How PTC Uses AI to Create Value for Customers

Opportunities for applying AI in industrial software

For over a decade, PTC has released AI-powered software features. Whether it’s applying machine learning or the latest technology like generative AI to our products, we are focused on this guiding principle—create value for our customers at scale and responsibly.

Manufacturers stand to gain immense benefits from AI innovation. McKinsey (2018) estimates AI could add $13 trillion to the global economy, increasing GDP by 16%. Gartner (2023) reports how engineering leaders are prioritizing AI investments, second to cloud computing. In manufacturing, McKinsey (2023) reports that AI can potentially add $1.2 – 2.1 trillion in value through factory automation, and IDC (2023) forecasts that 70% of G2000 OEMs will apply AI to automatically trigger or deliver service recommendations.   

But there are challenges, too. In the enterprise IT landscape, manufacturers work with disparate data across multiple software systems that are managed within complex product development processes. Moreover, these solutions need to meet compliance requirements and address critical business goals such as cost and productivity.   

These challenges are amplified when implementing new AI technologies such as large language models (LLMs). Given the novelty of LLMs and complexity of enterprise software, it’s no surprise that the current state of generative AI (GenAI) is characterized as having hyper-inflated expectations with less than 10% of companies releasing production solutions (Gartner, 2024).    

PTC’s AI advantage

PTC offers a distinct advantage in navigating AI opportunities and challenges. We are keenly aware of problems that manufacturers want to solve, we know how to build enterprise-grade software, and we have a track record of implementing practical applications with AI.  

For instance, we’ve applied machine learning (ML) in ThingWorx to enable predictive maintenance and OEE root cause analysis in the factory. And in the service space we’ve applied ML to enable spare part inventory management with Servigistics and remote trouble shooting of field service delivery with ServiceMax.  

We will continue to release AI-powered features across our products. In this pursuit, we look for opportunities to scale our AI investments by applying analytics patterns to multiple products. For instance, we are currently developing a solution in Windchill that utilizes computer vision capabilities from Vuforia to search 3D shapes, enabling use cases such as detecting duplicate parts or assisting in classifying part IDs.  

In addition to product innovation, we consistently identify ways to improve our operational execution. One area of focus is our AI Governance Program, established in 2023. It is a cross-functional group that monitors and guides AI use at PTC. This includes how our employees use AI and how we implement AI in our products. According to IDC (2024), our leadership in AI governance represents the top 17% of companies. These governance activities have not only enhanced compliance and the ethical use of AI but also fostered increased collaboration among teams. 

AI-enabled value drivers across the product lifecycle

In a strategic assessment of our products, we identified top value drivers benefiting from AI. When organized across engineering, manufacturing and service several themes rose to the top, including (but not limited to) accelerated time to market, cost reductions, and workforce enablement.   

AI in engineering

Accelerating product development, reducing cost of goods sold, and managing quality and compliance rose to the top as engineering value drivers benefiting from AI. Key trends from this assessment include:  

Software-driven innovation with generative AI  

ALM solutions like Codebeamer provide a rich set of unstructured data ideal for LLM use cases. For example, quickly analyzing requirements against INCOSE standards or automating requirements authoring through ingest of external documents. AI capabilities like this are crucial for manufacturers addressing themes such as software-driven engineering or complex supply chain demands, as they must expedite production in a competitive, resource-limited market without compromising quality. 

Search similar or duplicate requirements in Codebeamer using large language models (LLMs). 

Multi-pronged AI strategy in PLM  

PLM software manages product data that benefits numerous use cases across the digital thread. Given this variety and range, manufacturers must take a multi-pronged approach when implementing AI. For example, ML can be applied to change management data to detect bottlenecks or forecast project delivery dates. GenAI can be used in product data management for tasks such as document search or assessing the quality of design review documents. Additionally, computer vision technology can search for 3D parts and enable part reuse, addressing a carrying cost problem that can amount to millions of dollars per year for a single manufacturer. 

Intelligent automation in CAD

AI innovation in CAD centers on intelligent automation, and there is a maturity model to realize that value. This model starts with assisting designers with AI-driven tool tips as well as AI copilots that support training and troubleshooting. The next level is augmenting a designer’s work with tools like Creo Generative Design where AI-powered features receive inputs from users and then implement tasks independently, such as generating new 3D models based on material optimization parameters. The top level is full automation. At this level, AI models enable complex problem-solving with minimum to no human input. For example, automatically converting meshes and point clouds to solid geometry or generating new CAD parts from simple text prompts like we see with LLMs today that generate documents and 2D images.  

