Inside the Surprising Ways Tech Professionals Say Gen AI Is Reshaping Their Work

December 18, 2025

Nearly anywhere you look, there’s a new, eye-popping statistic about generative AI. From the size of the generative AI market to the number of people using it, it’s hard to believe that these tools have only been widely available to the public since November 2022.

Despite the hype, a recent IEEE survey suggests that organizations are struggling to integrate the technology into their larger operations. People are using it, but everyone seems to be using it a little bit differently.

We asked a panel of IEEE members — they are researchers, cybersecurity specialists, semiconductor designers and educators — to share exactly how generative AI is (and isn’t) changing their workflows, what they are doing now that they couldn’t before and where human judgment still matters most.

How are you currently using AI?

IEEE Senior Member Shafeeq Rahaman: I lead analytics and data infrastructure at a global advertising agency. My team builds machine learning models to optimize total marketing spend across channels, and to measure the impact of specific campaigns. AI automates data ingestion, identifies causal relationships between spending and performance and simulates future outcomes under various budget scenarios.

How has generative AI changed the way you approach solving problems in your field?

IEEE Senior Member Nicholas Napp: We provide business strategy, product strategy and some product development services to tech industry clients. The history of tech is filled with companies that launch a product without considering what pain point it will actually solve. AI has reduced the cost of execution; it is easier than ever to explore ideas. I still firmly believe in identifying real customer needs, but I think AI changes the process to be far more iterative.

What’s something you can do now that wasn’t possible before generative AI?

IEEE Member Man Zhang: My profession involves teaching the fundamentals of machine learning, neural networks, natural language processing and computer vision to undergraduate and graduate students. The most significant change is the ability to create highly personalized, adaptive learning pathways for students at scale. Before generative AI, creating customized tutorials or practice problems for a class of 100 students was logistically impossible. Now, I can design systems that use generative AI to dynamically create explanations and exercises tailored to a specific student’s demonstrated weaknesses.

IEEE Senior Member Sarat Chinta: I work as a semiconductor chip design engineer. Within any semiconductor company, there are several past designs often built on similar or evolving technology nodes. Each new project, whether it’s a fresh design or an existing one being adapted to a new node, requires extensive design and technology analysis before the actual work begins. Traditionally, analyzing such massive datasets and cross-node variations was extremely time-consuming. Generative AI can now process large volumes of design data in real time, extract meaningful insights and help teams make faster, smarter design decisions.

Where does the human still matter most in your AI workflows?

IEEE Member Juan Galindo: It matters most in the definition and assessment of goals, requirements and restrictions. It is also important in the analysis of the workflows themselves to make them more precise.

If you could fix one misconception people have about generative AI, what would it be?

IEEE Member Ning Hu: Generative AI excels at flexible operations and human interaction not precision engineering. It is an excellent decision support tool. It helps us explore options, understand trade-offs and communicate ideas effectively.

Traditional deterministic programming remains essential when you need precise, sophisticated system behavior. Generative AI augments the human decision-making process; it doesn’t replace precise technical execution. The key is matching the tool to the task: use generative AI for exploration and communication; use traditional methods for precision and reliability.

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