Most training programs get launched because someone noticed a problem – a project went sideways, a new tool rolled out and nobody knew how to use it, or leadership decided it was time to “invest in the team.” While the motivation behind these decisions is generally valid, the approach taken to implement a training program often is not. Most organizations jump prematurely to training their people before taking the time to properly assess their needs and identify the issues that are preventing their organization from operating at peak performance.
Define what “good” actually looks like before you measure anything
You can’t measure a gap without two reference points. Most teams obsess over the current state of their engineers and hardly ever define in any detail what their ideal engineers could accomplish. Before you do any kind of evaluation, gather your senior engineers and subject matter experts and break down what proficiency means in real, concrete, observable terms for your unique workflows and challenges.
This is not about creating lists of required skills in a job ad. It’s about making explicit what you believe a competent team member should be able to deliver and which level of independence and quality you expect – per stage of your local engineering process. This is an important distinction that is often overlooked as most competency models are too high-level and hence not really useful for actual day-to-day evaluations. The process-derived competency matrix that you create based on these discussions will at least offer a vague idea about what you should aim for.
Use layered assessment methods to get past self-reported data
Self-evaluations have limited but consistent value. People tend to overestimate their capabilities in skills they only use infrequently and underestimate themselves in areas where they have a great deal of experience. However, it’s a good practice to conduct them, but in combination with technically auditing some of their recent work and peer reviews.
By technically auditing, you might simply ask an engineer to solve a problem that would be typical of their actual current work, and have a senior peer review their solution against whatever your internal standards are. Peer reviews provide the kind of checkpoint that self-reported scores can’t. It’s a simulation of how the engineer will perform when the work is shared and the consequences are real.
This isn’t about discipline or punishment. It’s calibration. Where self-reported scores and actual performance are misaligned, that misalignment actually tells you something. It’s either a signal about the engineer’s metacognitive skills or it’s a signal about how you’re communicating your standards.
Prioritize gaps by risk, not just frequency
Once you have assessment data, resist the temptation to treat every identified gap as equally urgent. The right filter is criticality versus frequency – how often does this skill get used, and what happens when someone gets it wrong? A gap in a rarely-used peripheral tool carries different weight than a gap in a core modeling methodology that sits at the center of every major project. 44% of workers core skills are expected to change by 2027 (World Economic Forum) which means the skills at the center of your workflows today may not be the same ones in three years. Your risk prioritization has to account for both current gaps and anticipated capability requirements. This framework shifts the conversation from HR-led compliance to leader-led risk management. When you can show that a specific technical gap creates exposure on a specific class of projects, training decisions become easier to justify and easier to fund.
Build learning paths from the data, not around a catalog
Off-the-shelf training modules are easy to purchase, but they seldom do the job for your team. And we all know that if you don’t offer training, the job won’t change. For training to be an appropriate response, two conditions have to apply. The gap between the skills your engineers currently have and what they need to deliver results must be a skills gap that training can bridge. This is the one that most trainees, managers, and training coordinators think about.
The new skills must be something the engineer can learn in a training room, alone or with peers, from an instructor or recorded lecture, rather than on the job from an experienced mentor or via self-instruction. This factors out some of the important, but usually unsung, ways people actually acquire the skills they need to get their work done. It’s unglamorous (who brags about the fantastically effective self-study program they taught themselves over a long series of nights and weekends?), and worse, it doesn’t scale to transferring skills to a large, disparate, or constantly refreshing team.
For teams moving from document-centric workflows to model-based systems engineering, structured sysml training gives engineers the technical language to work in formal modeling environments without the guesswork that comes from learning on the job. When that training is preceded by a competency assessment, you can deploy it to exactly the engineers who need it and calibrate the entry point to their actual starting position.
Build assessment into your operating rhythm, not just your onboarding
Doing a one-shot assessment can give you a rough idea of where your team might be lacking in proficiency. But a few months after training, you’re likely to lose any gains you made from that information as the rapid pace of tech undermines the relevance of that snapshot.
The assessment isn’t separate from the training investment. It’s what makes the training investment defensible.