Introduction Regulatory momentum in the EU
Regulatory compliance demands are tightening at the same time that product innovation is accelerating, and the combination is compelling manufacturers and private label retailers to rethink how they govern product data, orchestrate cross-functional work, and prove compliance at scale. In the European Union, the Artificial Intelligence Act formally entered into force on August 1, 2024, with staged obligations beginning in 2025 and running through 2026–2027; early provisions address prohibited practices and AI literacy, mid-term provisions cover governance for general-purpose models, and later phases apply to high-risk systems.
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Sustainability data depth via ESPR and DPP
In parallel, the EU’s Ecodesign for Sustainable Products Regulation (ESPR), adopted in 2024, will progressively introduce Digital Product Passports (DPPs) and broader sustainability disclosures, requiring manufacturers to capture deeper, lifecycle-rich product data and keep it traceable across supply chains (see the Commission’s ESPR FAQ and its DPP consultation call for input: European Commission, European Commission).
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North American context for labeling and allergen control
In North America, labeling rules continue to evolve in ways that intersect directly with specification and artwork governance; the FASTER Act made sesame the ninth major allergen as of January 1, 2023, elevating the need to keep labels synchronized as suppliers and formulations change.
The system implications for PLM
For global manufacturers, the practical conclusion is straightforward: the system of record for product truth and the system of work for compliance can no longer be a patchwork of spreadsheets, email threads, and local drives; they must converge in a smart, AI-enabled PLM platform that moves faster while demonstrating control in line with accepted governance frameworks.
North American context for labeling and allergen control
In North America, labeling rules continue to evolve in ways that intersect directly with specification and artwork governance; the FASTER Act made sesame the ninth major allergen as of January 1, 2023, elevating the need to keep labels synchronized as suppliers and formulations change.
The system implications for PLM
For global manufacturers, the practical conclusion is straightforward: the system of record for product truth and the system of work for compliance can no longer be a patchwork of spreadsheets, email threads, and local drives; they must converge in a smart, AI-enabled PLM platform that moves faster while demonstrating control in line with accepted governance frameworks.
What this model covers
This white paper introduces an AI-powered PLM and regulatory compliance maturity model designed for manufacturers in the food and beverage, cosmetics and personal care, and specialty chemicals space. It provides a practical path to evolve from rules-based automation to AI-driven autonomy, aligning product, regulatory, and digital teams on a common roadmap and sequencing the data, governance, and change-management capabilities that need to come first. It also anchors AI adoption in widely recognized standards so that progress on LLM-assisted authoring, retrieval-augmented regulatory intelligence, predictive risk scoring, intelligent workflow orchestration, and ultimately autonomous agents remains compatible with board-level expectations and emerging regulation. Industry research underscores both urgency and opportunity: a majority of surveyed enterprises report regular use of AI in at least one business function, yet value capture is uneven when pilots stay siloed—a gap that argues for a staged, capability-based approach.
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What this model covers
This white paper introduces an AI-powered PLM and regulatory compliance maturity model designed for manufacturers in the food and beverage, cosmetics and personal care, and specialty chemicals space. It provides a practical path to evolve from rules-based automation to AI-driven autonomy, aligning product, regulatory, and digital teams on a common roadmap and sequencing the data, governance, and change-management capabilities that need to come first. It also anchors AI adoption in widely recognized standards so that progress on LLM-assisted authoring, retrieval-augmented regulatory intelligence, predictive risk scoring, intelligent workflow orchestration, and ultimately autonomous agents remains compatible with board-level expectations and emerging regulation. Industry research underscores both urgency and opportunity: a majority of surveyed enterprises report regular use of AI in at least one business function, yet value capture is uneven when pilots stay siloed—a gap that argues for a staged, capability-based approach.
Why a smart maturity model for PLM and compliance?
Modernization is no longer about converting paper to screens or codifying a handful of deterministic checks; it is about accelerating time-to-market without accruing compliance debt and scaling innovation without scaling headcount linearly. A maturity model helps because it translates those aims into a sequenced set of operating capabilities that compound rather than collide. It encourages teams to locate themselves candidly—by examining how data behaves and how work actually moves—and to choose a next step that is both technically feasible and governance-ready.
When the model is explicit about data prerequisites such as normalized vocabularies, canonical specifications, and curated regulatory content; human-in-the-loop controls such as approval gates with evidence; and model oversight such as monitoring and escalation, improvements in one layer unlock the next. Introducing LLM-assisted specification drafting and claim validation can deliver immediate productivity and quality gains, but the full benefit appears once those assists are grounded in authoritative content and tied to orchestration logic that can propagate impact across formulation, sourcing, labeling, and e-commerce.
Aligning the roadmap with recognized AI governance standards ensures automation advances in lockstep with risk controls and auditable accountability. The point is not to declare a finish line but to sequence capability development so that small wins pave the way for big ones, making clear that AI in PLM is a progression from assistance to orchestration to bounded autonomy.
