How Amgen Uses GPT-5 to Scale Pharma AI Workflows

Most companies think “AI in pharma” means molecule generation or a flashy research demo. Amgen’s GPT-5 story points to something more practical: using advanced AI to scale the everyday digital work that determines how fast science turns into impact.

Amgen’s adoption of GPT-5 matters because biotech is a high-stakes, high-regulation environment where mistakes are expensive—and slow processes cost real time. If a pharma leader can safely operationalize an AI system here, it’s a strong signal for what’s coming across U.S. technology and digital services more broadly.

This post is part of our “AI in Pharmaceuticals & Drug Discovery” series, but I’m going to focus less on hype and more on the operational reality: where GPT-5 fits, how teams set it up, and what other organizations can copy—even if you’re not building drugs.

Why Amgen’s GPT-5 adoption is a big deal for U.S. digital services

Answer first: Amgen’s GPT-5 implementation is notable because it shows how U.S.-based enterprises are embedding frontier AI into core workflows—not as a side experiment, but as a way to increase throughput, standardize work, and reduce cycle time.

Biopharma runs on documentation, coordination, and analysis. There’s research, yes—but there’s also a massive amount of writing, review, summarization, compliance mapping, and cross-functional communication. Those are exactly the activities where modern language models can produce immediate ROI.

This connects directly to the broader U.S. digital economy: once an organization learns to deploy AI safely in one complex domain (biotech), the same playbook travels well to insurance, financial services, government contractors, and any regulated digital service provider.

The underappreciated bottleneck: knowledge work at pharma scale

Drug discovery pipelines generate:

• Internal reports, protocols, and study documentation
• Scientific literature reviews and evidence summaries
• Clinical and regulatory narratives with strict formatting constraints
• Revisions and comments across legal, quality, medical, and safety teams

When you hear “AI accelerates drug discovery,” a large part of that acceleration is simply reducing the time it takes humans to turn information into decisions.

Where GPT-5 fits in pharma workflows (beyond research)

Answer first: GPT-5 is most valuable in pharma when it’s used as a “work compressor” for text-heavy processes—summarizing, drafting, classifying, and translating complex information into formats that teams can review and act on.

The OpenAI story page signals a real enterprise use case: Amgen is applying GPT-5 through the API, which usually implies controlled integration into internal tools rather than ad hoc consumer usage.

Below are practical, high-impact workflow categories where GPT-5 typically shows up in pharma AI workflows.

1) Scientific and medical summarization that stays auditable

Teams don’t just need summaries—they need traceability.

A strong pattern is:

1. Retrieve approved sources (internal documents, validated knowledge bases, or curated literature sets)
2. Ask GPT-5 to summarize with structured outputs (headings, evidence tables, “what changed” sections)
3. Require citations to the provided excerpts (not external browsing)
4. Route outputs into human review

This is especially relevant for medical affairs and clinical development teams that live in a world of version control and review cycles.

2) Drafting and revising regulated documents faster

Pharma documentation often has repetitive structure: background, methods, endpoints, safety, risk mitigation, appendices. GPT-5 can accelerate first drafts and revision passes if the organization enforces guardrails:

• Allowed templates only
• Approved language libraries for sensitive sections
• Redline-friendly outputs (what changed, why it changed)

In practice, the win isn’t “AI writes the document.” It’s AI reduces blank-page time and makes SMEs spend effort on judgment instead of formatting.

3) Internal support for teams (knowledge base + workflow help)

A lot of enterprise AI value comes from building an internal assistant that can:

• Answer process questions (“Which SOP applies?”)
• Locate relevant internal references (“Where is the latest protocol template?”)
• Explain acronyms and program context for new hires

This is how AI powers digital services inside an enterprise: it reduces interruptions, shortens onboarding, and keeps work moving.

4) Automation for “glue work” across tools

Biotech operations rely on ticketing systems, document repositories, and collaboration tools. GPT-5 is a natural fit for:

• Classifying requests and routing them to the right queue
• Summarizing long threads into action lists
• Extracting structured fields from emails or PDFs

This sounds mundane. It’s also where you typically find the fastest payback.

The real lesson: the value is in the system design, not the model

Answer first: The organizations that get results with GPT-5 treat it as one component of a controlled workflow—paired with retrieval, permissioning, and review—rather than a standalone chatbot.

If you’re evaluating AI in pharmaceuticals and drug discovery, this is the point many teams miss: model capability is only half the story. The other half is the scaffolding that makes outputs reliable enough for regulated work.

