Billow AI

Billow AI cofounder and CTO Philip Moniaga was recently featured in Forbes, where he explored how AI-powered deep research tools are reshaping financial planning and analysis for biotech companies.

The piece examines a shift we've been tracking closely: biotech FP&A teams moving away from fragmented workflows (Excel models in one place, manual data pulls in another, chat tools somewhere else) toward integrated AI systems that can reason through complex financial models in a single environment.

Read the full article on Forbes →

Why AI Adoption in Biotech Finance Is Accelerating

The numbers tell the story. According to Gartner, 58% of finance functions deployed AI in 2024, a 21-percentage-point jump from the prior year. Yet only about a third have integrated it into core accounting and financial planning workflows.

That gap represents a massive opportunity. Biotech finance teams are small (often single digits, even at companies with 250+ employees), and the work is complex: DCF modeling, rNPV calculations, clinical trial forecasting, runway scenario planning. AI can absorb the repetitive parts so teams can focus on the decisions that actually matter.

Why Vertical AI Outperforms Generic Tools for Biotech FP&A

General-purpose AI tools struggle with biotech-specific financial modeling. They don't understand the nuances: probability-weighted milestone payments, Phase 1/2/3 cost structures, FDA timeline assumptions, or how indication expansion affects terminal value.

Vertical AI, meaning tools trained specifically on life sciences finance patterns, consistently outperforms generic models on these tasks. That's why platforms built for biotech FP&A deliver better results on rNPV modeling, stress testing scenarios, and compensation benchmarking than off-the-shelf alternatives.

But there's an honest caveat worth repeating: AI doesn't fix broken processes. As one finance leader quoted in the Forbes piece put it, "It's going to be garbage in and garbage out." Clean data and solid foundations come first.

How AI Financial Modeling Tools Work Inside Excel

One of the most important shifts Philip highlights: the best AI tools aren't asking teams to abandon Excel. They're embedding directly into it.

This matters because biotech finance models carry years of institutional knowledge. Assumptions, edge cases, formatting conventions that would take months to recreate. Spreadsheet-native AI lets teams keep their existing valuation models while automating the tedious parts: building DCF projections, pulling benchmark data, running what-if scenarios on burn rate and runway.

The result is faster cycle times without the migration overhead. Some teams report cutting planning cycles in half; others have reduced month-end close from eight days to four.

What This Means for Biotech Runway Planning

EY's 2025 Biotech Beyond Borders report found that more than one-third of biotechs have less than one year of runway. Teams can't afford to wait for quarterly model rebuilds.

AI-powered scenario planning changes the equation. Instead of spending hours manually adjusting assumptions, you can ask "What if we delay the Phase 2 start by three months?" or "How does cutting two headcount affect our runway?" and get answers in minutes, with full traceability back to the underlying cells.

For lean FP&A teams managing hundreds of millions in R&D spend, that speed matters.

Frequently Asked Questions

How is AI used in biotech financial planning? AI tools help biotech finance teams automate repetitive tasks like building DCF and rNPV models, pulling benchmark data from proxy statements, running scenario analyses, and validating formula logic. The most effective tools integrate directly into Excel rather than replacing it.

What is AI deep research? Deep research refers to AI agents that can reason through multi-step problems. They gather data, analyze it, and execute tasks rather than just answering one-off questions. In finance, this means tools that can build a complete valuation model or stress test multiple scenarios, not just chat about them.

Why do biotech companies need specialized AI tools? Biotech financial models have unique structures: probability-weighted valuations, milestone-based cash flows, clinical trial phase costs, and regulatory timeline assumptions. Generic AI tools don't understand these patterns, which leads to errors. Vertical AI trained on life sciences data performs significantly better.

Is AI financial modeling secure? Security varies by vendor. Key things to look for: zero-retention agreements with AI providers (so your data isn't used for training), AES-256 encryption at rest, TLS 1.3 in transit, and SOC 2 compliance. Role-based access controls matter too, especially for sensitive compensation and runway data.

Why We Built Billow for Biotech Finance

Scattered workflows, manual modeling, generic tools that don't understand biotech. This is exactly the problem we're solving at Billow.

Our AI copilot lives inside Excel, handling rNPV and DCF automation, scenario stress testing, and proxy benchmarking. You keep your models; we handle the busywork.

If you're curious what this looks like in practice, schedule a demo or join our waitlist.