Refining the T-Shaped Skill Model Theory: AI Skills for Engineers

AI skills for Engineers

In 1991, David Guest of McKinsey & Company wrote about the concept of the ideal “T-shaped” employee, mainly applying to those trained in computers and technical fields. The theory, in its simplest terms, according to various articles and easily explained here by Indeed, says,

“The T-shape is a metaphor for an individual’s strengths, with the vertical line representing expertise, discipline, and knowledge of a particular field, and the horizontal line representing cross-discipline competencies and the ability to collaborate with professionals in other industries or roles.”

While its original intent, as stated above, was geared towards computer technicians, the same theory can be applied to most professions— for example, financial experts have a base knowledge in financial planning and investments with a breadth of knowledge that covers taxes, economics, negotiations, and more.

 Engineering is a prime example of having specialized knowledge as your base, with overarching supplemental skills to support that knowledge and the work at hand. For many engineers well-versed in a specific discipline, their ancillary knowledge is vast and overlapping – biomedical engineers understand engineering principles rooted in the knowledge of the human body mechanisms, but it also overlaps with manufacturing principles, testing, efficacy, safety, as well as technical knowledge that supports product development, data analysis, documentation skills and more. These skills are further enhanced by the necessary soft skills to be successful at their job, like critical thinking, collaboration, troubleshooting, and communication, to name a few.

As the employment landscape continues to evolve, more and more T-models will also add AI and machine learning knowledge and skills to the horizontal bar, which will be particularly important to engineering professionals, as explained further below.

AI Skills Will Make Engineers More Marketable and Refine Their T-Shape


For engineers, professionals whose everyday work is steeped in logic, formulaic procedures, and provable outcomes, the integration of AI and machine learning into their industry has been underway for some time. It’s just now on hyper-speed, where, when used in conjunction with human ingenuity, it revolutionizes the way engineers work. When it comes to assessing engineering talent, it is on the radar for most hiring organizations as an in-demand skill, especially when combined with cross-over tech knowledge.

Engineers who want to make themselves more marketable are embracing AI and machine learning advances. When blended with their learned skills, engineers who have the ability to train an AI tool for engineering purposes can boost productivity immensely through:

  • improving automated tasks
  • generating learning patterns adapting from data
  • simulating environments that allow for experimentation, testing, and refinement

Boosting productivity with AI

While the headlines insinuate continued threats of AI stealing jobs, most staffing experts would agree that AI should be viewed as an enhancement rather than a replacement. True, there will be activities that will ultimately phase out due to AI innovation, but human ingenuity stands the test of time. Engineers who can successfully use AI and machine learning to automate mundane tasks, create efficiencies, and produce solutions quickly and more accurately are in demand for both tech and non-tech organizations seeking to optimize output for their products and services.

Adapting to learning patterns

Engineers often employ a systematic approach to solutions, consuming data, analyzing it, and configuring outcomes, whether that be for civil engineering projects, electrical engineering, biomedical and biopharma products, and more. Imagine a trained engineering professional harnessing AI’s ability to intake, identify, classify, and quantify colossal amounts of data in a fraction of the time. Then, utilizing their creativity and critical thinking skills to analyze, assess and ultimately solve for the task at hand with the output. The accuracy and the application of this learned data that offer predictive modeling could lead to innovation beyond our expectations, accelerating product and service launches and adding to the bottom line—which are all vital actions that propel sought-after engineering talent to the top of the hiring pipeline.

Using AI to Optimize Costs and Engineering Processes

Most engineering professionals produce a product, a service, or a program, but it’s often something tangible and usable in its function. However, one of the most critical components in engineering is testing in its most basic form to ensure the end product works as intended. In many instances, test environments are developed, with their creation and replication of a live output in and of itself an integral part of the entire engineering process and one that can be costly. The introduction of AI offers time-saving, streamlined development of testing environments through simulation that can be continually replicated and refreshed. Of course, simulation environments are nothing new. Still, newer AI iterations continue to improve them, allowing engineers to work faster, have more controls, and make processes repeatable for more accurate results.


 The T-shape theory has long been applied to recruiting and hiring the ideal employee who is well-rounded and will fit well into an organization. This idea underscores the benefits to engineers augmenting their skills with AI and machine learning, one of the most important being to remain competitive in the job market and broaden career opportunities. At SSi People, we specialize in finding the best and brightest in the engineering sector that will bring game-changing thinking and skills to an organization. For help in identifying talent with skills that align with the future of work or for more information on engineering job opportunities that will put these skills to use, contact our recruiting and staffing experts today.

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