AMGNAmgen Inc.
AI adoption · Q1 2026 earnings call
Health CareScaling
14
extracted from this call
4 / 5
quantified with specifics
Not disclosed
no breakout in this call
Amgen discussed AI as a cross-enterprise transformation initiative spanning R&D, manufacturing, and regulatory functions, with Jay Bradner assuming leadership of AI and data activities following David Reese's retirement. Management cited several specific operational outcomes including a 50% acceleration in antibody lead optimization, up to 3x improvement in clinical trial enrollment rates via a proprietary site selection model, and a reduction in manufacturing line clearance time from ~30 minutes to ~2 minutes. Large language models and Agentic AI are being piloted for regulatory filing preparation, described as early-stage but promising.
Adopter
See full leaderboard →60/ 100
78
stage: scaling · max spec: 4
55
3 quant outcomes
35
1 scope
internal_use
14 AI mentions from this call.
Extracted verbatim from the AMGN Q1 2026 earnings call transcript. Speaker, section, and specificity tier surfaced for each mention.
- T4Prepared remarks· Other· Internal use
“In clinical development, we have designed and implemented a proprietary site selection model that improves clinical trial enrollment with a significant and in some cases, up to threefold improvement in enrollment rates.”
— James Bradner, AMGN earnings call - T4Prepared remarks· CFO· Internal use
“In AI-enabled automation, it has reduced production line clearance time at one of our manufacturing sites from approximately 30 minutes to about 2 minutes per batch run.”
— Peter Griffith, AMGN earnings call - T4Prepared remarks· Other· Internal use
“Antibody lead optimization has accelerated by 50% from contributions both to lead discovery and lead optimization.”
— James Bradner, AMGN earnings call - T3Prepared remarks· Other· Internal use
“Integrated multi-omics data resources at Amgen deCODE Genetics identify new targets for therapeutic consideration, in particular, within noncoding regions of the human genome studied at population scale.”
— James Bradner, AMGN earnings call - T3Prepared remarks· Other· Internal use
“Leveraging large language models and Agentic AI for regulatory filing preparation, we are seeing early promising results in data ingestion, integration and document drafting.”
— James Bradner, AMGN earnings call - T2Prepared remarks· Other· Internal use
“Over the last several years, amidst the rapid advances in artificial intelligence, we've taken a principled approach to reconsidering and augmenting drug discovery and therapeutic development. At the intersection of powerful AI models developed both externally and internally with Amgen research and insight-rich proprietary data sets, we are beginning to see meaningful tangible advances across Amgen R&D.”
— James Bradner, AMGN earnings call - T2Prepared remarks· CEO· Internal use
“we took steps early on to have Dave Reese lead these efforts, and we're grateful to him for the success he's achieved with this initiative. And we're encouraged that Jay Bradner will build on Dave's accomplishments, leading our artificial intelligence and data activities across the company.”
— Robert Bradway, AMGN earnings call - T2Prepared remarks· CEO· Internal use
“recognizing long ahead of many others the growing importance of AI and what he called the Hinge moment, Dave both raised his hand to be Amgen's first Chief Technology Officer and helped attract Jay Bradner to be his successor as Head of R&D.”
— Robert Bradway, AMGN earnings call - T2Prepared remarks· Other· Internal use
“With the retirement of our revered and beloved colleague, David Reese, I'm excited to lead the AI and data transformation across our business at the enterprise level, working in partnership with our leadership, staff and collaborators.”
— James Bradner, AMGN earnings call - T1Prepared remarks· CEO· Internal use
“We've talked about the excitement we feel about the convergence of technology and biology, including the application of artificial intelligence across the company. Here, too, we're making great progress. And there's no question we're in a period of tremendous change, and we're encouraged by the progress we're making in embedding new capabilities.”
— Robert Bradway, AMGN earnings call - T1Prepared remarks· Other· Internal use
“These are early innings, but we are captivated by the potential for AI and data science to deliver measurable impact and value in R&D and across the enterprise, as Peter will highlight in a few moments.”
— James Bradner, AMGN earnings call - T1Prepared remarks· CEO· Internal use
“we're thrilled, excited about the progress that we made in artificial intelligence and data under Dave's leadership as well as the other businesses that Dave has had responsibility for.”
— Robert Bradway, AMGN earnings call - T1Prepared remarks· CFO· Internal use
“We see technology and artificial intelligence as increasingly important tools to help Amgen operate with greater speed, productivity and scale across the enterprise.”
— Peter Griffith, AMGN earnings call - T1Prepared remarks· CFO· Internal use
“We are also seeing promising results as our colleagues across Amgen use AI to enhance productivity.”
— Peter Griffith, AMGN earnings call
What management wouldn’t quantify.
Analyst questions where management declined to share a specific number. The pattern of refusals is often as informative as the disclosures.
- No quantification of AI-related capex or opex investment provided; AI spending is embedded within broader R&D and capex figures.
- No disclosure of AI revenue contribution or cost savings in dollar terms.
- Manufacturing automation benefit (line clearance time reduction) cited for only one unnamed site; no indication of how broadly this has been or will be deployed.
- Productivity benefits from employee AI use described as 'promising' with no metrics provided.
- No detail on which external AI models or platforms are used (e.g., specific LLM providers for regulatory filing preparation).
- No timeline or quantification provided for the regulatory filing LLM/Agentic AI pilot outcomes.
- deCODE Genetics multi-omics AI target identification described qualitatively with no pipeline output metrics.
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