How Arena’s AI Assistant Transforms Search in PLM and QMS
Inside This Article
Search is one of the most heavily used features in any enterprise application, yet one of the biggest drains on productivity. It is estimated that employees spend 20%-30% of their workweek searching enterprise systems for information.1 That’s nearly a third of the workweek spent just hunting for answers.
The Intelligence Age promised technology would make us more efficient. In many ways it has, but it also introduced complexity. The next generation of enterprise solutions must simplify how we interact with data, and smarter search is the starting point.
Nowhere is this more pressing than in product lifecycle management (PLM) and quality management system (QMS) solutions. In these environments, search is a core workflow. Users must know exactly which filters to apply, where specific attributes live, and how objects such as items, changes, requests, and files are structured. Even slightly ambiguous or imperfect queries can return incomplete results or miss critical information altogether. As a result, users are forced to manually refine filters and rerun queries repeatedly.
Over time, this friction adds up, prohibiting product teams from focusing on work that moves innovation forward.
In our recent PLM and QMS software release, Arena by PTC introduced AI‑Assisted Advanced Search. This new capability in the Arena AI Assistant enables users to easily find product and quality information using natural language without complex filters or institutional knowledge of how the system is organized.
We sat down with Arena Product Manager Jonathan Cohn to discuss how AI‑Assisted Advanced Search works behind the scenes, and what it means for product teams looking to make faster, more confident decisions.
Jonathan, how does the Arena AI Assistant simplify the search experience for PLM and QMS users?
Jonathan: Anything you can do with traditional advanced search, you can now do through the Arena AI Assistant. The biggest shift is that PLM and QMS users can simply ask questions naturally, in their native language. Instead of thinking, “Which advanced search template do I need” or “Which attributes live on this object,” they can ask questions the way they ask a colleague.
Under the hood, the AI Assistant translates those natural language questions into Arena’s advanced search filters. It removes a huge learning curve while still preserving the precision and governance customers expect from Arena.
What happens if a user’s question isn’t clear or could be interpreted in multiple ways?
Jonathan: The conversational aspect of Arena’s AI Assistant enables it to ask for clarification. If a question has multiple interpretations, it will prompt the user for additional questions until it can perform the most accurate search possible. If the AI Assistant doesn’t quite find what you were looking for, it displays the applied filters so you can make a correction.
These guided prompts eliminate a lot of trial and error. Users don’t have to start over or manually adjust filters. They simply refine the question in context and move forward quickly.
What does the user see in Arena’s AI search results?
Jonathan: The AI Assistant returns the top 20 results directly in the conversation. It also displays the exact filters that were used to generate those results.
Additionally, an “Open Search Results” link takes users directly to the advanced search results page in Arena. There, they can see all matching objects beyond the top 20 and drill down for additional details. You can also save that search for future use. This keeps everything transparent and gives users a bridge between AI assisted discovery and traditional workflows.
Can you share a practical example of how the AI-Assisted Advanced Search works?
Jonathan: A good example is a term like “orphan items.” This phrase is widely used across the manufacturing industry, but it’s not a formal Arena attribute. With traditional search, you would need to know exactly how to define that term (i.e., items not in assemblies) and configure the filters manually. With Arena’s AI-Assisted Advanced Search, the assistant understands what the user means. It maps that industry terminology to Arena data, runs the appropriate search, and returns the results. Users can then drill in, open the full search results page, or refine the query further.
Another example is combining searches that would normally require multiple passes—like finding requests, changes, and files. The AI can interpret that intent and run multiple searches across the entire Arena workspace, presenting the results in a clear, organized way.
Earlier, you talked about keeping everything transparent. Why is this an important AI principle with Arena?
Jonathan: Trust is critical when you’re dealing with product and quality information. We want customers to understand exactly how the AI Assistant arrives at its results. The user is always in charge of what actions our AI can perform and can always see exactly what it is doing.
By showing the applied filters and providing a direct link to the advanced search page, users can validate the results, explore the information further, and even save those searches. The AI builds directly on Arena’s existing, trusted search capabilities.
How does AI-Assisted Advanced Search support Arena’s role as a single source of product truth?
Jonathan: Arena has always been the trusted system of record for product and quality information. AI-Assisted Advanced Search builds on that foundation by helping teams access the information quickly and intuitively.
When users can confidently find accurate, current information without second-guessing filters or search terminology, it drives better alignment and decision-making across engineering, quality, operations, and supply chain teams.
What does this capability mean for adoption and time to value, especially for new Arena users?
Jonathan: New users don’t have to be Arena experts to get the product and quality information they need. They can ask questions, learn the system through interaction, and become productive faster. If you have a question about how Arena works or want to learn best practices in PLM, the AI Assistant is a great resource to go to first.
For experienced users, it removes friction from everyday tasks. Either way, customers see accelerated time to value because they spend less time searching and more time acting on information.
How does AI-Assisted Advanced Search support PTC’s broader AI strategy?
Jonathan: Strictly developed following PTC’s Responsible AI governance principles and processes, this capability expands upon the improvements we’ve made across the AI Assistant—better models, more responsive UI, conversational memory, and ongoing refinement based on user feedback. As the underlying models improve, so does the search experience.
What excites you most about Arena’s AI-Assisted Advanced Search capability?
Jonathan: As manufacturing organizations scale and product lifecycle information becomes more complex, smart, intuitive search is critical. Arena’s AI-Assisted Advanced Search embeds that intelligence directly into the PLM and QMS workflows our customers rely on every day. It is the first of many AI-enabled search features designed to make data more useful and help teams work more efficiently.
References
Ann McGuire