How Arena AI Turns Product Information Overload Into Instant Insight
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Manufacturers generate vast amounts of product information every day—from technical specifications and work instructions to change orders, quality records, and supporting documentation. But how do they turn that information into actionable insights in an environment where speed is critical?
Arena by PTC introduced two new AI-enabled capabilities in its recent product lifecycle management (PLM) and quality management system (QMS) software release: AI File Insights and AI Item Redline. Together, these innovations make it easier to uncover critical information buried in documents, understand changes faster, and keep product development moving forward.
We caught up with Arena Product Manager Jonathan Cohn to discuss these new capabilities in depth and learn how they help engineering, quality, and operations teams make faster, more informed decisions.
What inspired AI File Insights and AI Item Redline in Arena’s latest PLM and QMS software release?
Jonathan: One of the biggest hurdles product teams face is information accessibility. Important knowledge exists throughout specifications, procedures, test reports, and other documents, but finding the right information often requires knowing exactly where to look. This is especially painstaking when information is scattered across spreadsheets, file cabinets, shared drives, and other disconnected systems.
For electronic PLM and QMS solutions, users typically need to enter exact keywords, file names, or document titles when searching. If they don’t know the precise terminology—or if they make a typo—they often come up empty-handed. As a result, valuable insights remain hidden within documents. AI File Insights was developed to address this challenge by enabling users to ask questions and uncover valuable information from their documentation.
Another major challenge is managing engineering changes. As products become more complex, understanding what changed—and why—can be difficult.
When a change order includes multiple items, several revisions, or significant updates across bills of materials (BOMs), reviewers often need to perform manual comparisons to understand what changed. That’s time-consuming and increases the risk of overlooking important details.
AI Item Redline was designed to provide immediate clarity. Rather than forcing users to manually analyze every revision, the system automatically summarizes changes and highlights the most significant ones.
Let’s start with AI File Insights. How exactly does it work?
Jonathan: AI File Insights lets users ask questions about their documentation in everyday language. Instead of searching for file names or specific keywords, users can ask questions the way they naturally think about a problem.
The AI Assistant searches approved workspace documents and returns answers based on their content. It understands language variations, synonyms, and even typos, making it much easier to find information regardless of how the question is phrased.
What’s especially powerful is that it doesn’t just return files; it returns answers. The system analyzes document content, identifies relevant information, and presents it in an easy-to-understand format.
Can you share some examples of the types of questions users can ask?
Jonathan: Users can ask highly specific questions, such as which environmental conditions are specified for a product, or which documents reference standards like ISO 13485 or 21 CFR Part 820. The AI Assistant searches specifications, procedures, and other documentation to identify relevant content and surface the appropriate documents.
It can also tackle much broader questions. One example I like is, “What are the top 10 things a new engineer joining this team should know based on our documentation?” Arena AI reviews the available documents, identifies key information, and recommends the most relevant content for onboarding and knowledge transfer.
This transforms documentation from a static repository into a knowledge resource that teams can actively use.
How does AI File Insights help bridge information silos across teams?
Jonathan: Product development requires collaboration among engineering, quality, operations, and supply chain teams. Each group creates and consumes information differently, and those insights often become distributed across many documents.
AI File Insights helps break down silos by making information easier to find, regardless of its origin. If a team member needs information from a quality procedure, engineering specification, or operating instruction, they can ask a question and quickly get answers from the relevant documentation.
The result is better alignment across teams and faster decision-making because employees spend less time searching and more time acting on information.
Trust is critical when AI is involved. How do Arena PLM and QMS users know where answers originate?
Jonathan: Transparency is extremely important. Every AI response includes citations that direct users to the original source material. We want users to quickly verify information, review the original context, and make informed decisions. Arena AI doesn’t hide the source of information. It helps users get there faster. They can immediately open the referenced documents to review additional details, if needed.
This combination of AI-generated answers and source-level traceability enables organizations to use Arena AI confidently while maintaining trust in the underlying data.
How are security and governance handled?
Jonathan: Security and control were key design considerations. Administrators control which file categories are included in File Insights, allowing organizations to determine which content is available to the AI Assistant and which is excluded.
Like the rest of Arena, File Insights respects user permissions. Users receive only the information they are authorized to access, helping organizations comply with internal governance requirements and data privacy regulations.
Let’s switch gears to AI Item Redline. How does it work?
Jonathan: Users can generate AI-powered summaries directly from the Item and Change worlds in Arena. The system compares revisions, identifies modifications, and generates an easy-to-understand overview of what changed.
Users can review up to 20 items at once, covering the vast majority of changes processed in Arena. They can also drill into individual items for a more detailed review.
The AI Assistant produces a high-level change summary, identifies significant areas that may require additional attention, and presents detailed redline information in one place.
How does this improve the review and approval process?
Jonathan: One of the most exciting benefits is efficiency. Instead of spending significant time figuring out what actually changed, reviewers receive an instant summary and can focus their attention on evaluating the change itself. This accelerates review cycles, improves process consistency, and helps teams move products through development faster.
What level of detail can users expect in the change summaries?
Jonathan: The summaries are designed to support both quick reviews and in-depth analysis.
Users can see lifecycle changes, BOM updates, specification modifications, file additions, and other relevant updates. They can review high-level summaries or dive into detailed redline views that show additions, deletions, and modifications in context.
The goal is to provide the appropriate amount of information for the task at hand—whether someone needs a 30-second overview or a comprehensive change review.
What excites you most about the new Arena AI capabilities?
Jonathan: I’m excited by how these advancements transform the way product teams process information.
With AI File Insights, our customers quickly unlock critical information from their product documentation. Using AI Item Redline, they can understand changes faster and with greater confidence. Together, these capabilities reduce time spent searching, comparing, and reviewing, allowing teams to focus on innovation and execution.
That’s exactly the value our customers seek as they navigate an increasingly complex product development landscape.
Ann McGuire