AI can simplify financial management in clinical trials, but only when applied through a clear, well-defined strategy, said Sitaram Srivatsavai, senior director and head of engineering for product suites at IQVIA. For CFOs and finance executives, AI is moving from a future consideration to a leadership competency that determines how financial operations scale, stay compliant and support faster trial execution.
"Organisations that benefit most from AI will be those with a strategy that treats it as an end-to-end transformation, not a point solution," Srivatsavai said.
Identifying high-value use cases
Clinical trial agreement (CTA) processing remains a manual, high-volume task. A single trial may require data for hundreds of CTAs, each entered by hand into a payment system. AI can ingest data from a CTA, extract unstructured information and render it into a structured format, saving significant time.
Three signals indicate where AI can deliver the most value: repetitive, high-volume workflows; complex manual processes that are error-prone; and workflows with a clear path for human review. These signals help finance leaders prioritize investments that show near-term returns while building trust and momentum. Starting with contained, reviewable workflows allows organizations to demonstrate value early.
Justifying the investment
Applying AI for Finance to clinical trials requires a clear business case. Benchmark data and proofs of concept can show feasibility, but leadership also needs to understand how end-to-end processes will improve. Questions about shortening the time from contract execution to first site payment, reducing errors and rework, and cutting back-and-forth between teams are central to the justification.
"When AI investments are tied to clearer visibility, fewer handoffs and faster financial execution, the business case becomes less about savings through automation and more about enabling better decisions," Srivatsavai said. This shift makes the investment harder to refuse.
Data, workflows and trust
A common misconception is that AI requires perfectly clean data. Waiting for perfect data stalls implementations. Instead, organizations should assess whether data is structurally consistent and relevant enough to start. While AI can help interpret data, fragmented systems with missing relationships and inconsistent identifiers remain a constraint. Unifying financial systems on a common data model is a necessary step.
AI deployments also require rethinking how work gets done. Redefining roles, decision points and workflow ownership is as important as the technology itself. An AI solution for CTA processing must work operationally, ingesting contracts from wherever they reside-a contracting system, a PDF, a Word document or an email.
Human involvement remains central. AI automates routine tasks, freeing finance teams to focus on interpreting contracts, resolving exceptions and managing stakeholder relationships. Trust depends on transparency. Features like confidence scoring, anomaly escalation and visibility into source data help teams embrace AI.
Governance sustains adoption
Strong governance does not slow AI adoption; it makes it sustainable. AI-enabled processes must meet the same auditability, traceability and compliance standards as traditional ones. Organizations should establish validation checkpoints, exception thresholds and reconciliation processes. AI cannot operate as a black box-it must provide visibility into data sources and decision logic.
Why this matters for finance leaders
AI in clinical trial finance is not a technology upgrade; it is an operational transformation. Finance leaders who approach AI as a long-term capability, aligned across data, workflows and governance, will be able to scale financial operations with confidence. Success depends on how deliberately AI is embedded into how trials are financed and managed, not on how quickly it is deployed. For those overseeing clinical trial budgets, the priority is to start with high-volume, reviewable workflows, build a unified data foundation, and enforce governance that earns trust.
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