Reimagining Investigator Grants with AI

Here's what we keep hearing from clinical finance and ops teams: "I know we need to be doing something with AI. I just don't know how to start. Can you share practical, hands-on examples of how to use AI in clinical finance?" That question is why we kicked off our webinar series, Practical Applications of AI in Clinical Finance & Ops. It's built around one simple premise: real use cases, real workflows, real results. No theory, no hype, just what actually works.

Our first session tackled CRO change orders. Our latest webinar took on the biggest line item in almost every clinical budget: investigator grants.

Here's the webinar recording.

Why Focus on Investigator Grants?

Investigator grants aren't a line item you can afford to get wrong. On average, site payments for administrative fees and patient visits account for roughly 48% of total per-trial cost. The average investigator grant runs about $6,900 per patient across therapeutic areas, and oncology trials push that closer to $18,700 per completed patient. On a study of any real size, that's tens of thousands of individual transactions, each one needing to be verified against whether the activity actually happened and whether the invoiced amount matches what was contracted.

The people on the other end of those payments can't absorb your payment terms. Sites paid on a monthly cycle wait an average of 45 days to be reimbursed for work already performed. On quarterly terms, that stretches to 137 days, nearly three times the wait for the same work. Forty-three percent of sites report three months or less of operating capital in the bank, and CTTI estimates that 40% of site dropout is tied to payment delays. Twenty-three percent of sites report delays of 90 days or longer. Payment accuracy isn't a back-office hygiene issue. It's site relationship and enrollment risk.

Grants are also just structurally hard to get right. The activity and the money live in different systems: your EDC knows which visits happened, but finance typically only sees the invoice and payment files, with no line-by-line reconciliation between the two. Every site contract is its own snowflake of rates, invoiceable procedures, screen-fail caps, holdbacks, and overhead, all buried in contract PDFs that range from clean to, frankly, illegible. Protocols now average 3.3 substantial amendments, and each one can reprice visits and admin fees mid-study. And invoiceables like screen failures and unscheduled visits often accrue in the dark, surfacing all at once as a true-up at closeout instead of being tracked as they occur.

Most sponsors respond by outsourcing grants management to a CRO or payments vendor, and that's often the right call. CROs bring dedicated staff, global payment rails, and the ability to manage hundreds of site relationships at once so you don't have to build that function internally. But outsourcing the work isn't the same as outsourcing the accountability. Payments are frequently a low-margin, pass-through afterthought inside a much larger CRO contract, and you're trusting a vendor to approve, calculate, and report on its own work. When your auditors and your board have questions about the numbers, they're asking you, not your CRO.

We shared two stories from the field that show what happens when convenience wins out. In the first, a top-five global CRO replaced its investigator payments team with an automated pay platform that auto-paid every invoice it received. Sites started getting checks for patient visits that hadn't happened yet, and it took months to unwind the confusion. In the second, a mid-cap oncology sponsor closed out a trial and received a $5 million bill from its CRO for invoiceables that had never been tracked or accrued along the way. The money had already been spent; the sponsor was simply the last to find out. Automation without reconciliation just makes the wrong payment happen faster.

How to Reimagine Investigator Grants with AI

To show what's actually possible today, we ran the same investigator grant payment report through two very different tools: Microsoft Copilot, something most finance teams already have access to, and Condor's purpose-built platform.

Using Microsoft Copilot

Jeff started by dropping a CRO investigator grant payment file, covering the last two quarters of a phase 3 study, into Copilot with the following prompt:

I'm the head of clinical finance at a biotech company. Attached is the investigator grant payment report from our CRO for our phase 3 study covering the last two quarters. Please analyze it and give me: total payments by site and month, highlighting the five highest-paid sites; a breakdown of visit payments versus procedures versus invoiceable pass-through line items; any payments that look inconsistent with each site's contract budget or with the visits patients actually completed; what we should accrue for work sites have performed but not yet been paid for; and five questions I should ask the CRO before approving the next payment run. Format the output as a brief memo I can share with my CFO.

Two things make this prompt work. First, it's specific about exactly what the output should contain, since a general ask invites the model to fill gaps with something that merely looks credible. Second, it gives the model context on audience and intent, telling it who's asking and who the memo is for, so it knows what level of detail and framing to use.

