AI in life sciences: Practical use cases for research, regulatory, and clinical teams

Artificial intelligence is reshaping the life sciences industry — not through hype or distant promises, but by solving real problems that slow innovation, increase risk, and inflate costs. AI is helping teams spend less time searching for answers - accelerating discovery and clinical development, confident regulatory submissions, resilient supply chains, and better-informed commercial decisions - and more time making breakthroughs. 

‍Life sciences organizations are under pressure. Data volumes are growing. Regulations are evolving. And critical knowledge is scattered across dozens of disconnected systems. Moving a single project forward can require combing through hundreds of pages of protocols, reports, and documentation. That’s why AI has become indispensable across multiple functions - research, clinical, regulatory, supply chain & manufacturing, and GTM teams.

What is AI in life sciences?

AI in life sciences refers to technologies like large language models (LLMs), machine learning, and natural language understanding that help teams access, analyze, and act on complex scientific, operational and regulatory information. . Consulting analyses suggest that GenAI alone could unlock $60-110B in annual value for life sciences companies by boosting R&D productivity, speeding clinical development, and improving commercial execution.

Unlike traditional automation, which follows structured workflows and predefined rules, AI can interpret unstructured content and respond to ad hoc requests — whether that means answering a scientific question, tracing a cold-chain excursion, or summarizing a payer dossier. 

AI isn’t replacing scientists or compliance teams - but it can help reduce the estimated ~30% of time scientists and operations staff spend simply searching for information. It’s helping them work more efficiently by doing what systems and static dashboards can’t: finding the right version of an SOP, retrieving experiment results, surfacing shipment and stability records, summarizing protocols, or locating supporting documentation buried across disconnected tools. The result is faster decision-making, better compliance readiness, and stronger use of institutional knowledge.

AI in research and discovery

Accelerating early-stage discovery

Scientific knowledge is often scattered across electronic lab notebooks, SharePoint drives, publication databases, and internal reports. AI helps researchers search across all of these systems in plain language — making it easier to find prior work, avoid redundancy, and uncover insights from past studies. These benefits compound in life sciences, where only an estimated 10-14% of drug candidates that enter clinical trials reach the market - so every avoided re-run and faster decision matters.

For example, a scientist exploring a new compound might ask, “Have we done any research on GXA-91?” and receive a summarized view of related internal experiments, formulation documents, and published literature. That context can reduce duplicative work and accelerate hypothesis generation.

Reusing validated methods and protocols

Planning a new experiment doesn’t always start from scratch, but it can feel that way when documentation is hard to find. AI helps teams resurface validated protocols, historical data, and the rationale behind past decisions. This makes it easier to build on proven methods, reduce trial-and-error, and plan smarter using what the organization already knows.

AI in clinical development

Supporting trial coordination and clinical onboarding

Clinical teams often need to review eligibility criteria, deviation logs, and training materials, but that information is spread across systems like SharePoint and Box. AI helps by retrieving and summarizing key documents with full source context, so site coordinators can get up to speed quickly and with confidence. One Tufts-led analysis found that substantial protocol amendments add a median 65 days to implementation timelines - and other studies estimate direct costs in the hundreds of thousands of dollars per amendment in late-stage trials.

A trial manager might ask, “What’s the I/E criteria for Protocol CT-123B, and how many deviations have been logged?” and get an accurate, up-to-date answer with links to supporting records, reports, and procedures.

Improving compliance and data consistency

During audits or inspections, it’s critical to surface the right SOPs, change control logs, and training history without delay. AI reduces the time teams spend tracking down these materials by enabling permission-aware search across systems, with version control and traceability built in. That means faster answers, cleaner audit trails, and fewer surprises when regulatory deadlines hit.

AI in regulatory and quality management

Enabling faster submission preparation

Regulatory submissions can exceed 100,000 pages, pulling together documentation from dozens of sources. Teams must locate CMC records, prior correspondence, labeling files, and other supporting materials — often while juggling multiple systems and tight timelines.

AI simplifies that process. Instead of relying on institutional memory or manual search, regulatory affairs teams can ask targeted questions and retrieve validated, signed documents with full source context. Whether compiling an IND, NDA, or responding to a Health Authority query, they can move faster and stay confident in the accuracy of every file.

Maintaining audit readiness

For quality teams, staying inspection-ready means being able to produce the right SOPs, batch records, or CAPA documentation without delay. Reviews of FDA Form 483 data show that laboratory controls and records consistently rank among the most common GMP observation categories worldwide, underscoring how often data-integrity and documentation gaps surface in inspections. But as standards evolve — from FDA 21 CFR Part 11 to EMA IDMP — and documentation grows more complex, preparation becomes more burdensome.

AI helps manage that complexity. It centralizes access to version-controlled records and organizes submission artifacts so they’re easy to locate, verify, and share. That means less time spent preparing for audits and more time ensuring products meet the highest quality standards.

AI in supply chain and manufacturing

Supporting cold-chain and distribution investigations

Cold‑chain networks are critical for the effective distribution of biologics and cell and gene therapies. They generate massive trails of sensor data, lane maps, deviation reports, and CAPA records - along with disruptions that cost the industry an estimated $35B per year. When a temperature excursion occurs, supply‑chain and quality teams must quickly assemble logger files, shipping documents, stability protocols, and batch‑release criteria from many systems to decide whether to quarantine, re‑test, or scrap product.

AI streamlines that work. Instead of manually stitching evidence from Benchling sample records, SharePoint deviation logs, sensor CSVs, Box shipping PDFs, courier emails, Slack channels, and Jira CAPA tickets, teams can ask targeted questions and get a unified, source‑linked view of each excursion. That helps cut investigation cycle time, avoid unnecessary write‑offs, and strengthen GDP compliance.

