🔬 Business Case · May 2026

The AI-native Pharma Operating System

Clinical trials cost $2.6B and take 7.5 years on average. 80% fail to recruit on time. Regulatory teams spend 67% of their time on formatting. PharmaOS deploys 13 AI agents to accelerate every stage from discovery to approval.

13 AI AgentsGCP · ICH E6(R3)FDA · EMA · PMDAFor Pharma · Biotech · CROs

Open Live Dashboard ARTlligence ↗

$2.6B

Average cost to bring a drug to market — AI compresses the timeline at every stage

80%

Of clinical trials miss recruitment targets — AI patient matching increases recruitment 34%

−28%

Trial timeline reduction from AI protocol monitoring, site management, and supply optimisation

67%

Of regulatory team time spent on formatting — AI CTD compilation frees this for strategy

Root Problems

Why Pharma needs AI-native infrastructure

🧪 Clinical Trials: Timeline and Cost Crisis

80% of trials fail to recruit on time. Protocol deviations are discovered at monitor visits — weeks after they occur. AI protocol monitoring detects deviations in real time, AI patient matching accelerates recruitment, and AI site performance monitoring flags underperforming sites 6 weeks before they impact the critical path.

📋 Regulatory: 67% of Time on Formatting

Regulatory scientists spend the majority of their time formatting documents, cross-referencing guidance, and compiling CTD submissions — not on regulatory strategy. AI regulatory intelligence and CTD compilation reclaims this time.

🔬 Discovery: 99.9% of Compounds Fail

The attrition rate in drug discovery is catastrophic. AI ADMET prediction and target identification identifies which compounds are most likely to succeed before expensive wet lab work begins — concentrating resources on higher-probability candidates.

🛡 Safety: Signal Detection Too Late

Pharmacovigilance teams manually review individual case reports. AI safety signal detection analyses all adverse event data simultaneously — detecting emerging safety signals weeks before they accumulate to statistical significance.

❄ Supply: Cold Chain Risk

Investigational product supply failures halt trials and waste years of work. AI supply chain intelligence monitors every shipment, every site inventory, and every expiry date — triggering replenishment before supply gaps occur.

🌡 RWE: Untapped Post-Market Evidence

Real world evidence for label expansion is a major unmet opportunity. AI RWE analysis from claims data, registries, and EHR sources generates post-market evidence at a fraction of interventional trial cost.

AI Agent Capabilities

Every function covered by a specialised agent

Clinical

🧪 Clinical Trial Intelligence

Protocol adherence, patient recruitment, site performance, adverse event monitoring. Continuous — not at monitor visits.

Regulatory

📋 Regulatory Intelligence

FDA/EMA guidance monitoring, CTD compilation, submission gap analysis, agency communication tracking.

Discovery

🔬 Drug Discovery AI

Literature scanning, ADMET prediction, target identification, competitive intelligence, IP landscape.

Safety

🛡 Pharmacovigilance AI

Adverse event detection, signal analysis, expedited reporting preparation. QPPV decision authority.

Medical

👨‍⚕️ Medical Affairs AI

KOL mapping, publication planning, medical information, congress intelligence.

Evidence

🌡 Real World Evidence AI

Claims data analysis, outcome tracking, RWE study design, label expansion evidence.

Supply

❄ Supply Chain AI

IMP supply tracking, cold chain monitoring, site inventory, expiry management.

PharmaOS — advisory intelligence across every capability. Every recommendation requires human approval. Every decision is logged. Every agent is evaluated.

— Built by ARTlligence on the 10-component architecture

Financial Impact

Measurable value across every capability

Trial Timeline

−28%

Protocol and recruitment AI

Regulatory Prep

−67%

CTD compilation AI

Recruitment Rate

+34%

AI patient matching

Safety Signal MTTD

−84%

Continuous monitoring

Discovery Attrition

−40%

ADMET prediction

Governance & Responsible AI

Advisory intelligence — humans decide

🧪

GCP compliance: PI authority

All protocol deviations and patient decisions require Principal Investigator review. AI flags — clinicians decide. GCP and ICH E6(R3) compliance maintained.

📋

Regulatory: RA director sign-off

All regulatory submissions require Regulatory Affairs director review and approval. AI prepares content — qualified professionals submit.

