Engineers are not suddenly being replaced by machines. Instead, parts of the job that once required hours of manual work are becoming easier to automate. Research can be done faster. Engineers are already using AI for coding, document review, and early design work.
Engineers will simply need to be comfortable working with AI as part of their usual tools and knowing when its output needs to be checked.
Understanding what AI can and cannot do
Engineers do not need to become machine learning specialists to work effectively with AI. They do, however, need enough AI fluency to understand what is happening when they use these systems.
Engineers need to know the basic differences between generative AI, traditional software, machine learning models, AI agents, and search or retrieval systems. It also means understanding why an AI model can produce an answer that sounds convincing without actually being correct.
Engineering work often depends on details that a general-purpose model simply does not have. A model may understand a question about a mechanical component but have no knowledge of a project’s internal specifications. It may produce technically valid code that does not account for the actual operating environment. It may summarize a document while overlooking the one exception that matters.
Microsoft and LinkedIn’s 2024 Work Trend Index illustrates how quickly this expectation is moving into the workplace. Sixty-six percent of business leaders surveyed said they would not hire someone without AI skills, while 71% said they would prefer a less-experienced candidate with AI skills over a more-experienced candidate without them.
For engineers, that does not mean replacing years of technical education with a collection of AI prompts. It means adding another layer of competence to an existing engineering foundation.
Knowing how to test an AI answer
One of the biggest changes AI brings to engineering is the speed at which possible answers can be produced.
The engineer’s job then becomes less about coming up with an answer from scratch and more about figuring out whether the answer is actually worth trusting.
That matters when AI is used for calculations, code, technical research, or design recommendations. Important information still needs to be traced back to the source. Generated code needs to be tested. Assumptions need to be checked. If there is an established way to verify the result, use it.
A calculation can be done independently to see if the numbers match.
A script can be tested against known inputs. A technical recommendation can be checked against the relevant specification. An AI-generated design can be reviewed against the actual manufacturing and operating constraints.
Turning engineering data into something AI can use
Most engineering organizations already have plenty of information. It just tends to live in different places: spreadsheets, CAD files, databases, PDFs, maintenance records, inspection reports, emails, and project management systems.
An AI system cannot automatically turn that mess into reliable knowledge.
Engineers who understand things like APIs, databases, data structures, document retrieval, and basic data cleaning can do more with AI than simply plug a model into an existing tool. They can build workflows around the data the business already has and, just as importantly, notice when that data is causing a problem.
If the documents are outdated or the data is incomplete, the answer can be wrong even when the model itself is working as expected.
Instead of asking a model to answer from its training alone, a RAG system can pull relevant information from a set of approved company or project documents and use it as context for the answer.
The difficult part is not simply adding an AI model. Someone still has to decide which documents belong in the system, how access should be controlled and how conflicting versions should be. How users can verify the information they receive.
Learning enough code to automate repetitive work
Programming is another area where AI can be useful for engineers. Engineers do not need to become software developers to get something out of it. Knowing some Python, SQL, JavaScript, or another language is often enough to start automating the smaller jobs that take up time.
There are plenty of those jobs around an engineering project. Cleaning test results. Comparing spreadsheets. Renaming and sorting files. Pulling information out of reports. Running the same calculations every week. Creating the same type of document again and again.
None of these tasks are particularly exciting, but they add up. AI can make it easier to write a script for them instead of doing the same work manually every time.
Engineers still need to understand the code they are using. AI-generated software can contain subtle errors, make incorrect assumptions about inputs, or work perfectly in one situation and fail in another. Someone needs to test it and maintain it after the original prompt has been forgotten.
Designing AI workflows instead of collecting AI tools
Using an AI tool for one task is fairly simple. Building it into a larger workflow is a different job.
An engineer might use AI to summarize a report. The bigger opportunity is what happens before and after that step.
A bigger workflow could pull information from the right project sources, find the documents that matter, run an initial analysis, flag anything unusual, and put together a draft for an engineer to review.
Engineers who know a process well have an advantage here. They know which steps depend on each other, where things usually go wrong, who needs to approve something, and which exceptions do not show up in the standard workflow.
