Fear of AI. We get it—it’s a real thing. There is a very real outrage that can be experienced from people who misguidedly believe that artificial intelligence will replace jobs at their workplace.
It’s understandable. Many people had just gotten used to the concept of AI, when ChatGPT entered the arena. Once again, panic set in.
Yes, ChatGPT is going to create
disruption in some industries—particularly in marketing and customer relations. Is it a cause for alarm? We think not. It is more of an opportunity to adapt and pivot.
What about process industries? Should reliability engineers be concerned for their jobs?
At VROC, we believe the answer is no. Here’s why.
- AI can’t be left to make decisions at an industrial plant or facility. It will never know what you or your supervisor think, or what conversations you’ve had that informed your decision-making process. It can certainly help provide insights to support your process, but it doesn’t know your reasoning and situational context. It just learns from the data it is provided.
- AI may be used to automate simple tasks, but not complex tasks such as design, risk workshops, forming strategies, or devising and implementing solutions. AI is a tool to help eliminate tedious tasks, such as data manipulation, continuous analysis, or identifying the contributing factors to an incident. As a result, engineers can prioritize the more interesting, knowledge-based tasks where they add immense value to the organisation.
- AI cannot form a problem statement or know intrinsically what your goals and KPI’s are. It needs to be directed by an engineer or a subject matter expert. It is these individuals who can interpret the results and provide rational explanations to others.
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AI can only learn from the data that we choose to expose it to, therefore, if we decide to train models on only three months of data—as opposed to twenty-four months’ worth of data—the results can be very different. The same applies to the type of data it is trained on: holistic data across the plant or facility, or data from one process or one piece of equipment. Engineers are required to choose the dataset that is used in modelling based on their experience. They just need to be cautious not to inadvertently add bias.
What should engineers do about AI?
Embrace AI! It is here to stay. Choose to become more data literate. Flex those ‘analytical muscles’, and discover how to use artificial intelligence, machine learning, and predictive maintenance to your advantage.
Businesses might not expect this of an engineer just yet, but the day will come when you’re expected to work smarter not harder, and AI will be commonplace.
Does this mean you need to become part engineer, part data scientist?
It is well-established that there is a
shortage of data scientists, especially data scientists well adverse in industrial operational nuances. Even if there was no longer a skills shortage, businesses realistically wouldn’t be able to fund additional data specialists to work alongside each industrial process.
So, does this mean engineers and SME’s will now wear two hats? One data scientist and one engineer? Well, thankfully, the majority of problems industrial processes face can already be solved by engineers with the help of no-code AI and their existing analytical, problem-solving skillset.
No-code AI is the creation of artificial intelligence models without any programming or coding. It will become common in the industry, just like asset management software, ERP systems, and risk management tools have become. No-code AI tools will eventually be the normal method to generate predictive analytics and insights for predictive maintenance, root cause analysis, and process optimisation.
These no-code AI tools remove the dependency on data specialists and they help make AI and machine learning accessible to engineers—without an extra hat.
Why embrace AI as anengineer?
So, you don’t need to be concerned for your job, and you won’t suddenly need to become a data scientist overnight. What are the other ways that AI can benefit engineers?
- Job Satisfaction – doing less of the things you don’t enjoy and more of the things you do, such as knowledge-based tasks, and devising solutions to problems.
- Learn New Desirable Skills – Embracing AI will help develop specialist skills that are going to continue to increase in demand.
- Productivity – AI is expected to reduce bottlenecks and increase the ability to solve a wider range of problems, ultimately improving the productivity of engineers.
Still not convinced?
A study of the
Impact of Artificial intelligence on Engineering: Past, Present and Future across different disciplines of engineering, states that respondents believe that over the next 20 years, there will be a 45.55% improvement in the productivity of engineers, and that it will create more jobs for engineers.
From our experience, we’ve seen engineers switch from spending most of their time investigating the root cause of a problem, to devising and implementing solutions based on AI insights. This is satisfying and rewarding, as it is
solutions that get management’s attention, not the analysis.
AI was utilised by a South-East Asia
oil and gas platform during a rapidly developing safety incident. The engineering team decided to build an AI model to help identify the root cause of the problem. The team was able to come up with a solution using the insights provided to avoid a major gas leak, saving close to one million USD. This is one of those types of solutions that gets management’s attention.
You may still not be convinced in the role that AI will have in the day-to-day jobs of reliability engineers. Hopefully this article has reassured you that with or without AI, the position of an engineer is a safe one and it won’t be easily replaced anytime soon.