AI in manufacturing 

Designing for manufacturing, improving operational efficiencies, and upskilling the workforce rose to the top in our assessment of top manufacturing value drivers. Highlights from this domain include:  

Criticality of integrating enterprise systems  

Data derived from integrating PLM, ERP, and MES software can unlock high-value AI opportunities. For example, with design for manufacturing use cases, PLM manages product quality and process plan data, ERP provides production order and inventory data, and MES systems track performance and work instruction execution data. With this data, AI can benchmark production quality with tolerance requirements or provide analytics on material inventory levels. In forward-looking use cases, GenAI can automatically create work instructions that are based on PLM process plans as well as resourcing criteria in ERP, and then optimize these work instructions for specific MES specifications such as translating content for operators or creating reference images for work instruction steps.  

AR and IoT as enabling technologies 

Enabling technologies like augmented reality (AR) and IoT are vital for delivering AI-powered capabilities in the factory. Take IoT software like ThingWorx, which connects to factory assets and industrial software like Kepware to deliver continuous improvement solutions. With machine learning, ThingWorx can quickly analyze data and identify the root cause of production inefficiencies, enabling faster resolution times.

Likewise, using computer vision technology, Vuforia processes streams of spatial data and augments this physical mapping with digital assets. This enables operators to view digital representations of tooling, products, machines, and other content in real-time and in their physical space, assisting training and work instruction procedures. AR also affords for the verification of manufacturing quality by using computer vision to inspect end-of-line deliverables or verify maintenance assembly procedures.  

Validate assembly and maintenance procedures in Vuforia using computer vision.

AI in service 

Optimizing spare parts management, delivering proactive service, and reducing service delivery costs were the top value drivers in our strategic assessment of the service domain. Key highlights in this space include:  

Sustainability as strategic theme for spare parts management   

AI plays a crucial role in optimizing the service supply chain. By harnessing PLM, ERP and service data industrial software like Servigistics can address complex issues like sporadic demand or supply chain disruptions caused by events like COVID. Servigistics harnesses ML and proprietary algorithms by creating a predictive twin of equipment and associated service supply chain to deliver precise inventory optimizations, demand forecasting of parts, and improve pricing strategies for service components.

An emerging theme in this space is sustainability. For instance, using AI to deliver insights on the entire service lifecycle, the carbon footprint of service parts, or running predictive failures to manage reuse and replace use cases. Features like this give companies an advantage in managing costs as well as meet increasing regulatory compliance such as the Corporate Sustainability Reporting Directive (CSRD), requiring manufacturers to report product footprints (European Commission, 2023).  

Analyze carbon footprint of spare parts supply chain in Servigistics with machine learning.

Early mover advantages in the field service space

Field service management software such as ServiceMax is strategically positioned to benefit from generative AI. There are number of levers accelerating this innovation. ServiceMax stores text-rich work order data that is highly suitable for LLM analysis. SaaS delivery is well-adopted in the service space and affords for consistent updates associated to LLM model improvements. Also, service technicians work heavily with mobile devices where text and chat interactions are commonplace, a primary vehicle for interfacing with copilots. As a result of these synergies, service organizations are poised to benefit from LLM use cases such as asking copilots to summarize prior work orders or to get support on scheduling optimizations. Features like this scale knowledge in the organization and expedite inquiries given LLMs’ capacity to quickly scan vast data sets at scale. 

Ask questions about service work order history and scheduling with copilot capabilities.

What's next for PTC

It’s an exciting era for manufacturers. In front of us are great opportunities and significant challenges that we can solve together. This includes AI innovation and other market trends such as software-driven engineering, sustainability, or service optimization.  

As we deliver future AI applications and optimize our internal operations, we will stay focused on creating value for our customers at scale and responsibly. We invite you and your team to reach out to us so that we can chart a course together on practical ways to implement AI in your business. Areas of AI innovation that we will be evaluating include:

• Generative AI applications, including new features within specific products as well as features that source data from multiple software systems
• Building out critical integrations that enable data orchestrations across the digital thread both in PTC and external systems
• Coupling AI models together so that we have multi-modal AI systems that yield higher-value outcomes 
• Researching new ways of interacting with your industrial data through chat and audio interfaces  
• Monitoring advances in the regulatory and compliance space to ensure data security and ethical use of AI
• Creating and deepening strategic partnership with companies like Microsoft to scale AI solutions in market
• Building a community of champions who can work together to address value, data, technology, and change management topics in AI innovation

For information on certain products, please reach out to your PTC point of contact.