AI is changing how engineering work gets done, but the core message for students and recent graduates is consistent: The fundamentals still matter.
For early-career engineers, AI is becoming part of how teams review data, compare options, test assumptions and move through technical work. But it is not a substitute for judgment. The engineers who will be prepared for modern project work are the ones who pair strong technical foundations with curiosity, adaptability and a disciplined approach to checking results.
Madison Hewitt, early careers recruiter, and Robert Tipton, innovation development director, share what early-career engineers should understand about AI, how to talk about it in recruiting conversations and why human accountability remains central to engineering work.
What does “AI-ready” mean for an early-career engineer?
Madison Hewitt: Being AI-ready does not mean you need to be a software programmer or know every tool. It means you are curious, willing to learn and able to partner with technology thoughtfully. An AI-ready engineer can use AI to generate ideas or support modeling, but more importantly, they know how to question and validate the results.
Robert Tipton: I would add that AI starts with understanding what it is, where it is reliable and where it is not. AI has been marketed as a simple solution, but that’s not always the case. Early-career professionals who understand that are already better prepared.
How should students and recent graduates talk about AI experience on a resume or in an interview?
Madison: Don’t just list AI tools as keywords. That tells us what you used, but not how you think. Instead, tell a story about how AI supported your process. Maybe you used it to analyze data faster, automate a repetitive task or brainstorm design options for a class project.
Then explain what came next. What did you question? What did you validate? What decision did you make because of your own technical judgment? That is the stronger story.
Where is AI making the most practical difference in project work today?
Robert: Time savings are often where teams start. But the value does not stop there, and sometimes time savings are not the most important part.
What I find more meaningful is what teams do with the time they gain back. They can run additional checks, pull more representative datasets or complete analysis they did not have room for before. In that sense, the win is not only doing things faster but also creating more room for rigorous analysis.
What technical fundamentals should early-career engineers continue to prioritize?
Madison: The first principles of engineering remain nonnegotiable. Even as AI becomes more common in modeling, calculations or drafting, engineers still need to understand the why behind the math. They need to spot errors, evaluate safety, and review work against codes, standards and project requirements.
AI can generate information. A skilled engineer knows how to verify and check it.
Robert: Every major technology changes the list of technical skills engineers use day to day. When CAD replaced pencils and drafting tables, some tasks changed, but the engineering fundamentals underneath them did not. AI is doing something similar, at a much broader scale.
This is a great opportunity for early-career engineers. You are building your foundation as these tools become part of the work. You can help experienced engineers carry forward hard-earned judgment while also bringing fresh fluency with new methods.
How can early-career engineers balance speed with careful technical review?
Madison: AI can help move early stages of work more quickly, whether you are brainstorming, drafting or comparing options. But speed can never come at the cost of safety or quality. There still needs to be human-in-the-loop review. At Burns & McDonnell, trust is central to our work. AI may support the process, but people remain the final authority. AI tools are incredibly fast, but speed should never outpace safety or quality.
What soft skills become more valuable as teams adopt more digital tools?
Madison: Active listening and technical translation become even more important. Engineers need to take complex data and turn it into clear, actionable information for teammates and clients. As workflows become more digital, strong communication helps keep projects aligned.
Confidence also matters, but it should be paired with coachability. If you hit a roadblock, try to work through it, form a preliminary idea and bring your logic to a mentor. That shows initiative. Knowing when to elevate the question shows maturity.
What habits help engineers check and validate AI-generated information?
Robert: Checking, questioning and validating are already part of engineering work. The challenge is making those habits function inside AI-assisted workflows.
Sometimes AI can be invisible, running in the background without users realizing it. Other times, it can be opaque. You know AI is involved, but you cannot see the instructions, source data or assumptions behind it. If the output is all you can test, your ability to assess quality is limited.
The habit early-career engineers should build is asking AI for visibility into its logic. How does the tool work? What data is it using? Where could it fail? That is not skepticism for its own sake. That is engineering.
What role does data literacy play?
Robert: Data literacy is critical because poor data does not become reliable just because AI can process it. If you run inconsistent or biased data through an AI system, you can get inconsistent results. Early-career professionals need to understand what the data needs to be useful, where it is fragile and what happens downstream when those needs are not met.
What advice would you give students who feel pressure to know every new AI tool before applying?
Madison: Don’t worry. No one expects you to know every tool. The technology is changing too quickly for anyone to master it all. Focus on strong engineering fundamentals and show that you are an agile, thoughtful learner. We love meeting candidates who are curious problem-solvers, ready to innovate and solve some of our clients’ toughest challenges.
What is the main takeaway for early-career engineers?
Robert: Approach AI like an engineer. Do not sacrifice quality for speed. Do not step outside your domain just because a tool makes it possible. Understand the work well enough to stand behind it.
Madison: Technology is a tool, but you are the problem-solver. AI will not replace the need for strong engineers. It can amplify your ability to learn, contribute and take on meaningful work earlier in your career.