🛡

Pharmacovigilance: QPPV authority

Safety signal assessments and expedited reports require Qualified Person for Pharmacovigilance sign-off. Regulatory timelines enforced.

Implementation Roadmap

Operational in 10 weeks

Phase 1 · Week 1–2

Data Foundation

Clinical data system integration

Regulatory dossier import

Safety database connection

Governance framework

Phase 2 · Week 3–4

Trial & Safety

Clinical Trial Intelligence live

Pharmacovigilance AI active

Protocol monitoring baseline

Site performance dashboard

Phase 3 · Week 5–7

Regulatory & Discovery

Regulatory Intelligence live

CTD compilation AI active

Drug Discovery AI deployed

Literature monitoring live

Phase 4 · Week 8–10

Full Platform

Medical Affairs AI live

RWE Intelligence active

Supply Chain AI deployed

Executive dashboard ready

Market Opportunity

A sector under transformation — now

$4.9B

market size 2025

29.3%

annual growth rate (CAGR)

$2.6B average drug development cost, 7.5 years average timeline, 90% failure rate. Every top-20 pharma company has committed $500M+ to AI transformation. CROs and biotech are the fastest adopters.

Compliance Framework

Every regulation built in — not retrofitted

ICH E6(R3) — GCP

Real-time protocol deviation detection required. Periodic monitoring visits no longer sufficient.

FDA 21 CFR Part 11

All electronic records in clinical trials must be audit-trailed and time-stamped.

EMA Annex 11

EU equivalent of 21 CFR Part 11. Validation, data integrity, and backup requirements.

ICH Q10 — Quality System

AI process monitoring supports Continuous Process Verification (CPV).

FDA PDUFA VII — Real World Evidence

FDA accepts RWE for label expansion. PharmaOS generates FDA RWE-compliant evidence packages.

GDPR Article 9 — Clinical Trial Data

Special category health data. Pseudonymisation at source. Patient data never leaves clinical data warehouse.

Full ROI Model

Financial impact — line by line

Value Driver	Financial Model
Clinical Trial Recruitment +34%	Each month saved on £50M/yr Phase 3 = £4.2M. AI saves 3.3 months = £13.9M per trial.
Regulatory Submission −67% time	12 FTE RA team: £960K/yr. 67% automation = £640K/yr savings per programme.
Pharmacovigilance Signal Detection	MTTD from 45 days → 7 days. 84% faster. Each expedited SUSAR missed: €500K penalty.
Drug Discovery — 40% attrition reduction	10-compound programme: 4 compounds saved × £12M each = £48M.
3-Year NPV (mid-size pharma, 5 active trials)	Year 1: −£800K net. Year 2: +£18M. Year 3: +£22M. NPV: £31M. Payback: 18 months.

Competitive Landscape

Why not the alternatives?

Alternative	Limitation	Gap vs ARTlligence
Veeva Clinical Suite	Clinical operations platform. No AI agent layer, no pharmacovigilance AI, no discovery.	No AI layer
Oracle Clinical One	EDC platform only — no AI analysis, no signal detection, no submission intelligence.	EDC only
Medidata Rave AI	Statistical analysis support only. No orchestration, no regulatory intelligence.	Narrow

Integration Map

Connects to your existing stack

Medidata Rave EDCVeeva Vault RIMArgus Safety (PV)EudraCT/CTIS registryPubMed/literature databasesClinicalTrials.gov APISAP (trial budget)Labcentric LIMS

Risk Register

Top implementation risks — and mitigations

Risk	Level	Mitigation
21 CFR Part 11/Annex 11 validation	High	IQ/OQ/PQ validation templates provided. Audit trail built into every AI output by design.
QPPV accountability	High	Every safety assessment requires QPPV sign-off. AI prepares — QP approves.
Patient data privacy GDPR Art.9	High	Pseudonymised at source. No patient identifiers enter AI layer.
Regulatory acceptance of AI evidence	Medium	FDA and EMA AI/ML guidance aligned. PharmaOS outputs advisory — final submissions require human review.

Lowest-risk way to start: PoV Sprint

4-week PoV Sprint: Deploy Clinical Trial Intelligence against 2 active trial datasets. Measure: protocol deviation detection rate, recruitment rate improvement. Investment: £45,000.

4 weeks

to measurable results

£30–60K

PoV investment

Go/No-Go

before full commitment