Clinical Trials
May 5, 2026

Best Clinical Trial Accruals Software: A Plain-English Guide for Pharma Finance Teams

Jen Kyle
CEO and Founder, Condor

Key takeaways

If you work in pharma finance, you already know how painful clinical trial accruals can be. Every quarter, your team is hunting down CRO status reports, updating spreadsheet models, chasing vendors for activity data, and trying to make sure the numbers hold up under audit — all while managing a dozen other priorities.

Most pharma companies still do this the hard way: manually, in Excel, with a lot of back-and-forth emails and a lot of crossed fingers. But there's a growing category of software built specifically to fix this problem.

This guide walks through what clinical trial accruals software does, what separates the good tools from the mediocre ones, and how the major vendors compare. Whether you're evaluating options for the first time or trying to replace a clunky legacy setup, this should help you get oriented quickly.

What Are Clinical Trial Accruals — and Why Do They Matter?

Here's the basic idea: when you run a clinical trial, you're paying vendors — CROs, investigator sites, labs, imaging centers, and others — over a long period of time. But those vendors don't always bill you right when the work happens. They might send invoices weeks or months later.

Accruals are how your finance team accounts for money that's been spent (or is owed) even before an invoice shows up. Under GAAP, you have to record expenses when they're incurred — not when you get the bill. For public companies, this is also a SOX requirement, meaning auditors will scrutinize your methodology closely.

In practice, that means your accounting team has to estimate, every single month or quarter, what work has been completed and what it costs. That includes:

  • Pulling activity data from your CROs, study sites, and clinical systems (like EDC and IRT platforms)
  • Estimating how far along each service category is — direct fees, pass-throughs, investigator grants
  • Reconciling those estimates against your contracts, including any change orders or amendments
  • Accounting for currency differences if you're running global studies
  • Producing journal entries and documentation that auditors can actually follow
  • Doing all of this repeatedly, across every active trial, every single period

A single Phase 2 or Phase 3 trial can involve $50M to $500M in vendor spend. Getting accruals wrong in either direction — too high or too low — can misstate your financials and create serious problems at audit time.

The core problem: Clinical trials don't follow clean billing cycles. A CRO might send a monthly summary weeks after the period ends. Sites submit expenses on their own schedule. Protocol changes can shift costs mid-stream. Your finance team has to turn all of that into accurate, period-specific estimates — month after month, for every trial on your plate.

Why Spreadsheets and Manual Processes Don't Cut It Anymore

A lot of pharma finance teams — even ones running late-stage trials — are still doing accruals by hand. They've built elaborate Excel models, they chase CRO contacts for status updates, and they manually reconcile everything before close. It works, sort of. But it has some serious downsides.

1. You're always working with old data

When your accruals are built on delayed invoices or CRO reports that arrive two weeks after the period ends, your numbers are always behind. By the time finance has a complete picture, the trial has moved on. There's no good way to see what's actually happening right now.

2. It eats up your best people's time

Building accruals from scratch every period is slow. Senior accounting staff spend days pulling data, updating models, and chasing down approvals — work that should take hours. That leaves almost no time for anything more strategic.

3. Everything is siloed

Clinical data lives in one system. Contracts and change orders live in another. Financial actuals are in your ERP. None of these talk to each other automatically. So when a protocol amendment happens or a CRO submits a change order, someone on your team has to manually figure out what that means for the financials.

4. Your forecasts go stale fast

A static spreadsheet model can't keep up with what's actually happening in a trial. When enrollment slows down or a vendor changes scope, updating the forecast means rebuilding parts of the model from scratch. By the time you're done, something else has changed. Finance ends up reactive instead of ahead of problems.

По цифрам (в оригинале — три стат-карточки; в блоге можно дать строкой или списком):

  • 70–75% — Efficiency gain. Reported by biopharma teams using purpose-built accruals software
  • 90%+ — Forecast accuracy. Achievable with automated, data-connected accrual platforms
  • >30% — Vendor budget savings. Average per trial with proactive financial management

What Good Clinical Trial Accruals Software Should Actually Do

Not all tools are built the same. Some are purpose-built for pharma accruals. Others are general finance platforms that teams try to adapt. Here are the six things that actually matter when you're evaluating options:

1. It should connect to your clinical data automatically

The whole point of software is to stop manually gathering data. Look for tools that pull directly from the systems your clinical teams already use — Medidata, Veeva, Suvoda, your CTMS — so you're not uploading spreadsheets or waiting on CRO portal exports. If it requires manual data entry, you haven't actually solved the problem.

