Accelerating Safety Data Sheet Management with AI and Automation

Critical Issue

A large industrial organization depends on a small internal team to create and manage safety documentation, with much of the workflow centered around a single expert. The process includes manual data collection from various systems—one for product and bill of materials data, another for formulation management, and a third for authoring safety documents.

This manual approach is time-consuming, error-prone, and creates business continuity risk. Delays or inaccuracies in SDS documentation can block shipments, incur fines, and erode customer trust. The client partnered with Concurrency to conduct a focused discovery engagement to identify where AI, automation, and improved UX could deliver the greatest impact.

Customer Profile

Privately held chemical manufacturer specializing in custom blends and technical solutions for food processing, water treatment, agriculture, industrial manufacturing, and energy sectors.

Key Problem

The SDS workflow relied heavily on a single expert, causing slow turnaround, limited throughput, compliance risks, and knowledge gaps. The organization needed a scalable solution to automate data retrieval, streamline drafting, and preserve institutional knowledge.

BUSINESS CHALLENGES

Before engaging with Concurrency, the client faced several challenges:

• Manual, Fragmented Workflows: SDS creation involved multiple disconnected systems and repeated data entry.
• Key-Person Dependency: Operations relied heavily on a single subject matter expert, posing continuity risk.
• Regulatory & Compliance Pressure: Errors or delays could trigger fines, shipment holds, or reputational damage.
• Scalability Limitations: Growth in product lines risked overwhelming the existing SDS team without automation.

Outcomes

Reduced Authoring Cycle Times

Consolidating information from multiple systems into a single AI-driven assistant significantly decreases the time needed to gather data and prepare draft SDS documents.

Increased Throughput Without Added Headcount

Automation allows the small SDS team to handle more requests efficiently, providing capacity to support product growth without hiring additional staff.

Knowledge Retention & Continuity

AI agents capture institutional knowledge, reducing reliance on a single expert and mitigating business continuity risks associated with turnover or absence.

our solution

Focused Discovery Engagement
Spent three weeks mapping workflows, identifying pain points, evaluating integration points, and validating opportunities for AI-driven assistance.

AI-Driven Insights
Explored leveraging Copilot Studio, Azure AI services, and reusable multi-agent patterns to accelerate data gathering and draft SDS creation.

Future-Ready Architecture
Designed a scalable solution foundation to reduce errors, embed validation, and create reusable agents for broader automation across systems.

LESSONS LEARNED & NEXT STEPS

• Discovery engagements help identify high-value opportunities for automation and AI in critical workflows.
• Capturing expert knowledge digitally mitigates key-person dependency and continuity risk.
• Early design of scalable, multi-agent architectures enables compounding benefits as additional processes are automated.

The client plans to advance to implementation following this discovery, focusing on accelerating SDS workflows, reducing regulatory risk, and building a platform for future AI-driven process improvements.

CONCLUSION

By partnering with Concurrency, this chemical manufacturer laid the groundwork for transforming SDS management from a manual, error-prone process into a scalable, AI-assisted workflow. Through strategic discovery and technology planning, the organization is now positioned to accelerate product launches, maintain regulatory compliance, and secure institutional knowledge for long-term operational continuity.