Why a 'Gen AI' Revolution Is Coming for the Engineering Profession

TL:DR

• 30% of traditional engineering roles will be replaced by Gen AI by 2030 (<5 years).
• Engineering companies risk driving off a skills-deficit cliff in 10–15 years if they don’t actively manage Gen AI implementation.
• Organic Gen AI implementation will erode the need for entry-level positions and rob the profession of the next generation of experienced engineers.
• Gen AI–enabled organisations will outperform competitors by developing work processes that couple humans with technology to:
  1. improve work outcomes (better, cheaper, faster), and
  2. more efficiently train our future engineers.

Introduction

In the past five years, Generative Artificial Intelligence (Gen AI) has burst into our lives at a rapid pace, demonstrating amazing results that would have been unthinkable only a decade ago.

What Is Generative Artificial Intelligence?

In the simplest terms, Gen AI refers to smart computers that can perform tasks for us. For more details, check out ‘Wikipedia – Generative AI’.

My Future Prediction for the Engineering Profession

30% of traditional engineering roles will be replaced by Gen AI by 2030.

As shocking as this may sound, it’s not hard to believe when you look at how other industries have already been impacted. The lowest-hanging fruit so far has been fields with significant amounts of open-source data that can be used to train AI models.

Here are a few industries already undergoing significant transformation:

• Software development
• Journalism
• Visual arts
• Legal services

What About Engineering?

Currently, AI is being used by early adopters for some of the more repetitive and mundane tasks that—let’s face it—we hate doing anyway. Things like report writing, research and information retrieval, and general administrative duties are easy targets.

But as AI tools become even more powerful, expect increasingly complex tasks—those involving reasoning and industry-specific knowledge—to also become automated.

Where Do the Humans Fit In?

Human input in engineering will likely always be necessary, as the consequences and legal responsibilities for errors will remain with real people.

AI will likely become more like a personal assistant—doing most of the work while checking in and confirming actions. A single engineer may work alongside multiple AI agents, each with different capabilities, functioning like a conductor of an orchestra.

Companies Have Two Paths From Here:

1. No Planning – Gen AI Implemented Organically

   • Low-skilled, repetitive tasks are replaced across all levels, but most significantly at the graduate engineering level.
   • Senior engineers are required to perform quality control of AI outputs.
   • Competitors who embrace the technology strategically will gain a competitive advantage.

2. Active Gen AI Organisational Integration

   • Gen AI acts as an efficiency multiplier, allowing more output at higher quality (better, cheaper, faster).
   • Graduate engineers become AI power users.
   • AI tools are used to teach and expand skills—not just to generate results.
   • AI systems communicate internally, becoming organisational glue that fosters collaboration and knowledge sharing.

What Can Gen AI Tools Already Do?

Over the past few months, I’ve explored many of the most popular AI offerings and ran some basic tests to see whether they could be applied to engineering tasks.

I’ll share more detail in future blog posts, but as of June 2025, my conclusion is that Gen AI can perform basic tasks—provided it’s given the right guidance.

Here’s what I tested:

• Can AI interpret engineering drawings (e.g. P&ID, PFD)?
• Can AI complete engineering calculations (e.g. pipe sizing, free-body diagrams)?
• Can AI interact with external programs (e.g. FreeCAD via MCP)?
• How does AI perform in risk assessment and problem analysis?

A Must-Have Skill Set for Young Engineers

1. Broad/general knowledge of coding
   You don’t need to be a software developer, but you should understand how to read and work with code. Python is a great starting point, as it underpins many current AI frameworks.

2. Be process-focused
   If a human can’t follow a work process, the AI won’t be able to either. Understanding how to solve problems, manage risk, and plan tasks will be essential skills.

3. Strong communication and interpersonal skills
   There will always be humans in an organisation, so communication will remain a valuable soft skill—especially when collaborating with both people and AI agents.

This is the first in a series of blog articles capturing my journey along the generative AI path and its influence on the future of the engineering profession.

The future is here, and we all need to get on board fast!