AI agents are rewriting biopharma’s $140B playbook - ending manual, outsourced drug development

For decades, biopharma has relied on outsourcing as the default solution to complex problems. CROs, CDMOs, and specialized consultants became extensions of the pipeline, offering speed and predictability, but at the cost of moving critical capabilities outside pharma’s walls. That reliance has ballooned into a $140 billion market that grows every budget cycle. CROs alone are projected to nearly double, from $70B in 2025 to $126B by 2034. Today, roughly half of all pharma R&D spending flows to external providers. Outsourcing isn’t just part of drug development; it is drug development.

But the cracks are showing. Despite billions invested, outsourced workflows are bloated and fragile. PDFs ping-pong through inboxes. Lab notes are retyped multiple times. Critical insights vanish into spreadsheets and file vaults. The result: slow, error-prone, and unsustainable processes in a world that demands speed and precision more than ever.

Meanwhile, pharma is staring down a $236 billion revenue cliff as some patents expire over the next five years. Leaders face mounting pressure to squeeze every ounce of output from their teams while hiring curves stay flat, all during one of the most aggressive waves of layoffs the industry has ever seen. Simply layering on more outsourced services is no longer a viable answer. That’s why pharma leaders are turning to AI, not as a side experiment, but as a fundamental shift in how drugs are developed.

For healthcare founders, the message is clear: CRO/CDMO moats weren’t just built on scale, but on bundling dozens of line items into a single RFP, priced at cost, plus 10–20% markup. Even if a startup offered a better solution, unbundling wasn’t worth the friction, until now. With new tech delivering 2x-10x efficiency gains, pharma is now willing to break apart vendor packages to adopt best-in-class tools.

The sheer scale, fragmentation, and criticality of this outsourced machine create massive opportunities to inject new tech, rewire workflows, and build AI-native platforms that can slot into—or completely upend—billion-dollar contracts. This thesis explores where those opportunities lie and how the next wave of category leaders will seize them.

What’s driving pharma’s rapid shift toward AI

Pharma’s C-suites are turning to AI as a lifeline, seeking tools that eliminate dead time, surface faster decisions, and strip out costly points of failure. CB Insights has even launched a  ‘Pharma AI Readiness Index’ to track how the top 50 companies are gearing up for what looks like a full-blown AI arms race. In this market, the winners won’t just cut costs; they’ll rewrite the entire playbook for how drugs reach patients.

Four forces are accelerating pharma’s AI shift, and each one creates a founder’s wedge:

• A productivity crisis = a massive market gap: In most industries, technology drives exponential productivity. In pharma, it’s the opposite. Drug discovery costs have doubled roughly every nine years while new approvals slowed (Eroom’s Law).
• R&D returns are still weak and ripe for disruption: After collapsing to a 1.2% internal rate of return (IRR) in 2022, big pharma clawed back to ~4.1 % IRR in 2023 and then ~5.9% in 2024. Better, but still inadequate when a single drug costs $2.2 - $2.3 billion to bring to market. Bending this cost curve even slightly can create enormous enterprise value.
• Budgets are shifting and up for grabs: With sweeping layoffs and flat hiring curves, leaders must “do more with less.” For the first time in decades, outsourced service budgets are vulnerable to redirection toward AI-powered platforms.
• A $350 billion AI prize pool: Analysts project AI could unlock more than $350 billion in biopharma value. The winners will be founders who can translate that potential into measurable efficiency gains in one of the world’s most regulated, capital-intensive markets.

The convergence of cost pressure, budget availability, and AI readiness makes this a once-in-a-generation wedge for founders who can deliver speed, accuracy, and automation where outsourcing has failed. 

Together, these forces are creating immediate openings across the biopharma value chain. From discovery through compliance, nearly every stage of development is riddled with inefficiencies that AI is now positioned to attack head-on.

Here’s where the biggest opportunities lie:

• Discovery: Replace siloed spreadsheets with smart bioinformatics that cut cycle times.
• Clinical: Use AI to fix trial bottlenecks with faster design, recruitment, and monitoring.
• Manufacturing: Deploy predictive systems and digital twins to boost yield and cut waste.
• Pharmacovigilance: Automate safety monitoring for faster, more accurate compliance.
• Compliance: Build trust with AI platforms that embed auditability, transparency, and safeguards.

These aren’t edge cases; they’re daily, universal points of friction in a $140 billion outsourced services market. The startups that thrive will be those that pair cutting-edge AI with airtight regulatory compliance, embedding guardrails into the core of their platforms. If you can deliver faster, cheaper, and more compliant solutions, you’ll tap into massive and recurring budget line items.

But even fixing individual bottlenecks is only the beginning. The real unlock comes when AI stops acting as a point solution and starts running the entire process end-to-end. That’s where AI agents step in by transforming biopharma’s relay race into a fully automated, orchestrated system

Full-stack automation for biopharma: AI agents take the wheel

Biopharma’s old model works like a human relay race: one person completes their task, passes it along, and waits. The next repeats the cycle. The result? Slow, error-prone, and staggeringly expensive.

