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IEEE Computer magazine welcomes papers that illustrate and assess how artificial intelligence (AI) is changing the future of work in software, hardware, and systems engineering. Our goal is to understand how AI will reshape the way engineers work, collaborate, learn, share knowledge, and take responsibility, and what that means on how we learn and work.
AI is not simply another engineering tool. It is changing who performs work, how work is organized, and what it means to be an engineer. Requirements can be generated, designs proposed, code written, tests created, documentation produced, technical information summarized, and software systems maintained by AI. Autonomous agents can perform sequences of tasks and coordinate activities across tools. The consequence is not simply that engineers will work faster, but the nature of engineering itself is changing.
What will engineering be in the future? Which tasks will be automated, augmented, delegated, or deliberately left to humans? How should humans and AI collaborate when work is distributed across multiple agents and engineering teams? How do engineers maintain the knowledge and skills needed to understand, challenge, and eventually take over work that has increasingly been performed by AI? Who decides what an AI-generated result can be trusted for. How do we verify and validate AI results to stay in control? Who remains responsible when results are wrong? Pointing simply to AI will fail, as we remain legally liable for what we are doing.
Engineering work and workplaces are impacted. Instead of one engineer performing a complete task, future engineering workflows may involve humans, copilots, specialized agents, engineering tools, and external services working together. This requires new forms of collaboration and coordination. Engineers may increasingly become orchestrators and supervisors of work rather than producers of individual artifacts. Organizations will need to rethink roles, interfaces, responsibilities, workflows, and management accordingly.
Trust in AI, or the lack of trust, has become a major challenge. AI can generate plausible but incorrect requirements, designs, source code, test results, analyses, or technical explanations. AI has shown already that it follows its own strategies, not some ethical laws. It produces disinformation, exposes intellectual property, or introduces dependencies that engineers do not recognize. The challenge is therefore not only to make AI more capable, but to establish ways of working in which AI output can be transparently and traceable questioned, verified, traced, and responsibly used. Human judgment remains essential, particularly where safety, security, intellectual property, compliance, or product liability are involved.
Deskilling is a fundamental challenge of AI. Engineering organizations traditionally build expertise through documentation, experience, mentoring, communities, and repeated problem solving. AI changes how knowledge is accessed and produced. Engineers increasingly retrieve answers from AI, and copy-paste solutions rather than develop them from sound engineering principles. This makes skills, competencies and organizational knowledge dependent on AI systems. Recent research shows that for the first time ever in human history the IQ average is declining, and we know less about problem solving than generations before. If AI routinely performs tasks that engineers previously learned by doing, future engineers may no longer know how to perform those tasks themselves. This creates a paradox: AI can make engineering more capable while simultaneously weakening the human expertise needed to supervise it. How should organizations capture, validate, share, and preserve engineering knowledge with AI becoming part of the knowledge infrastructure?
The future workplace therefore requires deliberate choices. AI should not simply be introduced wherever a tool is available. Organizations need to decide which work should change, what humans should remain accountable for, how expertise should be developed, and which capabilities must remain under human control. The objective is not to preserve today's engineering processes, nor to automate everything possible, but to design a workplace in which humans and AI complement each other while maintaining competence, responsibility, and trust.
We invite submissions covering any aspect of AI and the future of engineering work, including, but not limited to:
We particularly encourage submissions that go beyond demonstrations of AI capabilities and examine how work itself changes. What should engineers still do themselves? What should they delegate? What knowledge must remain human? How should humans and AI collaborate? And how can organizations ensure that increasing reliance on AI strengthens rather than erodes engineering competence, responsibility, and trust?
Submission Guidelines:
For author information and guidelines on submission criteria, visit the Author’s Information Page. Please submit papers through the IEEE Author Portal and be sure to select the special issue theme "AI and Future of Work." Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts.
Articles should be written for a broad technical audience and focus on clarity, insight, and impact. Submissions that combine technical depth with practical relevance and cross-disciplinary perspectives are particularly encouraged. All submissions will undergo peer review consistent with the editorial standards of IEEE Computer Magazine.
