Expedite products to market

How can AI in PLM help you launch better products faster?

AI in PLM puts artificial intelligence inside your product lifecycle management software, where your formulas, specifications, supplier data and regulations already live. It removes manual data work, answers questions in everyday language and supports decisions, so your experts spend their time on innovation. Trace One PLM is AI-native PLM for food and beverage, cosmetics and personal care, and specialty chemicals, with AI built into the platform.

Summary

  • AI in PLM automates manual data work, speeds up regulatory research and supports formulation decisions.
  • AI works inside Trace One PLM for manufacturers and retailers. It extracts supplier data, answers regulatory questions and verifies supplier certificates.
  • People stay accountable. Critical decisions stay under expert control, and AI takes on more routine work within clear rules.
  • Customer data stays isolated, follows user permissions and is not used to train public AI models.

In This Guide

Key definitions

Term Definition
AI-native PLM PLM software built around AI: governed AI agents work with product context and tools, coordinate tasks across workflows, and take on more work over time within defined rules and permissions.
AI-powered feature A feature that uses AI to complete a specific task, such as extracting data or answering a question.
Trace One Devex PLM Trace One PLM for manufacturers, purpose-built for formula-based product development in food and beverage, cosmetics and personal care, and specialty chemicals.
Trace One Copilot The agentic AI layer across Trace One solutions. Specialized AI agents work inside each application, and the architecture is designed to connect customer and third-party AI agents.
Human-on-the-loop An oversight model where AI agents carry out lower-risk tasks within set rules, and people step in for exceptions and critical decisions, keeping final accountability.
AI agent Software that uses an AI model to carry out tasks, such as finding data or filling in fields, within the permissions it is given.
Tenant isolation Each customer's data is kept in its own separate environment, never mixed with another customer's data.
Bring your own model (BYOM) An architecture designed to support customer-selected AI models that meet the vendor's technical and security requirements.
Closed AI system An AI setup where the application, the AI agent and the AI model all run inside the customer's own network, with no external AI service.

What is AI in PLM?

AI in PLM means using artificial intelligence inside the system that manages your products from idea to launch. PLM already holds the data AI needs: formulas, ingredients, specifications, supplier documents, packaging and regulatory requirements. Because this data is connected, AI can do more than answer questions. It can find, compare, extract and act on product information in one place.

Many AI tools can now connect to company data. AI in PLM goes further: it works natively on product context, meaning the relationships between formulas, ingredients, suppliers, specifications, markets and regulations, within your own permissions and with regulatory content your teams can trust.

Key Takeaway

AI in PLM puts AI where product data already lives: formulas, specifications, suppliers and regulations. That is what lets it act on real product information instead of answering general questions.

Where does AI make the biggest difference in product development?

AI has the most impact on six types of work across new product development and introduction.

Area What AI does What your team gains
Automation Takes over repetitive tasks such as data capture and document management Less manual entry and fewer errors
Discovery Lets anyone query the system and take actions in everyday language Non-technical users find answers on their own
Comparison Compares text and images and supports proofreading Faster checks for compliance and brand consistency
Mapping Maps data between systems during integration Faster integration and cleaner data
Decision support Suggests raw materials and formulation options Faster decisions and quicker response to consumer demand
Orchestration Coordinates tasks across workflows and specialized AI agents Fewer handoffs and faster end-to-end processes

What can Trace One AI do today?

Trace One AI works across Trace One solutions: Trace One PLM for manufacturers (Trace One Devex PLM), Trace One PLM for retailers managing private label products, Trace One Regulatory Compliance and Trace One SDS. In each solution, specialized AI agents extract data, answer questions and check information.

How does Trace One AI help manufacturers in Trace One PLM?

For formula-based product development in food and beverage, cosmetics and personal care, and specialty chemicals:

  • Supplier document extraction. AI reads supplier technical data sheets and safety data sheets and fills in the right fields, so R&D and regulatory teams stop retyping data from PDFs.
  • Regulatory answers in everyday language. Regulatory teams ask a question in everyday language and get answers drawn only from Trace One's validated regulatory content, including the Food Law Library, without manual filtering.
  • Product search in everyday language. R&D teams ask questions about ingredients, packaging, formulas and finished products, and the AI turns them into the right searches across the PLM.
  • Ingredient and packaging suggestions. The AI suggests ingredients and packaging options that fit the project's requirements, ready to test in a trial formula.

