Updesh Dosanjh, practice leader, PV Technology Solutions, IQVIA explores the lesson: digital transformation in pharmacovigilance (PV) must be preceded by foundational business transformation.
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The function of pharmaceutical safety continues to evolve as organisations are turning to artificial intelligence (AI) to manage mounting data volumes and complexity.
A growing challenge
Pharmaceutical companies face mounting pressures in PV operations that cannot be addressed effectively with traditional approaches. The exponential growth in the volume and complexity of safety data, driven by diverse clinical trials, increasing patient reporting and expanding digital channels, has strained manual processes.
A recent tier 1 client, Sanofi, is a prime example, processing over 600,000 adverse event report versions annually, with 5-20 percent growth in case volumes each year. Traditional workflows reliant on manual inputs to maintain compliance across a complex global regulatory landscape. More about how Sanofi implemented a business transformation framework to enhance their AI usage is discussed below.
Technology alone is not enough
The harsh reality is that most pharmaceutical companies struggle to achieve their goals with AI adoption because they are attempting to add new tech on top of existing workflows instead of transforming from the ground up. This is utilising AI as a supporting tool instead of a transformational enabler. They are locked into existing workflows and processes, when true success with AI requires re-engineering workflows and not just adding AI to existing processes.
For many organisations, the root issue is institutional inertia. Certain divisions within pharma companies have historically perceived PV as a cost centre, rather than a value generator, and tend to hold onto legacy processes that are no longer fit for purpose.
Pharma companies are not alone. MIT’s State of AI in Business 2025 report said that despite $30-40 billion spent on enterprise GenAI, 95% of organisations are seeing zero ROI from their AI initiatives. The research exposes what has been called the "GenAI Divide" — a gap that cuts across every market segment, from enterprise and mid-market organisations to small and medium-sized businesses, and affects everyone from startups and vendors to consultancies.
On one side of this divide is 5 percent of organisations whose end-to-end AI pilots are yielding millions in measurable value. On the other, the significant majority are stalled with no discernible
impact on their profit-and-loss statements. This divide doesn't appear to be the outcome of model quality disparities or regulatory constraints; rather, it's purely driven by methodology.
The business transformation framework
AI adoption must begin with wholesale business transformation along four key dimensions:
- Workflow Reimagining. This involves completely re-engineering PV processes based on AI capability instead of attempting to insert AI into conventional workflows. Organisations should identify what is required for regulatory and business stakeholders; not the process, but the outcomes. Use this as the springboard to challenge every process.
- Strategic Human Oversight. Successful transformation does not rely on blanket automation. It puts qualified professionals at key decision points while automating routine tasks. This approach recognises that human expertise remains invaluable for compliance and ethical questions, particularly in healthcare settings.
- Value Redirection. The objective is to shift skilled resources away from routine administrative tasks to high-value analytical tasks. This realignment enhances both efficiency and safety results by permitting expertise to be concentrated where it has the greatest impact — medical review and complex decision-making. In addition, users are given time to train on AI skills and knowledge to prepare for the evolving capabilities of AI and to ensure a continually smooth adoption. Bringing AI skills into a business can drive process optimisation and enhance data analysis. Having this mindset ensures companies really focus on what matters and is a good way to ask whether processes and skills should remain as-is.
- Cultural Change Management. Perhaps the toughest aspect is overcoming deep-seated organisational barriers and perceived regulatory challenges through evidence-based implementation. Workers who have mastered current processes may feel threatened by automation, requiring tactful change-management that demonstrations on how AI augments human expertise, instead of replacing it.
Measurable impact
This business transformation strategy has yielded impressive results that confirm the approach. Organisations have realised huge operational efficiency gains while ensuring complete regulatory compliance, proving that automation and regulatory compliance are not incompatible. Most importantly, these implementations have received regulatory acceptance by FDA and EMA for properly implemented AI solutions, creating precedents for future innovations. Improved patient engagement has also been an outcome, because call agents have more time to engage with patients in a meaningful way instead of being bogged down by administrative processes.
Strategic value creation
This transformation plan shifts PV from a compliance cost centre to a strategic asset on various levels. Patient-centric benefits emerge as the primary value driver. Call agents spend more time engaging with patients, building valuable relationships that were previously unaffordable due to the administrative burden. Improved data quality and faster processing enable more responsive patient care and improved potential to identify critical safety signals earlier in the process, meaning problems are caught earlier in the cycle.
The plan also promotes building regulatory trust through systematic steps that supply data provenance and promote proper governance at every step. Maintaining proper human oversight of machine automated processes offers assurance to regulators that essential decisions still incorporate human judgment. An open, evidence-based implementation approach earns the credibility of regulatory agencies and offers clear, auditable trails.
Operational excellence transforms how organisations utilise their most valuable assets. By allocating assets to augment signal handling and analytic activities, specialists are freed to focus on complex decision-making instead of routine processing. Scalable templates adapt to diverse regulatory climates, enabling worldwide institutions to be uniform while adhering to localised requirements.
This increased standardisation improves reliability in processes, reduces errors and improves overall operational performance.
Key success factors
Organisations seeking similar transformation should prioritise a series of critical success factors. Shared leadership and cross-functional alignment between business and digital teams are central to success. Siloed approaches often fail to recognise the interconnectedness of business and technology, making early and sustained integration across departments critical.
Controlled implementation minimises risks and enables adaptation throughout the organisation. Big changes can be made as long a process is in place to deal with issues in a systematic fashion and continue to develop confidence in AI-based solutions.
Preserving human oversight remains essential, even as automation brings greater efficiency. Human judgment is critical for compliance and ethical matters, particularly in healthcare settings where patient safety is paramount. The goal is always to augment, and not substitute, human capabilities.
Finally, change-management paves the way for success by engaging stakeholders throughout, creating alignment and facilitating smooth adoption of new processes and systems. This entails addressing concerns proactively and demonstrating value at each implementation phase.
The future of pharmacovigilance
As governments and regulators continue to develop frameworks for evaluating AI implementations, organisations that have had their business processes re-engineered will be well-positioned to derive maximum value from AI while ensuring patient safety remains the paramount consideration. The path ahead entails incorporating intelligent workflows that adapt in real-time to evolving regulatory stipulations. AI-driven tools will enable the faster identification of priority cases, flagging them for immediate review while filtering routine cases with minimal or no human intervention.
Conclusion
The pharmaceutical industry stands at a juncture where the volume and complexity of safety data demand revolutionary solutions. However, technology alone cannot provide solutions to such problems. Those companies that realise the necessity of fundamental business transformation — workflow redesign, human talent repositioning and management of cultural change — will be better positioned to successfully harness AI's potential.
Effective deployments demonstrate that when business transformation precedes technology implementation, the results can be revolutionary: dazzling efficiency gains, improved patient safety outcomes and regulatory approval. As the industry continues to evolve, this approach will mark the difference between organisations that truly transform and others that merely install new tools.
The future of PV is not in choosing between human judgment and AI, but in tactically integrating both through comprehensive business transformation with patient safety and operational excellence at its centre.