The 2024 HazCom final rule puts a specific number on that exposure. The rule updates the U.S. standard to reflect GHS Revision 7 and select elements of Revision 8, and OSHA's Final Economic Analysis projects that approximately 94% of SDSs currently in use will require revision ahead of the May 19, 2026 manufacturer compliance deadline. That is a one-time structural event landing on top of a continuous stream of individual product updates, separate from any predictable planning cycle.

The enforcement picture is consistent with how this plays out in practice. Hazard Communication ranked second on OSHA's Top 10 most-cited standards in fiscal year 2024, with 2,888 citations, and held that position through FY2025. Violations most frequently trace to records that are missing or outdated and labeling that lags behind supplier revisions, which are precisely the failures a maintained SDS program exists to prevent. As of OSHA's 2025 civil penalty adjustment, a willful or repeat citation carries a maximum penalty of $165,514 per instance.

The Structural Gap Manual Systems Cannot Close

The traditional SDS library, built on shared drives and manual review cycles, fails in predictable ways as a chemical inventory grows. Indexing a single SDS document requires extracting every structured field from a PDF that may arrive in any number of source layouts or languages. Multiplied across thousands of products and dozens of supplier updates per quarter, that task outpaces most safety departments well before the next scheduled program review.

The deeper problem is invisible divergence. A database populated with last year's sheets from a supplier who has since reclassified a component will pass a quick count but fail a line-by-line inspection, or, far more consequentially, fail a worker who needs accurate exposure information at the moment of an incident. The distance between holding a document and holding the right document is undetectable by any method short of systematic, field-level verification.

Where Precise Document Processing Earns Its Place

AI fits naturally into the extraction and indexing portion of this problem. Optical character recognition converts scanned and PDF sheets into machine-readable text, and natural language processing models can identify the 16 standardized GHS sections and pull structured data regardless of source layout or formatting variation. A study published in Scientific Reports in 2024 reported that a machine-learning system achieved a precision of 0.93 at the document level across 20,000 annotated SDS documents, a performance benchmark that gives teams a credible reference point for weighing automated indexing against manual processing at volume.

The operational value that follows from that extraction capability runs in a specific direction. A structured, searchable library replaces the folder system, and every indexed field becomes a queryable parameter a worker or first responder can retrieve in seconds. Automated currency checks compare the workplace library against supplier databases and regulatory revision schedules, surfacing sheets that pre-date a GHS reclassification before an inspector does. Version control and access logging produce the audit trail OSHA expects, sparing teams the retrospective reconstruction that most programs undertake only after an inspection is announced.

Teams that have moved through this build tend to find the extraction architecture more decision-intensive than anticipated, particularly once non-standard document formats enter the picture. 

The Accountability Line AI Cannot Cross

Precision of 0.93 also means roughly seven documents in a hundred will need human correction. High-hazard substances and non-standard source documents warrant professional verification regardless of model confidence scores. Beyond accuracy rates, the judgments that require a safety professional's credential and accountability do not transfer to software. Exposure assessment and training adequacy determinations belong to people by design and by regulation.

Read against those constraints, the productive framing for AI in SDS management is task allocation. The tools absorb document-intensive extraction and gap-flagging work that consumes safety staff hours and demands no professional judgment. That absorption redirects EHS capacity toward the decisions that actually require it.

Building a Defensible Program Ahead of the 2026 Deadline

For teams approaching the HazCom transition, a sequenced starting point reduces program risk appreciably.

• Begin with an inventory audit. Confirm that every chemical on site has a current, manufacturer-specific SDS and that any worker can retrieve it in under a minute. That single test routinely exposes more gaps than program administrators expect.
• Digitize before automating. AI-assisted extraction tools produce reliable output only from a connected, structured data foundation, and a cloud-based SDS library must be in place before any extraction tool can add value.
• Use the incoming wave of HazCom-driven SDS revisions as a natural pilot opportunity. Run automated extraction against the revision backlog and keep human review running in parallel to establish accuracy baselines for your specific document set. Before removing human checkpoints from any part of that process, define in writing which document categories require professional sign-off regardless of model output. That documented policy is also what an auditor will want to find.

The benchmark that applies across every step of this process is the same one a HazCom inspector will use on arrival. Can a worker retrieve the correct, current SDS for the product in front of them in under a minute? Programs that build extraction automation and version management into their SDS infrastructure ahead of the 2026 deadline will answer that question consistently, in every inspection year that follows.