Start Study Budgets Sooner With AI-Enabled Protocol Translation

Sponsors trying to bring new treatments to market faster face a familiar set of bottlenecks, including contract negotiations, site activation, and recruitment. But one constraint that can also slow them down is less discussed — protocol translation.

Before budget builders can create a study budget, they often must manually translate protocols into workable budget inputs. This process requires budget builders to not only retrieve details from complex protocols, manually re-enter data, and verify accuracy, but also identify and select the appropriate line items from potentially hundreds of options.

Determining which procedures, visits, and activities belong in the budget can be a tedious and error-prone task that demands significant time and expertise. As protocol complexity continues to increase, manual protocol translation is becoming increasingly unsustainable. But there is a better way forward — using AI to automate the process.

This can reduce the risk of errors and allow budgeting to start sooner, while keeping humans at the center of the process to manage budget complexity and confirm data accuracy.

Taking on layers of complexity

AI helps streamline a key portion of budget creation by reducing burdens on human budget builders. At the heart of budget creation lies protocol interpretation.

Budget builders must review and analyze study procedures, visit schedules, assessments, and protocol requirements before they can begin building an accurate budget. Even for relatively simple studies, this process can take hours. It also introduces several challenges.

The first is understanding the protocol itself. Even experienced budget builders can spend significant amounts of time deciphering protocol language before they can begin creating a usable study budget.

Another challenge is the manual work involved in the process. Even with a user-friendly, digital budgeting platform, entering all the required schedule of activities and schedule of events data can take a long time.

Finally, activity-code selection is labor-intensive and prone to mistakes. Determining which codes belong in which section can be frustrating.

Selecting the incorrect codes can result in budgeting errors and further lost time before negotiations. AI provides relief by extracting and structuring protocol data, mapping activities to budget inputs, and creating a first-pass budget framework for review.

This not only shortens the path from protocol approval to budget creation, but it also elevates the work of budget builders.

A new role for budget builders

Automating protocol translation enables budget builders to shift their focus — from doing to reviewing. Instead of spending time on manual tasks, such as data entry, they can put their time and attention toward higher value activities, such as validation, refinement, and strategic adjustments.

All of this enables budget builders to create budgets faster and begin negotiations sooner in support of quicker study startup. For sponsors and contract research organizations, one of the most meaningful benefits of AI-enabled automation is the ability to apply experienced budget builders’ expertise where it delivers the greatest value: managing complexity, evaluating exceptions, and supporting better financial decision-making.

One misconception about AI is that it can simply take over processes such as protocol translation. In practice, AI does not replace or remove humans from the process; it changes how their expertise is applied.

Over time, AI solutions can learn different protocol structures and study designs, as well as inputs such as procedures, visits, and footnotes. But because protocol language is complex and variable, human budget builders still need to review AI-generated outputs to confirm that data are captured correctly.

Additionally, protocols do not always contain all the elements needed to create a budget. For example, non-procedures typically are not outlined in a protocol.

AI can help by incorporating default activities and predefined codes that give budget builders a starting point. From there, budget builders can validate what has been included, add what is missing, and verify that the budget aligns with sponsor requirements.

Part of a larger transformation

Protocol translation is one piece of the larger AI transformation under way in clinical trial financial management. Financial activities touch nearly every stage of the study lifecycle, and finance-related delays risk impeding trial progress.

As AI-enabled automation evolves, its value is expected to come not only from accelerating tasks but also from improving financial visibility and reducing mistakes and rework that can slow studies. Protocol translation is an early example of this shift. Ingestion of clinical trial agreements (CTAs) is another.

Today, a single trial can generate hundreds of CTAs, and entering their data into payment systems is laborious manual work for financial teams and prone to errors. Teams can use AI to ingest CTAs, extract key data from them, and structure those data for payment systems.

This reduces the need for teams to manually re-enter data and instead allows them to focus their attention on tasks such as verification and exception handling. Protocol translation and CTA ingestion are notable because both occur early in clinical trial financial workflows.

Delays, manual interpretation, and data entry challenges at these stages can create ripple effects throughout the study lifecycle. By helping teams structure and validate information sooner, AI has the potential to reduce rework before it affects budgeting, contracting, and payment activities.

Over time, AI use is likely to expand into other areas, such as forecasting, anomaly detection, and reconciliation. This expansion is expected to create even more opportunities to uncover new efficiencies, reduce rework, and further accelerate study startup.

Accelerating with control

Clinical trial financial management can happen more seamlessly when sponsors successfully combine automation and human expertise. Those that get this balance right can not only build budgets faster but also execute financial activities with greater efficiency, consistency, and control.

As study complexity continues to grow, the organizations that gain the most value from AI may not be those seeking to replace human expertise, but those using automation to apply that expertise more effectively across the study lifecycle.

About the Author

Deepak Dwivedi, Director, Product Management, IQVIA Technologies. Dwivedi is a product leader at IQVIA with more than 20 years of experience leading strategy and innovation for life sciences clients. He plays a key role in advancing capabilities within the Clinical Trial Financial Suite (CTFS), with a particular focus on solutions such as CTFS GrantPlan, CTFS Site Payments, and CTFS Participant Payments. In his role, Dwivedi contributes to the development of digital approaches that help sponsors and research sites better manage clinical trial financial planning, grants, and budgeting processes. He is known for translating complex operational challenges into scalable, customer-centric product solutions that support greater efficiency, transparency, and control across global clinical trials.

Aaron Squires, Product Manager, IQVIA Technologies. Squires is a product manager for the CTFS GrantPlan application, bringing more than 20 years of experience in clinical trial budgeting and forecasting. He has a strong track record of leading enterprise technology initiatives and cross-functional teams, with deep expertise in aligning business and clinical requirements to scalable fair market value (FMV) solutions. Squires focuses on enabling faster, more efficient study startup by reducing complexity in budgeting, forecasting, and FMV decision-making. He partners closely with clinical, operational, and technology stakeholders to deliver measurable business impact across global trial programs.