Not every step needs to be automated either. If the system is unsure, there should be a clear way to stop, flag the issue, and hand it back to a person. Otherwise, automating one part of the process can just create another problem somewhere else.
Seeing the whole engineering system
AI can optimize an individual task while making the overall process worse.
An AI model might become very good at identifying production anomalies. But if every alert requires a manual investigation, the factory could end up with more work rather than less.
The same issue appears in design. Generative tools can create hundreds of designs. That does not mean all of them are useful. Many ideas may look good on paper. Fail in the world. Material availability, how parts need to fit together, the manufacturing methods available, production cost, installation, maintenance, and safety can rule out most of those ideas pretty quickly.
For example AI could do a job producing design ideas quickly. It can suggest layouts or structures from a set of inputs, and it can come up with ideas much faster than a person could. But choosing which design actually works is a different problem.
A design might look efficient on the screen and still be impossible to build with the tools, materials, or equipment available.
The engineer is not simply asking, “Can this be automated?” They are asking, “What happens to everything else if we automate it?”
Communication will become even more valuable
AI makes it easier to produce information. It does not make that information easier to act on.
An engineer may have an AI-generated analysis containing dozens of observations, possible causes, and recommendations. A project manager may need only three points from it. A customer may need one clear recommendation. A technician may need a completely different level of detail.
Someone still has to make that translation.
Strong communication allows engineers to explain what an AI system found, what remains uncertain, and what action should be taken. It also makes it easier to challenge an AI-generated result without turning every technical discussion into an argument about the technology itself.
Autodesk’s 2025 AI Jobs Report supports the importance of this broader skill set. Its analysis found design skills among the leading requirements in AI-related Design and Make job postings, alongside technical skills, coding, and cloud skills. Communication, leadership, people skills, and collaboration also appeared among the skills identified in the report.
How engineers can prepare for 2027
Start with something you already do regularly. Pick a repetitive task and see if AI can make it quicker without creating more work somewhere else.
It could be working through documents, cleaning up data, putting together reports, writing or checking code, or doing research. What matters is that it takes time and follows a fairly predictable process.
Then learn what you need to make the solution reliable. If there is code involved, understand enough of it to test and change it. If the system needs to work with company documents, look at how that information is stored and retrieved and who can access it. If AI is doing analysis, have a way to check the results before they are used.
This approach also fits the wider labor-market trend. The World Economic Forum’s Future of Jobs Report 2025 identifies AI and big data as the fastest-growing skills through 2030. It also lists creative thinking, resilience, flexibility, curiosity, and lifelong learning among the skills expected to increase in importance.
LinkedIn’s 2025 Work Change Report estimates that 70% of the skills used in most jobs will change by 2030, with AI acting as one of the major drivers.
The tools will change. Engineers will have to keep learning them, testing them, and deciding where they actually fit.
What employers will look for in 2027
A resume that simply says “AI experience” will not tell an employer very much.
A stronger candidate will be able to describe a real problem they solved. Perhaps they automated a reporting process, built a small internal application, created a document-retrieval system, reduced manual data processing, or used AI to explore a design problem.
An engineer who says, “I used AI to automate this process,” has demonstrated tool familiarity. An engineer who can explain what was automated and how they tested it has demonstrated much more than basic familiarity with the tool. They understand where it worked, where it failed, and where a person still needed to be involved.
Steve Blum, Autodesk’s Chief Technology Officer, recently discussed the growing gap between rapidly changing AI requirements and traditional engineering education on LinkedIn, pointing to the sharp increase in AI-related skills appearing in Design and Make job listings.
The real advantage will be knowing where AI belongs
Engineers will need to look at a piece of work and decide what can be automated, what is better left to AI-assisted tools, and what still needs an experienced person.
An AI system can produce plenty of options. The engineer still has to decide which one makes sense for the actual project.
If you’re building an engineering team for that future, Apollo Technical can help you find the professionals who bring both technical expertise and the skills needed to work effectively with emerging technologies.
