Choose a defined task for AI

Begin with a specific task, such as extracting declared substance identifiers from supplier documents or flagging records with missing dates. Define the input, expected output, and reviewer before testing the tool. A broad instruction to determine whether a product is compliant leaves too much scope unresolved.

Separate the techniques involved. Document extraction may use optical character recognition, rules, machine learning, or generative AI. A rules-based comparison against a substance list is not necessarily an AI function. Generative AI produces content and requires particular attention to unsupported statements. NIST's voluntary Generative AI Profile identifies confidently presented false content, often called hallucination, as a risk. [2]

Normalize records while preserving their meaning

Normalization puts information into consistent fields so it can be compared. A proposed record might contain the manufacturer part number, declaration date, covered revision, substance name, Chemical Abstracts Service (CAS) Registry Number, concentration, and unit, where those details are available.

Retain the original document and link each extracted field to its location in the source. Keep a supplier's stated value separate from an AI-generated inference. Do not fill an empty field with a plausible answer or silently merge conflicting declarations.

Review identifiers and units carefully. A similar chemical name does not establish an exact match, and a CAS number alone does not establish that a substance falls within every regulatory definition. Missing concentrations, unclear scope, or uncertain component matches should remain unresolved until investigated.

Use screening to route investigation

Screening compares available information with a defined reference set to identify items needing attention. Record which reference list or requirement version was used, the date of the screen, and the products or components assessed. Reviewers need that context to understand a result and repeat the check.

A potential match is a reason to investigate applicability and evidence. A result with no matches does not prove that a product contains no relevant substance: the input may be incomplete, the identity uncertain, or the reference set narrower than the requirement.

Use prioritization to organize that investigation. Give reviewers visibility into the affected products, the evidence gap, and the decision or deadline at stake. A tool-generated priority or confidence value should not become a compliance conclusion or override a consequential gap.

Make human review visible in the record

Consider an illustrative case: a supplier declaration names a connector family but omits the exact suffix used in a released BOM. AI extracts the family name and proposes a match. The reviewer checks the source, records that coverage is unconfirmed, and asks the supplier whether the declaration covers that suffix and revision. Receipt of a readable summary does not close the question.

Supply chain can own the supplier clarification, while engineering confirms the component mapping and compliance evaluates the applicable requirement. Record the next action and due date. Keep the prior evidence and the reason for accepting or rejecting the proposed match.

Human review needs a defined decision, sufficient source access, and authority to correct or stop the output. Reviewers should verify material extracted values and assess scope, exceptions, and conflicting evidence before those outputs inform a release, declaration, or report. A checkbox saying reviewed is insufficient if nobody can retrieve what was checked.

Test the workflow before expanding it

For 2027 planning, pilot one task within one product family. Use a reference set of documents that knowledgeable reviewers have assessed, including clear declarations, incomplete responses, conflicting revisions, and difficult scans. Define what a correct extraction or match means for that task before comparing results.

Measure errors as well as effort. Track incorrect matches among proposed matches and missed matches among known matches. For extraction, compare the fields with known values against the source, including units and document coverage. Record the time required for verification and corrections as part of the total workflow effort.

Agree acceptable performance and escalation rules with the responsible team based on the consequence of an error. Retest after material changes to the model, prompts, source documents, or reference data. Use your organization's approved environment for supplier documents and BOMs, with appropriate access and data-handling controls.

These tests assess a workflow's performance under defined conditions. They do not establish product compliance.

Evaluate tools against your evidence needs

When evaluating a platform, bring a defined use case and representative documents. Ask whether reviewers can inspect source evidence, identify uncertainty, correct outputs, and retain the basis for decisions. Confirm which capabilities are available for your configuration and how they fit your process.

SiliconExpert's AI materials describe contextual assistance and specialized agents across electronics workflows, including compliance. Evaluate the relevant capabilities with your team rather than assuming that every proposed use case is a supported feature. [3]

Use the 2027 Compliance Readiness Playbook for the broader framework and the two-page Compliance Readiness Checklist to identify a process gap. Start with the task where your team spends the most time reconstructing evidence, then test whether AI assistance improves that task without obscuring unresolved questions.

Explore our compliance readiness resources.

Request a SiliconExpert Free Trial to explore the platform for your team's workflow. To discuss your requirements with a specialist, request a Demo.

This article provides general information and is not legal advice. Obligations depend on the applicable law, product, jurisdiction, and circumstances.