Not in theory. In your workflows, your data, your regulatory environment.
Every life sciences organization is asking the same question: how do we move from AI experiments to AI that actually delivers — safely, defensibly, and at scale? The answer requires more than great technology. It requires a partner who understands what it means to operate in a regulated world.
01 — THE LANDSCAPE
The State of AI: Hype Has Given Way to Hard Questions
The conversation has shifted. A year ago, leadership teams across pharma, biotech, and medtech were asking whether AI was real. Today, they are asking something more urgent: how do we actually put it to work?
What we hear consistently from customers — from emerging biotechs running their first Phase II to global pharmaceutical companies managing hundreds of concurrent submissions — is a version of the same thing: we know AI can help, but we don’t know where to start, what to trust, or how to make sure we aren’t creating new risk in the process.
That hesitation is understandable. The technology is advancing faster than most organizations can absorb it. Vendors are making sweeping promises. And in life sciences, the cost of getting it wrong — a failed inspection, a compromised submission, a data governance breach — is not abstract. It can be existential.
At the same time, the organizations that are moving deliberately and thoughtfully are beginning to pull ahead. They are compressing document-intensive workflows that used to take weeks. They are enabling quality teams to stay ahead of inspections rather than react to them. They are giving commercial and regulatory teams access to the institutional knowledge they need, precisely when they need it.
“The question isn’t whether AI can help your organization. The question is whether you have a partner who understands what it means to put AI to work in a regulated environment — and can do it in a way you can stand behind.”
The industry also has an AI sprawl problem. Organizations are running multiple AI tools — ChatGPT, Copilot, point-solution agents, bespoke builds — often without a coherent strategy connecting them. The result is high spend, fragmented value, and governance gaps that carry real risk. The organizations moving fastest are not the ones with the most AI tools. They are the ones who have operationalized AI into their actual workflows, with the right data access, the right guardrails, and the right people guiding the process.
02 — AGENTIC AI
Operationalizing Agentic Workflows: What It Actually Means to Put AI to Work
There is a meaningful difference between using AI as a productivity tool and deploying AI as an operational asset. The first category — a chatbot that summarizes a document, a copilot that autocompletes an email — delivers incremental convenience. The second category changes how work gets done.
Agentic AI is the term the industry has landed on for this second category. Agents don’t just respond to prompts — they execute multi-step tasks, connect to enterprise systems, reason across data sources, and act autonomously within defined boundaries. They draft a clinical summary by pulling from your document management system, your QMS, and your regulatory history — not a general knowledge base. They prepare an inspection readiness report by synthesizing your actual SOPs, deviations, and CAPA data — not a template.
This is where many AI initiatives stall. The technology is capable. The enterprise infrastructure — data connectivity, permissions management, governance frameworks — is not yet ready to support it. Agents that can access everything but have no defined scope create risk. Agents that know nothing about your organization deliver no differentiated value.
Getting agentic AI to work in a life sciences organization requires bridging that gap: connecting AI to the right knowledge, building the right guardrails, and designing workflows that keep humans appropriately in the loop. In regulated environments, where the concept of GxP impact is real and the question of whether an AI-assisted output is audit-ready is never theoretical, this is not optional. It is the work.
Making AI defensible — ensuring that outputs are traceable, that decisions are explainable, and that the system behaves consistently — is what validation means in this context. It is what separates AI that an organization can actually rely on from AI that creates more exposure than it removes.
03 — THE PARTNERSHIP
Why USDM Partners with Glean — and Why Glean Partners with USDM
● USDM is the only Glean partner dedicated exclusively to life sciences. That distinction reflects a deliberate choice on both sides about how to serve a market that demands more than general-purpose AI deployment.
Glean is the enterprise AI platform built for how organizations actually work. It connects across 100+ enterprise systems — Veeva, SharePoint, Salesforce, Box, ServiceNow, and more — without moving or duplicating data. It respects existing permissions structures so the right people see only the right information. It delivers enterprise search, an AI assistant, and agentic workflows from a single unified platform, and provides access to more than 35 large language models through one interface — consolidating both spend and governance in a single layer.
What Glean provides in platform breadth, USDM provides in domain depth. USDM brings the life sciences expertise, the regulatory fluency, and the implementation methodology that makes Glean’s capabilities actually land in a regulated environment. That is not a layered add-on. It is a genuine division of capability that neither organization has alone.
| WHY USDM CHOSE GLEAN
The platform built for enterprise knowledge — at life sciences scale
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WHY GLEAN CHOSE USDM
The domain expertise life sciences demands
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For customers, this means a single partnership that can hold the AI conversation and the regulatory conversation in the same room — without needing two separate partners for the technology and its compliance implications.