2. It needs to understand how pharma contracts actually work

Clinical trial contracts are complicated. They have fixed fees, pass-throughs, investigator grants, milestones, and change orders — all in different currencies, across different vendors. Your accruals tool needs to handle all of that natively, not require you to build custom formulas on top of a generic finance platform.

3. Forecasting should be built in, not bolted on

The best tools use the same data for accruals and forecasting. That way, when actuals come in, your rolling forecast updates automatically. You should be able to model "what if enrollment is 20% slower?" or "what if this amendment goes through?" without rebuilding your model from scratch.

4. Auditors need to be able to follow everything

For public companies and pre-IPO biotechs, SOX compliance is a real concern. Every calculation needs to be traceable and documented. Look for tools with full audit trails, role-based access controls, and ideally SOC 1 or SOC 2 certification. Bonus points if the tool was designed with Big 4 audit requirements in mind.

5. It should plug into your existing ERP and finance tools

Accruals end up as journal entries in your general ledger. Your tool needs to integrate cleanly with your ERP — whether that's SAP, Oracle NetSuite, or Sage — so you're not manually rekeying data. Integration with FP&A platforms like Anaplan, Planful, or Adaptive Insights matters too if you're running separate planning workflows.

6. It should scale as you add more trials

A tool that works fine for two trials should still work when you have fifteen. Look for portfolio-level dashboards, multi-entity support, and the ability to handle more volume without proportionally increasing your team's workload.

The Top Clinical Trial Accruals Software Tools

Here's an honest look at the tools pharma finance teams most commonly evaluate — including purpose-built platforms, clinical data systems, ERP tools, and yes, Excel. Some of these are genuinely built for accruals. Others are tools that teams try to stretch to fit the use case. It's worth knowing the difference before you start demo calls.

Condor Software — Purpose-Built · Biopharma

Condor is built specifically for pharma R&D finance — not adapted from a general accounting tool. It automates clinical trial accruals, forecasting, budgeting, and benchmarking. The platform pulls data directly from clinical systems your team already uses (Medidata, Veeva, Suvoda) and connects to your ERP and FP&A tools through its Condor Connect integration layer. The result: your accruals are based on live activity data, not lagging invoices or manual estimates. Finance teams using Condor report closing 60% faster and achieving 90%+ forecast accuracy without growing headcount.

Strengths:

  • Native clinical data integrations (EDC, IRT, CTMS)
  • Purpose-built accrual engine for biopharma contracts
  • 90%+ forecast accuracy, 70–75% efficiency gains
  • SOX/SOC-compliant, audit-ready workflows
  • Scales from 2 trials to 20+ without headcount growth
  • Big 4–trusted outputs

Considerations:

  • Focused on biopharma — not a general-purpose finance tool
  • Best fit for companies with active clinical programs

Clario (formerly BioClinica) — Enterprise CRO Services

Clario is primarily a clinical services and data capture company — they do endpoint adjudication, eClinical technology, and imaging. Some sponsors using Clario as a CRO get access to spend-tracking portals, but these are built for Clario's own team to manage, not for sponsor-side accounting. If you're looking for a tool to run your own accruals, this isn't it.

Strengths:

  • Deep clinical data capabilities
  • Established enterprise relationships

Considerations:

  • Not a finance/accounting platform
  • CRO-side reporting, not sponsor-side accrual automation
  • Limited ERP or FP&A integration

Veeva Vault eTMF / CTMS — Clinical Operations Platform

Veeva is the dominant clinical operations platform in pharma — eTMF, CTMS, EDC, and regulatory are all in their suite. It's widely used and holds a lot of the site-level data that feeds into good accrual estimates. But Veeva doesn't calculate your accruals. Think of it as a data source that should connect into a financial platform, not a replacement for one.

Strengths:

  • Comprehensive clinical data repository
  • Industry-standard in mid-to-large biopharma
  • Good API connectivity for downstream finance tools

Considerations:

  • Not an accruals or finance platform
  • Requires integration with a financial layer for accruals
  • High cost and implementation complexity

SAP / Oracle NetSuite / Sage (ERP) — ERP General Ledger

Your ERP is where accruals land as journal entries — it's not where they get calculated. SAP, NetSuite, and Sage are great at what they do, but none of them understand how a CRO contract works. They don't know what investigator fee structures look like or how to handle percent completion across service categories. Companies that try to run clinical accruals directly in their ERP almost always end up layering Excel on top of it anyway — which defeats the purpose.

