Pharmaceutical companies are quietly moving the clipboard — and, increasingly, whole chunks of clinical development — from manual, contractor-heavy workflows into AI-powered pipelines that promise to shave weeks to months off site selection, patient recruitment and the tedious document assembly that accompanies regulatory submissions.
The math that drives the industry’s urgency is familiar: bringing a new medicine from discovery to market commonly takes a decade or more and costs in the low billions of dollars, depending on how costs are counted. Those averages hide huge variance by therapeutic area, but they explain why even modest percentage improvements in trial speed or operational overhead matter. Published analyses from academic and industry sources place the clinical development window at roughly 7–10 years of the total timeline, and traditional cost estimates from long-term studies often cluster around the $2–3 billion mark when capitalized.
What changed in the last two years is not a sudden discovery of an AI “silver bullet” for novel molecules, but a steady roll-out of AI in the so-called messy middle of drug development: trial site identification, patient outreach and retention, medical writing, and the compilation of regulatory dossiers. At the JP Morgan Healthcare Conference in January, executives and investors from a cross-section of large pharma and smaller biotech described real, measurable reductions in work time — the kind of operational wins that stack up across multiple programs.
That McKinsey projection is emblematic of the industry narrative: incremental, function-level improvements aggregate into program-level time and cost savings. But the consultancy also emphasizes the need for governance, model validation and integration work before those numbers become routine.
For Windows-focused IT teams and enterprise leaders, the imperative is to adopt a disciplined, compliance-first approach that combines strong endpoint and tenant controls (for copilots and collaboration tools), defensible model governance (data lineage, validation and monitoring), and infrastructure decisions that preserve IP while enabling agility. The benefit is clear — faster trials, lower operational drag and the ability to redeploy expensive human expertise toward the hardest scientific problems — but the path forward will reward organizations that treat AI like regulated software from day one.
If your organization is evaluating pilots this quarter, start with well-scoped, high-repeatability workflows (site scoring, document conversion, recruitment outreach) and require measurable KPIs, documented validation and human sign-off. Those conservative steps are how the industry’s incremental AI wins become lasting competitive advantage.
Source: The Economic Times Drugmakers turn to AI to speed trials, regulatory submissions - The Economic Times
Background
The math that drives the industry’s urgency is familiar: bringing a new medicine from discovery to market commonly takes a decade or more and costs in the low billions of dollars, depending on how costs are counted. Those averages hide huge variance by therapeutic area, but they explain why even modest percentage improvements in trial speed or operational overhead matter. Published analyses from academic and industry sources place the clinical development window at roughly 7–10 years of the total timeline, and traditional cost estimates from long-term studies often cluster around the $2–3 billion mark when capitalized. What changed in the last two years is not a sudden discovery of an AI “silver bullet” for novel molecules, but a steady roll-out of AI in the so-called messy middle of drug development: trial site identification, patient outreach and retention, medical writing, and the compilation of regulatory dossiers. At the JP Morgan Healthcare Conference in January, executives and investors from a cross-section of large pharma and smaller biotech described real, measurable reductions in work time — the kind of operational wins that stack up across multiple programs.
Where AI is already being used in drug development
Site selection and trial operations: from weeks to hours
Novartis reported cutting a typical four- to six-week site selection process to a single two-hour session during a 14,000-person outcomes trial for Leqvio, after applying AI to identify higher-performing sites and optimize enrollment. That one example illustrates how analytics and models can compress previously manual discovery and scoring workflows — and how time saved at each trial start-up can compound across a global program.Patient recruitment, outreach and retention
Recruitment remains a “leaky funnel”: candidates fail to progress from outreach to screening to enrollment, and drop-out rates eat into timelines and budgets. Startups and in-house teams are using AI for segmentation, targeted outreach, automated education and scheduling — automations that can increase screening yields and reduce no-shows. Venture investors are backing companies that position AI as a scaffold to patient engagement and operational orchestration.Regulatory document assembly and template conversion
Regulatory filings still require thousands of pages of clinical, safety, manufacturing and quality documentation. Companies reported using AI to cross-check, standardize and convert long reports into agency-specific templates, sometimes saving weeks of manual reformatting and review work. Small firms and regional sponsors — which often lack deep regulatory writing teams — find these tools particularly attractive.Post-trial analysis and medical writing
Generative models and purpose-built agents are being piloted for transforming raw trial outputs into tables, figures and clinical study reports, and for automating sections of regulatory submissions where standardization is high. Some teams describe these capabilities as augmenting human writers rather than replacing them: models accelerate drafting and formatting, while experienced clinicians and writers retain final sign-off.The “agentic AI” promise — and where the numbers come from
Consulting firms and analysts have framed the possible upside in hard numbers. McKinsey’s life-sciences work argues that agentic AI — semi-autonomous systems able to orchestrate multi-step workflows with minimal human intervention — could boost clinical development productivity by roughly 35–45% within five years if broadly and correctly deployed. Those gains are not about discovering new drugs overnight; they’re about extracting efficiencies across protocol design, site operations, data management, medical writing and other repeatable functions.That McKinsey projection is emblematic of the industry narrative: incremental, function-level improvements aggregate into program-level time and cost savings. But the consultancy also emphasizes the need for governance, model validation and integration work before those numbers become routine.
