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AI Is About to Break the CRO Pricing Model 

Expert insights by Rich Polgar, Managing Director, Danforth Advisors 

For decades, CRO pricing has largely been built on a simple concept: 

Hours × activities = unit price. 

A CRO estimates how many hours it will take a functional expert to complete an activity, applies the relevant labor cost and margin, and converts that calculation into a unit price. 

It is essentially an activity-based costing (ABC) model. 

Sponsors rarely see all of the assumptions underneath that calculation. CROs generally do not provide the hours behind each unit unless specifically asked, and many proposals blend multiple activities into broader units. 

However, AI creates a new challenge to the model. 

What Happens When Hours are No Longer a Good Proxy for Value? 

Consider an activity historically priced assuming 40 hours of work. 

If AI can complete the same deliverable with equivalent or better quality in 15 hours, should the price immediately fall by more than 60%? 

Probably not. 

The sponsor is purchasing the output, expertise, infrastructure, accountability, and risk associated with the deliverable, not simply 15 hours of someone’s time. 

That begins to move the CRO industry away from pure activity-based costing and toward something much closer to value-based pricing. 

And that transition could have enormous implications for CRO economics. 

The Five-Year Pricing Problem 

Clinical development makes this transition particularly interesting because sponsors routinely negotiate prices today for work that will not occur for years. 

Think about a Phase II or Phase III program being contracted today. 

The sponsor may already be locking in unit prices for: 

  • Biostatistical analyses 
  • TLF development 
  • Data review 
  • Database activities 
  • Statistical programming 
  • Clinical Study Report writing 
  • Submission-support activities 

Some of those tasks may not occur for three, four, or even five years. Now consider how much AI-assisted clinical work could change during that period. 

We are effectively negotiating 2026 prices for work that might be performed in 2030 or 2031 using an entirely different operating model. 

If a CRO prices a CSR today based on its current operating assumptions, but five years from now AI enables the organization to produce that CSR using a fraction of the labor, the CRO could generate substantially greater margin on that unit. 

That isn’t necessarily unfair. 

CROs are making substantial investments in technology, AI platforms, data architecture, training, validation, process redesign, and change management. The companies taking the investment risk should expect to capture some of the economic benefit. 

But it changes the pricing conversation. 

AI Doesn’t Automatically Mean Lower CRO Prices 

There is an assumption among some sponsors that AI will simply make CRO services cheaper. 

Eventually, it probably will. But I don’t expect that to happen immediately. 

The first financial impact of AI may actually be margin expansion for the CROs that implement it successfully. 

Imagine two CROs charging the same $100,000 for a deliverable. One still requires 500 labor hours to produce it. The other has redesigned its workflow around AI and requires 250 hours. 

From the sponsor’s perspective, the output may look identical. From the CRO’s perspective, the economics are dramatically different. 

That is why the winners from AI will not necessarily be the CROs with the most AI press releases. They will be the CROs that can systematically reduce the amount of human effort without reducing the value, quality, or accountability of the service. 

The Largest CROs May Have an Early Advantage 

In the short term, I believe the larger CROs may be particularly well positioned to benefit. 

Large CROs have enormous volumes of historical data, repeatable processes, global delivery organizations, and thousands of employees performing similar activities across hundreds of studies. 

A 10% productivity improvement across that infrastructure is meaningful. 

A 30% improvement is transformative. 

And once an AI-enabled process is developed, validated, and deployed, the incremental benefit can be repeated across a very large book of business, creating the potential for substantial operating leverage. 

Smaller and mid-sized CROs can absolutely compete (and in some cases may be able to innovate faster), but they will need to be deliberate about where they invest rather than trying to replicate the technology infrastructure of the largest providers. 

Sponsors Need to Rethink Procurement Too 

This isn’t only a CRO issue. Sponsor procurement and outsourcing teams also need to evolve. 

If we continue evaluating CRO proposals primarily by comparing hundreds or thousands of unit prices, we may increasingly be measuring the wrong thing. 

The better questions may become: 

  • What outcome are we buying? 
  • How much operational risk is the CRO assuming? 
  • How quickly can the work be completed? 
  • What technology and AI capabilities are embedded in the delivery model? 
  • How much of the productivity benefit is being shared with the sponsor? 

And perhaps most importantly: Are we paying for effort, or are we paying for value? 

I don’t think activity-based pricing disappears tomorrow. Sponsors will still need units, assumptions, and transparency to manage budgets and change orders. 

But I do think we are at the beginning of a significant transition. 

What This Means for Biotech Sponsors 

At Danforth Health, we manage clinical outsourcing across multiple biotech companies, applying best practices in CRO sourcing, negotiation, and governance across a broad portfolio of clinical programs. 

That perspective allows us to see how CRO pricing, commercial models, and contracting practices are evolving across the market, not just within a single sponsor or sourcing event. 