A practical “enterprise GPT” architecture that scales

A common pattern looks like this:

• Identity and access controls: users only see what they’re allowed to see
• Retrieval-augmented generation (RAG): responses grounded in approved internal content
• Structured outputs: JSON-like fields for downstream systems (e.g., risk category, summary, next step)
• Human-in-the-loop review: approvals and sign-off flows
• Logging and monitoring: who asked what, what sources were used, and what the model returned

Put bluntly: if you can’t explain where an answer came from, it doesn’t belong in a high-stakes pharma workflow.

What “good” looks like: outputs built for review

In regulated settings, I’ve found the best prompts don’t ask for “a summary.” They ask for:

• A one-paragraph executive summary
• A bullet list of claims
• A supporting evidence table (source excerpt + location)
• A risk/uncertainty section (“what we don’t know yet”)

That format makes it easy for reviewers to validate, correct, and approve.

Guardrails that matter in pharma AI (and translate to other sectors)

Answer first: To use GPT-5 responsibly in pharma, you need controls for privacy, validation, and change management—because the risks are operational and legal, not theoretical.

Amgen’s GPT-5 usage sits inside a U.S. healthcare ecosystem with strict expectations around patient safety, IP, and compliance. Even when you’re not working with direct patient data, you’re still managing sensitive program details and proprietary research.

Privacy and data handling: decide what never enters the model

Set clear rules for:

• Whether personally identifiable information is permitted (often it’s not)
• How confidential program names and assets are handled
• Redaction or tokenization for sensitive fields

A simple but effective approach: classify data first, then route it.

Validation: treat AI outputs as drafts until proven otherwise

High-performing teams set expectations early:

• The model can be wrong.
• The model can sound confident.
• The model’s job is speed, not authority.

Then they design workflows that force verification:

• Required citations to provided sources
• “Checkable” intermediate outputs (extracted facts before narrative)
• SME review before anything leaves the team

Change management: the hard part isn’t model rollout

Model rollouts fail when leaders skip two steps:

1. Training people on how to ask and how to review (prompting and verification)
2. Updating SOPs so AI-assisted work has an approved path

If AI use is unofficial, you’ll still get AI use—just without controls.

What other U.S. companies can copy from Amgen’s approach

Answer first: You don’t need Amgen’s scale to benefit from GPT-5—you need a narrow workflow, clean inputs, and a review path.

Here are repeatable starting points I recommend to teams building AI in pharma and biotech (and to adjacent digital services):

Start with one workflow that has three properties

Pick a process that is:

1. High-volume (happens weekly or daily)
2. Text-heavy (summaries, drafts, extraction)
3. Reviewable (a human can validate quickly)

Good examples:

• Literature triage and structured summaries
• Drafting meeting minutes into action items
• Converting unstructured notes into a standardized template

Measure impact with metrics people actually trust

If you want buy-in, track:

• Cycle time reduction: “days to first draft” or “hours to review-ready summary”
• Throughput: number of summaries/reports completed per week
• Quality signals: reviewer edits per document, rework rate
• Adoption: active users and repeat usage

One opinionated stance: avoid vanity metrics like prompt counts. They don’t map to business value.

Build for integration, not novelty

Frontier models shine when they’re embedded in the tools people already use:

• Document management systems
• Clinical operations platforms
• Ticketing and request queues

That’s how AI powers technology and digital services: it becomes part of the workflow fabric, not another tab to open.

People also ask: practical GPT-5 questions in pharma

Can GPT-5 help with drug discovery directly?

Yes, but the quickest wins are usually upstream and downstream of discovery—evidence synthesis, experiment documentation, and decision support. Discovery teams still benefit when they spend less time wrangling text.

Is GPT-5 safe to use in regulated environments?

It can be, if you implement access controls, grounding in approved content, logging, and mandatory review. “Safe” is less about the model and more about the workflow around it.

What’s the fastest pilot to prove value?

Start with summarization + structured extraction for a single document type (e.g., study reports or literature abstracts). Keep scope tight, then expand once reviewers trust the outputs.

What this means for the “AI in Pharmaceuticals & Drug Discovery” roadmap

GPT-5’s significance in a company like Amgen is straightforward: it signals that AI is becoming infrastructure for pharma knowledge work, not just a research tool. That shift is what will compound over time—faster decisions, fewer bottlenecks, and better reuse of organizational knowledge.

If you’re building AI in pharmaceuticals and drug discovery in the U.S., the next step isn’t chasing the most advanced demo. It’s choosing one workflow, grounding the model in trusted information, and designing reviewable outputs that your quality and regulatory partners can live with.

Where do you see the biggest bottleneck in your pipeline right now: literature review, document drafting, cross-team handoffs, or something else? That answer usually tells you where GPT-5 will pay off first.