Automation in engineering covers everything from IoT sensors to AI-powered design. But will automation lead to complete engineering role transformations? Professionals who can adapt and learn new skills will surely have more opportunities as engineering job market trends shift to show the rise in organizations seeking experts in emerging technologies.
Explore how engineering industry innovations are changing roles, and how to adjust recruitment and retention strategies to adapt.
Key Technologies Driving Change in Engineering Roles
Traditional engineering and design are still relevant, but automation and other factors are creating new roles and transforming existing ones. Organizations could experience engineering workforce challenges if they don’t adjust their hiring strategies to prioritize skills in emerging technologies.
Robotics Engineering
Designing, building, and integrating robots is relevant across multiple industries, from automotive to aerospace. Advancements in collaborative robots, or cobots, place emphasis on machine and human working in tandem.
Additive Manufacturing
The 3D printing industry is set to reach a value of $88.2 billion by 2030, with innovations like automated real-time layer defect monitoring and the ability to create lightweight building materials transforming industries and roles. Engineers will need to adapt to the increased use of additive manufacturing and its impact on product design and prototyping.
Artificial Intelligence (AI) in Engineering
AI is changing the way engineers work, with 60% of engineering professionals utilizing AI for designs that cut costs and save time. AI in engineering can also aid with simulation, predictive maintenance, and diagnostics.
Already, 70% of engineering organizations have implemented some form of AI, with 90% planning to utilize AI tools by 2027. The benefits? Significant reductions in downtime and defection rates.
Emerging Engineering Roles and Skill Sets
Some new roles created by these technological innovations include:
- AI systems integrators, with an understanding of machine learning algorithms and coding
- Digital twin analysts who can create digital copies of physical objects, requiring skills in sensor technology, data analysis, and data integration
- Advanced prototype and testing roles, utilizing technologies like 3D printing
- IoT experts for remote monitoring and the automation of systems in hazardous environments
Engineering career advancement opportunities may depend on developing skills in the above areas.
Engineering Workforce Development Strategies
Engineering skills development programs now need to include reskilling and upskilling on AI, advancing robotics, and other relevant technologies. Implementing continuous learning initiatives and incentivizing their use could be another strategy.
Organizations can also collaborate with institutions that offer engineering education programs, creating effective talent pipelines and helping develop curricula aligned with industry needs.
Apprenticeships, on-the-job training, and mentorship schemes can also create a highly skilled workforce, familiar with current new technologies and adaptable to change.
Retention Practices in the Automated Era
One of the downsides of investing in skilled engineers is that they’ll be in demand from competitors. Retention is critical to protect bottom lines and avoid frequent, costly recruitment campaigns. Engineering organizations must create engaging work environments that encourage innovation, creativity, and information sharing.
Leaders also need to check that they’re offering competitive benefits packages to avoid top talent leaving and heading to a competitor. Create compensation packages that either align with or exceed industry standards.
It’s also important to offer advancement opportunities and clear career progression paths. Engineers who know they have an exciting future with an organization are more likely to stay.
Ethical Considerations and Challenges
Engineering job displacement is a common concern. It’s important for leaders and change managers to emphasize that AI and other automation tools are just that: tools to support human ingenuity and hard work. Engineering requires complex decision-making, creative thought, and collaboration, none of which is replaceable by technology at this time.
Other considerations include data governance, particularly privacy and security. Engineering organizations must ensure they work with tech partners who meet the data protection regulatory standards, as well as promote those standards and requirements throughout their organization.
Importantly, all aspects of automation must have human oversight. Engineers and engineering leaders should monitor all tools in use — just like you would a piece of physical equipment — to ensure they remain fit for purpose, safe, and secure.
Combining Evolving Technologies with Human Expertise
Learning to implement automation and work with robotics and AI tools could future-proof the careers of many engineers. Organizations that invest in these adaptable, tech-forward professionals are more likely to edge ahead of their competitors. Firms should also consider engineering training and retention policies to support and upskill their existing teams.
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