The Challenge: Licensed AI Capability Without a Regulated Path to Use It
The biopharmaceutical company already had Box Enterprise Advanced and Shield Pro licensing, including high-value capabilities such as Relay, Shield, Hubs, Apps, Metadata Extraction, and Agent Studio. The issue was not access to technology. The issue was whether Legal could use those tools in a controlled, validated, and audit-ready way.
Legal also had an immediate operational problem. Roughly 1,000 legacy contracts were spread across shared drives with no consistent metadata for parties, dates, contract type, expiration terms, or renewal obligations. Expiring agreements were tracked manually, and the team lacked a structured intake, review, approval, and execution workflow.
In a GxP environment, enabling AI without governance creates real risk: unclear intended use, uncontrolled prompts or outputs, weak auditability, and avoidable inspection exposure. The company needed a path that would let Legal use the AI capabilities it had already paid for without stepping outside regulated controls.
USDM's Approach: Validate the Platform, Then Govern the AI Use Case
USDM supported the work in two connected phases: first establishing a validated Box foundation, then building a Legal Contracts Intelligence Hub on top of that foundation.
For the Box Enterprise Advanced and Shield Pro validation effort, USDM updated the validation plan, user requirements, configuration specification, IQ and PQ protocols, traceability matrix, and validation summary report. The team also updated leveraged Box Validation Accelerator Pack content, including the functional requirements specification, FRS traceability matrix, and high-level risk assessment.
USDM aligned Sandbox and Production configuration for GxP-relevant settings and supported IQ execution in Sandbox, along with Production and PQ execution support. The result was a controlled validation path for the Box environment before AI-enabled legal workflows went live.
USDM also delivered the governance foundation needed for regulated AI adoption:
- AI Governance and Risk Assessment Framework to define how AI use cases should be assessed, controlled, and monitored.
- AI Roadmap and Use Case Matrix to prioritize where AI could deliver value while staying inside the company's risk tolerance.
- GxP Use Case Decision Tree to help teams decide when an AI-enabled workflow requires additional validation, documentation, or oversight.
The Legal AI Hub: From Contract Sprawl to Controlled Intelligence
With the validated Box foundation in place, USDM built a Legal AI Hub designed around contract operations. The work included ingesting approximately 1,000 legacy agreements, applying metadata extraction, designing folder structure and retention logic, defining tags and permissions, and configuring hub-specific prompts, tasks, access controls, and AI guardrails.
The hub was designed to make contract work easier without making it less controlled. USDM configured workflows for contract intake, expiration alerts, review, approval, and execution. Dashboards gave the team visibility into contract aging and status, replacing scattered manual tracking with a more structured operating model.
Because the hub used AI in a regulated context, USDM also delivered a GxP Assurance Package for the Legal AI Hub itself. That package included validation planning, scripts, reporting, a GxP impact assessment, metadata-extraction accuracy testing, controls and auditability verification, and an AI drift and bias monitoring plan.
What Changed
Before USDM, Legal had licensed AI features that were effectively blocked by governance and validation concerns. Contract data was fragmented, expiration tracking was manual, and review workflows were not structured enough for scalable, auditable operations.
After the engagement, the company had a validated Box Enterprise Advanced and Shield Pro environment, a live Legal AI Hub, metadata-tagged contract content, configured AI guardrails, and workflows for contract intake, renewal visibility, review, approval, and execution. Legal could begin using AI-enabled capabilities with a defensible control model instead of ad hoc experimentation.
Business Impact
The engagement gave the company a practical way to turn paid-for AI capability into a working regulated solution. Legal gained a better way to manage contract volume, expiration risk, metadata quality, and workflow visibility. IT and Box administrators gained a validated configuration path. QA and validation stakeholders gained traceable evidence and a governance model for future AI decisions.
The model is also reusable. The AI Governance and Risk Assessment Framework, AI Roadmap and Use Case Matrix, GxP Use Case Decision Tree, and drift and bias monitoring plan can support future AI-enabled hubs in functions such as Quality, Regulatory, Clinical, and IT.
Why It Matters for Life Sciences AI Adoption
Many life sciences companies already own AI-enabled platform capabilities inside enterprise systems. The hard part is making those capabilities usable in regulated work. That requires more than feature activation. It requires intended-use clarity, risk-based validation, role-based access, data controls, workflow design, monitoring, and audit-ready evidence.
USDM helped the company bridge that gap. The result was not AI for its own sake. It was a validated, governed, and usable AI-enabled contract management process that Legal could operate with more confidence.
Build AI Workflows That Can Stand Up to Review
USDM helps life sciences organizations validate AI-enabled platforms, define appropriate GxP controls, and build workflows that are useful, governed, and inspection-ready. If your team has AI features available but not safely enabled, talk to USDM about building the governance and validation path.