How does Trace One AI help retailers in Trace One PLM?

  • Certificate extraction and verification. When a supplier uploads a certificate, AI extracts the information and fills in the certificate fields. Verified certificates are marked "Verified by AI," and any later manual change is recorded, so retailers always know what was checked against the source document.
  • Specification review. AI checks supplier specifications against the retailer's guidelines and points to missing, unclear or inconsistent data, so suppliers fix issues before submission and quality teams spend less time on corrections.

Does Trace One AI help in Trace One Regulatory Compliance?

  • Formula compliance assessment. Teams upload a formula and run a compliance assessment across several markets at once, with clear go or no-go results linked to the relevant legislation.

How does Trace One AI help in Trace One SDS?

  • Supplier SDS extraction. AI extracts hazard classification and composition data from Sections 2 and 3 of supplier safety data sheets. Regulatory teams review and approve the result before it is saved.

Key Takeaway

Today, Trace One AI extracts supplier data from documents, answers regulatory questions from validated content and verifies supplier certificates, across Trace One PLM, Trace One Regulatory Compliance and Trace One SDS.

How does AI change the work of your teams?

AI amplifies expertise. It does not replace it. Your experts stay in the driver's seat while AI removes manual, low-value tasks and frees time for innovation. The value shows up at three levels:

  • For each user: less data entry and searching, more time for expert work.
  • For each department: R&D, quality and regulatory teams work faster from the same reliable data.
  • For the business: brands and retailers launch remarkable products faster and respond sooner to consumer and regulatory change.

People stay accountable. Critical decisions stay under human control, while lower-risk activities can increasingly be delegated to AI agents working within defined permissions, rules and exception thresholds.

What are the stages of AI maturity in PLM?

AI in PLM grows in stages. Each stage gives AI more responsibility, with oversight matched to the risk of the task.

  1. Inform and recommend. AI finds information, answers questions and suggests options. People act on them.
  2. Prepare actions. AI prepares the work, such as extracting supplier data into the right fields, for people to review and approve.
  3. Execute lower-risk tasks. AI agents carry out lower-risk tasks on their own, within predefined rules, permissions and exception thresholds.
  4. Orchestrate workflows. AI coordinates broader workflows across specialized agents and teams. People oversee exceptions and critical decisions.

At every stage, people keep final accountability. Trace One AI-native PLM is designed to progress along these stages. Today, Trace One AI informs, recommends and prepares actions for people to approve. Trace One's direction also includes a semantic layer that maps the relationships between products, formulas, ingredients, suppliers, markets and regulations, so AI agents understand product context, not just documents.

Key Takeaway

AI maturity in PLM moves from informing, to preparing actions, to executing lower-risk tasks, to orchestrating workflows. Oversight is proportionate to risk, and people keep final accountability at every stage.

How does Trace One AI protect your data?

Trace One AI keeps your data yours. Each customer's data stays in its own isolated environment, the AI sees only what each user is allowed to see, and customer data is not used to train public AI models.

  • Your data trains no one else's AI. Customer data is not used to train public AI models, and Google does not keep prompts or responses.
  • The AI respects your permissions. It sees only what each user is allowed to see, with the same roles and single sign-on as the rest of the platform.
  • You choose where it runs. For manufacturers, Trace One PLM and its AI agent can run in Trace One Cloud, a dedicated private cloud or on-premises. AI models currently run on Google Cloud, and the architecture is designed to support customer-selected models for fully closed deployments.

Read the FAQ below, or request the full Trace One AI Security & Governance Statement.

What should you ask an AI-PLM vendor before you sign?

AI claims are easy to make and hard to compare. These questions separate what works today from what is promised.

  1. Which AI capabilities are live today, and which are on the roadmap? Ask to see them on your own data, not on a demo dataset.
  2. Is our data used to train AI models? Get the answer in writing, including for the vendor's AI providers.
  3. Does the AI follow our user roles and permissions? An AI assistant should never show users data they cannot see in the application.
  4. Where does the AI run, and where does our data go? Ask this for each deployment option, including private cloud and on-premises, and ask whether a fully closed setup is possible.
  5. Which AI models are used, and can we bring our own? Model choice affects performance, cost and your own AI policy.
  6. How long are prompts and responses kept, and who can review them? Retention and audit trails matter for regulated product decisions.
  7. Who governs AI at the vendor? Look for a cross-functional review covering security, privacy and legal, and a clear position on the EU AI Act.

