Globally, the oil and gas industry, directly and indirectly, accounts for 42% of global emissions, according to a Mckinsey study. In Canada, the oil and gas industry is the single biggest source of Greenhouse Gas (GHG) emissions, contributing 10% to the country's total gas emissions. At the same time, the sector is crucial for Canada's growth, accounting for 5% of its GDP and generating employment for several thousands. It is then no surprise that the industry is under tremendous pressure to produce energy with reduced emissions.

AI plays a pivotal role in helping the oil and gas industry to reduce their emissions. In fact, the WEF estimates that with AI the oil and gas industry can reduce 350 million tonnes of CO2 emissions and 800 million gallons of water consumed by 2025. When it comes to process and asset optimization, oil and gas companies can reduce greenhouse gas emissions by 20% with minimal capital investment.

However, deploying AI is not without challenges. If not implemented properly, it can prevent the company from realizing the benefits of the deployment. In fact, Gartner says that 85% of the AI projects will continue to fail by 2022. World Economic Forum states that 36% of oil and gas companies have already invested in big data and analytics. However, only 13% use the insights from this technology to drive their approach towards the market and their competitors. Both of these point to companies applying the technology in a piecemeal manner and how a lack of lack of effective strategy can make it challenging to accomplish the desired goals.

In this presentation, Humera Malik and Forogh Askari will outline the three-step plan for oil and gas companies to effectively deploy AI across their operations that augments their workforce with AI insights to accelerate their sustainability efforts in the race to net zero.

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