Artificial intelligence (AI) has already transformed industries from finance to healthcare, and engineering is no exception. In the world of static equipment design and analysis, AI has the potential to dramatically improve workflows. From automating code-compliance checks to suggesting optimized parameters, detecting design inconsistencies, or generating calculation reports, AI can act as a powerful assistant to engineers tasked with ensuring both safety and efficiency. Despite this promise, the integration of AI into pressure vessel software remains very limited. The vast majority of commercial tools used today were built decades ago in older programming languages and frameworks. While robust and reliable, these platforms are difficult to adapt for modern AI technologies.
Integrating advanced machine-learning models into such legacy systems would require costly rewrites and careful revalidation, which makes vendors hesitant to move forward quickly. As a result, many of the tools engineers rely on every day have remained largely unchanged in their core architecture, even as other industries surge ahead with AI-driven innovation.
There is, however, one notable exception: VCLAVIS. Unlike traditional software, this was designed on a modern web-based architecture, giving its engineers the flexibility to integrate new technologies more seamlessly. Leveraging this foundation, the software team has developed an in-app assistant powered by OpenAI. This assistant is not just a marketing add-on; it provides practical, real-time support for engineers, helping with code interpretation, documentation, and general design tasks. The fact that VCLAVIS could incorporate AI so quickly highlights the advantage of selecting forward-looking programming frameworks from the start.
Other commercial software providers have begun experimenting with AI in limited ways, such as adding small assistive features or exploring AI-augmented simulation. However, these steps are incremental rather than transformative, and they fall short of the comprehensive AI integration seen in VCLAVIS. The underlying challenge remains the same: legacy code bases make deep AI adoption both technically and financially challenging.
Looking ahead, the potential applications are compelling. AI could eventually guide engineers through design decisions by suggesting compliant configurations, highlight overlooked details in calculation models, or even predict maintenance needs by analyzing operational data. It could serve as a tutor for younger engineers, translating complex code clauses into plain language. And with generative models advancing rapidly, we may even see AI-driven optimization that balances cost, weight, safety, and compliance automatically across thousands of design iterations.
For now, though, most of this remains aspirational. The software industry as a whole still lags behind in AI adoption, held back by legacy infrastructure and the conservatism that naturally surrounds safety-critical engineering. VCLAVIS stands out as the first commercial platform to truly embrace AI, proving that with the right technical foundations, it is possible to build smart, adaptive tools that support the engineer rather than simply calculate results. The industry may eventually follow, but this software has taken the first definitive step into the AI era of pressure vessel analysis.