Strengths:

  • System of record for GL and financial reporting
  • Strong SOX and audit controls natively
  • Already in place at most companies

Considerations:

  • No clinical-specific accrual calculation logic
  • Requires extensive customization for biopharma use cases
  • Acts as destination, not source-of-truth for accruals

Microsoft Excel (Manual Accrual Models) — Spreadsheet-Based

Most pharma finance teams reading this are still using Excel for at least part of their accrual process — and that's fine for one or two trials. But Excel doesn't connect to live clinical data. It has no audit trail. Formulas break. Models get inherited from people who've left the company. And as your trial count grows, the amount of time your team spends maintaining these models grows right along with it. Every other tool on this list should be measured against what it would take to just keep doing things in Excel.

Strengths:

  • Universally available, no procurement required
  • Fully flexible for any model structure
  • Finance teams know it well

Considerations:

  • No live data connections — fully manual input
  • High error risk; no audit trail
  • Does not scale beyond 3–4 concurrent trials
  • Cannot support SOX compliance requirements
  • Enormous opportunity cost on senior finance staff time

Anaplan / Planful / Adaptive Insights — FP&A / Planning Platforms

Tools like Anaplan, Planful, and Workday Adaptive Insights are widely used by pharma finance for budgeting, planning, and rolling forecasts. Some teams try to build accrual models inside these platforms. The problem: they're not built for clinical contract logic. They don't have native connections to EDC or IRT systems. You'd need to custom-build everything, and it still won't be as accurate as a tool designed specifically for this. They're better used as the downstream home for accrual data than as the engine that produces it.

Strengths:

  • Powerful scenario planning and modeling
  • Strong financial consolidation capabilities
  • Good integration with ERP systems

Considerations:

  • Not built for clinical accrual calculation logic
  • No native clinical data (EDC, IRT) connectors
  • Significant custom build required for R&D use cases

LedgerRun — Clinical Finance

LedgerRun is one of the purpose-built clinical trial finance tools in the market. It handles CRO accruals, contract management, and financial tracking for biopharma. It's generally a good fit for smaller biotech teams looking for something more structured than Excel without a heavy enterprise implementation. Forecasting and benchmarking capabilities are more limited compared to newer platforms, but for teams just starting to move off spreadsheets, it's a reasonable option to evaluate.

Strengths:

  • Purpose-built for clinical trial finance
  • Relatively fast to implement
  • Accessible for smaller biotech teams

Considerations:

  • Limited AI/ML-driven capabilities
  • Less robust forecasting and benchmarking
  • Fewer integrations compared to newer platforms

Medidata (a Dassault Systèmes company) — Clinical Data Platform

Medidata is the industry standard for clinical data capture — EDC (Rave), IRT, and increasingly AI-powered trial analytics. Like Veeva, it holds a huge amount of the patient and site activity data you'd want driving your accrual estimates. But Medidata doesn't do sponsor-side financial accounting. It's the source of the data, not the tool that turns that data into journal entries. You still need something else to close your books.

Strengths:

  • Gold standard for clinical data capture
  • Extensive data for driving accrual estimates
  • Broad industry adoption

Considerations:

  • Not a finance or accruals platform
  • Expensive; complex implementation
  • Finance teams still need a separate accrual system

How They Compare Side by Side

[EMBED: comparison-table]

Questions to Ask Before You Buy

Before you book demos or send out an RFP, get your internal team aligned on what actually matters. The Controller, VP FP&A, and whoever owns clinical finance should all be in the room — they care about different things, and you'll want to surface those differences before a vendor does.

Things you need the tool to do

  • Connects directly to our ERP (know which one)
  • SOC 1 Type II or SOC 2 Type II certified
  • Handles our CRO contract types (fixed-fee, FTE, milestone)
  • Automates investigator fee and grant calculations
  • Handles multiple currencies and FX automatically
  • Full audit trail for every number and journal entry
  • Different access levels for accounting, clinical, and FP&A
  • We can be up and running in 8–12 weeks

Things that separate the good tools from the great ones

  • Live data from EDC/IRT (not just file uploads)
  • Rolling forecast uses the same data as accruals
  • Can model "what if enrollment slows" scenarios easily
  • Can benchmark costs across trials
  • Flags variances automatically instead of relying on manual review
  • References from companies at a similar stage to ours
  • Connects to our FP&A tool (Anaplan, Adaptive, Planful)
  • Designed for regulated environments with a validated release process

One thing most teams skip: ask each vendor to walk through a full close cycle using data from a trial that looks like yours. Generic product demos are easy to polish. What you want to see is how the tool handles a messy change order, a mid-period protocol amendment, or a site that's billing late. Bring your Controller and your most complicated CRO contract to that call.