As AI changes the economics of clinical delivery, sponsors will need greater transparency into how CROs build their pricing, stronger protections around scope changes and change orders, and more sophisticated ways to assess whether they are receiving appropriate value. 

If you are looking to improve the outcomes, pricing, and transparency of your CRO relationships, contact us to discuss how Danforth Health can help. 

AI in Clinical Outsourcing: What’s Signal, What’s Noise? 

Written by Rene Stephens, Managing Director, Clinical Business Operations, Danforth Health  

AI in clinical development is everywhere right now. Headlines promise transformation. Vendors promise acceleration. Teams are running pilots. 

But beneath the noise, where does the industry actually stand? 

To better understand the reality, Danforth Health conducted a focused industry pulse survey both before the 2026 SCOPE Summit Conference and during a live panel discussion with Outsourcing, Clinical Operations, and healthcare Innovation experts on The Impact of AI on Outsourcing and Clinical Trial Execution. The findings provide a candid snapshot of adoption, constraints, and where real value is (and isn’t) emerging.  

View the survey results here and continue reading for the 5 biggest takeaways… 

1. AI Adoption Is Real — but Still Fragmented 

Survey respondents confirmed that AI is no longer theoretical in clinical development, yet enterprise-wide adoption remains the exception, not the rule. 

  • 22% report actively using AI in a subset of trials 
  • 30% are implementing or formally allowing AI use 
  • 37% are still evaluating 

Nearly 90% of organizations are somewhere on the adoption curve, but most remain in pilot mode, functional silos, or narrowly defined use cases rather than scaled transformation. 

The takeaway: AI credibility has arrived. Operational maturity has not. 

2. Deployment Is Concentrated Where Risk Is Measurable 

Where AI is being deployed, the survey shows strong clustering around activities with: 

  • High data density 
  • Clear quality metrics 
  • Auditable outputs 

Respondents reported AI usage primarily in: 

  • Data capture and quality oversight 
  • Protocol design and optimization 
  • Patient population and cohort identification 
  • CSR preparation and submission support 

Adoption is split between internally built tools and vendor/CRO-provided solutions, raising a strategic question many sponsors are still debating:

Is AI a capability to own, or a service to buy?  

3. The Business Case Is Speed — Not Headcount Reduction 

One of the clearest signals from both survey responses and the panel discussion: 

AI’s value is being measured in cycle-time compression, not FTE elimination. 

External benchmarks point to an average six-month reduction in development timelines per asset driven by: 

  • Improved protocol feasibility 
  • Faster cohort identification 
  • Earlier quality signal detection 

Sponsors are not viewing AI as a replacement for CROs or clinical teams. Instead, it’s emerging as an execution accelerator, a tool to reduce rework, friction, and downstream risk. 

4. Governance, Not Technology, Is the Primary Bottleneck 

The biggest obstacles aren’t algorithmic; they’re organizational and are likely more difficult to overcome without a deliberate approach to change management.  

Transparent positioning of the impact to current and future functions, both positive and potentially negative, and honest discussions with key stakeholders to address changes to operating processes and team roles, among others, are key considerations for POC pilots as well as full implementation. 

Despite optimism, respondents expressed clear confounders to value: 

  • Unclear ownership across Clinical Ops, IT, Data Science, and Procurement 
  • Variability in CRO AI maturity and transparency 
  • Questions around validation, auditability, and regulatory defensibility 

At the same time, ICH E6(R3) and the FDA Diversity Action Plan (effective January 1, 2026) will increasingly force sponsors to integrate AI into risk-based quality management and enrollment analytics, whether they feel operationally ready or not. 

In short: AI adoption is no longer optional, but unmanaged adoption is risky. 

5. 2026–2027 Will Separate Experimenters from Operators 

The survey reinforces a forward-looking conclusion: 

  • Organizations can realize measurable gains in speed and predictability, and possibly see cost savings when AI is embedded into core trial design and operations (i.e., feasibility, site ID, data management, safety reporting, etc.) 
  • Those that treat AI as an “innovation sidecar” risk falling behind, even if they run more pilots 

This is less about tools and more about operating model design: 

  • Sponsor-led vs. CRO-led AI 
  • Centralized vs. functional deployment 
  • Governance that enables scale without slowing execution 

My Thoughts? 

This industry pulse survey’s results tell a clear story: 

AI in clinical trials has crossed the credibility threshold—but not the execution threshold. 

The next phase will be defined less by better algorithms and more by better integration: 

  • Into outsourcing strategy 
  • Into vendor governance 
  • Into clinical operating models 

That is where real competitive advantage will be created. 

If your team is navigating similar decisions around AI adoption and governance, we’d welcome the opportunity to compare perspectives. Reach out for a brief follow-up discussion to explore how your approach aligns with broader industry trends.