AI was used first by individual engineers typing in prompts. But it’s not staying there, as companies are now understanding the movement must happen at scale. Check out the data to see just how big this shift is. 

Sometimes zero is the biggest number.

In a CoLab survey of 250 engineering leaders, across sectors from aerospace to medical devices, 0% said full AI adoption was unimportant at the company level. 

Every single leader agreed their teams must fully adopt AI within the next 12–24 months. 

• Nearly half (47%) went further — calling it “critically important,” the difference between staying in business or going out of business. 
• Another 48% said failing to adopt AI would prevent them from hitting company performance goals. Only 5% called it “somewhat important.”

AI adoption isn’t about early-mover advantage or making  breakthroughs at the individual level. 

We’ve moved past that. It’s no longer adapt or die for individual engineers trying ChatGPT prompts. The reality now is adopt or die at the organizational level. And that’s not us saying that. That’s the 250 engineering managers, directors and VPs we interviewed. 

AI: From individuals to organizations

Engineering companies now recognize that AI must be built into processes and systems company-wide, from the CEO down. 

This new movement is about companies understanding they need to absorb the work, understanding that adopting AI is more than typing “You are a leading expert at CAD design …” as a ChatGPT prompt. It’s about building AI into strategic engineering workflows company-wide, starting with leadership and working its way through the organization from there. 

That’s a different scale of change. Because building AI into an organization isn’t just about prompts. There are issues using AI that only company leadership can properly address. These include: 

• IP security: Who sees what, and how do you protect sensitive designs?
  
  
• Access control: How do suppliers, partners, and internal teams get the right visibility and ability to contribute — and nothing more?
  
  
• Consistency: How do you ensure every design review applies the right standards, across global teams.

Engineers aren’t skeptical of AI itself. Their concerns are practical, mostly involving issues that begin with how it’s implemented. Areas of key interest include data hygiene (20%), change management (20%), security (18%), and integration complexity (16%). 

Individuals asking for the right tool are trying to stay ahead of the curve: it’s impossible not to respect that. But what the top companies are doing is asking “what organizational barriers do we need to break down first to set up AI for success across the entire company?” 

What happens if you don’t shift: survey results

If AI adoption doesn’t happen company-wide, the risks compound quickly:

• Your competitors are moving faster. If they’re automating reviews and iterations while you’re stuck in manual checks, they’ll bring products to market months — or years — sooner.
  
  
• Manual work drags down engineering time. Imagine flipping through 50,000 drawings a year to check tolerances and finish specs. That’s weeks of wasted time that AI can cut.
  
  
• Targets slip. Missed performance, quality, and cost goals cascade into rework, warranty claims, or worse — recalls. Our data shows that only % of companies have fully documented, up-to-date standards that are consistently referenced in reviews, despite them being seen as critically important. With AI adoption at scale, those issues can go away. 

We’ve already seen the dangers of slow, inconsistent adoption in other areas of engineering: Airbus delayed the A380 launch by nearly two years because teams used different CAD systems. 

Now an error was made there. But a case like that does draw the same lesson: processes that can’t keep up with complexity introduce risk.

Ensuring standards are met is one area where AI can excel, if employed at scale. Companies that integrate it into engineering workflows now will widen the gap between themselves and everyone else.

Here’s how AI can work. Instead of flipping through PDFs or relying on memory to catch mistakes, CoLab’s AutoReview automatically checks drawings against your company’s standards, everything from missing tolerances and ambiguous notes to GD&T compliance and surface finish requirements. 

Because the feedback is captured directly on the drawing, in context, engineers don’t waste time hunting through email threads, drowning in meetings or updating spreadsheets by hand. Every comment is tracked and traceable, creating a design history that teams can reference in future reviews. The result is faster cycles and fewer errors slipping through. 

Where the company-wide movement starts: drawing reviews

Our survey shows most companies believe change will occur first with drawing reviews. This makes sense. It is entirely logical that engineering companies tasked with making products want to simplify the process behind making them. 

• 68% of engineering leaders predict AI will handle ambiguous notes and missing views in drawings within two years.
  
  
• More than half expect AI to enforce standards like GD&T and surface finishes in just a few months.

More than a quarter of teams are already piloting AI-powered 2D drawing reviews, and over half plan to apply AI to 3D model reviews and DFM analysis within the year.

Drawing reviews the logical first step for several good reasons:

• They’re fed by data you already have (CAD,checklists, simulation results).
  
  
• They’re repetitive, rules-based, and high-volume.
  
  
• They’re currently a massive drain on time.

Consider this: at many large manufacturers, engineers manually review tens of thousands of drawings every year (and every company does this work just a little differently). That means flipping through PDFs, cross-referencing standards from a file share (if they can find them), or relying on memory and institutional knowledge. Even when they catch mistakes, the feedback often gets buried in email threads or PowerPoint decks or missed in meeting notes.

What AI presents is a superior method of doing what engineers do now: AI can systemize and automate drawing reviews. Changing drawing reviews methods often requires a company-wide adoption of a new process, as opposed to any one individual being able to take up the cause and alter a process.  

Benefits of targeting AI at design reviews

Drawing reviews are a form of design reviews. So it makes sense that design reviews would be the next logical step in an AI workflow. Engineers themselves see significant gains ahead there:

• 32% expect design cycles will be 3–5× faster.
  
  
• 46% expect 2–3× faster.
  
  
• 23% expect at least 1.5–2× faster.

There’s that number again. Once again, 0% said design reviews won’t get faster with AI. 

So if you’re a company not contemplating change in this area, know that your peers are. Because they have already decided improvement is coming. 

Engineering companies have begun wide AI adoption

The current AI movement in mechanical engineering isn’t about individuals adapting and experimenting with what can be done. That’s where it started. 

Today it’s about organizations learning how to adopt AI processes at scale. Our research data shows this is happening, why and where it’s happening. 

Drawing reviews are the perfect place to start. The capability to improve company-wide processes exists today. Book a demo with CoLab, and we’ll show you exactly how much faster your design reviews can be, with industry-specific data.