On some days I have the feeling that I wounded again until 1997 when the Internet began to develop. At that time it was technology circles against the skeptics – those who swore computers would never be the office capacity. Now everyone is in the technical circle, and the sums around artificial intelligence (AI) is global. This is not a conversation about future potential. It is the new operational reality.
The US Ministry of Labor has made it clear that the time for the debate with the latest training and employment officer (TEGL No. 03-25) has ended and the states proves to use grants for the innovation and opportunities laws for workforce in order to improve the AI alphabetization. This is a federal mandate for quick retrofitting.
In the AEC sector, in which the modeling of building information (BIM) and computer design are already standard, AI is not a supplementary tool for employees. It is a structural disruptor. While many still discuss existential fears about AI, such as: B.: Will it take jobs? Will it be human? The crucial questions for industry leaders are: How can it maximize efficiency? How do we protect proprietary information? And how do we ensure that our workforce does not become negligent?
My experience in the integration of large -scaling models (LLMS) in my workflow at mothers Professional Engineering in combination with my focus on AI certification taught me that curiosity alone was not enough. Only implementable, methodological introduction will move into the future.
If you are an AEC professional who is waiting for the rules to set, you are already back. Here are the 5 non-negotiable rules for survival and leadership of the AI-controlled workforce.
The 5 non-negotiable rules for the survival of the AI-controlled AEC workforce
Rule 1: Don't be lazy. Create existing input requests.
The “Wilde West” mentality to play around with a generative AI is a necessary first step, but it is a non -sustainable strategy for a professional environment. You cannot break the model, but you can waste billing hours and achieve useless results.
Use AI Daily for time -consuming tasks, from researching future projects to the creation of technical graphic concepts. For example, if you enter identification information in a model and query certain technical details, manual research saves hours. However, this only works if your input is precise.
Advice: Your phase of experimentation has to go into a quick strategy. Start with an end result, however, make different input requests across models and compare the results for distortions, accuracy and source quality. This disciplined approach includes the definition of specific parameters: persona (e.g. “as a civil engineer …”), format (e.g. “edition of the information as a implementable summary …”) and restrictions (e.g. “Use only data that were published after 2023 …”). This shifts their role, only “asking” in the professional “director” the machine.
Rule 2: Learn the Lingo not only. Lean into the matrix.
The initial alphabet soup of AI, machine learning (ML), LLM, Deep Learning (DL) and artificial neural network (Ann) can be overwhelming. As in every industry, you need a cheat leaf that can easily provide AI. But a cheat sheet is just a vocabulary list. The survival requires understanding the underlying matrix of models.
The risk of plagiarism: A large part of the AI certification process is devoted to the learning and evaluation of models for ethical standards. We found that certain models tend to generate subtle, outdated and/or biased information due to their training data. The solution was not to give up AI, but to understand how a model was trained (ethical or with which bias) and which human reviews were used.
Advice: Move your focus from easy define an LLM to understand its origin. For professional use, you must understand:
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The training data used (and your procurement).
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The specialty of the model (e.g. code, text, image, research).
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Have human control points used?
With this deeper understanding you can select the correct, legal solid tool for the job. You should always check the results for accuracy, distortion and plagiarism before use.
Rule 3: Set on human control points before using
The “wild west” of the rules and the lack of regulations is a great risk in a regulated environment like the AEC. Although I am enthusiastic about AI's potential, my role as a manager and business development and business marketing requires a strong AI government. An LLM can hallucinate a fact via a local construction code, and the downstream costs for correcting this error can destroy trust.
Advice: If you opt for the use of KI for content, implement a non-negotiable, three-stage human checkpoint for every externally or internally used piece of AI-generated content.
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Facts test: Check all claims, statistics and references against primary sources.
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Ethics & Bias Review: Rate the content for unintentional prejudices or ethical drift (e.g. exclusion language in job descriptions).
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Brand/legal regulations: Make sure that the sound, style and the legal framework match the fixed standards, insurance protection and contracts.
The human role will finally shift from the producer to the editor and guardian of the edition.
Rule 4: The aha moment: Ai does not solve the problem, it accelerates the solution
The true “aha moment” came for me when I realized that Ai did not solve the problem, but accelerated the way to a solution. I started tackling a little problem that developed into a new prototype -ai agent. The original problem was simple; The solution had to dive deep into the “rabbit hole” of data and test different models until the realization that I could create everything I could imagine.
Advice: Do not be stopped by the complexity. Go deeper by concentrating on a repetitive problem (e.g. information on the latest and accessible). This deep dive will inevitably force you to Upskill. At this point in my process, the initial experimentation remained at a standstill, and I decided to pursue a formal learning path in the AI for product design. For each specialist there is a way to maintain the relevance of deepening the skills through targeted training and certifications in connection with their specific role.
Rule 5: The final goal is to stop and start the search and with the production
Many experts use AI as an “assistant” or as a detailed search engine. The future is one of those who develop AI as a solution for these gears in the efficiency of the daily workflow. My current Capstone project, in which a KI agent is created, is an example of how this development occurs.
Advice: hug a digital notebook. Treat your AI development like a strict research project. Manage a digital notebook to save your input requests, test results and comparative model analyzes. Documenting which models have failed, which was successful, and why is the fundamental difference between casual “play” and strategic integration at the company level.