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January 30, 2026
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Artificial intelligence shows promise in its ability to predict smoke spread, optimize suppression strategies, and detect fire hazards early. But can AI be trusted to make crucial safety decisions? What does this mean for the fire protection industry as we know it?
The fire consulting and protection industry is currently exploring optimal ways to use AI, determining what’s effective, what’s not, and identifying new opportunities to enhance the industry's capabilities. Given the extensive nature of fire codes and documentation, AI presents itself as a useful tool for fire engineers. However, we need to exercise caution when integrating AI into existing workflows and processes.
This raises critical questions for fire engineers:
Take a look at the table below for a quick overview of the pros and cons of AI use in fire protection.
AI is carving out a significant role within the fire consulting and protection industry. This sector is characterized by extensive fire codes, regulatory documentation, and technical specifications. By efficiently processing vast amounts of data, AI empowers engineers and consultants to make critical decisions with greater speed and accuracy.
AI-powered note-taking and transcription services are playing a vital role in capturing and organizing detailed records during meetings. These tools document key discussions and critical decisions, especially those pertaining to high-priority safety assessments, allowing you and your team to focus on the conversation itself rather than manual documentation.
AI's speed in information retrieval is fundamentally streamlining engineering workflows. Instead of manually navigating extensive documentation, AI systems can quickly sift through fire codes and regulations to pull up relevant standards and precedents instantly.
This capability accelerates the decision-making process, minimizes the potential for human error in interpreting complex regulations, and allows engineers to dedicate more time to advanced problem-solving and strategic design.
Related: What Is a Fire Risk Assessment?
Our engineers found that AI works well with commodity classification. By providing the AI with a database of example materials, packaging, and classifications, it was able to accurately classify commodities per NFPA 13 definitions (e.g., Class I, II, III, IV, Plastics).
This process can be time-consuming because it requires cross-referencing multiple tables and definitions, but AI significantly streamlined this initial step.
While AI is significantly assisting fire engineers with meticulous, time-consuming work, there are still plenty of hurdles it cannot overcome just yet.
When utilizing AI tools in fire consulting, the input of sensitive project data, proprietary designs, or confidential client information raises significant information security concerns. Many AI models, especially large language models, learn from the data they process, meaning that data entered into these systems might not remain private and could potentially be exposed or used to train future iterations.
Ensuring that all data is protected against unauthorized access, breaches, or unwanted data retention is crucial. Fire consultants must therefore prioritize tools that offer robust security protocols, guaranteed data privacy, and compliance with industry-specific regulations to safeguard both your work and your clients' trust.
AI "hallucinations" pose a major risk when applying the technology to fire consulting. This phenomenon occurs when an AI model generates highly plausible, yet entirely false or misleading information, occasionally citing non-existent codes or misinterpreting complex regulations.
In an industry where accuracy is required for public safety, relying on potentially fabricated AI outputs could lead to dangerous design flaws, non-compliance with critical safety standards, and severe legal liabilities.
Fire engineers must treat AI-generated code references and design recommendations as starting points only, requiring rigorous, independent verification against official source documents before implementation.
Related: NFPA 101: Life Safety Code Requirements
AI models are only as current as the data on which they were trained or have access to. In the fire protection industry, codes, standards, and best practices are frequently updated, and an AI model trained on older datasets will inevitably lack knowledge of the most recent amendments, additions, or jurisdictional changes to critical documents like NFPA standards or IBC codes. As a workaround to this limitation, many models have the ability to search the internet for updated information; however, users of AI must understand if the model they are using has this ability and whether the feature is active.
This creates a significant risk of generating non-compliant or obsolete design recommendations, requiring constant vigilance and cross-referencing by the fire engineer to ensure compliance with the current authority having jurisdiction (AHJ) requirements.
AI excels at data analysis and pattern recognition, but it fundamentally lacks the intuitive judgment, contextual understanding, and creative problem-solving skills that human fire engineers possess.
Fire protection often involves navigating ambiguous situations, complex site-specific constraints, and collaborative negotiation with other stakeholders and the AHJ. These situations demand nuanced reasoning and real-world experience, which current AI models are incapable of providing.
Our engineers found that AI struggled significantly with complex, performance-based design scenarios that require nuanced interpretation of intent and engineering judgment. These advanced tasks demonstrated the current necessity for human expertise and critical thinking over purely algorithmic output.
One of the most profound challenges in AI adoption is establishing clear accountability when an AI-driven decision fails. If a design flaw resulting from an AI recommendation causes a fire safety failure, who is legally responsible: the engineer who accepted the output, the firm that deployed the AI, or the developer who created the model?
Current legal and professional frameworks are not fully equipped to address these "accountability loopholes." Ultimately, the professional engineer maintains the legal and ethical responsibility for the safety of a design, meaning fire engineers must own and meticulously verify all AI-generated outputs, as the liability for failure will fall squarely on their shoulders.
Related: What Do Fire Protection Engineers Do?
AI's role in fire protection is to augment, not replace, human expertise. Strategic, incremental integration is key.
Fundamental challenges need to be addressed for AI to be effective. First, fire protection knowledge is fragmented; unlocking this proprietary data through licensing or data-sharing is essential for training models that truly understand fire dynamics.
Second, rigorous testing and validation are crucial. The industry must start small, perfecting AI on defined tasks through extensive testing and peer review to build confidence in its recommendations.
The economic benefit extends beyond cost savings. By automating routine tasks like code research and calculations, AI allows you and your team to focus on complex challenges requiring human insight. This efficiency also makes performance-based analysis less time-intensive and more economically viable for everyday projects, democratizing advanced safety solutions.
Making AI truly effective in fire protection will require cross-disciplinary collaboration. Fire engineers, software developers, code officials, and researchers will need to work together to establish data standards, validation protocols, and best practices for AI integration.
Industry organizations will play a crucial role in developing guidance documents, case studies, and training programs that help practitioners understand both the capabilities and limitations of AI tools.
The future of fire consulting with AI is one of partnership, where technology handles the tedious and time-consuming aspects of analysis, freeing engineers to focus on innovation, safety, and the kind of nuanced judgment that only human expertise can provide. The question isn't whether AI will transform fire protection, but how quickly the industry can build the infrastructure, training, and trust needed to realize that transformation responsibly.
The impact of artificial intelligence on fire consulting and protection is set to be transformative. While AI offers immense potential for automating tedious tasks like code classification and information analysis, its current limitations demand a cautious, human-centric approach. Essentially, AI should be viewed as a sophisticated tool for augmentation, not a substitute for a licensed fire engineer's expertise.
For successful AI integration, fire protection demands a balance: harnessing AI's speed and power while maintaining stringent standards of accuracy and accountability. Summit Fire Consulting (PBFPE) is committed to the future, consistently integrating modern change. Contact us today to explore how we can help you upgrade your systems and structures for protection and innovation.
