The 5 Mandatory Soft Skills Engineers Must Have in the Age of AI

https://ift.tt/bcuE7aW

For about six years, I’ve supported enterprise customers at a major global technology company, working mostly in cloud support and messaging systems, i.e. the information backbone that lets large applications talk to each other without falling over. Across more than 1,200 customer engagements, I’ve sat with government agencies, critical-infrastructure operators, and Fortune 500 companies on some of their worst production days. Since 2023, I’ve also served as a tenured technical interviewer, running more than two dozen hiring loops and helping decide who joins the team. From both chairs, the one solving problems with customers globally and the one evaluating a candidate for a hiring recommendation call, the same pattern keeps showing up: the tools we use change constantly, but the skills that actually move a career are still owned by us, the human.

Without a doubt, generative AI boosts performance. It drafts code, summarizes logs, and accelerates research that used to take even tenured engineers an entire week to complete. What it doesn’t do is earn a customer’s trust, own an ambiguous problem nobody else has explored before, or make the judgment call when the data is incomplete. Those still belong to us. Here are the five soft skills I believe every tech professional needs to build, and that I, as an interviewer, am actively looking for.

1. Communication Skills that Deliver

Early in my career, a mentor taught my team with a simple exercise: price out each written communication we offer to our customers. Take a customer’s monthly spend, divide it across the average interaction count per customer, and it turns out that one well-crafted response is worth several hundred dollars. The lesson wasn’t about money. It was that every word carries weight.

AI can generate a grammatically perfect paragraph in seconds. What it can’t decide for us is whether those paragraphs project confidence, whether the generated response quietly assigns blame or takes ownership of the findings, or whether it matches the customer’s urgency and level of knowledge. The highest-value writing is specific, sets clear expectations, and is positioned correctly to the right audience, not just its correctness. AI gives us a faster first draft. Knowing what makes that draft land and tailored to the customers we serve is still our job.

2. Empathy and Emotional Intelligence

A few years ago I took the lead on an escalation from a Fortune 500 airline whose production messaging system was running with eight-second latency. The numbers mattered, but the bigger problem was sentiment: the customer felt unheard, and their trust in our support team was dropping sharply. I joined a call, dug into their architecture, metrics, and historical logs, and helped them reconfigure to achieve an overall system latency under one second. The latency fix is what got measured. The reason customer smoothly collaborated is that I treated a frustrated team like people first.

Research keeps confirming this. Surveys of executives consistently rank empathy, active listening, and interpersonal skills as more important in an AI-assisted workplace, not less. We are, as one neuroscientist put it, not thinking machines that feel, but feeling machines that think. AI has no idea how the person on the other side is feeling. We can match and improve the customer’s experience, if we choose to pay attention.

3. Adaptability and a Learning Mindset

The pace of technical change right now is faster than any period I’ve worked through. That’s exactly why the most valuable and talented professionals I meet are “learn-it-alls,” not “know-it-alls.” They treat challenges as opportunities rather than threats, see feedback as fuel rather than criticism, and stay comfortable in ambiguity instead of freezing when there’s no obvious best practice.

This mindset is what lets us use AI well in the first place. Tools that felt cutting-edge last year are baseline today. If our identity is wrapped up in being the person who already knows the answer, every new tool feels like a threat. If our identity is built on curiosity, every new tool is a force multiplier. Adaptability isn’t a personality trait we’re born with. Instead, it’s a discipline we practice, by rethinking setbacks and deliberately seeking out the perspectives that disconfirm what we already believe.

4. Earning Trust and Collaborating Through Disagreement

I once took over a messaging-delivery engagement for a customer team that had not made progress for over a month, with a hard feature launch bearing down on them. Solving it meant coordinating across an internal service team, an external service partner, and the customer simultaneously. I had to consistently replicate the reported failures, isolate the cause, and eventually delivered a solution, in conjunction with the external service partner, four days before their deadline. No single person had the full picture. The fix came from getting several parties to trust each other enough to work the problem together.

Trust, the research shows, is built less on raw competence than on relationships and consistency. And professional collaboration includes healthy disagreement. The constructive discussions are the ones that challenge the idea, not the person. The absence of conflict on a team usually isn’t harmony; it’s apathy. AI can route information between people, but it can’t make them trust one another or disagree productively. That’s the human role that holds projects together, and it’s one of the first things I probe for in an interview, by asking candidates about their experience with team collaboration activities or projects.

5. Values-Driven Ownership and Judgment

The thread running through all of this is judgment grounded in values. The strongest teams I’ve been part of operate from a shared compass: start with the customer and work backward, take ownership beyond the written job description, earn trust, dig into the details, and deliver results despite setbacks. Those principles tell us what good looks like when the path isn’t written down. And in the new AI era, uncharted territory is common.

This is also where my two professional roles converge. As an interviewer, I’m not impressed by candidates who can recite frameworks; AI can do that. I’m listening for a real story: a specific situation they faced, the actions they took, and the result they delivered. That’s the one thing generative AI cannot fabricate on a candidate’s behalf: a track record of judgment under pressure.

In conclusion, AI is a remarkable tool, and we should readily embrace this change. But stakeholder communication, human connection, professional reputation, and project collaboration all still run on soft skills, and so do our careers. Growth depends on us, the human, demonstrating potential and impact to our organization. There’s no AI shortcut around that. The professionals who thrive in this era won’t be the ones who lean on AI to coast. They’ll be the ones who let AI handle the mechanical work so they can spend more of their energy on the things only humans can do.

The post The 5 Mandatory Soft Skills Engineers Must Have in the Age of AI appeared first on SD Times.

Tech Developers