At their core, oil and gas companies are process-driven. From upstream to downstream, every step of the value chain is organized by rules and regulations designed to achieve a safe and efficient working environment.
We can see this reflected in the kind of cultural inflexibility that poses a major obstacle to effective transformation in the sector. When asked in a recent EY survey what the greatest strategic challenge was in adopting digital technologies in the sector, 41% of respondents indicated difficulties in aligning digital roadmaps with the executive team and the board.
What is needed is a new generation of leaders and decision-makers who actually move the needle on progress. However, the industry will need to devise new ways of thinking to accommodate advanced ways of doing business.
However, disruptive thinking does not necessarily come naturally to large, process-driven companies. Because of this, a new approach like introducing AI is bound to cause growing pains, especially among veteran workers.
Company-wide training that provides employees with a better understanding of the technology and its benefits can help mitigate some of these initial concerns. Indeed, our survey found that 81% of oil and gas executives agree that the industry needs to work on developing a digital-first workforce over the next 10 years.
Appropriate training should reassure workers that as AI increasingly automates certain tasks, workers instead will get to focus on more rewarding activities or take up more sophisticated, problem-solving roles. Intelligent machines can go beyond just optimizing existing jobs to creating new ones. In fact, they could create more jobs than they eliminate.
Breaking down data siloes to create value
Oil and gas companies, especially large integrated ones, generate reams of data. Much of this data gets siloed within different business lines, geographies, or even in single operating units.
By not consolidating this data, AI-enabled solutions will fail to deliver maximum value.Fortunately, however, many companies are looking to break down data silos. For example, last year, some oil and gas companies expanded their collaboration with technology companies to consolidate data on custom AI platforms.
These data hubs hold, process, and analyze reams of structured and unstructured data. Such capacity can open up data silos and provide the rich, holistic data sets that AI needs to succeed. One major oil and gas company has a “data lake” that will hold data emanating from its entire downstream portfolio. The data won’t be siloed off but will be easily accessible and sharable.