Why Supply Chain Transparency and Ethical Sourcing Are Critical
Achieving visibility into Scope 3 emissions isn’t just a reporting exercise – it’s now a strategic imperative for cosmetics brands. Supply chain transparency and ethical sourcing are critical for several reasons:
Effective Emissions Reduction: You cannot reduce what you can’t see. Transparency is the first step to action – brands need granular data on where emissions are coming from in their supply chain in order to target reductions. For example, if a particular ingredient supplier or packaging component is responsible for outsized emissions, that insight allows the company to pursue alternatives (such as lower-carbon materials or new sourcing locations). Many cosmetics companies have set science-based targets that include supply chain (Scope 3) cuts, and meeting these commitments requires pulling accurate data from suppliers and contract manufacturers. Indeed, as of 2025 the EU’s Corporate Sustainability Reporting Directive (CSRD) mandates large companies to report Scope 3 emissions - forcing organizations to gather verified emissions data across their value chain. Transparent supply chains thus underpin compliance with emerging regulations and ensure brands can actually hit their public climate goals.
In parallel, regulatory pressure is rising across the EU and UK. Extended Producer Responsibility (pEPR) schemes now mandate detailed reporting on packaging composition, weight, recyclability, and recycled content at the SKU level. Regulations such as the UK Plastic Packaging Tax and France’s AGEC law introduce not just disclosure requirements but also financial consequences for non-compliance. These policies are expanding rapidly across jurisdictions, and brands that fail to keep pace face penalties, supply chain disruptions, and even market access restrictions.
To meet these twin pressures—from both consumers and regulators—cosmetics brands must that can consolidate and manage packaging and sourcing information across the product lifecycle. Embedding compliance and sustainability tracking into product development processes enables companies to ensure audit-readiness, reduce Scope 3 risks, and maintain credibility in a market increasingly defined by accountability and environmental performance.
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In essence, transparent supply chains are more resilient – companies can identify weak links or hot spots early and work with suppliers to improve them, rather than reacting to crises blindly. As an added benefit, transparency and data sharing foster stronger partnerships: suppliers who know they are being measured on sustainability metrics often become more proactive, driving innovation (for example, using renewable energy or cleaner processes) that ultimately benefits the brand.
The four stages of smart PLM and compliance maturity
Rule-Based automation
Organizations in the first stage have digitized workflows and embedded gated approvals and validations as static rules. This represents a substantial improvement over document-only processes because it creates consistency, reduces manual rework, and surfaces status. The limitation is brittleness: rules encode yesterday’s assumptions, and exceptions multiply as formulations, suppliers, claims, and regulations change. Compliance checks often occur late; artifacts are scattered across shared drives; and audits can devolve into resource-intensive hunts. At this point, automation is deterministic and struggles with nuance, and AI is not yet participating in the creation or interpretation of product truth. The strategic risk is not failed digitization but an inability to scale under regulatory churn and portfolio complexity.
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AI-Augmented tasks
The second stage begins when the PLM experience incorporates assistive AI within bounded activities while keeping humans firmly in the loop. Authors see LLM-based suggestions in context—ingredient substitutions that respect allergen and claim logic, narrative explanations of regulatory changes mapped to internal policy, and risk scores that flag likely labeling or documentation gaps. Teams ask natural-language questions—whether a formulation is compliant across specific markets—and receive traceable answers grounded in internal and external sources using retrieval-augmented generation. Productivity improves, error rates fall, and PLM shifts from hurdle to helper. This is also where AI governance must become operational: intended use, human oversight, data minimization, and model monitoring should align to NIST AI RMF 1.0 and ISO/IEC 42001 so early wins do not outpace controls.
Intelligent orchestration
The third stage marks the shift from helpful tools to system-level intelligence. PLM no longer offers isolated recommendations; it manages end-to-end workflows across formulation, sourcing, labeling, and commercialization. When a supplier updates a material, the system identifies affected SKUs, evaluates allergen and claim impacts by jurisdiction, initiates change orders, requests new certificates, prepares artwork tasks, and sequences work to the right people with the right context. External signals—regulatory updates, ingredient-database changes, allergen alerts, and DPP data needs—are ingested into a product knowledge graph so orchestration policies adapt to live information rather than static swimlanes. Time-to-market improves because inter-task latency collapses, and compliance becomes scalable because validation runs continuously. By this stage, data quality is strong, role-based access is enforced, integrations with ERP, QMS/LIMS, PIM/MDM, and labeling systems are in place, and AI governance is operational in anticipation of risk-based obligations under the EU AI Act.
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Autonomous AI agents
The fourth stage is not about ceding control to machines; it is about enabling bounded, goal-driven agents to execute well-specified change tasks inside clear guardrails. A product owner sets a goal—optimizing a formula for cost and compliance across North America and the EU while preserving a defined sensory profile—and the agent decomposes the work, interacts with PLM, QMS, and sourcing systems, engages suppliers for missing data, proposes label adjustments, and assembles a change packet ready for human approval. The guardrails derive from agreed policy and standards: human-in-the-loop controls, action logging, confidence thresholds, and escalation rules mapped to NIST AI RMF 1.0 and ISO/IEC 42001. With the AI Act’s timeline and the ESPR/DPP data burden, manufacturers at this stage scale innovation and adaptive compliance without proportionally scaling overhead.
