AI in Engineering Software: How AI Is Transforming PTC Tools Like Windchill, Creo, and Codebeamer and What’s Still To Come

Artificial intelligence is no longer a standalone technology layered onto engineering software. Across the PTC portfolio, AI is becoming part of the everyday engineering experience. This helps teams design products faster, improve data quality, automate repetitive work, surface insights from connected lifecycle data, and make better decisions throughout the product lifecycle.

Across Windchill, Creo, Codebeamer, Arbortext, Mathcad, and ThingWorx, PTC is enabling teams with AI features. What do these PTC AI updates look like? Let’s explore these additions, their benefits, and their limitations.

Where AI Shows Up in Today’s Engineering Stack

Before we go into the weeds, we should address the elephant in the room. AI in engineering has exploded. And that isn’t just focused on one single area of engineering. It’s a growing set of capabilities embedded across multiple systems. Have a look at the high level focuses below:

Each of these tools applies AI to improve specific tasks. To get the full picture, let’s look at what AI is actually doing within each system.

AI Is Only as Good as Your Engineering Data

The AI capabilities built into PTC’s engineering solutions can help teams work faster and make better decisions, but only if they’re built on a strong data foundation. AI relies on accurate, connected, and well-governed engineering data to deliver meaningful results. If product information is scattered across disconnected systems or inconsistent throughout the product lifecycle, AI can only provide limited value.

That’s why becoming AI-ready is just as important as adopting AI itself. EAC’s AI Readiness Discussion helps manufacturers evaluate whether their engineering data, PLM processes, and digital infrastructure are prepared to support AI-driven workflows. We’ll help you identify gaps, uncover opportunities, and build a practical roadmap for implementing AI with confidence.

AI in Windchill (PLM): Unlocking Product Data

Windchill is the backbone of product data for many engineering organizations, making it a natural place for AI to deliver value. AI capabilities in Windchill include:

These capabilities help engineers find the information they need faster, reduce duplicate work, and make more informed decisions.

However, most AI functionality remains focused within the PLM environment itself. Access to insights is often limited to Windchill users and interfaces, leaving broader workflow opportunities untapped.

AI in Creo (CAD): Faster Design and Simulation

In Creo, AI is focused on improving how engineers design and validate products. Key capabilities include:

These features allow engineers to explore more design options, iterate faster, and reduce reliance on physical prototypes.

The result is better-performing products developed in less time. While AI enhances design tasks, it does not inherently connect those insights to downstream systems like PLM or manufacturing.

AI in Codebeamer (ALM): Smarter Requirements and Traceability

For organizations managing complex or regulated products, Codebeamer uses AI to improve development processes. Key capabilities of this addition include:

These features reduce manual effort, improve compliance, and help teams identify issues earlier in the development lifecycle.

Still, these insights often remain within the ALM domain, without full integration into product data or engineering workflows.

AI in Arbortext: Smarter Technical Documentation

Arbortext applies AI to one of the most time-consuming areas of product development: technical documentation. AI capabilities include:

These features help organizations produce accurate, consistent documentation more efficiently while reducing redundant work.

For service, manufacturing, and support teams, this means faster access to reliable information. However, documentation insights are still often disconnected from real-time engineering and product data.

AI in Mathcad: Improving Engineering Calculations and Knowledge Capture

Mathcad brings a different kind of intelligence to engineering, one focused on calculations, validation, and knowledge transfer. Key capabilities include:

While not always labeled as “AI” in the same way other tools are, these capabilities reduce errors and make complex calculations easier to understand and reuse.

This is especially valuable for organizations looking to preserve engineering knowledge and improve collaboration. However, these calculations are typically not connected to broader product data systems or workflows.

AI in ThingWorx and Kepware: Operational Intelligence

On the operations side, ThingWorx and Kepware enable AI-driven insights using real-world data. Capabilities include:

These tools help organizations improve uptime, optimize performance, and make better operational decisions. But like other systems, these insights often remain siloed unless integrated with engineering and product data.

The Gap: AI Is Still Siloed Inside Each System

As evidenced product by product, AI is clearly delivering value across the PTC ecosystem. But this value is mostly within individual tools. That’s where the limitations currently lie. And those limitations pave the way for potential challenges:

As a result, organizations still struggle to answer some fundamental questions. Questions like “Where has this design been used before?”, “What issues are associated with this component?”, or “What data across systems is relevant to this decision?”

The problem isn’t a lack of AI. It’s a lack of integration. AI inside tools improves individual tasks. AI across systems transforms entire workflows.

AI delivers its greatest value when it’s built on connected engineering data, not isolated applications. EAC’s AI Readiness Discussion helps manufacturers evaluate their current systems, identify integration opportunities, and develop a practical strategy for enabling AI across the entire product lifecycle.

What Engineering Teams Actually Want from AI

Most engineering teams aren’t looking for standalone AI features. They’re trying to solve practical problems:

These workflow challenges aren’t limited to isolated tools, but span multiple systems.

Why Windchill Is the Foundation for Engineering AI

If you’re looking to apply AI across engineering workflows, Windchill is the logical starting point. Why?

First, it contains structured, governed product data. Second, it connects to the other systems: CAD (Creo), ALM (Codebeamer). Finally, it represents the digital backbone of product development.

By anchoring AI to Windchill, organizations can ensure that insights are grounded in accurate, up-to-date product information.

Connecting AI to Windchill: Where the Real Value Happens

The next step is not adding more AI tools. It’s connecting AI to your existing environment. When AI is integrated with Windchill, organizations can enable:

This is where AI moves from isolated capability to enterprise value.

How EAC Helps You Integrate AI with Windchill

PTC provides powerful tools with embedded AI, but most organizations need help connecting those capabilities across their environment. That’s where EAC comes in. EAC specializes in integrating AI with Windchill and related systems to support real engineering workflows. Our approach focuses on:

We’re not introducing disconnected AI tools. We’re helping you make AI work within the systems your teams already rely on.

Getting Started with AI in Your Engineering Environment

The question for manufacturers is no longer whether AI will influence engineering. It already is. The greater challenge is ensuring the underlying engineering data, product lifecycle processes, and digital infrastructure are prepared to support it. Organizations that invest in connected lifecycle management today will be better positioned to realize the full value of AI tomorrow.

Learn more about EAC’s Windchill AI Integration services today.