Sitaram Srivatsavai, senior director and head of engineering for product suites, IQVIA, explores how the right AI strategy can unlock faster, smarter financial operations.
Financial management underpins every clinical trial and can create friction that slows studies and strains research sites if not implemented strategically. Activities such as budget negotiations, invoice processing and payment distributions are often done manually and across disparate systems. AI can streamline these workflows, but only when it’s applied through a clear, well-defined strategy.
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Embedding AI in clinical trial financial management involves more than using the right tools. It requires selecting the right use cases, establishing governance and aligning the organisation while also addressing misconceptions about what AI can and can’t do. For sponsor and CRO leaders, AI is no longer a future consideration; it is becoming a leadership competency tied directly to 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.
Identifying use cases
AI can transform financial management across the clinical trial lifecycle. Opportunities to use it already exist in applications where fragmentation and manual work are commonplace.
One of these use cases is clinical trial agreement (CTA) processing. This work remains highly manual and of high volume. A single trial can require data for hundreds of CTAs, which must be manually entered into a payment system. AI can save significant time by ingesting data from a CTA, extracting the relevant unstructured information and rendering it into a more structured format.
Still, how and where organisations should use AI will depend on their unique needs. Three signals that indicate strong opportunities for AI include:
- Repetitive and high-volume workflows that are time-consuming
- Complex manual processes that are burdensome for staff and introduce the risk of errors
- Workflows with a clear path for human review that allow AI to assist without removing control.
For leadership teams, these signals provide a practical filter and help prioritise AI investments that deliver near‑term returns while building organisational trust and momentum.
Starting with contained, reviewable workflows allows organisations to demonstrate value early and shorten the path from experimentation to enterprise‑level adoption.
Justifying the investment
Even the most compelling AI demos need proof that they will create real-world improvements before leadership signs off on them.
Benchmark data can reveal what gains are possible, and proofs of concept can demonstrate feasibility. However, leadership also needs to understand how end-to-end processes will evolve and improve with AI embedded in them.
Will the time from contract execution to first site payment shorten? Will fewer errors lead to less rework? Will automation reduce time-consuming back-and-forth interactions between teams? At their core, these questions are about confidence, whether financial operations can keep pace with trial execution without introducing friction, delays or downstream risk.
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, which makes these investments harder to say no to.
Keys to a successful implementation
A successful AI deployment isn’t determined solely by the model used. A variety of organisational factors also play a significant role. They include:
Data readiness
A common misconception is that AI requires perfectly clean, standardised data before it can be deployed. The truth is, waiting for perfect data only stalls AI implementations and slows organisational progress.
Organisations should instead determine whether the data is good enough from the start to use. Is it structurally consistent and relevant to the problem they’re trying to solve? Even if all the necessary data isn’t available, financial systems or the AI model itself can be enhanced where needed to get to that “good enough” point.
At the same time, data fragmentation is a constraint. While AI can help interpret data, it can’t fully overcome all the challenges of data being produced by disparate systems with missing relationships and inconsistent identifiers. This is why organisations adopting AI should plan to unify their financial systems on a common data model.
Redesigned workflows
Embedding AI in workflows requires fundamentally rethinking how work gets done. AI deployments are not only technology upgrades but also operational shifts. To work, they require organisations to redefine roles, adjust decision points and rethink workflow ownership.
At the same time, it’s just as important that AI solutions align with the actual work of financial teams, sites and other stakeholders. Even if an AI model is technically strong, its odds of success are lower if it doesn’t work operationally. For example, an AI solution used for CTA processing will only be valuable to an organisation if it can ingest CTAs from wherever they reside, whether it’s in a contracting system, a PDF, a Word document or an email.
Human involvement
AI can automate repetitive, high-volume tasks, but humans remain at the centre of clinical trial financial-management activities. By automating routine work, AI allows financial teams to focus on complex tasks where their expertise is needed more, such as interpreting contracts, resolving exceptions and managing stakeholder relationships.
Earned trust
Teams will only embrace AI if they trust it. They may push back on it if they feel it’s unreliable, taking control of their work or creating compliance risks.
AI can earn trust through transparency, such as by showing users how it’s making decisions or how confident it is in its outputs. Features such as confidence scoring, anomaly escalation and visibility into source data can provide this transparency and help build trust.
The right governance
Strong governance doesn’t slow AI adoption; it makes it sustainable. Transparency, auditability and clear exception handling give teams the confidence to rely on AI outputs without sacrificing control or compliance. AI-enabled processes must meet the same standards of auditability, traceability and compliance as traditional processes. AI can’t operate in a “black box.” It should provide visibility into data sources and decision logic. Organisations should also establish validation checkpoints, exception thresholds and reconciliation processes to maintain accuracy.
Navigating a long-term change
AI is more than a technical upgrade. It’s an operational transformation. Organisations that approach AI as a long‑term capability aligned across data, workflows and governance will be best positioned to scale financial operations with confidence. For leaders, success will depend not on how quickly AI is deployed, but on how deliberately it is embedded into the way clinical trials are financed and managed.