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How to assess your current maturity Reading the signals in your operations
A credible self-assessment begins with observed behavior rather than aspiration. If workflows execute predictably but exceptions stall for weeks, you remain largely rules-dominant. If authors receive AI suggestions and inline claim checks yet work still moves linearly and hand-offs are manual, you have entered the AI-augmented phase. If the system can propagate supplier and regulatory changes across specifications and labels and pre-stage artwork, evidence collection, and approvals without prompting, you are operating in intelligent orchestration.
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Two questions that clarify your PLM stage
The first question is whether work moves because humans tell the system what to do, or whether the system guides people based on live data. The second is whether rules are tethered to forms, or whether policies are applied to knowledge graphs and external signals, including regulatory content and supplier performance. Organizations that answer “the system guides us” and “policies run on live data” are beyond augmentation and on the path to orchestration and agents, assuming governance remains aligned with NIST AI RMF 1.0 and ISO/IEC 42001.
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Moving from One Stage to the Next
Sequencing capability with governance
Progress is fastest when capability and governance advance together. The jump from rules to augmentation succeeds when teams target repeatable, rules-heavy tasks where AI can add immediate value with low risk. In a process manufacturing context, that often includes LLM-assisted field completion for specifications, AI-based OCR for supplier documents and certificates, retrieval-augmented claim validation, and risk scoring for documentation completeness. These steps increase authoring velocity and reduce errors without changing approval authority, while providing a proving ground for human-in-the-loop controls and model monitoring consistent with leading standards.
Building the orchestration foundation
The transition from augmentation to orchestration requires connected data. In practice, this means rationalizing specification vocabularies, implementing a product knowledge graph that maps formulations, packs, markets, and policies, and curating regulatory content stores so retrieval-augmented generation is grounded in authoritative sources. Once those pieces are in place and PLM is integrated with ERP, QMS/LIMS, PIM/MDM, and labeling systems, orchestration policies can route work based on impact analysis rather than static swim lanes. With ESPR and DPP requirements approaching, traceable sustainability attributes and evidence are better designed into the specification backbone than bolted on late.
Agents with guardrails, not autopilot
The step to agents is agency under constraints. Start with bounded domains such as cost-and-compliance optimization within an existing sensory profile or supplier risk mitigation when certificates lapse. Define objective functions and escalation criteria, run in shadow mode, then progress to suggest/execute-with-approval as confidence grows. The governance that protects you—accountability, transparency, and oversight—remains constant even as tools grow more capable.
Talent and change as First-Class work
Each stage rebalances roles. Analysts become curators of knowledge and policy stewards; product owners become objective setters and exception resolvers; regulatory experts become explainability reviewers and AI-profile custodians. Embedding these expectations in role design and enablement keeps humans leading even as AI scales execution.
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Conclusion and next steps
Operating-Model upgrade, not a tool swap
The journey from rules-based automation to AI-driven autonomy is an operating-model upgrade that re-platforms how product truth is created, governed, and proven. Manufacturers that move now will be better aligned with the EU AI Act timeline and better prepared for ESPR/DPP data depth, positioning themselves to exploit generative AI and machine learning to compress cycle time without accumulating compliance risk.
Practical priorities for the next quarter
In the near term, baseline cycle times, first-time-right rates, and regulatory incident costs; land AI-assist in low-risk tasks to build confidence while establishing governance that includes human oversight and model monitoring aligned with NIST AI RMF 1.0 and ISO/IEC 42001; and prepare the data foundation—knowledge graphs and curated regulatory content—so orchestration can operate on live information rather than static forms. When these pieces are aligned, autonomous agents stop being a buzzword and become an auditable way to scale change tasks that used to consume weeks, freeing experts to focus on objectives and exceptions rather than paper-chasing. For multi-market portfolios, additive requirements like sesame labeling under the FASTER Act only reinforce the value of a single governed backbone that keeps specifications, labels, and evidence synchronized as rules evolve.
How Trace One Helps
The governed backbone for AI-Enabled PLM
Trace One PLM solutions are the governed backbone and collaboration layer that manufacturers use to move from rules to autonomy without losing control. In practice, PLM solutions can provide a single source of product truth and a configurable workflow that connects R&D, Regulatory, Quality, Packaging, Sourcing, and Commercialization in one secure, scalable, modular SaaS platform.
The PLM overview emphasizes compliance by design, auditability, and traceability, and highlights integration with ERP, PIM, and e-commerce systems so manufacturers complete the digital thread and retire manual reconciliation. For teams building AI-enabled PLM, Trace One PLM operates as the authoring and governance layer—with version control, role-based permissions, and evidence capture—that provides the policy guardrails and data consistency required for LLM-assisted authoring, retrieval-augmented regulatory intelligence, intelligent workflow orchestration, and bounded autonomous agents that execute change tasks with human oversight.
Book a guided demo today to understand how Trace One applies these principles to its PLM solutions.
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