By Sandy Tammisetty, VP of the Veeva Services Practice Group at Conexus Solutions. 

Artificial intelligence (AI) is no longer a future concept in clinical trials. It is already influencing how clinical data is reviewed, monitored, and analysed, particularly as trials become more complex and data volumes continue to grow. From anomaly detection to advanced analytics, AI is increasingly positioned as a solution to the mounting pressure facing clinical data teams. 

For sponsor organisations, especially small and mid-sized pharmaceutical and biotechnology companies, this creates both opportunity and uncertainty. While AI promises efficiency and earlier insight, clinical data management remains a GxP-regulated function where data integrity, traceability, and accountability are non-negotiable. 

That tension is where many AI initiatives struggle. Understanding where AI can realistically add value, and where caution is required, is critical for sponsors navigating this evolving landscape. 

Why AI Is gaining traction in clinical data management 

Clinical trials today generate data from a growing number of sources, including electronic data capture (EDC) systems, electronic patient-reported outcomes (ePRO), wearable devices, central laboratories, and other external vendors. Managing and reviewing this volume of data using traditional, manual processes is becoming increasingly complex. 

At the same time, expectations have shifted. Clinical teams are under pressure to identify potential data quality issues and clinical risks earlier in the trial, rather than discovering them late in the process or at database lock. Regulators and internal stakeholders alike expect sponsors to demonstrate proactive oversight across increasingly distributed data landscapes. 

AI appears well-suited to help address these challenges. By identifying patterns, surfacing anomalies, and prioritising risk areas, AI can help data teams focus their efforts more effectively. In principle, this makes sense. In practice, regulated clinical environments introduce significant constraints that must be carefully managed. 

The hype vs. the reality in GxP environments 

Much of the hype surrounding AI suggests it can automate data cleaning, significantly reduce manual effort, and accelerate database locking with minimal human involvement. These claims often overlook the realities of working in GxP-regulated environments. 

In clinical data management, systems must be validated, decisions must be traceable, and outputs must be explainable. Most importantly, accountability must remain clear and firmly assigned to qualified individuals. 

AI can support clinical data management activities, but it cannot operate outside governed workflows. An algorithm that flags a potential anomaly only adds value if the underlying data source is trusted, the logic behind the output is understandable, and the resulting action is documented in an auditable manner. Without these controls, AI introduces risk rather than reducing it. 

Where AI Is delivering value today 

When applied thoughtfully, AI is already delivering measurable value in several areas of clinical data management. 

Many sponsors are using AI-enabled analytics to identify data quality issues and outliers earlier in the trial lifecycle. Rather than relying solely on manual review, teams can be alerted to unusual patterns that warrant closer investigation. 

AI is also being used to detect emerging trends that may indicate site-level issues, subject-level risks, or operational bottlenecks. This allows data managers and clinical teams to intervene earlier, when corrective action is more effective. 

Another area of value is risk-based prioritisation. By helping teams focus attention on higher-risk data, sites, or subjects, AI supports more efficient allocation of limited resources. In addition, AI-driven operational analytics can provide insight into data flow, query volume, and resolution timelines, helping teams improve overall trial execution. 

Notably, early success is most often seen in narrow, well-defined use cases, such as data quality signal detection or review prioritisation, rather than broad, end-to-end automation. In all these scenarios, AI works best as an augmentation tool. It supports human decision-making, but responsibility for interpretation and action remains with the clinical data team. 

Common pitfalls sponsors often underestimate 

Many of the risks associated with AI adoption in clinical data management are not technical; they are operational. 

One common pitfall is deploying AI tools outside validated systems of record. Even when these tools generate valuable insights, outputs produced outside governed environments can be difficult to operationalise and challenging to defend during audits or inspections. 

Another frequent issue is reliance on fragmented or unmanaged data pipelines. AI models are only as reliable as the data they consume, and inconsistent or poorly governed data sources undermine both accuracy and trust. For many sponsors, the challenge is not whether AI is available, but whether their current data, systems, and operating models are ready to support it. 

Lack of explainability is another critical concern. If clinical teams cannot clearly explain how an AI-generated output was produced, they may be reluctant, or unable, to act on it. Many organisations also underestimate the extent to which AI represents an operating model change rather than a simple technology implementation. 

In regulated clinical trials, speed without control does not create progress. It creates risk. 

AI in clinical data management is not a replacement for validation, oversight, or clinical judgment. It does not remove regulatory responsibility, nor does it shift accountability away from sponsors. While validation is sometimes viewed as an obstacle to AI adoption, it is ultimately what allows AI outputs to be trusted, operationalised, and defended during inspection. 

How AI is changing the role of clinical data managers 

AI is not eliminating the role of clinical data managers; it is elevating it. 

As automation increases, data managers are spending less time on manual data cleaning and more time serving as data stewards and oversight leaders. Their role increasingly centres on ensuring that data is reliable, traceable, and defensible. 

Clinical data managers are also becoming more deeply embedded in cross-functional teams, partnering closely with clinical operations, quality, regulatory, and IT colleagues. They act as quality gatekeepers, ensuring that AI-assisted insights are used appropriately, consistently, and with proper documentation. 

This shift places greater emphasis on judgment, governance, and communication – skills that are essential to successful AI adoption in regulated environments. 

What leaders should focus on now 

For clinical, IT, and data leaders evaluating AI, several priorities consistently emerge. Data quality and governance must be addressed before introducing AI at scale, as weak foundations will limit the value of even the most advanced analytics. AI should be embedded within validated systems of record whenever possible to support compliance and inspection readiness. 

Just as importantly, traceability and human oversight must be maintained so AI outputs are explainable, documented, and clearly owned. Finally, organisations should focus on readiness before scale, piloting AI in well-defined use cases to build confidence before broader deployment and approaching AI as a capability to be governed and evolved – not a tool to be deployed and forgotten. 

From automation to trust 

AI adoption in clinical data management is inevitable. Successful adoption is not. 

Organisations that realise value from AI will not be those chasing the most advanced algorithms. They will be those focused on building trust in their data, systems, and processes. 

In GxP-regulated environments, AI is not about replacing people. It is about helping people make better decisions, faster, without compromising the integrity that underpins clinical trials. In regulated trials, AI only works when people, processes, and platforms work together. That balance between innovation and trust is where AI truly works. 

About the author 

Sandy Tammisetty is the VP of Veeva Services Practive Group at Conexus Solutions. With over 15 years of experience in the life sciences industry, she oversees six teams, including Veeva Commercial and Vault Services. Sandy specialises in optimising client sales and marketing operations and has led over 40 Veeva implementations. She holds a Masters in Computer Science from Monmouth University, and MBA from the Indian Institute of Management Bangalore, and is certified in six Veeva specialties.