Key Takeaway

A strong AI-PLM evaluation asks for proof, not promises: live capabilities on your own data, written commitments on data use, and clear answers on where the AI runs.

Frequently Asked Questions

What is Trace One Copilot?

Trace One Copilot is the agentic AI layer across Trace One solutions, launched with the 2026 release. Specialized AI agents summarize, find information and complete tasks inside each application, within each user's permissions, and the architecture is designed to connect customer and third-party AI agents.

What is the difference between AI-native and AI-powered PLM?

AI-powered describes individual features that use AI to complete a task, such as extracting supplier data. AI-native describes the platform: governed AI agents work with product context and tools, follow the same user permissions, coordinate tasks across workflows and take on more work over time within defined rules. Trace One PLM is built as AI-native PLM, with AI-powered features available today across product development.

Which industries use Trace One AI-native PLM?

Trace One PLM is purpose-built for formula-based product development in food and beverage, cosmetics and personal care, and specialty chemicals. Trace One PLM also serves retailers managing private label products. Trace One helps over 9,000 brands worldwide develop, launch and manage their products.

Is customer data used to train AI models?

No. Customer data is not used to train public AI models or shared with AI providers for training. Trace One AI runs on Google Cloud, and under Google's contractual commitments, prompts and responses are not retained by Google or used to train its models.

How is customer data kept separate from other customers?

Each customer has its own dedicated tenant, isolated at the application, file system and database level. The AI works only with that customer's own data and Trace One's managed regulatory content, never another customer's data. Data is encrypted in transit (TLS 1.2+) and at rest (AES-256).

Who can see the data the AI uses?

Only the users who are already allowed to see it. Every AI request is checked against the platform's role-based permissions before it runs, exactly as in the user interface and APIs. Trace One supports single sign-on, including Microsoft Entra ID and OpenID Connect.

Which AI models power Trace One AI?

Trace One AI runs on Google's Gemini models, hosted on Google Cloud Platform.

Can Trace One PLM AI run in a private cloud or on-premises?

Yes, for manufacturers. Trace One PLM for manufacturers (Trace One Devex PLM) and its AI agent can run in Trace One's cloud, in a dedicated private cloud, or on-premises. In private cloud and on-premises setups, the AI models run on Google Cloud, managed by Trace One. Trace One PLM for retailers runs in Trace One's cloud, with the same security controls.

Can Trace One AI run as a closed AI system?

Trace One's architecture is designed for it. In a closed setup, Trace One PLM for manufacturers runs on-premises with a customer-selected AI model, so the application, its AI agent and the AI model all run inside the customer's network, with no connection to external AI services. Availability depends on the bring-your-own-model capability and technical validation.

Can companies connect their own AI agents or models?

Trace One is designed to connect customer and third-party AI agents through open AI standards, and to support customer-selected AI models. Every connection follows the same permissions as the rest of the platform.

Does Trace One AI keep data after a session ends?

No. AI agents keep no data once a user's session ends. Prompts, responses and uploaded documents are stored only in the Trace One application, under the customer's own retention policy.

How does Trace One govern its AI?

A cross-functional team from Product, Engineering, Information Security, Legal and Customer Success reviews every AI use case for value, security, privacy and compliance. Trace One monitors AI regulation, including the EU AI Act and GDPR, and applies human oversight in proportion to risk, with people keeping final accountability.

Full details are available in the Trace One AI Security & Governance Statement, on request.

Learn More about AI in PLM

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About Trace One

Trace One is a global provider of AI-native Product Lifecycle Management (PLM) and regulatory compliance software for the food & beverage, cosmetics & personal care, and specialty chemical industries. With more than 30 years of industry expertise, the company helps over 9,000 brands worldwide develop, launch, and manage remarkable products faster while turning regulatory complexity into a competitive advantage. Trace One's AI-native PLM platform is purpose-built for formula-based product development, connecting the entire product lifecycle from ideation and portfolio planning to formulation, product and packaging specifications, supplier collaboration, quality management, regulatory compliance, and approval to launch. Integrated regulatory intelligence spanning 170+ countries helps R&D, regulatory, quality, and product teams understand requirements, assess product compliance, and make informed decisions throughout the product lifecycle.