Stevens Online Blog: Insights, Inspiration and Information
Stevens Online education blogs are excellent resources for current and prospective students and professionals looking to stay up-to-date on the latest industry developments supported by our programs.
Through our blog, you can access in-depth articles, interviews and other resources that provide valuable insights into the skills and knowledge needed to succeed in today's rapidly-changing business, technology and engineering landscapes. Whether you are considering enrolling in one of our programs, want to explore a new career path or deepen your knowledge in your current field, the Stevens online blog is an excellent source of information and inspiration for you to take advantage of.
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AI tools handle boilerplate, scaffolding, and routine tests, but system design, security, and distributed debugging still require engineers. Here is what the data shows, which roles face real risk, and the skills that compound.
AI tools now handle routine SQL, scheduled dashboards, and templated reports. The work growing in demand is question framing, stakeholder communication, and validating AI-generated analysis. Here is what the data shows and which skills to build.
Project coordinators and process-heavy middle managers face the highest automation risk. Strategy, change management, and people leadership roles are most protected. Here is how researchers assess the risk and what determines which side you are on.
Pursuing a master's degree in business administration, artificial intelligence, computer science, engineering, or data science while working full time is a strategic career move. But before you apply, take a smarter first step: build a clear, business-driven case for employer tuition assistance.
The artificial intelligence landscape is undergoing a fundamental transition. We are moving from stateless, prompt-based interactions to stateful, autonomous agentic systems. This evolution represents a departure from viewing Large Language Models as solitary oracles to treating them as cognitive engines within broader, engineered systems.
To operationalize the agentic shift, developers and architects are coalescing around specific design patterns. These patterns solve the fundamental problems of coordination, error handling, and task decomposition that plague traditional AI implementations.
The shift to agentic AI is not just technical. It is economic. The cost model of software is changing from fixed infrastructure to variable intelligence, and engineering leaders must understand the hidden economics that can make or break an AI deployment.
Alkis Vazacopoulos had already launched a successful business analytics and optimization career when he caught the teaching bug. In a recent interview, he explained that he was “inspired by a desire to share my real-world experiences with students and contribute to developing the next generation of analytics professionals… As someone who had implemented optimization solutions in companies across various sectors, I could help students understand not just the mathematical concepts, but their practical applications and limitations in real business settings.”
Artificial intelligence has made its presence felt across countless industries and professions, with engineering management close to the epicenter. Companies leverage data to make important decisions about resources, risk, and recruiting; AI has made that process more powerful and more fraught with potential complications for engineering managers.
Demand for engineering managers grows across healthcare, technology, construction, manufacturing and other industries. If you plan to pursue an engineering management career, an advanced degree – such as a master’s in engineering management – can provide a pathway to reaching your goals.