Why Condor Was Built for This

Condor was started because the people who built it had been on the finance side of pharma companies and knew firsthand what a mess clinical trial accruals could be. No existing tool — not the ERP, not the FP&A platform, not the spreadsheet — actually spoke the language of clinical contracts. So they built something that did.

The Condor Platform — three modules that work together as a single financial system for pharma R&D. Each one handles a different piece of the problem — and they share the same underlying data, so nothing falls through the cracks: Connect (Data Integration), Copilot (Workflow Automation), Compass (Intelligence & Benchmarking).

Condor Connect pulls data automatically from the systems your clinical teams already use — Medidata, Veeva, Suvoda, IQVIA, Labcorp, ICON, PPD, Worldwide Clinical Trials, and others — and syncs with your ERP and FP&A platforms. No file uploads, no waiting on CRO portals, no manual reconciliation to get data flowing.

Condor Copilot runs the accrual calculations: applying your contract structures, figuring out percent completion by service category, computing investigator fees and grants, handling currency, building journal entries, and generating the documentation your auditors need to sign off. Finance teams using Copilot report cutting their close time by 60% and reducing the team hours spent on accruals by 70–75%.

Condor Compass takes the data further — connecting clinical and financial information across your full trial portfolio so you can benchmark costs, spot overruns early, and model out long-range scenarios. It's the difference between reporting on what happened and actually steering where things are going.

The platform has been validated by Big 4 auditors and is built with SOX compliance at its core. Customers have gone from managing 2 trials to 10 without adding headcount — and have found more than $5M in savings on individual programs by catching accrual discrepancies and change order exposures before they became problems.

How to Pick the Right Tool

The good news: you have real options now. A few years ago, the honest answer for most pharma finance teams was "use Excel and hope for the best." Today there are tools built specifically to solve this problem, and the case for switching is pretty clear.

If you're running more than two or three active trials — or you know you will be soon — the time and error cost of manual accruals is almost certainly higher than the cost of a purpose-built platform. The finance teams that move first don't just close faster; they get visibility that actually helps with budget decisions and audit prep.

When you evaluate tools, keep the focus on three things: does it connect to your clinical data automatically, does it actually understand how pharma contracts work, and can your auditors follow every number it produces? Everything else is secondary.

The best clinical trial accruals software isn't the one with the longest feature list. It's the one that makes close week less painful, keeps your forecasts accurate, and lets your team spend time on things that actually matter.

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AI
September 13, 2026

Introducing the World’s First Clinical Finance AI Agent Purpose-Built for Biopharma

Jen Kyle
CEO and Founder, Condor

Today we unveiled Condor's Clinical Finance AI Agent — the world's first AI agent purpose-built for biopharma R&D finance.

Ask it why a trial's actuals and forecast diverged, and it reasons across your full budget and forecast history to give you the answer in seconds, not the days it takes to reconcile across your ERP, CTMS, EDC, and a dozen spreadsheets. Ask it what a change in site mix or enrollment timing will cost you, and it runs the scenario and builds the resulting model directly in Condor. It doesn't just surface a number. It gives you the "why," and then it does the work.

This is a big milestone for our company and industry. It's also the moment I've been building toward since the day I started Condor.

The vision I had five years ago

When I founded Condor, I believed the financial machinery underneath every clinical trial could be fully automated, end-to-end, with AI reasoning on top of it. 

No more manually managing or outsourcing your finances. The numbers, built by an engine you can trust. Workflows run by AI. The why behind the numbers, uncovered in seconds instead of weeks, while there’s still time to act. 

Our new agent is the realization of that vision.

Why it took five years

Building AI that produces numbers you can actually trust is unbelievably hard. 

It took building Condor's financial engine first — a deterministic layer that follows defined rules, produces consistent output every time, and is fully auditable. No guessing, no black box, no "the model thinks it's probably right." Every number has to tie back to the clinical activity that actually drove it, because in this industry, a number nobody can defend is a number nobody will use.