Large-language-model-powered AI agents are breaking this model apart. They can now orchestrate dozens of micro-steps into one seamless workflow: fetching datasets, cleaning them, running analyses, generating outputs, and only flagging humans when judgment or approval actually matters.

This isn’t theoretical; it’s happening now. Several of the top 20 pharmaceutical companies are already piloting AI agents across target identification, clinical data management, manufacturing, and supply chain operations. Sanofi’s partnership with OpenAI and Formation Bio is one early example of how proprietary models, paired with strong engineering, can shepherd a drug candidate from concept to trial-ready faster, cheaper, and with fewer human choke points.

For founders, the message is clear: In a $140B outsourced-services market built on manual hand-offs, AI agents aren’t incremental; they’re disruptive. Build the right domain-specific agent, and you can replace or radically augment workflows that have been static for decades.

Here’s where AI is already breaking through in biopharma:

R&D: High-performance computing at scientists’ fingertips

Autonomous design agents are transforming discovery. They can mine omics datasets, sketch novel chemotypes, predict ADMET liabilities, and give chemists ranked hit lists in record time. Startups using workflow automation are already doubling scientist output while building moats of proprietary training data that compound in value.

These “AI scientists” now propose hypotheses, schedule experiments, manage instruments, and update pipelines in a closed loop, from hypothesis to in silico validation to wet-lab testing. The impact: fewer hand-offs, faster turnaround, and less room for costly error across molecular design, lab automation, data QC, and downstream logistics, all stitched into one integrated workflow.

Clinical development: Cracking the costliest bottleneck

Clinical development is the single largest cost driver in drug development and one of the slowest-moving parts of the process. Over 80% of drug trials miss enrollment timelines, bleeding cash, and delaying launches. Historically, 37% of trial sites under-enroll or don’t enroll at all, creating billions in “dead capital.”

AI is finally changing that. By scanning EHRs, real-world data, and even social media, startups are surfacing patients that match complex inclusion criteria, boosting both efficiency and diversity. Predictive models also forecast site performance, giving sponsors a sharper view of which locations will deliver results.

But the opportunity goes beyond recruitment. AI can simulate protocol designs before enrollment, optimize trial size, and suggest novel endpoints that boost statistical power without inflating costs. During trials, AI platforms act as real-time nerve centers by monitoring data continuously, flagging early efficacy or risk signals, and enabling adaptive designs sooner.

Operations are being rewired too:
- NLP tools auto-generate clinical study reports and regulatory submissions
- AI assistants guide patients through scheduling and FAQs
- Site staff are freed from repetitive admin.

The outcome: faster workflows, safer trials, and higher retention, without more headcount.

Manufacturing: AI on the factory floor 

Pharma manufacturing has long run on brittle, decades-old systems, which are now buckling under complex biologics, personalized therapies, and rising quality demands. CDMOs remain central to production but often operate with outdated infrastructure that leaves efficiency gains untapped.

AI is reshaping the industry’s engine room. Predictive systems now analyze terabytes of process data (temperatures, pressures, yields, assay results) in real time, spotting anomalies, predicting failures, and tweaking parameters before they derail production. Some startups are going even further with AI-driven digital twins that virtually simulate entire manufacturing lines (powered by live process data), testing thousands of variables to find the optimal settings (e.g., temperature, pH, feed rates) for yield and quality.

The payoffs are significant: Pfizer deployed a suite of AI tools—including the generative AI platform Vox—and reported a 20% throughput boost in certain processes. Their goal is to increase yield by 10% and cut cycle times by 25%. For founders, the opportunity is not just marginal efficiency but foundational reinvention of pharma’s production model.

Regulatory tailwinds creating AI-ready supply chains

The regulatory environment is accelerating change. The Biosecure Act, introduced in 2024, aims to cut dependence on Chinese CROs and CDMOs, which currently handle 80% of U.S. outsourcing. If passed, it could trigger one of the largest onshoring waves in biopharma history.

Designed to restrict federal contracts and partnerships with Chinese biotech giants like WuXi AppTec and WuXi Biologics, the legislation is forcing a mass rethinking of outsourced biopharma services and is catalyzing a major shift toward onshoring critical pharmaceutical R&D and manufacturing.

That shift will be messy with fragmented suppliers, capacity bottlenecks, and inspection backlogs, but it’s also a greenfield market for startups that can solve these headaches with speed, precision, and compliance baked in. New U.S. facilities will require real-time supply chain visibility, predictive capacity planning, continuous quality monitoring, and more efficient systems than legacy platforms can deliver. Founders who pair deep regulatory knowledge with AI-first infrastructure could become the backbone of a modern, U.S.-centric supply chain that’s more transparent and resilient than the offshore model it replaces.

