Faced with the explosion of data, multiplied by fifteen between 2025 and 2035, technological acceleration and increasingly stronger global competition, manufacturers must more than ever optimize their operations and accelerate innovation to remain competitive. Artificial intelligence (AI) is therefore becoming a major and essential driver of transformation, capable of converting these massive volumes of data into strategic advantages.
TL;DR: Industrial companies generate massive volumes of product data that remain hard to exploit efficiently, even within a PLM. AI changes this by enabling smart knowledge access, automated impact analysis, and faster processing of technical changes. Integrated into a PLM, it makes data more actionable, decisions faster, and regulatory compliance more reliable.
A major challenge: managing data effectively
Each industrial project or product generates a considerable volume of technical, operational and commercial data. These may include technical specifications, design models (CAD), customer data, industrial documentation, regulatory documents, or even test and quality reports. Even if they are centralized in a PLM, their fast and reliable operation often remains complex, especially in the following cases:
- Information search accurate: find, for example, the dimensions of a specific thread buried in long functional specifications.
- Impact analysis : assess the consequences of a technical modification with multiple use cases, such as the updating of a screw used in all modules and machines from a manufacturer.
- Unstructured data migration : map and migrate a large volume of unstructured data into a PLM solution, for example when receiving a customer datapack.
Manufacturers must find solutions to effectively exploit this data in order to quickly respond to market requirements and growing customer expectations, while respecting regulatory constraints. It is precisely here that AI integration for the Product Lifecycle Management makes perfect sense.
What are the applications of AI in PLM?
The purpose of PLM systems is to centralize all technical data and product information. However, without artificial intelligence, key tasks such as information research or documentary analysis remain mostly manual. Here are three major applications of AI in modern PLM.
Smart access to technical knowledge
Integrating virtual assistants into PLM simplifies information access and sharing knowledge within the company. Employees can ask specific questions - about a product, a technical specification or a regulatory standard - and get instant, contextual answers. Thanks to AI, the whole of business knowledge quickly becomes available to everyone.
Fast and reliable impact analysis
Faced with the constant evolution of technical documents and products, manufacturers suffer from inefficiencies and risk major non-conformities. Indeed, determining the changes between two versions of a 300-page customer specification and analyzing its impact on the entire article database is very difficult and can be a source of errors. AI makes it possible to automatically compare multiple versions, identify critical differences, and assess the impact of each change. The processing of technical modifications is thus much faster and manufacturers are strengthening their regulatory compliance by limiting the risks of errors or omissions.
Automating repetitive tasks
AI can take care of repetitive actions such as mapping data, enriching them or even automatic generation of technical documents. This frees up time for teams to focus on high value-added missions.
Expected benefits for industrial companies
Increased efficiency
AI allows businesses to deal more effectively with massive volumes of data generated throughout the product life cycle. By automating processes and speeding up analyses, it allows significant time savings to focus on core business and innovation.
Faster and better informed decisions
Through instant and reliable analysis of critical data, AI consistently agile and reliable decisions, which are essential in a constantly changing industrial context. It aims to accelerate cycle times, reducing time-to-market and strengthening the responsiveness of businesses in the face of the unexpected.
Assured compliance
PLM software ensures the complete traceability of data and processes throughout the product life cycle. With AI, businesses have an even more powerful tool to guarantee their regulatory compliance. AI makes it possible to quickly anticipate and integrate regulatory changes, to identify the products or components concerned and to implement the necessary adjustments in the shortest possible time. This synergy between PLM and AI limits risks and improves the ability of manufacturers to ensure compliance in a regulatory environment that is constantly evolving.
AI in PLM: what Aletiq has built
The three challenges described in this article, accessing technical knowledge, analyzing the impact of changes, and automating repetitive tasks, are precisely what Aletiq has chosen to address by integrating AI directly into its PLM platform.
Instant access to company knowledge. Aletiq's AI assistant searches across all product data centralized in the platform and delivers contextual answers in seconds, with sources. From the technical specifications of a component to applicable regulatory requirements: the information is accessible to everyone, without going through an expert or manually searching through documents.
Version comparison and gap detection. Aletiq AI compares two versions of the same document in one click, whether it is a customer specification running several hundred pages or an updated standard, and highlights every change. Impact analysis on affected products and components is the next step on the roadmap.
Automatic enrichment of the product database. As soon as a document is imported into the platform, the AI analyzes it, identifies its nature and extracts key information to complete the associated fields. No more manual data entry, no more risk of omission.
The underlying model is Mistral, a French solution compliant with GDPR, hosted on Aletiq's servers. The access rights defined in the platform also apply to the AI: the assistant cannot access documents a user is not authorized to view.
The integration of artificial intelligence into PLM marks a decisive step in the digitalization of industrial processes. It is no longer limited to a distant technological promise, but is already concretely transforming the way in which companies design, manufacture and evolve their products.
By making data more accessible, analytics faster, and processes smoother, AI is taking PLM to a new level: smarter, more responsive, and more reliable. It allows manufacturers to gain operational efficiency, reduce the risks of non-compliance and accelerate innovation.
FAQs
What does AI concretely bring to a PLM?
AI goes beyond simple data centralization. It makes information instantly accessible, automates repetitive tasks such as data entry and product record enrichment, and speeds up impact analyses during engineering changes. What used to take hours of manual research can now be handled in seconds.
What is the difference between a standard PLM and an AI-powered PLM?
A standard PLM centralizes and structures product data. An AI-powered PLM makes it actionable in real time: teams can query the database in natural language, automatically compare document versions, and receive alerts on the potential impact of a change. Data is no longer just consulted, it is questioned.
Is AI in PLM compatible with industrial regulatory requirements?
This is a critical point for manufacturers operating in regulated sectors such as aerospace, medical devices, or defense. At Aletiq, the AI relies on Mistral, a French solution compliant with GDPR, with data hosted on Aletiq's servers. The access rights defined in the platform also apply to the AI: the assistant can only access documents the user is authorized to view.
How can AI help manage engineering changes?
When a customer specification is updated or a standard evolves, identifying the differences between two versions and assessing their impact across the entire product database is a time-consuming and error-prone task. AI automatically compares versions, highlights critical changes, and will progressively identify the affected products and components. Teams gain in responsiveness and reduce the risk of non-compliance.
