Blogs

The AI boom has disrupted the way engineers work, introducing new tools to learn, raising expectations for what teams can achieve in a workday, and making it harder to get hired in the first place. This makes it difficult to advise students on which specific coding languages or technical skills they should learn. So amidst the uncertainty, advice for young professionals often turns to a common refrain: Be adaptable. But what does adaptability look like in practice? Engineers often operate on the cutting edge of technology, so dealing with change is a normal part of the job, says Samantha Brunhaver, an associate professor of engineering at Arizona State University, in Tempe. Yet university curricula and training in the workplace often don’t prepare students for this. “We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it,” says Brunhaver, who received a National Science Foundation award in 2020 to study how to foster greater workplace adaptability among young engineers. For this ongoing project, she has interviewed engineering managers, early career employees, and undergraduates about their experiences. Part of the problem, she says, is that every employer has its own idea of what to be adaptable means. Generally, Brunhaver defines adaptability as “the ability to recognize that a change or uncertainty is occurring, and then respond effectively to that change.” But the skill is context-dependent. In software engineering, that might mean responding to turnover in the tools you use on a daily basis, while aerospace or biomedical engineers may need to keep track of changing procedures and regulations. “Managers are all saying adaptability is important,” Brunhaver says, “but defining it in different ways.” At the same time, engineers are all contending with changes beyond these industry-specific expectations. Jobs in the technology, media, and telecom sectors are experiencing the fastest pace of skill turnover, according to a June 2026 report on the effects of AI from the professional services network […]

This article is brought to you by Emerson.I’ve spent much of my career as an engineer, including years in the semiconductor industry. And one lesson has stayed with me through every major technology shift: innovation always creates new complexity.In semiconductors, we have seen that repeatedly. Every generation has delivered breakthroughs in performance and capability, but each step forward made it harder to understand system behavior. What used to be easy to validate on the component level with a test bench now needs a much wider view. “The future of engineering will be defined by who can verify, understand, and improve complex systems fast enough to safely keep innovation moving forward,” says Ritu Favre, President of Emerson’s Test & Measurement business group.EmersonChiplet-based designs are a prime example. A chiplet from one supplier, an interposer from another, and a packaging process from a third may all perform perfectly on their own. Yet there is a chance for unexpected behavior when you put them together in a system. More and more often, the hardest engineering challenges are not in the individual components themselves. The problems are found when we start to combine components and have them interact with each other.These challenges extend far beyond semiconductors. Products are becoming more software-defined and dependent on interactions across different technologies and environments. Think about the interactions needed for a modern car using adaptive cruise control on a bumpy road in a rainstorm. Or a passenger jet adjusting wing flaps and engine speeds in turbulent weather to maintain safety and stability. Both the car and the jet are being guided by complex computer systems with thousands of sensors leading to thousands of interactions every second. And in many cases, there are multiple computer systems working together. We are building systems of remarkable capability but understanding how they will act under real-world conditions is getting harder.That is why I believe we are entering a new era of test. The defining challenge of modern engineering is no longer simply what we can design and build. It is what we can confidently […]

How do I touch youacross the ocean,across cold depthswhere light travels through glass.Not copper—fibers. Optical. Through liquid glass, through flickering lightthat carries you in fragments. Light broken into pulses. You say: it’s easier this way. What are we missing like this? You smile.Safe distance. I say: network. Signals slide beneath the sea, through cables thinner than trust, faster than touch,slower than longing. We stand alone, together. Synchronous, yet apart. Icons replace skin, latency replaces breath. This distance protects us. Silence that feels intentional. Everything is under control as long as nothing truly hurts. And we choose itbecause it shields usfrom what we might become if we actually met. You are my counterpoint. My response.My reflectionat a safe distance. Beneath the ocean, nodes remember paths. Packets shake hands without bodies. If we get lost,we resend everything, with error,with noise,with hope.

Balaji Ingole rarely saw televisions while growing up in Udgir, India. No one in the small Maharashtra village had computers or phones. Only one household owned a television, and neighbors often gathered there to watch shows together.Ingole never even saw a computer growing up. It wasn’t until he reached middle school that he encountered a computer lab, an experience he says changed his life. Almost immediately, he says, the machine felt like a window into a different scale of possibility for him.Balaji IngoleEmployer Amla Commerce in MilwaukeeTitle Project managerMember grade Senior memberAlma maters COEP Technological University and Welingkar Institute of Management, both in India“I was very studious and not very social, always reading or solving problems in a math textbook,” he says. “At the computer lab, I began learning the C programming language—which was like discovering a whole new world. I was fascinated that you could create something with just a few lines of code.”His early interest grew into a self-directed education. Outside of Ingole’s formal classwork, he taught himself to build database-backed applications, wire up hardware, write software, and trace error logs.Today the IEEE senior member similarly splits his time. During the week, he’s a project manager in Milwaukee at B2B e-commerce company Amla, leading AI-driven digital transformation initiatives to help the company’s clients boost their sales. On weekends, he leads a similarly demanding life as an independent researcher. His current projects include developing AI-enabled health care diagnostic tools and assistive technologies to support people with physical disabilities.“I believe in ‘learn by doing,’” he says. “I really like to test my knowledge and prototype ideas to find out if they truly work.”A college project becomes an inspirationIngole’s tendency to go beyond his coursework continued after he graduated high school in 2004. As a mechanical engineering undergraduate at The College of Engineering, Pune (now COEP Technological University), in India, he participated in several extracurricular activities. One was interviewing entrepreneurs and writing […]

About this WebinarTurn Yield Excursions into Faster, More Confident Root Cause AnalysisWhen a yield issue emerges, the answer rarely lives in a single system. Critical clues are spread across metrology data, tool traces, chemical analysis, and facilities systems, while growing data volumes make traditional dashboards slow, fragmented, and difficult to act on.What You’ll Learn:Discover how a purpose-built semiconductor analytics platform can help engineers connect insights across domains without moving data. See how Agentic AI, semiconductor-specific visualizations, and push-down compute enable faster investigation of yield excursions and process issues, even across billions of data points. In the session, a live demonstration shows how to conduct a multi-domain root cause investigation using Spotfire® Industry Pro.Key Takeaways:Understand why siloed manufacturing data delays yield recovery and inflates costsLearn how Agentic AI automates complex cross-domain analytics and visualization generationExplore methods for scaling high-performance analytics across massive fab datasetsWho Should Attend:Yield, Process, and Integration Engineers; Fab and Manufacturing Operations Managers; Quality and Reliability Engineers; and Data & Analytics leaders supporting wafer fabs, foundries, OSATs, and IDMs who need to identify issues faster while maintaining confidence in decision-making.Save Your Spot!Join this webinar to learn how leading semiconductor teams are accelerating root cause investigations, scaling analytics across massive datasets, and transforming disconnected data into actionable manufacturing intelligence. Reserve your seat today.Register now for this free webinar!