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.
“Developing strategies for integrating AI into existing workflows is a critical step,” Adeva co-founder Tosho Trajanov observes. “This integration involves understanding the strengths and limitations of AI tools and seamlessly incorporating them into current processes.”
AI has already made substantial inroads with software developers. A recent Github survey revealed that 92 percent of U.S.-based developers use AI coding tools in and away from work. The survey also found that developers:Â
- Find that waiting on builds and tests is still a problem
- Want more collaboration
- Think AI will facilitate collaboration
- See significant benefits to AI, including better code quality, completion time, and resolving incidents
The need for human evaluation and input means AI won’t replace people any time soon. That said, people who know how to use AI may replace their less-knowledgeable peers.
Industry Innovations: AI in Engineering Management Jobs
AI can be useful in multiple ways; Trajanov lists automating routine tasks, aiding in complex problem-solving, and optimizing overall workflow among its applications. Engineering managers can make good use of AI if they apply it judiciously and recognize its limitations.
Some observers — Nickelfox, for one — see AI as a fount of opportunity for engineering managers: “By tapping into the predictive capabilities of AI, engineering professionals can make data-driven decisions, allocate resources more effectively, and significantly boost operational efficiency. The result? Faster project completion, reduced costs, and superior outcomes.”
That potential must be counterbalanced by AI’s vulnerabilities, LeadDev reports. “AI is very experimental,” says Piyush Tripathi, a lead engineer at Square. “I’ve seen teams where people had high expectations and thought it was going to be a game changer, and then realized it’s buggy and has lots of issues.” According to Tripathi, current AI tools work best in environments where everything doesn’t have to work 100 percent of the time.
AI trends point to leadership needs in engineering management subfields, such as prompt engineering, which involves designing and refining inputs on generative AI platforms. Forbes calls prompt engineering a “hot job,” with salaries ranging from $200,000 to more than $300,000. AI engineering offers similar opportunities; AI engineers build, implement, and maintain AI-based systems. They are primarily responsible for implementing machine learning that applies AI to perform tasks automatically.
Turning to the personnel management side of engineering management, AI can help track the work of team members. “Predictive AI can automate insightful performance reports telling leaders where they should be making improvements,” TechCrunch reports. “The main advantage here is that AI has a greater ability to identify patterns.”
Let’s look at a few other ways AI innovations impact engineering management.
- Risk management: According to SN Computer Science, historical business data related to financial institutions can be used to make high-stakes business decisions in risk management, fraud prevention and credit allocation.
- Quality control: Leveraging AI in the quality control of supplier management enables companies to automate manual and time-consuming tasks; improve accuracy, efficiency, and sustainability; and make warehouse processes greener, according to the IEEE Computer Society.
- Predictive maintenance: AI can create weekly targets and offer built-in advice and use cases for reaching those targets, TechCrunch reports.
- Operations optimization: According to the IEEE Computer Society, effective supply chain management is essential to optimizing the flow of products and services and streamlining business operations. AI can apply predictive analytics to sustain optimal inventories.
How an MEM Degree Prepares You for the Future of Engineering Management
The ever-evolving engineering field increasingly requires specialized skills at every job level, from programming and field techs to the C-suite. In today’s competitive marketplace, a master’s degree can set leaders apart, no matter where they are in their careers.
A modern master’s program in engineering management should provide students with technical, analytical, and practical skills they can apply to solve problems in data science jobs in any field. Most programs teach math, programming, and data visualization concepts, providing a cross-disciplinary skill set. High demand for employees with expertise in machine learning, neural networks, and natural language processing (NLP) points toward a program like the Online Master of Engineering in Engineering Management (MEM) at the Stevens Institute of Technology.
A combination of in-demand skills and a master’s degree can help you land the job of your dreams. Let’s explore what Stevens has to offer.
A Look at the MS in Engineering Management at Stevens: Curriculum Highlights
Students in a modern engineering management program study business management principles, data science, project management, technical leadership, cybersecurity and different types of engineering. Stevens’s MEM program features core coursework built on three pillars: management for engineers, data science and management, and engineering modeling and risk analysis. This foundation addresses students’ needs from a variety of engineering disciplines.
While many online programs divide coursework between engineering and business schools, MEM students at Stevens complete all their classes within the Schaefer School of Engineering. Stevens online master’s in engineering management students learn how to:
- Leverage advanced techniques and analysis to estimate and use cost information in decision making
- Form and manage an effective engineering design team in a business environment
- Handle and process information using tools such as Python
- Master the fundamentals of system dynamics and build system dynamic models
Courses focusing on interpersonal and management skills are always contextualized through a lens of technical expertise. Required classes include:
- Engineering Economics and Cost Analysis
- Project Management of Complex Systems
- Systems Modeling and Simulation
- Decision and Risk Analysis
Stevens gives MEM students a choice between recommended electives and up to four courses focused on a particular specialty: construction management, mechanical engineering, electrical engineering, and systems and software engineering. Electives include:
- Leader Development
- Data Analysis and Visualization Techniques for Decision Making
- Supply Chain and Logistics Management
Data Exploration and Informatics
Students gain the knowledge and skills to handle the variety and volume of information encountered in today’s workplace, working with structured and semi-structured data. The course uses Python, a favored language for information handling and data analysis.
Decision and Risk Analysis
This course explores analytic techniques for rational decision-making that address uncertainty, conflicting objectives, and risk attitudes. Topics covered include modeling uncertainty; rational decision-making principles; representing decision problems with value trees, decision trees, and influence diagrams; solving value hierarchies; defining and calculating the value of information; incorporating risk attitudes into the analysis; and conducting sensitivity analyses.
Data Analysis and Knowledge Discovery
Students examine methods that have evolved from statistics and AI. They investigate methods that have emerged from both fields and proven valuable in recognizing patterns and making predictions from an applications perspective. The curriculum covers a combination of methodologies, tools, techniques, algorithms, and ingenuity. Those skills are employed in creating views, extracting trends, defining patterns, and identifying clusters to manage large data.
Forecasting and Demand Modeling
This course covers the theory and application of modeling aggregate demand, fragmented demand, and consumer behavior using statistical methods for analysis and forecasting for facilities, services, and products. Students study the conceptual basis and tools necessary to conduct market segmentation studies, defining and identifying criteria for effective segmentation. They also learn techniques for the simultaneous profiling of segments and models for dynamic segmentation.
Earn Your MEM Degree Online with Stevens
AI plays a fundamental role in the work of engineering managers, making a tech-related master’s degree more important than ever. The online MEM program at Stevens delivers the technical, analytical and practical skills needed in every engineering field. Stevens faculty consists of experienced educators and active researchers who offer industry insights. Earning a degree online allows students to continue working while completing the program in less than two years.
Ready to take the next step? Apply for the Stevens online MEM or contact an enrollment advisor to schedule an application walkthrough.