different asset management systems, global information systems, monitoring systems and derive actionable insights related to drilling operations. In the process, GenAI-powered solutions can address the challenges associated across the entire drilling process lifecycle such as rig site preparation, drilling, cementing and testing, well completion, and fracking.
Transportation
AI-powered solutions can help optimize supply chains by autonomously monitoring assets like oil and gas pipelines distributed across geographies. It can enhance the functionalities of supervisory control and data acquisition (SCADA) systems by analyzing patterns of large volumes of operational data and providing actionable alerts to ensure adherence to industry-specific key performance indicators.
AI-assisted bots can be trained on images and videos of defects in pipelines such as corrosion and cracks, which can improve the speed and accuracy of defect detection and prediction in pipeline integrity processes. Layering AI models on top of traditional hydraulic simulation methods can significantly improve understanding of flow dynamics in pipelines to drive productivity, efficiency, and capacity utilization of assets.
Refining
Refineries are growing increasingly sophisticated in pursuit of more efficient and sustainable operations. Control systems are becoming more capable and intelligent. AI can enable such sophistication with tools such as intelligent workbenches for safe and efficient operations. Machine learning applications are providing more current and detailed information on operations through applications such as AI-assisted mass balancing.
Sales and marketing
AI can enable energy consumers and industrial customers to analyze customer energy usage and buying patterns. Consumers benefit from intelligence at the point of sale. Industrial customers take advantage of customized B2B intelligent AI applications that help lower delivery costs.
38% of energy and resources executives are interested in using AI to enhance engagement with their market initiatives beyond chatbots, according to the TCS AI for Business Study.
When it comes to customer engagement, while chatbots for sales and product support remain important to energy and resources companies, their use is being supplanted by other AI-driven means to accomplish these goals.
31% of respondents in the AI study say they are looking to use AI for more personalized interactions with their products and services while another 24% say that AI will help obtain insights to analyze customer behaviors and data.
Field service management is another area benefiting from this use case of AI for experience enhancement. AI equips those in customer-facing positions with intelligent troubleshooting support and contextual knowledge repositories.
Natural language models can guide customers through self-help processes that offer accurate customized responses, and personalized assistance can aid responses to more complex inquiries. AI can also help efficiently monitor and analyze social media data to identify potential adverse events and product quality complaints.
Most (63%) energy and resources executives believe AI will augment and enhance human capabilities—not replace them, with AI enabling people to focus on higher-value activities that require creativity, empathy, and strategic thinking.
What it takes
A tailored fit and not a one-size-fits-all solution will ensure success.
Transforming the potential of AI into sustained performance requires a multidimensional strategy and an enterprise architecture optimized for cost, quality, security, and privacy that also ensures compliance with regulations.
Built on the principles of an industry-led, cloud-fueled, and ecosystem-enabled foundation, an AI strategy that considers the entire organization is required to make AI consumable for an enterprise-grade transformation.
Considerations for successful AI implementations
These four principles underlay the path of AI potential to performance, a phased roadmap that builds upon and reinforces each stage—Assist, Augment, Transform (see figures below).
Case in point
Though A look at AI infusion into the daily activities of a field operator.
AI-powered field operator reimagines the process of field operations and maintenance in upstream operations of oil and gas organizations by enabling on-field technicians to collaborate across the enterprise for accessing work logs, SOPs, safety data, and troubleshooting drilling equipment.
This helps in predictive maintenance of on-field equipment and assets, enabling an oil and gas organization to unlock business benefits such as achieving operational efficiency, reducing operating costs, preventing revenue loss due to unplanned outages.
The multi-layered AI architecture
How do energy organizations prepare themselves for an AI evolution?
Data is integral, as is a cloud foundation to scale and optimize AI outcomes. The data volumes required to drive a real-time operational AI effectively must be part of the infrastructure consideration. Further, energy organizations have unique regulatory, compliance, and data privacy requirements, which must be factored in during the initial stages of developing an AI model.
Finally, developing a robust business case can be challenging when quantifying the business benefits and costs of AI is difficult. Energy consumption to train larger AI models is significant and must be evaluated in accordance with the business benefits. Any AI solution must start with a value-augmentation opportunity for the business; prioritizing top-down structures, rather than starting with technology adoption.
For energy organizations to fully capitalize on AI’s potential, access to a multitier architecture and integration to enterprise systems is essential. The dimensions of AI applicability in the AI architecture for the energy value chain can be segmented into four layers.
Making AI pervasive
A balanced approach is critical to success in AI adoption.
AI is not plug-and-play technology with a one-size-fits-all strategy, and the findings from the energy industry executives reflect their varied approaches to AI. More than a third favor establishing an enterprise-wide AI strategy—but a little over a quarter are content to hold back and see how others in their industry apply the technology before making any major moves.
Only 3% of E&R executives in our survey say AI is a differentiating factor for business transformation and 23% have only just begun to explore AI. Most of the respondents say they have a long way to go to realize transformational outcomes.
AI adoption needs to be pervasive across the organization and at three levels.
- Process operations level: Day-to-day operations, especially where knowledge extraction or synthesis is involved, are low-hanging fruits that demonstrate the success of AI infusion. Companies now accept that AI can rapidly improve both productivity and effectiveness of such processes. Enterprises are currently more active in AI infusion at this level, both to prove applicability via proof of concepts and in terms of targets for production deployments.
- LOB level: In a few advanced companies, lines of businesses (LOBs) are seeking opportunities to transform and create significant business value in activities driven by insights, for example, in areas like customer and partner experience. They have started integrating AI into operations at a business unit level.
- Enterprise or strategy level: Top management and board initiatives intervene to define a company level strategy and roadmap for AI.
Enterprises have set up committees for this purpose, and the path to responsible AI adoption through an institutional, policy-driven approach is gaining pace.
The merging of reasoning and recognition intelligence into AI models offers tremendous potential to help companies reimagine entire value chains and transform the way they do business. A strategic, business-first approach is essential for successful AI implementations and enterprise-wide adoption.
EXECUTIVE CHAMPIONS
Jan Johansson
Industry Advisor, Upstream Oil and Gas
Tom Franklin
Industry Advisor
Dipayan Mitra
Client Partner
Shalini Manchanda Mehtani
Head of Industry Offerings, AI.cloud
Dibyatanu Banerjee
Lead ERU, Industry Offerings, AI.cloud
