By the year 2025, Artificial Intelligence (AI) will have transformed every aspect of the energy business and delivered over $800B of value across the energy value chain.

The world of energy is undergoing rapid evolution mainly driven by climate change and the resulting deployment of renewable energy resources that are distributed and variable in nature. This rapid change is leading to technology and business challenges for traditional oil & gas and renewable energy companies. Covid has thrown more headwinds and made the business environment even more challenging. These challenges include:

• Improving profitability

• Increasing productivity

• Extending asset life

• Optimizing operations

• Enabling safer operations

Energy companies need to decarbonize, decentralize, automate & digitalize at an extremely fast speed to survive, sustain, and compete in such disruptive times. This means that over the next 5 years, the business and operating models of energy companies and the energy ecosystem will undergo massive change. AI will be a critical tool enabling these changes.

AI algorithms make it possible for machines aka computers to learn from historical data and experience, adjust to new inputs, and perform human-like tasks. In essence, AI enables systems to learn and understand context. This falls into two categories:

1. Performing human-like tasks like seeing, conversing, reading, understanding, drawing, etc. Siri and self-driving cars fall into this category.

2. Identifying and dealing with variances in vast datasets that may be beyond the ability of the human mind to comprehend and assimilate. High-frequency sensor data from a sensor-rich environment is an example.

Traditionally, tabular data used to be the only form of data used in analytics and data models. AI is now enabling multiple forms of unstructured data to be converted into insight, information, and action. Datasets now include images, video, sound, voice, documents, social media, etc. This allows companies to tap into massive amounts of knowledge stored in the form of documents, reports, chats, etc.

This combination of AI and Machine Learning (ML) techniques, AI/ML native applications that would not exist without AI/ML algorithms and the unlocking of non-traditional datasets enables companies to achieve:

• Greater efficiency by enabling faster execution with lower error rates at lower costs

• Higher capacity by enabling a higher volume of higher quality work to be executed

• Finally enabling newer channels for revenue

AI and digitalization can lead to exponential and innovative growth for companies. The following case studies illustrate how AI can power innovative businesses.

• Ant Financial is Jack Ma’s 8-year-old digital financial services company spun off from Alibaba. Its planned, albeit delayed $34.5B IPO will be the largest in the world overshadowing Saudi-Aramaco’s. Ant sells financial products like loans & insurance and financial services like portfolio management, trading, and financial advice. The reason Ant Financial is valued so highly when there are so many other financial services companies comes down to how it has built AI into its operations from the ground up. Its adoption of AI has enabled it to offer its services to 10 times as many customers as other companies at 1/10th the cost as compared to its competitors. So Ant can expand into new markets and geographies much faster compared to its competitors.

• AI enables Adidas to provide personalized sneakers to its customers who can change the material, color, logo, etc. of their sneakers. Using AI, Adidas can incorporate the production of these highly customized sneakers into Adidas’ normal production line without manual intervention and ship the sneaker to the customer within 10 days without any additional cost.

• Bottomless.com delivers fresh ground coffee powder to their customers’ homes through a subscription model. They send a small wifi and IOT-enabled weighing scale to the subscriber. The subscriber is asked to store their coffee bag on a weighing scale. The scale enables Bottomless.com to measure coffee consumption and its AI algorithms order and ship freshly ground coffee from the nearest coffee supplier at the right time ensuring waste reduction and higher customer satisfaction and profitability.

These three forces of AI which power incredibly fast, super economical, and hyper scalable operations, allow extreme customization and personalization, and enable new sensor and data-based business models, will act as catalysts for groundbreaking transformations in energy businesses. Some areas where AI will have an enormous impact are :

• Completely autonomous oil & gas wells and plants. AI coupled with sensors and digital controls will monitor, control, manage, and optimize these wells and plants. AI will assist in the predictive maintenance of these operations and lead to greater efficiency, productivity, and lower operating costs. Operators will supervise these wells and plants remotely using cameras, drones, computer vision-based surveillance, and augmented reality enabling safer operations. Speedier and predictive AI-controlled autonomous operations will enable cleaner energy extraction leading to lower emissions.

• Highly dynamic power generation, transmission, and consumption. As installs of variable renewable energy resources become prevalent and electric vehicles become more widely adopted, the supply and demand for power will fluctuate widely. Supply from solar and wind-based sources will change by the minute due to local weather conditions. Demand will be highly mobile and variable as hungry power consumers like EVs can appear without any notice. Additionally, a person who has installed renewable power can change from a consumer of power to a supplier of power in seconds. This highly dynamic environment would require billions and billions of decisions to be made. AI will enable a huge number of complex decisions to be made and executed for monitoring operations, profiling, controlling and smoothening power consumption, and making optimal decisions to buy, sell, store, or trade power depending upon forecast demand and supply.

• Digitalized business of energy. Bots and digitalization will improve productivity and efficiency of business execution across every aspect of business operations be it engineering, finance, HR, marketing, or logistics. AI will assist in managing risks and automating tasks across the design, build, commission, and operate phases of energy projects. Conversational AI bots will respond to routine queries and speed up communications. Digitalized workflows will reduce errors and highlight risks to ensure that the real-time value of energy assets is maximized.

All said and done, AI technology has strong potential to transform the energy industry in all areas – upstream, midstream, and downstream. But to ensure success, the barriers between data silos need to be broken and employees reskilled for an AI-enhanced future. Energy companies that learn to adopt and adapt to Artificial Intelligence will attract capital and talent, deploy groundbreaking innovations, and become the titans of Clean Energy.

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This article was first published in Scovan Engineering’s e-magazine Ignite