It took building a knowledge graph grounded in a clinical and financial ontology we developed over years of work with Big 4 accounting firms — mapping how budgets, vendor contracts, clinical sites, and clinical activity actually connect to each other, across hundreds of studies and therapeutic areas. That ontology is what lets our agents understand a change order or a forecast variance the way a clinical finance team does, instead of the way a generic model guesses.

We built all of that first, five years ago, before there was a market pulling us to do it, because we knew it was the only foundation AI could stand on and still be trusted with a number that ends up in a board deck.

Recently, competitors that built their entire business model around outsourcing clinical finance — putting bodies behind the work instead of automating it — have realized AI is where our industry is headed. They're years behind, so the best they can offer is AI bolted on top of their services model. 

Layering AI onto a services model doesn't change what the AI is standing on. If the underlying data was never built for automation — if it was always meant to be assembled by a person — AI on top of it can move faster, but it can't reason with the same grounding as our AI platform. It will take those companies years to build what we have been building for the last five years, because an ontology and a knowledge graph like ours can't be retrofitted. They have to be the starting point.

Why our AI platform matters now, more than ever

For most of the last century, science was the bottleneck in drug development. AI is closing that gap fast, and pipelines are about to fill with more candidates than this industry has ever had to fund at once. Every one of those candidates still has to be forecasted, funded, and managed. Right now, the financial infrastructure doing that job is still, for almost everyone, a spreadsheet.

The bottleneck didn't disappear. It moved from the lab to the ledger. Today, Condor is the only platform built from the ground up to run biopharma R&D finance and operations at the scale AI-driven pipelines are about to demand. Because we built the engine, then the knowledge graph, then the agents, in that order, on purpose.

What comes next 

Our Clinical Finance Agent is one of a growing team of agents built on Condor's knowledge graph. Each one, including our forthcoming investigator grant agent, is purpose-built to remove a specific piece of manual work slowing R&D finance and clinical operations teams down.

My vision is here: No more manually managing your finances. The numbers built by our engine. The workflows run by AI. The why behind the numbers uncovered in seconds, not weeks, while there’s still time to act. 

This is the start of something big for our industry. 

If you want to see the agent for yourself, book a demo.

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AI
August 30, 2026

Reimagining Investigator Grants with AI

Jeff Jahnke
Director, Customer Operations, Condor

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.

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AI
July 26, 2026

Reimagining CRO Change Order Management with AI

Jeff Jahnke
Director, Customer Operations, Condor

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?"

The answer is yes, absolutely. To help, we’ve kicked off a series we call Practical Application of AI in Clinical Finance & Ops. It’s built around one simple premise: real use cases, real workflows, real results. No theory or hype. Just what actually works.

Our first webinar focused on the three levels of AI maturity, the AI mindset teams must have to successfully implement and scale AI, and how AI is evolving and what that means for pharma clinical finance and ops teams. 

Our latest webinar focused on reimagining CRO changes order management with AI. 

Why Focus on CRO Change Orders?

If you work in clinical finance or clinical operations, you don't need convincing on this one. Change orders are one of the most painful, recurring problems, and the data backs up what everyone already feels. Substantial protocol amendments, which are the main trigger for change orders, have gone from affecting 57% of trials a decade ago to 76% today. In Phase 3, it's 82%. On average, a Phase 3 change order runs $535,000, which is close to four times the Phase 2 figure, and takes about three months to negotiate.

Scale that across a typical portfolio, say eight active studies, and you're looking at 40 to 50 change orders across the portfolio, including ancillary vendors. That represents $10 million or more in unplanned, unbudgeted costs. Meanwhile, change order volume has roughly doubled over the past decade, while finance headcount at a typical biotech has stayed flat. That gap gets closed one of three ways: with tooling, headcount, or with weekends.

On top of the volume problem, change orders are just hard to review. The explanations on each line item are often a sentence or two. CROs sometimes lowball the initial bid, knowing the change order process is where they recover margin. And retrospective work, cost for activity that already happened, routinely shows up buried in a document you're given a week or two to evaluate. It's a lot of financial exposure moving through a process with very little tooling behind it. That combination of high stakes and low tooling made it the obvious place to start.

