As published on: Engineering.com (April 17, 2025).
There’s a perfect storm brewing with the potential to dramatically transform design and manufacturing industries. Think of it as a Venn diagram, where technology, humans and geopolitics intersect. At that intersection lives a huge opportunity for industrial brands, and the thousands of manufacturing and design engineers working for them. The time is now for engineering’s AI revolution, and here’s why:
It’s been more than two years since ChatGPT burst onto the scene and in that time, we’ve witnessed the incredibly accelerated maturing and democratizing of the technology. What started out as hype has evolved into a viable enterprise technology. Businesses today are rapidly moving beyond mere experimentation to nascent or wide-scale AI deployments and there are multiple use cases with proven results. In fact, C-Suite leaders today are not only curious about how AI might transform their organizations, they’re also actively mandating that staff seek out AI tools so that they can tap into efficiencies and uplevel in their roles, ultimately benefiting the bottom lines.
However, it’s become increasingly clear that the broad, generic open-source AI technologies are not a good match for narrow or domain-specific use cases; they are often unreliable and prone to hallucinations. Specialized use cases require specialized, high-quality data to feed and train their LLMs (Large Language Models) and Machine Learning (ML) engines. The good news is that vertically-specialized AI technologies are on the rise – in law, marketing, medicine, autonomous vehicles – and soon, engineering. And, if you haven’t already read about agentic AI, get ready. Agentic AI is rapidly transforming and organizations are catching on to how it automates tedious error-prone tasks all while adapting and collaborating with humans.
Manufacturing is incredibly complex. The multi-step processes needed at every stage – from R&D to design, production and documentation is intense. While agile, lean, smart and just-in-time processes have been deployed to attempt to streamline and eke out efficiencies, today there’s plenty of opportunity to harness the power of AI to digitally reshape this centuries-old industry. Siloed teams and data sets can be unified through AI-powered agents that take advantage of natural language processing, transforming imprecise or unstructured processes into precise actionable code – resulting in faster, more accurate processes, and a more intelligent digital foundation for the entire production lifecycle.
It’s been reported that 40-50% of the design manufacturing industry’s $40T market value is being eroded due to legacy processes, causing production delays and stagnant revenues — equivalent to $8T of economic waste. Surely that huge amount is incentive enough to harness the power of AI to modernize its processes and bring observability, auditability, and transparency – not to mention business continuity – to this vital market?
Now let’s consider the demographics of the design engineering workforce and the stark fact that by 2030, all Baby Boomers will reach retirement age – a phenomenon some call the Silver Tsunami. Of course, only a portion of those will be engineers, but decades of valuable engineering experience are often locked in the minds of long-serving employees, instead of being documented and accessible to all. This means vital institutional knowledge will be lost when they retire. In their place will come a new generation of younger, AI-literate engineers, skilled and eager for innovation.
This means now is the time to modernize and digitally codify all that foundational engineering intelligence – or risk getting left behind. If we can use AI to supercharge the automation, accuracy and efficiency of every stage of product lifecycle management, engineering teams will be able to not only cut costs but also be more nimble, productive and creative. Ultimately, they’ll be able to build better products faster, driving healthier revenues for the world’s leading industrial brands.
Geopolitical and economic forces are recasting global manufacturing and supply chains. Shifts are underway that will inevitably impact where things are made, who makes them and how much they will cost. Time will tell how this plays out, but the writing is certainly on the wall. Industrial brands would be smart to invest now in foundational AI technologies to ensure their people, operations and ecosystems of supplierS, partners and customers are agile, resilient and ready.
Manufacturers should pay attention to these prevailing tailwinds: advancements in AI technologies, the need for digital transformation in engineering to overcome economic waste, and geopolitical forces. Together, they create both urgency and opportunity—signaling an AI revolution in engineering.