Copilot came back with a clean executive summary: roughly $270,000 in payments, weighted toward startup activity and pass-through costs consistent with early enrollment, no obvious duplicate payments, but several items flagged for CRO follow-up. It broke out the top five paid sites, plotted payments by month, and split the mix between visits, procedures, and invoiceable pass-throughs. It flagged a 4-5 month lag between when sites performed visits and when they were reimbursed, and called out specific items, like a biopsy and imaging reimbursement paid before the associated screening visit, and ECG charges appearing months after their related visits.

For a tool that's already sitting in most people's Microsoft 365 license, that's a genuinely useful starting point: a fast, readable summary, clean per-site totals, catches on the obvious duplicates and outliers, and a CFO-ready memo in seconds with zero procurement.

But here's where it stops. Copilot can't see the EDC, so it can't tie a payment to a visit that actually happened. It doesn't know the contracted rates, caps, holdbacks, or overhead in the site agreements, so it has no way to know whether a flagged charge is actually wrong. The accrual figure it generates is an extrapolation of past payments, not a measurement of completed activity, and it can't distinguish an early payment from an unearned one. Run the same prompt twice and you may get a different answer, with no evidence trail behind either one. It reads the payment file. It cannot read the trial.

From "What the File Says" to "What's True": The Condor Demo

Nim then walked through the same investigator grant scenario inside Condor. Instead of reasoning from a single spreadsheet, Condor's Intelligence Engine reconciles patient-level activity, visits, procedures, and site admin fees coming from the CRO, directly against the EDC data sponsors already have, using AI to map each payment to the business context behind it. The system proposes the mapping automatically; nothing overrides a user's judgment without review, but the matching itself doesn't require anyone painstakingly reconciling line by line.

Once that file is imported, two things change. The overview surfaces CRO and site performance alongside payment reconciliation, flagging missing invoices and overbilling immediately and pointing to which sites need attention. And a trend layer goes beyond flagging individual discrepancies to surface patterns: what share of spend is startup versus investigative, which sites show unusual or no activity, and how performance is trending over time, the exact kind of history and pattern-matching a generic LLM has no way to see. From there, users can drill into the raw patient data, overlay it against processed EDC data in native currency or USD, and investigate a specific negative variance down to the site, visit, procedure, or admin fee level.

The platform is built around four core principles: the full story lives in one place instead of scattered across systems; overbilling, missing invoices, and pricing issues are visible at a glance instead of requiring a manual dig; discrepancies and off-contract charges are flagged as they appear; and everything is oriented toward decision support, so teams spend less time combing through data and more time acting on it.

Mapped back to the two stories from earlier in the session: the auto-pay trap becomes activity matching, every payment tied to a corresponding EDC event, so a payment with no visit behind it is caught before it goes out the door instead of after. And the closeout surprise becomes continuous invoiceables tracking, watching that spend accrue monthly as it happens, so it becomes a line item you tracked all along rather than a bill that shows up at the end.

What We Heard From the Room

Several attendees asked some version of, "If I upload my EDC and contract data into Copilot and structure the prompt correctly, can't I get there myself?" Short answer: to a point, yes, but volume becomes the constraint fast. Most general-purpose tools cap how many documents you can attach to a single prompt, well short of the dozens or hundreds of contracts a real trial generates. Without a knowledge graph or ontology behind it, there's no guarantee you get the same answer twice.

On data sources, Condor works from whatever level of detail a CRO provides, whether that's an accrual report or the underlying invoices, and the more detail available, the more granular the mapping. Unmapped invoiceables and procedures are clearly flagged in the reconciliation table so they can be reviewed and remapped rather than silently dropped. Protocol amendments are handled by loading the full history of a site's CTA, so a visit performed before an amendment's effective date is recognized at the old rate and everything after at the new one, even across sites with seven or eight amendments over a study's life. The platform supports full multi-currency and FX conversion, any therapeutic area, and, per attendees who asked directly, no cap on site count. Some phase 3 oncology trials on the platform run 500-plus sites, each with its own contract history, and international sites are handled the same way as domestic ones, loaded through automation and human-in-the-loop review into the same normalized EDC and contract structure.

What's Next in the Series

This is only our second session, and we've already received multiple topic ideas from attendees. Our next webinar will be in early October, and we’ll share the topic soon. 

In the meantime, if you'd like to see what Condor can automate for your investigator grants process specifically, book a demo with our team here.