High-profile partnerships and infrastructure implications
AI in life sciences is not only a software story; it’s increasingly an infrastructure story. Major drugs firms are investing in on-prem or co-located GPU supercomputing and strategic vendor partnerships to host private models and preserve sensitive IP and patient data.- Eli Lilly’s big hardware and platform moves with leading GPU vendors reflect that trend: manufacturers and research organizations are constructing DGX SuperPOD–class clusters and tailored AI factories to train foundation models and run large-scale inference for scientific use cases. Those systems are designed to reduce latency, protect proprietary data, and offer control that public clouds alone may not provide for certain regulated workloads.
- At the same time, enterprises are layering cloud services, commercial LLMs and packaged “copilot” features into business processes for tasks like regulatory drafting and document summarization — a pattern that blends on-prem AI for heavy compute with SaaS copilots for day-to-day productivity.
What worked: early, verifiable wins
Several concrete claims cited by industry executives and covered in journalism are worth noting because they are quantifiable and verifiable in public statements:- Site selection acceleration: Novartis’ Leqvio program reduced a four- to six-week site evaluation to a two-hour decision meeting via AI-enabled site-scoring. That translated into faster enrollment and tight control of the recruitment target.
- Direct cost savings: GSK reported using digital and AI tools that collectively saved roughly £8 million (~$10.9M) in late-stage trial costs for its asthma candidate Exdensur — a tangible example of how automation of data aggregation and manual paperwork can yield near-term savings.
- Productivity projections: McKinsey’s modeling shows 35–45% clinical development productivity gains are plausible with agentic AI in the near term, contingent on governance and integration.
Regulatory context: cautious acceptance and new guardrails
Regulators are not sitting on the sidelines. The U.S. Food and Drug Administration and European agencies have been steadily building guidance and internal tooling to handle AI-enabled development and submissions:- The FDA has acknowledged the increasing presence of AI across drug development and published draft guidance outlining considerations for the use of AI to support regulatory decision-making. The agency’s public resources emphasize credibility, validation, explainability and early engagement with reviewers.
- The European Medicines Agency has issued reflection papers and set up coordinated workplans that call for a risk-based approach: high-impact AI tools used in trial decisions or safety analyses warrant earlier and deeper regulatory dialogue. Regulators in Europe are also updating GMP and documentation expectations to incorporate AI validation and lifecycle monitoring.
Key risks that must be managed
AI-driven efficiencies come with a specific risk profile in regulated biomedical work. Below are the major categories IT and compliance teams must tackle:- Data quality and bias: Models trained on non-representative clinical or electronic health record data risk producing skewed site scores or mis-prioritized subpopulations, which can threaten trial validity and downstream regulatory acceptance.
- Hallucinations and factual errors: Large language models can confidently fabricate details or misinterpret procedural nuance; when those errors enter regulatory drafts or safety narratives they carry material risk. Human review and source-linking are not optional.
- Auditability and explainability: Agencies expect traceable decision chains. If a model recommends excluding a site or altering an endpoint, sponsors must show the data and logic used to reach that recommendation. Black-box systems are less likely to pass scrutiny without compensating controls.
- Security and IP leakage: Housing proprietary development data on third-party models or tools without appropriate contractual and technical controls creates intellectual property and privacy risks. High-value firms are investing in isolated compute fabrics and strict data governance to avoid leakage.
- Supply chain and vendor lock-in: Dependence on a single model provider or GPU vendor for mission-critical workflows creates operational concentration risk and negotiating leverage for vendors.
- Regulatory divergence: Different jurisdictions are converging but not identical. What regulators in one region accept may trigger additional scrutiny elsewhere; global programs must plan multi-jurisdictional validation strategies.
Practical guidance for Windows-oriented IT and compliance teams
Pharma IT is often heterogenous — a mix of Windows endpoints, Linux compute, cloud services, and specialized medical systems. For WindowsForum readers responsible for enterprise environments, here are actionable steps to manage AI adoption responsibly in clinical development:- Inventory and classify data sources
- Identify any repositories that contain PHI, trial data, vendor data or proprietary models.
