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BRYX Resource Center

From the latest AEC trends to AI insights and success stories, we share ideas and knowledge to help tackle industry challenges.

Increased efficiency. Lack of control. Improved decision making. Job displacement. Streamlined tasks. Data breaches. In 2025, public perception about AI can be contradictory. Professionals within the AEC industry are both excited by AI’s potential and concerned by its impact. If your customer is skeptical or reluctant to adopt AI, how can you help them navigate their concerns and make informed decisions?  

Uncovering the Roots of Skepticism 

First, make sure you fully understand the reasons behind your customer's reluctance to adopt AI. The Pew Research Center states that 32% of workers think AI will lead to fewer work opportunities. Does your customer worry about potential job displacement? They might believe that AI will lead to a full automation takeover, operating without any human involvement. Or perhaps they have concerns about data privacy and security. By taking steps to understand your customers’ reluctance to adopt AI, you can build trust, create specific solutions, and work towards successful implementation.  

Steps Before Strides 

Although AI has made significant progress in the last few years, people's professional skills are still absolutely necessary in evaluating AI-generated outputs and balancing efficiency with accuracy. AI excels at performing repetitive engineering tasks, like organizing field data, running calculations, and preparing routine reports. As an extension of human expertise, AI can assist with a variety of tasks, such as updating project schedules, performing quality control, and spec writing, allowing teams to spend more time on other innovative and strategic activities. Although full automation may currently be out of reach, AI provides significant value in optimizing heavy workloads, freeing up time for higher-value work, eliminating repetitive tasks, and improving overall work quality.  

Bottom line? AI works best as a productivity booster, not a job replacement. The focus should be on working within existing workflows to see where AI can be used to increase efficiencies. 

Balancing Innovation with Privacy 

Customers may also be concerned about the privacy risks AI introduces, such as collecting personal information without consent, using it without permission, security breaches, and unchecked surveillance. These kinds of privacy concerns aren't particularly new; we've been facing the same risks over the past few decades with the rise of IoT devices, big data analytics, and the reliance on cloud computing. But the explosive growth of AI has introduced renewed scrutiny on data privacy since this type of technology depends on massive datasets for training and performance improvements.  

To address these concerns, clients should look for AI solutions that prioritize robust security measures, transparency, and strict access control. In addition, they should conduct regular audits of their AI systems to continually assess data privacy compliance.  

The BRYX Approach 
  • Optimization, Not Replacement. BRYX models are designed to improve productivity, not to replace jobs. As an example, the BRYX Sewer Pipeline Defect Detection model analyzes CCTV inspection video and automatically detects sewer defects to optimize QA/QC, freeing up wastewater professionals to focus on more creative, higher-value activities. For transportation professionals, the SoundScanNX machine learning model automatically distinguishes traffic noises from non-traffic sounds in audio files, speeding up traffic noise impact evaluations.  
  • Data Privacy Focus. BRYX protects user data by maintaining proper isolation of all data, preserving the integrity of each customer's data, and preventing unauthorized access across account boundaries. In addition, BRYX users have full control over whether their data can be used for model training. While users can choose to improve BRYX models by allowing retraining with their data, they can opt out of this feature entirely whenever they upload new data.     

Our BRYX catalog showcases machine learning and computational models designed to optimize engineering workflows. Take a look at our catalog now to find models that will take your operations to the next level. 

 

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