What happens when AI-enabled tools give engineers more time to focus on the work that matters?
There is no shortage of opinions on what AI means for the workforce, and that includes engineering. A lot of the conversation centers on AI as a replacement: if software can do something an engineer does today, does that mean we need fewer engineers tomorrow?
We at Onyx 360 see something different happening. Our view is relatively simple: AI is a force multiplier on skilled resources. The more skilled the person using the tools, the more value those tools can create.
An engineer who understands the process knows what information matters, what assumptions are reasonable, and when an answer simply doesn’t make sense. Giving that engineer better tools doesn’t make their expertise less valuable. It gives them more leverage.
The interesting question then becomes what we do with that leverage.
Engineering the engineering learning process.
When I was a co-op at MYNAH Technologies, a large portion of my time was spent reviewing engineering datasheets and entering that information into simulation models. The first few times were interesting. I was learning what was in the documents, how the pieces fit together, and how those inputs affected the model.
After enough repetition, I was no longer learning much by typing in the next set of inputs. But this is what a lot of day-to-day engineering work that looks like: Finding information, moving it between systems, configuring tools, checking documentation, updating models, and repeating tasks because two pieces of software don’t communicate particularly well. Some of that work is necessary. Very little of it is where engineers create the most value.
This is where the impact of AI becomes much more interesting. If we can dramatically reduce the effort required to configure, maintain, and interact with engineering technologies, engineers can spend more time actually using those technologies to solve problems.
Lowering the barrier for better engineering technologies
We design dynamic simulations, sometimes referred to as Digital Twins, every day. There are a lot of process industry challenges where simulation can improve projects, operations and decision making. Historically, the limitation has often been whether the value justified the engineering effort required to build and maintain the solution. That same problem exists across a much broader set of technologies.
Advanced analytics may have a strong use case, but someone must organize and contextualize the data. A knowledge management system may be valuable, but someone must capture and maintain the information behind it. Simulation can answer difficult engineering questions, but someone still has to build and maintain the model.
As the effort required to implement and sustain these solutions comes down, the number of instances where they make business sense goes up.
That is a significant shift in industry. We are not just talking about doing the same Digital Twin project faster. We are talking about applying simulation, advanced analytics, engineering knowledge, system optimization, and other transformative technologies to solve problems where a business case may not have existed before.
This opens the door for engineers to use much more sophisticated tools as part of their day-to-day work. It also does not mean we suddenly need fewer engineers.
Most process manufacturers we work with have more tasks to accomplish than they have engineering capacity to pursue. Projects take priority over improvement initiatives. New technology gets pushed out because the engineering team doesn’t have time to support it. Valuable ideas sit on a ‘nice to have’ list because something more urgent always comes up.
Today, nearly everyone is being asked to do more with less.
Making engineers more productive does not eliminate that backlog. It gives them a better opportunity to work through it. This might mean that engineers spend more time optimizing a process instead of collecting data. It might mean using simulation on a smaller project where it previously was not economical. It might mean evaluating three ideas instead of having barely enough time to evaluate one.
The opportunity isn’t simply getting today’s work done faster. It’s increasing the value of the engineering work we are capable of doing.
Here’s the catch: the way engineers learn is changing.
There is another important part of my co-op experience that matters here. Entering parameters from those datasheets wasn’t the highest-value thing I did for the company, but especially early on, I learned from doing it.
I learned what type of information engineers put on a datasheet. I learned what inputs mattered when building a simulation. I started recognizing what typical parameter values looked like. Eventually, I could look at something and realize when data was missing or incorrect.
A lot of entry-level engineering development works this way. You start with simpler tasks. You repeat them. You build context. Over time, you develop enough contextual understanding to take on more complicated challenges.
As some of the simpler work disappears, companies cannot assume that the learning that came with it will magically remain.
This will require a concerted effort in changing how companies onboard and develop engineers. The old learning model could afford to be somewhat passive. Give someone progressively harder work over several years and let their experience accumulate.
The next model will have to be much more intentional.
Learn the fundamentals first. Then use the multiplier.
This is something we have already started addressing at Onyx 360. Because of the nature of our work, we hires more engineers directly out of universities than most companies. Simulation maps well to the engineering fundamentals students are learning in school, but we also know there is a large gap in understanding theory and applying it in an industrial environment.
Our entry-level onboarding program intentionally begins without the use of AI tools. Engineers work through the simulation fundamentals first. They need to understand exactly what the model is doing, why it behaves the way it does, and how to recognize when something is wrong before we give them tools that can help them do it faster. Once that foundation is established, they progress into more complicated work where modern tools become part of how they solve problems.
The objective isn’t to keep new engineers away from technology. We want them to first understand the fundamentals, then quickly equip them with the best tools, engineering expertise, and organizational knowledge we have available. That is why experienced engineers remain so important. We hire experienced simulation and engineering specialists because deep expertise is incredibly valuable, particularly when technology can multiply its reach. Those experienced team members help define how we work, teach the next generation, and provide context that tools alone cannot.
Connecting experience engineers with next generation and empowering them to explore innovative ideas and build solutions together using powerful tools gives us an edge.
Experienced engineers also have a new responsibility.
Developing a next generation engineering workforce is not only an entry-level problem. This is because a tremendous amount of engineering knowledge still exists in people’s heads.
Why was the solution designed this way?
How did we try to solve this five years ago?
Which number in the calculation actually matters?
What looks acceptable on paper, but causes problems in the plant?
This is the tribal knowledge every organization worries about losing when their experienced engineers retire.
Today, we have much better ways to capture that information and make it available across an organization. But those systems are only as useful as the information behind them.
Someone still has to capture this information. Someone has to provide context. Someone has to recognize when the information has changed.
That makes knowledge capture a bigger part of everyone’s job. It’s not documenting your job away. It’s capturing what you learn so the next person doesn’t have to learn it from scratch.
In our view, every engineer increasingly becomes a curator of engineering knowledge. Organizations that do this well will have a distinct advantage because each lesson learned can become available to the rest of the organization rather than staying with the person who happened to learn it.
Connecting the Pieces.
Understandably, there is a lot of discussion about what AI means for engineering careers. Our view is that engineering fundamentals, experience, and judgment still matter. In many ways, they matter even more when the technology gives those skills more leverage.
What is changing is what engineers can accomplish with the time and tools available to them. That creates opportunity, but it also creates responsibility. Companies need to rethink how they develop new engineers, get better at capturing the knowledge of experienced engineers, and be willing to change how work gets done as the barriers to more sophisticated technologies continue to fall.
At Onyx 360, we use the term Connected Performance to describe how our full circle solutions connect people, process and technology. This same principle applies to how we are building our company.
Technology such as AI is only one part of the equation. The real opportunity comes when we connect it with the strong engineering fundamentals with processes to capture organizational knowledge and put it in the hands of people who know how to use it. This is the future of engineering we’re excited to be building.
Visit our careers page to learn more about exciting available roles with Onyx 360.