Artificial Intelligence (AI) in the oil and gas industry stands to reach US$2.85 billion by 2022. Why? Because data is never special. Oil rigs may generate somewhere around 50 terabytes a year, but that kind of big data needs to be applicable to be useful and, unfortunately, humans do a terrible job of classifying things into datasets. Indeed, a good scenario will see 10% of the resulting datasets actually be beneficial.
Most competing firms are also known to have access to the same datasets. This means that how they each choose to clean up, interpret, transform into information and disseminate that data throughout the organization is where the opportunity lies. More and more organizations understand that, even though their focus is often on mitigating risk (think health and safety), risk is now necessary in order to move the needle. They are ready to change the way they treat their people and their screens, and must use AI to do it.
The older generations now retiring from the workforce possess a great deal of knowledge that is, in fact, intuitive and difficult to convey to younger entrants. But when paired with a strong, AI-powered database structure, the learning process can be made infinitely easier by the adoption of user experience rules and trends that are more resonant and relevant to the next generations of workers – Millennials and Z’s – coming in.
Current employees, given their proficiency in the work and processes, are best suited to be the teachers and stewards of any new, AI-powered system. Beyond their knowledge of traditional systems, these pioneers must also accept that every generational worker after them will have different situations to deal with. The work focus of future employees will involve interacting with machines as they would with coworker tools more than anything else. And it is the job of current employees (together with AI specialists) to answer how this all happens from a knowledge perspective.
It stands to reason then that creating a document of information and sticking it in Sharepoint is not knowledge management. So, what exactly is at stake here? The answer is democratizing veterans’ knowledge in a quantified way that is useful for both the machines and humans. (This does not exist today.) Current employees’ responsibilities will shift towards that of AI instructors; they will continue to do their jobs alongside an intelligent machine, effectively defining what the future of that system will be. At the end of the day, these pioneers – those that have the traditional skills and knowledge – need to be the ones to validate that the machine is correct.
The evolution of that machine intelligence will, ultimately, result in the creation of new jobs and opportunities. Predictions suggest that AI will create more jobs than it eliminates by 2020 and will also be seen recovering 6.2 billion hours of worker productivity across sectors a year earlier. Promising, indeed.
Machine intelligence does not spell the end of human interaction in the oil and gas industry, but it does propose some modifications. Jobs such as oversight assessment and quality assurance or training and maintenance of the AI system are some of the more obvious ones. Not only does this mean field work, but it also means personnel in control centers monitoring both the machines and employees involved. Other roles will skew more towards performance management and leading bigger teams of digital specialists. All in all, future employees in this industry stand to be remote digital knowledge workers more than anything else.
Entering the industry will not look the same either – a picture painted clear by the potential brought on by natural language processing (NLP). NLP will eliminate the need for future employees to have deep, industry knowledge simply by mitigating the challenge of learning (and retaining) the current glossary of acronyms needed to speak intelligently with other industry members. Not to mention that once an employee is hired, he or she will enter a much more homogenized and automated organization. Just think about it… Satellite radar, in combination with geological drones and historical findings in the region, can be used to detect where wanted minerals are. The hot spots can then be identified by a robotic unit and only after a threshold is met would the human supply chain kick in. This level of automation is all possible once machine intelligence is finding and looking into goal-driven patterns.
Better yet, machine intelligence empowers the system to simultaneously learn the way each and every employee thinks and acts. It remembers this better than the user does and alters its approach when influencing each particular user’s decision-making process. This is not to say that the user does not also gut check the machine – after all, the AI (like humans) is also capable of developing bad habits. Indeed, processes are built and designed to enable this exact kind of verification.
The truth is that many traditional oil and gas workers have learned what they know on the job and, therefore, find themselves occasionally straying from established and documented processes to achieve success. This becomes a noteworthy obstacle when hiring new generations. Not only will they be trained less for the oil and gas industry specifically and more for engineering as a whole, but they will also be searching for the employer with the best tool set for new hires. All of a sudden, the AI system becomes the differentiating factor for top talent looking for its next employer.
Marc Boudria is the vice president of artificial intelligence at Hypergiant, where he applies the technology to solve real business problems of Fortune 500 companies.
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