AI Everywhere in Pharma: Lessons for U.S. Digital Teams

Most companies treat “AI adoption” like a single project: pick a tool, run a pilot, declare victory. Pharma is starting to prove that approach is too small.

Genmab’s “AI Everywhere” framing (even without the full public write-up available from the source page) captures a direction a lot of high-performing organizations are moving toward: AI as an operating model, not a side experiment. And it’s showing up far beyond the tech industry.

This matters for anyone building technology and digital services in the United States—SaaS leaders, IT teams, product owners, operations executives—because healthcare and biotech are some of the most complex, regulated, data-heavy environments in the economy. If AI can be made practical there, it can be made practical almost anywhere.

What “AI Everywhere” really means (and what it doesn’t)

“AI Everywhere” means AI is embedded into daily workflows across teams, not trapped in a center-of-excellence slide deck. It’s the shift from “Who’s our AI team?” to “How does every team work faster and safer because of AI?”

It doesn’t mean:

• Every employee building models
• Replacing regulated decision-making with black-box automation
• Rolling out one chatbot and calling it transformation

In pharma, the most valuable AI often looks boring from the outside: document drafting, literature triage, signal detection, data reconciliation, and query resolution. These tasks create measurable cycle-time wins because they sit on critical paths.

A practical definition: AI Everywhere is when AI reduces the time-to-decision across the business without increasing risk.

Why pharma is a good stress test

Pharmaceutical R&D and commercialization combine:

• Massive unstructured text (papers, protocols, submissions)
• High-stakes outcomes (patient safety, regulatory compliance)
• Long timelines and costly delays

If an “AI Everywhere” program can work under those constraints, it gives U.S. digital service teams a playbook for adoption in finance, insurance, manufacturing, and enterprise SaaS.

Where AI creates real leverage in pharmaceutical innovation

AI in pharma pays off when it compresses timelines and reduces rework. The biggest gains aren’t flashy predictions; they’re reliability and speed in processes that already exist.

1) Discovery and early research: speed matters, but traceability matters more

In discovery, teams face an attention problem: too much literature, too many hypotheses, too many experiments. Generative AI and machine learning help by:

• Summarizing research areas and surfacing contradictions
• Creating structured views of targets, pathways, and prior art
• Drafting experiment plans and translating them into checklists

The catch: every claim must be traceable. A useful system doesn’t just summarize; it shows what it used, what it ignored, and where uncertainty remains.

For U.S. digital services, the parallel is obvious: customers don’t need “smart”; they need auditable. If your AI can’t explain itself at the level your stakeholders require, adoption stalls.

2) Clinical development: documents, deviations, and operational drag

Clinical work is full of operational bottlenecks: protocol amendments, site communications, monitoring notes, deviation logs, and constant documentation.

AI helps most when it:

• Drafts and standardizes recurring content (without inventing facts)
• Flags inconsistencies across documents (e.g., endpoints and inclusion criteria)
• Speeds query resolution by pulling context from multiple systems

A strong stance here: document automation is underrated. In regulated environments, writing and review cycles dominate timelines. Reducing a review loop by even a day, repeated across programs, adds up quickly.

3) Regulatory and quality: the “digital services” side of biotech

Regulatory submissions are essentially a specialized digital publishing pipeline with strict controls.

“AI Everywhere” in this area looks like:

• Controlled drafting assistance with references and versioning
• Automated cross-checking (terminology, tables, consistency)
• Faster retrieval of prior responses and precedent language

This is where U.S. SaaS and digital service providers can learn the most: build AI that fits the control system. Permissioning, audit trails, and retention policies aren’t “nice to have”—they’re the product.

The operating model shift: from pilots to platforms

AI Everywhere requires a platform mindset: shared components, shared governance, shared measurement. In practice, that means building a stack that supports many use cases without rebuilding from scratch.