04 — DOMAIN DEPTH
What USDM Brings: 25 Years of Trust — and the Expertise That Earns It
USDM has operated in life sciences for over 25 years. That tenure means something specific: we have been through enough regulatory submissions, inspections, system implementations, and organizational transformations to understand what actually matters — and what looks compelling in a demo but breaks down in a GxP environment.
When we deploy AI in a life sciences organization, we bring that history with us. We know which workflows carry GxP impact and which do not. We know how to design human-in-the-loop checkpoints that satisfy an auditor’s inquiry. We know how to document AI-assisted outputs in a way that is traceable, reproducible, and defensible — the same rigor that applies to any validated system.
Validating AI is not a compliance checkbox. It is the practice of making AI safe enough to rely on — ensuring the system behaves as intended, that its outputs can be interrogated, and that the organization can stand behind them. In life sciences, that is not optional. It is the difference between AI that accelerates your business and AI that creates a liability you did not anticipate. USDM has developed governance frameworks and GxP impact assessment methodologies that translate these principles into operational practice — not just policy language.
We also bring something no technology platform can replicate: the credibility that comes from being a trusted partner across the life sciences ecosystem over decades. Our customers talk to each other. Our implementations are referenceable. Our team carries the domain fluency — in clinical operations, regulatory affairs, quality, commercial, and IT — to engage at the level where decisions are actually made.
USDM puts AI to work for life sciences. That means not just deploying technology — it means deploying technology that your organization can trust, your quality team can defend, and your leadership can rely on to deliver.
05 — HIGH-VALUE USE CASES
Where We’re Putting AI to Work Right Now
The following represent the highest-value use cases we are delivering with customers today — built on Glean’s platform, deployed with USDM’s life sciences methodology, and designed to operate safely within regulated workflows.
| CSR Drafting & Medical Writing
AI agents that synthesize clinical study data, TFLs, and regulatory history into structured first-draft CSR sections — freeing medical writers from document assembly so they can focus on higher-judgment work. |
Enterprise Knowledge Search
Connecting employees to institutional knowledge across Veeva, SharePoint, Box, Salesforce, and more — with permissions respected and AI surfacing the right answer, not just a list of documents to manually sift through. |
Inspection Readiness Agents
Agents that proactively surface open deviations, CAPA status, audit trails, and documentation gaps — so quality teams stay ahead of inspections continuously, rather than scrambling in the weeks before one arrives. |
| Commercial & Sales Intelligence
AI that gives commercial teams instant access to account history, competitor intelligence, clinical data, and market access information — turning pre-call preparation from a research burden into a quick briefing. |
Cross-System Integration
Connecting AI across the full technology stack — Veeva Vault, QMS, ERP, CRM, CTMS, and more — so agents operating on submissions, deviations, or regulatory responses have access to every relevant data source in context. |
Regulatory Intelligence & Response
AI assistants that monitor regulatory agency activity, surface relevant guidance updates, and help regulatory affairs teams draft faster, more consistent responses to health authority inquiries. |
Across each of these use cases, the same design principles apply: the AI operates on your data, within your permissions structure, with human oversight positioned where the stakes require it. In every GxP-adjacent workflow, outputs are documented and traceable — the standard that separates a working AI deployment from a pilot that never earns organizational trust.
“The goal is not to automate everything. It is to put AI to work on the highest-value, most time-intensive tasks — so that your best people can focus on the decisions that actually require human judgment.”
The organizations seeing the greatest returns are not asking AI to replace their teams. They are using AI to dramatically amplify what their teams can accomplish — compressing document-intensive workflows, enabling proactive quality management instead of reactive crisis response, and giving every function the institutional knowledge it needs to move faster and with more confidence.
06 — WHAT’S NEXT
The Moment to Move Is Now
The AI advantage in life sciences is not permanent. Use cases for organizations building real capability today — connecting their knowledge, deploying agents against high-value workflows, and establishing the governance frameworks that make AI defensible — are building a lead that compounds. The organizations waiting for a clearer picture are, in practice, falling behind.
USDM and Glean are uniquely positioned to help life sciences organizations move from AI strategy to AI execution — with the platform capabilities, the domain expertise, and the implementation methodology to do it right. Not just AI that works in a demo, but AI that works in your environment, with your data, under your regulatory obligations.
If your organization is asking how to start, how to scale, or how to make what you have already built more defensible — this is the conversation USDM is built to have. Engage USDM today.
USDM + GLEAN
USDM Puts AI to Work
for Life Sciences.
The only Glean partner dedicated exclusively to life sciences.
25+ years of life sciences trust. Deep GxP expertise. The implementation methodology to make AI defensible, scalable, and real. Let’s start with your highest-value use case.
