The Building Simulation Talent Gap Isn’t About Software

Part 4 – Unpacking the 3 T’s – Talent

Not enough qualified building simulation practitioners

The talent gap is not simply a shortage of people who can operate building simulation software. It is a shortage of practitioners who combine building-physics knowledge, modeling judgment, software fluency, and the ability to translate results into design and operating decisions. As in many other parts of the AECO industry, that combination is difficult to recruit, develop, and retain. Easier-to-use tools may allow architects and engineers in adjacent roles to perform occasional analyses, and they may lower the barrier to learning. But occasional tool use is not the same as practiced proficiency, and usability alone does not create the judgment needed to recognize bad inputs, unrealistic assumptions, or misleading results.

One rough indicator of the gap is the small number of recognized specialists relative to the scale of the built environment. A 2025 CalBEM presentation counted about 4,700 IBPSA members and 545 ASHRAE Building Energy Modeling Professionals. It then estimated that each IBPSA member would effectively need to support hundreds of commercial buildings per year for simulation to reach the expanding building stock.[1] Those figures are directional rather than a formal labor-market forecast, but they make the capacity problem tangible: the community of experienced practitioners is small compared with the number of buildings and decisions that could benefit from simulation.

Meanwhile, demand is growing. DOE describes building energy modeling as a multipurpose capability used for design, retrofit analysis, code compliance, tax credits, utility incentives, building controls, and policy analysis.[2] Each new use case requires people who can frame the question, select an appropriate method, validate inputs, interpret outputs, and communicate uncertainty. In short, it requires people who can apply simulation responsibly across a growing range of decisions.

So how do we close this talent gap and speed up talent development?

Developing building simulation talent requires a sound technical understanding of building physics, a grasp of modeling methods and when to apply them, and practiced judgment in interpreting results. It’s a bit like construction estimating: there are fundamental principles that must be brought to bear, but excelling in the profession takes judgment that only comes with experience.

That doesn’t mean only dedicated modelers can do the work. It means the talent pipeline should follow the lead of other professions: on-the-job training and experience built on a sound technical foundation.

Technology can help here. One of the biggest constraints on learning is that mentors in most firms are already overburdened with their own project deadlines. An integrated ecosystem that makes mentors more productive also frees up their time to grow the talent pipeline. The deeper benefit comes when mentors use a common platform as a kind of “teaching assistant.” That means not just research and testing, but also gamification, visualization, and validation. This also reduces risk in talent development, because learners can make (and learn from) mistakes before those mistakes reach a real project.

Continual talent development is essential for long-term success

It might seem that the opportunity with AI “in the loop” is to dispense with energy modeling expertise altogether. After all, the investment required to reach “mastery” is significant, as it is for architects, engineers, superintendents, and other professionals. But if we want the practice of architecture and engineering to keep improving and to remain led by humans, we must keep developing talent.

Talent development for this skill set is lengthy and lifelong, just as it is for architects, engineers, and other design professionals (PDHs, CEUs, etc.). The question then becomes: how can we accelerate it?

As noted, freeing up mentors is part of the answer. If technology is less of a barrier to proficiency, more time can go toward applying the physics and learning the craft. And if simulations ran faster, learners could study more options and explore each one more deeply.

In other words, a significant part of solving the talent problem lies in improving technology and easing time constraints. Today, experienced modelers spend much of their time learning, modifying, and sometimes outright fighting dated software just to get the work done. That is time they could otherwise spend mentoring and training new staff. It’s like continuing to drive an old car whose parts are no longer available: every time it starts to sputter, time is lost figuring out how to keep it running. Eventually, the people who know the “tricks” for keeping the tools working move on. Then the team really loses productivity, and risk increases. That hurts the business, but it doesn’t show up on the balance sheet. Opportunity cost never does.

^[1] Konis, K. (2025). “Growing the Pipeline for Building Performance Simulation in the Age of AI.” Presentation to CalBEM (California chapter of IBPSA-USA). https://calbem.ibpsa.us/wp-content/uploads/2025/12/CC_Kyle-Konis-2025-11-05-CalBEM-Talk_v2.pdf

^[2] U.S. Department of Energy (n.d.). “Building Energy Modeling.” https://www.energy.gov/cmei/buildings/building-energy-modeling