The three layers that make “AI Everywhere” scalable

1. Data layer

   • Clean, permissioned access to structured and unstructured sources
   • Metadata, lineage, and retention policies

2. Workflow layer

   • Where employees actually work: ticketing, document systems, CRM, LIMS, quality tools
   • Human-in-the-loop checkpoints by default

3. Model layer

   • A mix of foundation models and specialized models
   • Evaluation, monitoring, and rollback mechanisms

If you’re a U.S. digital leader, this is the practical takeaway: don’t measure AI success by number of demos. Measure it by number of workflows improved with reliable controls.

A December reality check: budgets and planning cycles

Late December is when teams lock roadmaps and budgets for Q1. “AI Everywhere” is a useful planning filter: instead of asking “What AI projects should we run?” ask:

• Which workflows cost the most time?
• Where does rework happen repeatedly?
• Where are we exposed to compliance or customer risk?

That’s how you move from experimentation to outcomes.

Governance that doesn’t kill momentum

The fastest AI programs are the ones with clear guardrails. In pharma, guardrails are unavoidable; in U.S. digital services, they’re often optional—until a customer audit or incident makes them mandatory.

What good guardrails look like

• Use-case tiering (low-risk internal vs. regulated external)
• Approved data sources (no copy-pasting sensitive content into random tools)
• Prompt and output logging for regulated workflows
• Red-team testing for hallucinations, data leakage, and prompt injection
• Clear accountability: who owns accuracy, who signs off, who monitors drift

A line I’ve found helpful: If you can’t audit it, you can’t scale it.

“People also ask”: Can generative AI be used in regulated pharma work?

Yes—when it’s used as an assistant, not the final authority. The winning pattern is:

• AI drafts, summarizes, or checks
• A qualified human reviews and approves
• Systems record what happened

That same pattern is now spreading across U.S. industries like insurance claims, financial operations, and enterprise support.

What U.S. tech and digital services can copy from “AI Everywhere”

The most transferable lesson is that AI adoption is a change-management problem disguised as a technology project.

Practical playbook: 90 days to “AI Everywhere” momentum

1. Pick 3 workflows, not 30 use cases

   • Choose high-volume, high-friction processes (support triage, compliance writing, sales enablement)

2. Instrument the baseline

   • Track cycle time, touches per ticket, error rates, rework loops

3. Build a “safe-by-default” assistant

   • Retrieval from approved sources
   • Citations to internal docs
   • Role-based access control

4. Deploy inside existing tools

   • If users must change tabs, adoption drops

5. Measure weekly, ship improvements biweekly

   • Treat prompts, templates, and retrieval as product features

The KPI set that actually reflects value

If you want leads (and credibility), talk in metrics buyers recognize:

• Cycle time reduction (hours saved per case; days saved per review cycle)
• First-pass quality (fewer revisions, fewer escalations)
• Deflection rate (tickets prevented through better self-service)
• Compliance performance (audit findings, policy violations, incident rate)
• Adoption depth (repeat users and tasks completed, not logins)

AI programs fail when they brag about model capability and ignore process outcomes.

The bigger series narrative: AI is powering U.S. digital services—by spreading into everything

This post fits squarely into the broader theme of How AI Is Powering Technology and Digital Services in the United States: the winners aren’t only “AI companies.” They’re organizations in healthcare, biotech, retail, and logistics that treat AI as a core competency and demand production-grade reliability.

Genmab’s “AI Everywhere” idea is a strong signal of where the market is headed in 2026: enterprise buyers want AI that lives inside their operations, supports their compliance needs, and makes employees faster without adding risk.

If you’re leading a U.S.-based digital service, the opportunity is clear:

• Build AI features that respect governance and data boundaries
• Start with workflows that create measurable time savings
• Make auditability and monitoring part of the product, not an afterthought

Where do you want “AI Everywhere” first—support operations, compliance workflows, or product development? Your answer will tell you what to build (and what to stop piloting).