AI Opportunities in Biopharma: Early Signals

Drug discovery is moving faster than ever. AI tools are helping researchers generate new molecules, predict protein structures, and evaluate potential drug candidates more efficiently. While much still needs to be proven about whether these assets will deliver on their promise in the clinic, the downstream stages already pose major challenges. Clinical trials, regulatory submissions, and manufacturing scale-up are slow, costly, and resource-intensive, and these bottlenecks will become even more pressing as discovery accelerates.

These areas are where delays and costs consistently accumulate. They are also where the newest wave of generative AI technologies can make a difference. Unlike earlier systems that were limited to structured databases or rule-based workflows, today's approaches span machine learning, statistical modeling, and LLM-based workflows. Generative AI and agentic systems in particular can handle unstructured notes, lab protocols, voice interactions, and documents in ways older tools couldn’t. The opportunity is significant, but success also depends on addressing EHR interoperability, regulatory conservatism, data privacy obligations, and the need to build confidence in AI-generated outputs.

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Four Areas of Opportunity

Clinical Trials
For many therapies, clinical trials are the stage where delays are most visible and costly. Patient recruitment and enrollment remain persistent bottlenecks, with many sites struggling to identify or retain participants. LLMs can read through complex medical records to flag potentially eligible patients and draft outreach messages or informed consent documents, while conversational agents help coordinators engage and follow up with participants.

Recruitment is only one part of the picture. Trials also generate an enormous volume of patient notes, lab reports, and safety narratives that must be reviewed and submitted to regulators. LLMs are strong at summarizing narratives and drafting reports, machine learning models can detect anomalies in clinical data streams, and statistical methods help standardize comparisons across sites. By combining these techniques, trial teams can move from raw data to regulatory-ready outputs much more quickly.

The opportunity is clear: compressing the time from trial launch to data readout, one of the most expensive stages of development. What determines adoption is whether startups can show accuracy, reliability, and fit within existing site and sponsor workflows, rather than adding yet another disconnected tool.

Bioprocessing & Manufacturing
Manufacturing biologics and advanced therapies requires complex processes and careful transfer from lab to factory. Traditional systems like electronic lab notebooks (ELNs), lab information management systems (LIMS), and manufacturing execution systems (MES) ensure traceability and compliance, but they do little to help operators understand why a process is drifting or whether a batch is at risk of failing.

Process optimization has long used machine learning and digital twins, but newer LLM and agentic capabilities can add a decision-making layer: automating batch records, explaining root causes, and suggesting adjustments in context. The main hurdle is making these systems reliable for GMP environments, where errors have regulatory consequences.

Regulation & Safety
Regulatory workflows remain heavily document-driven. Teams must prepare safety reports, clinical narratives, and submissions across geographies, often under tight timelines. LLMs can generate case narratives from patient records, draft periodic safety update reports, and reformat outputs to match regulator-specific templates such as FDA or EMA. Agentic workflows extend this by sequencing tasks. One agent extracts adverse event data, another drafts the narrative, a third checks alignment with regulatory guidelines, and a final agent packages the submission for review.

These applications do not require massive volumes of proprietary data to deliver value, only to structure and reformat existing trial data, which makes them attractive for early-stage startups. The challenge is building enough confidence that regulators will accept outputs, which requires traceability, version control, and clear evidence of accuracy.

Market Access & Strategy
Once a therapy is approved, bringing it to patients requires pricing decisions, reimbursement negotiations, and coordination with payers and health systems. The work involves both interpreting large volumes of unstructured documents and running complex quantitative analyses. LLMs can help distill payer policies, competitor filings, and scientific evidence into clear narratives for decision-makers, while machine learning and statistical models provide the quantitative backbone for scenario planning, cost-effectiveness studies, and reimbursement forecasts. When these capabilities are combined and orchestrated through agentic workflows, teams can move from scattered inputs to submission-ready payer dossiers or negotiation materials with far less reliance on consultants.

The upside is faster and more transparent decisions. For startups, the barrier is proving that these outputs are not only faster but also credible enough to influence negotiations with payers who demand rigorous, well-referenced evidence.

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Our Market Map

We have been mapping the early landscape of AI in biopharma across these four categories. Below is a snapshot of companies and approaches we have researched as part of building our understanding of the space. The companies shown here are either AI-native startups or established players that have layered new AI technology into their products. While not exhaustive, it reflects the range of activity we are seeing.

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What We’re Looking For

As early-stage investors, we are particularly drawn to teams that:

●      Start with a focused wedge: Tackling a narrow workflow pain point that is urgent enough to drive adoption.

●      Demonstrate potential for repeatability: Even if early, we look for products and go-to-market models that can scale beyond bespoke pilots into repeatable deployments across studies, sites, or customers.

●      Balance technical and domain depth: Combining advanced AI expertise with practical knowledge of regulatory, clinical, or manufacturing workflows.

●      Address core challenges: Demonstrating accuracy, ensuring privacy/security, navigating regulatory scrutiny, and integrating with EHRs and legacy tools.

Why Now

The balance of bottlenecks in drug development has shifted. AI is beginning to accelerate discovery, compressing the time it takes to identify new molecules and targets. As a result, the downstream stages, such as clinical trials, regulatory work, and manufacturing, are becoming more visible as the slowest and most expensive parts of the process.

What makes this moment different is that generative AI is suited to exactly these bottlenecks. It can:

●      Work with unstructured data: Clinical notes, lab protocols, and regulatory filings.

●      Automate repetitive, text-heavy tasks: Patient outreach, clinical narratives, and submission documents.

●      Scale human interactions: Conversational agents that engage patients, investigators, and site staff.

●      Bridge silos: Connecting trial data, manufacturing records, and regulatory systems.

These capabilities were not feasible with previous generations of clinical software. Combined with workforce shortages and growing trial complexity, they create a window where generative AI can become part of the core infrastructure of biopharma.

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Our Invitation

We see biopharma AI as a category where focused, workflow-native products can make a measurable difference. If you are a founder building in this space, or an investor tracking it, we would welcome the chance to connect.