Strengthening batch release and CDMO oversight

Manufacturing and MSAT teams rely on thousands of SOPs, master batch records, deviations, CAPAs, and training logs spread across MES/ERP, QMS, document repositories, and partner portals. Regulators and manufacturers increasingly track first-pass batch-release rate and right-first-time documentation as board-level KPIs, because each delayed or rejected lot ties up significant working capital and heightens inspection risk. Coordinating across internal sites and CDMOs makes it hard to see the full evidence chain behind each batch, understand recurring issues, or prepare for sponsor and regulatory audits.

AI helps bring that context together. By centralizing access to version‑controlled SOPs, batch and deviation records, CAPA histories, and training attestations, it enables teams to ask questions like “What evidence supports release of this lot?” or “Show similar deviations and CAPAs for this failure mode across our CDMO network.” The result is faster, more consistent release decisions, better tech‑transfer outcomes, and fewer surprises during GMP and sponsor inspections.

AI in GTM and commercial teams

Elevating evidence-based campaigns and content

GTM and commercial teams in life sciences depend on a steady flow of accurate, approved content: clinical and economic evidence, case studies, slide tracks, objection‑handling guides, and payer dossiers. Today, that material is often spread across CRM, content repositories, slide libraries, and shared drives, which makes it difficult to quickly assemble targeted campaigns or thought‑leadership pieces for specific segments or accounts.

AI can act as a connective layer across that content. Instead of starting from a blank page or hunting through folders, marketers and medical or commercial teams can ask questions like “Which studies and real‑world evidence support this indication for oncologists in the US?” or “Show me past campaigns and case studies for accounts similar to this payer or IDN.” AI can then surface the most relevant materials — along with their sources and approval status — and help draft outlines, emails, or slide tracks that stay aligned to brand, regulatory, and medical‑legal guidance.

Equipping field and account teams

Field teams and account managers need fast, reliable answers in front of customers and HCPs: the latest label information, key clinical results, safety and tolerability data, pricing and access details, and summaries of prior interactions. That information lives across CRM, medical information systems, MLR‑approved content hubs, email, and meeting notes — making it easy to miss context or default to generic messaging.

AI brings that context together. With a single query, a rep can pull a concise, source‑linked briefing: recent conversations, open issues, relevant studies, approved slide tracks, and payer or formulary status — all governed by existing permissions and approval workflows. That reduces prep time, supports more tailored discussions, and helps ensure teams stay on‑label and on‑message while still meeting customers where they are.

Why Glean for life sciences

Glean’s Work AI platform is built to meet the needs of regulated industries like life sciences. Unlike generic AI copilots, Glean connects directly to the systems your teams already rely on — including Veeva, Benchling, SharePoint, Box, Jira, and more — to index knowledge with full context, permission controls, and audit traceability.

Instant access to enterprise knowledge

Whether you’re a scientist searching for assay results, a QA lead retrieving SOPs, a commercial lead preparing for a customer meeting, or a trial manager onboarding a new site, Glean delivers fast, contextual answers from across your tools. You can ask questions in natural language and get a direct answer, along with the exact file, author, timestamp, and location.

When timelines tighten or an unannounced audit arrives, Glean helps teams stay inspection-ready. It surfaces the right version of the right document — so teams can respond confidently and avoid delays.

Designed for compliance and trust

Glean was built for environments with strict regulatory oversight. It:

• Respects user permissions and role-based access controls
• Maintains a traceable audit trail for every query
• Enforces version awareness to ensure source-level accuracy

These safeguards help organizations meet GxP requirements without creating operational overhead.

Built to work across your AI stack

Many life sciences organizations are already experimenting with tools like Microsoft Copilot, ChatGPT Enterprise, and Gemini as part of their broader AI strategy. Glean complements those investments by acting as the system of context across the enterprise. Glean connects to 100+ systems - including Microsoft 365, Google Workspace, Benchling, and Box - works across all the three major cloud providers, and provides access to leading models to ensure customers aren’t locked in to one productivity suite or model provider. That means life sciences teams can keep using the tools they already know, without migrating data or changing the way they work - whether that’s inside a lab notebook, a quality system, a logistics channel or a commercial workspace..

Getting started with AI in life sciences

You don’t need to overhaul your workflows to benefit from AI. When protocol amendments can cost $500K+ and add months to timelines, and when each new drug may require close to $2B in R&D spend, organizations can save millions of dollars across their portfolio by applying AI to everyday tasks — like finding a protocol, preparing a regulatory response, assembling content for a field campaign, investigating a cold-chain excursion or locating the right batch release document.

To get started:

• Look for teams slowed down by search fatigue — such as regulatory, quality, manufacturing, supply chain, marketing or R&D
• Prioritize repeatable, documentation-heavy use cases where accuracy and compliance matter
• Choose a platform that integrates with your systems, enforces security and permissions, and delivers answers quickly

Bringing AI to the frontlines of life sciences work

The challenge in life sciences isn’t a lack of information. It’s accessing the right information at the right time — without slowing down research, disrupting operations or putting compliance at risk. That’s where AI delivers real value. It doesn’t replace expertise. It unlocks it.

With the right foundation, AI helps teams move faster, stay audit-ready, and focus on what they do best. Whether they’re developing new therapies, running clinical trials, equipping field teams with the latest evidence, or managing quality at scale, AI keeps work moving.

It doesn’t need to be disruptive to make a difference. It just needs to work where your teams already do. That’s what Glean makes possible.

Ready to see it in action? Get a demo to see how Glean supports your teams across research, regulatory, and clinical operations. Or download the healthcare whitepaper to learn more about how Glean enables secure, AI-powered transformation in highly regulated environments.