Closing the loop: Post-approval safety and compliance

The pharma value chain doesn’t end at FDA approval. Pharmacovigilance (PV), regulatory compliance, and commercialization support remain critical for a product to be successful, and AI is transforming them too.

PV teams, that monitor adverse events and safety data after a drug hits the market, process staggering volumes of safety data from healthcare databases, patient reports, social media, and scientific literature. AI models can parse these streams, flag early safety signals, and triage true risks faster than manual methods. For example, natural language processing (NLP) tools can read through thousands of medical case reports and published papers to flag mentions of potential side effects related to a drug. Machine learning models can then assess patterns (such as clusters of adverse events) and estimate their significance, helping safety experts prioritize true signals over noise. NLP tools can draft adverse-event reports, auto-fill data fields, and reduce error rates, turning reactive safety monitoring into proactive risk prevention.  

Beyond safety, AI is streamlining post-approval regulatory affairs, where compliance requires mountains of documentation: new drug applications, periodic safety reports, labeling updates, and more. AI tools can now organize regulatory data and generate draft reports by extracting key points from source documents, reducing human error and freeing teams to focus on higher-value work. In medical affairs and commercialization, AI analyzes real-world data to reveal how drugs perform in the market, for example, spotting off-label use patterns or unmet needs.

AI-powered analysis of insurance claims and electronic health records (with appropriate privacy safeguards) can also help demonstrate a drug’s value to payers and providers by revealing efficacy in subpopulations or comparing outcomes to competitors.

Guardrails for global pharma’s regulatory maze

If you’re building for biopharma, the requirements vary vastly across geographies. The FDA (U.S.), EMA (EU), PMDA (Japan), CDSCO (India), ANVISA (Brazil), and others each have their distinct dossier formats, labeling, safety protocols, and review timelines. Navigating these differences adds layers of cost, risk, and administrative burden to drug approval and commercialization efforts.

As AI penetrates high-stakes workflows, regulators are tightening requirements. The FDA’s 2024 draft guidance emphasizes having a bulletproof audit trail. Expect questions like: When was the model retrained? On what data? Who signed off? How do you know it’s still fit for use? The agency is also zeroing in on sources of bias, like data diversity and explainability, which are no longer just nice‑to‑haves

As AI agents take on high-stakes pharma workflows, the battle lines will be drawn around IP and data ownership. The winners will be the startups that lock in clear terms early, with airtight MSAs that address these edge-case complexities before contracts are inked. The AI revolution in biopharma is likely to be won in the fine print as we see a new breed of MSAs tackle this technical complexity.

The next billion-dollar pharma “service” company will look like a tech company

Pharma’s old model assembly line is being rewired, and agentic AI is cutting out entire belts of that conveyor, delivering faster without sacrificing oversight or compliance. Every month shaved off discovery, every week gained in a trial, every hour saved in manufacturing ripples directly to patients waiting for cures.

For founders, the formula is deceptively simple: Find a critical workflow still running on copy-and-paste, wrap it in code, price it like software, and deliver it like a service.

In this shift, the next billion-dollar pharma “service” firm won’t look like a CRO or CDMO. It will look like a tech company, powered by AI agents that move data, decisions, and deliverables across the value chain without breaking regulatory trust.

SignalFire: A launchpad for Health & PharmaTech AI startups

At SignalFire, we partner with founders building exactly this future, and our portfolio companies consider us an extension of their team. Beyond our proprietary Beacon AI recruiting platform, we’ve assembled a full-time Portfolio Success Team with world-class operators, including the former Chief People Officer at Netflix, the former CMO at Stripe, and the former Editor-at-Large at TechCrunch. Our XIR program pairs unicorn-level industry leaders with high-potential startups at scale.

If you’re a founder building at the intersection of AI and biopharma, we’d love to hear from you. Email us at sahir@signalfire.com or sooah@signalfire.com. 

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Sources:

1. Grand View Research. “Biotechnology and Pharmaceutical Services Outsourcing Market Analysis.” 2025. (Grand View Research)
2. Clinion Insight. “AI in Clinical Trials: Key to Accelerated Timelines & Reduced Costs.” Clinion.com, 2024. (Clinion)
3. McKinsey & Company. “Reimagining the Future of Biopharma Manufacturing.” 2023. (McKinsey & Company)
4. “Contract Research Organization Market Size and Growth 2025 to 2034.” Precedence Research, 2025. (Precedence Research)
5. “Considerations for the Use of Artificial Intelligence.” U.S. Food and Drug Administration, Dec. 2024. (U.S. Food and Drug Administration)
6.  Scope Research. “Novo Holdings Acquires Catalent: Deal Analysis.” 2024. (Grand View Research)
7. Wired. “Where Are All the AI Drugs?” 17 July 2025. (wired.com)

Clinical trial recruitment statistics [infographic] (https://www.antidote.me/clinical-trial-recruitment-infographic)