How to Reimagine Change Order Management with AI

In the webinar, we walked through the same change order budget three different ways: first with generic AI, Microsoft Copilot. Then with Condor's purpose-built AI, Talon. And finally by flipping the process entirely and modeling the scope change in Condor before the CRO's document even arrives.

Using Microsoft Copilot

One of the most common requests after the panel was for something people could go use immediately. If you have Microsoft 365, you already have Copilot, and that's genuinely a useful starting point for a first pass at any change order. Here's the exact prompt we used in the webinar:

"I'm a finance director at a biotech company. I've received the attached change order budget from a CRO for a Phase 3 study. Please analyze it and give me a summary of the total cost change and the top five largest cost increases, a breakdown of direct fees versus pass-through costs versus investigator costs, any line items that appear to be retroactive or cover work already performed, and a list of five questions I should ask the CRO before approving this change order. Format the output as a brief memo I can share with my CFO."

A few things worth noting about why this prompt works: it gives the model a role ("I'm a finance director"), a specific task, and a defined list of deliverables. The more context you give a general-purpose model before you hit send, the better the output. Attach your change order file, run this, and in under a minute you'll have a readable first-pass memo.

Just know where it stops. A general-purpose tool can tell you what a document says. It can't tie units back to your original contract, it has no knowledge of your protocol history, and it won't reliably catch unlabeled retroactive work. It gets you from zero to informed. It doesn't get you to defensible. That's the gap purpose-built, ontology-driven tools like Talon are built to close, and it's why the answers looked meaningfully different once we ran the same budget through a model that actually understood the contract underneath it.

From "What Changed" to "What's Wrong": The Talon Demo

That's the gap we closed with the next demo. I ran the exact same change order budget through Talon, an AI-powered service offering we’re rolling out to customers, which has our clinical and financial ontology built into it. Instead of just summarizing what changed, Talon made judgment calls on every line item: it flagged that roughly 94% of the total dollar delta was unjustified, inadequately supported, or needed clarification, and sorted every line into a clear disposition, hard no, negotiate, needs clarification, or accept. It landed on a negotiation target of $1.4 to $1.7 million in recoverable savings, with a realistic settlement range of $556,000 to $856,000 given how CROs typically respond to pushback.

The reasoning behind that is what makes it useful. On the largest cost driver in the change order, added monitoring visits, Talon explained that monitoring spend is mechanically tied to patient volume, not site count, and since patient counts hadn't changed, the increase didn't hold up. It reverse-engineered the expected cost from the original contract's drivers (per-patient procedures, per-site fees) and showed the gap between that and what the CRO was billing, with every finding linked back to the source cell in the change order budget grid. Microsoft Copilot told us what the document said. Talon told us what it was hiding, and gave us a negotiation brief to send back to the CRO.

Getting Ahead of the Change Order Entirely: The Condor Scenario Planning Demo

The last demo flipped the sequence altogether. Instead of waiting for a CRO's change order and then analyzing it, I modeled the scope change myself, before any document arrived, directly in Condor's production environment. I typed a plain-language prompt asking the system to forecast a scenario for the study: expand from seven to 11 sites, keep the 48-month duration and hold patient counts flat, then show the incremental cost against the current $10.67 million contract value.

Condor's forecasting agent built that scenario in real time, referencing the study's actual contract structure and cost drivers, and landed on a $629,000 incremental increase. Compare that to the $2 million-plus the CRO's change order was requesting for the same scope, and the gap tells you everything: once you strip out costs that shouldn't move when patient volume doesn't (like a big chunk of that monitoring spend), the real cost of the change is a fraction of what was billed. That's the value of scenario planning: you walk into the negotiation with your own independent number already in hand, instead of reacting to whatever number the CRO sends first.

Here’s the webinar recording.

We’ve trimmed the Talon and Condor demos from it. Want to see all the demos, and see what this looks like with your own data? Book a demo with our team here.

What's Next in the Series

As I mentioned, this is the second of several webinars and workshops in our Practical Applications of AI in Clinical Finance & Ops series. We polled attendees live during the webinar on what they wanted to see next, and the results were clear. Here are a few of the topics we’ll cover:

  • Monitoring vendor spend pacing against contract value and identifying anomalies in vendor invoices (the top pick)
  • Building a first trial budget from a protocol
  • Building an AI prompt library for your clinical finance team

We'll be running these both as webinars and as in-person live workshops at upcoming industry events, where you can bring your laptop and work through the exercises with us in real time. Stay tuned for updates!

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