- Apply sensitivity labels and ensure DLP controls prevent uncontrolled model access.
- Choose deployment topology intentionally
- For highly sensitive model training or inference, prefer isolated or co-located GPU clusters with strict network controls.
- Where SaaS copilots (e.g., enterprise copilots) are used for drafting or summarization, configure tenant-level restrictions and ensure Business Associate Agreements (BAAs) if PHI may be involved.
- Enforce human-in-the-loop checkpoints
- Create mandatory review gates for any AI-generated regulatory text or safety analysis.
- Maintain versioned artifacts and human sign-offs to preserve audit trails.
- Harden and monitor endpoints
- Windows desktops running copilots must have strong EDR, managed identity, conditional access and controlled plugin policies to prevent data exfiltration via prompts or third-party connectors.
- Implement model governance and validation
- Operationalize model cards, dataset lineage logs and continuous performance monitoring to detect drift.
- Schedule periodic, documented validation cycles tied to the model’s intended regulatory role.
- Build defensible contracts
- Ensure vendor contracts include commitments on data handling, model retraining practices, access controls and liability limits for hallucinations or data loss.
- Plan for multi-jurisdictional compliance
- Align your validation strategy with FDA and EMA expectations; coordinate submissions if AI tools materially influence trial design or key safety decisions.
How CIOs and CTOs should budget for the next 24 months
Expect a hybrid spend profile:- Infrastructure: CapEx for GPU clusters or private racks if you plan in-house foundation-model training; OpEx for cloud inference and managed agent services where flexibility and scale matter.
- Integration and validation: Significant engineering effort goes into embedding AI into validated workflows — data pipelines, EHR connectors, audit logging and test harnesses.
- Compliance and people: Invest in model governance teams, regulatory liaisons and data stewards who understand both AI and GxP expectations.
- Security and resilience: Budget for hardened network architectures, privileged access management, and incident response practices tailored to AI supply chains.
Strategic implications for the drug-discovery value chain
AI’s immediate effect is operational — speeding the middle parts of development — but infrastructure investments and agentic automation also reshape organizational capabilities:- Companies that retain data control and build validated model operations (MLOps) will be the firms that extract the most predictable, repeatable value from AI.
- Startups and middle-market biotechs have a window to use AI to compensate for smaller headcounts, but they must be careful: reliance on third-party copilots without strong contract and validation controls can create downstream regulatory and IP friction.
- Vendors that offer explainable, auditable AI for GxP contexts will capture premium enterprise demand. The market is already bifurcating into raw compute vendors, model providers and compliance-oriented solution builders.
What still can’t be taken for granted
It’s important to call out limits and unverifiable enthusiasms:- There are no regulatory approvals yet for a therapeutic that was discovered and developed end-to-end by AI alone. Claims that AI will instantly start delivering “AI drugs” to market understate the scientific and clinical validation still required. Industry leaders say molecules informed by AI are already in pipelines, but regulatory acceptance hinges on classical clinical evidence.
- Productivity projections (35–45%) are models — they are plausible in aggregate but will vary widely by company, therapeutic area and how well governance is implemented. Early wins in site selection and document automation are verifiable; broad generalization to discovery-stage success remains speculative.
- Not all generative AI use is appropriate in GxP-controlled environments. Some regulatory updates explicitly caution against using generic LLMs for critical GMP tasks without rigorous controls. Plan for human oversight, reproducibility and documented quality checks.
Conclusion: pragmatic optimism and disciplined governance
The industry’s current AI story is less about miraculous molecule generation and more about operational transformation — the quiet reshaping of the activities that plug time and cost into every program. Verified case studies from large firms show concrete time and cost benefits in site selection, patient recruitment, document assembly and medical writing. The McKinsey productivity thesis gives a coherent picture of how agentic AI could amplify those wins at scale.For Windows-focused IT teams and enterprise leaders, the imperative is to adopt a disciplined, compliance-first approach that combines strong endpoint and tenant controls (for copilots and collaboration tools), defensible model governance (data lineage, validation and monitoring), and infrastructure decisions that preserve IP while enabling agility. The benefit is clear — faster trials, lower operational drag and the ability to redeploy expensive human expertise toward the hardest scientific problems — but the path forward will reward organizations that treat AI like regulated software from day one.
If your organization is evaluating pilots this quarter, start with well-scoped, high-repeatability workflows (site scoring, document conversion, recruitment outreach) and require measurable KPIs, documented validation and human sign-off. Those conservative steps are how the industry’s incremental AI wins become lasting competitive advantage.
Source: The Economic Times Drugmakers turn to AI to speed trials, regulatory submissions - The Economic Times
