Artificial intelligence is no longer a future concept in healthcare. It is here, which has been integrated into daily operations, including simplifying drug care workflows, analyzing unstructured data, predicting operational disruptions and personalized care services. Yet despite the growing investment and adoption, it is still difficult for organizations to define what AI success looks like.
Executives often rely on narrow metrics such as cost savings, faster processing and fewer manual tasks. While these are great starting points, these measurements don’t tell the whole story. In an industry based on trust, detailed clinical judgment and human insight, the value of AI must go beyond automation.
Establish a broader framework for success
Instead of asking, “How many hours did we save?” Consider, “What milestones or achievements did we achieve at that time?” Does AI help security teams identify risks faster? Does it enable nursing managers to intervene earlier? Does it allow analysts to discover patterns that would otherwise not attract attention?
Financial ROI remains crucial, but in healthcare, the larger returns are usually human. This includes the ability to make better decisions, build stronger teams and ultimately improve patient outcomes.
Redefine AI success in the entire organization
Artificial intelligence not only improves performance—it improves morale. A 2025 survey found that 82% of employees using AI said it helped them deliver better jobs, while 58% reported stress relief. When organizations track employees’ emotions, adoption rates, and retention, they really gain insight into how AI supports rather than stressed workforces.
In addition to employee engagement, AI improves analytical accuracy and reduces risks in high-risk areas such as safety, diagnosis, and population health. In these areas, even small mistakes can lead to serious consequences. By standardizing how data is collected and evaluated by standardizing AI can reduce variability and marker inconsistencies early. In drug advocacy, this leads to a more consistent narrative and stronger signal detection. Clean data can make faster and more informed decisions across the healthcare ecosystem.
AI also improves measurable operational efficiency. When deployed effectively, AI reduces manual workloads, eliminates redundancy and accelerates timelines. Many pharmaceutical and provider organizations now use AI to process safety case data, classify patient records and generate regulatory reports. These applications reduce cycle times and help teams meet critical deadlines. In fact, Deloitte reports that the internal rate of return on Pharma AI investments rose to 1.2% from 2023 to 4.1%, indicating an increasingly converged consistency between technology investment and business strategy.
The requirement of success is not just an indicator
Tracking results is crucial, but it is not the only part of the equation. The most successful organizations recognize that the impact of AI depends on how they implement, support and control these systems. To go from adoption to transformation, healthcare leaders should focus on three strategic priorities.
- Investing culture is not just a tool: Even the best AI tools can be without the correct support. Top-down tasks usually fail to gain traction. Instead, leading organizations form cross-functional teams to define requirements, pilot solutions and redefine workflows. They identified internal champions or “superusers” who bridged the knowledge gap, trained peers and helped teams see AI as an asset rather than a destructive threat. Transparency is also important. Establishing explanatory on the dashboard, documenting AI decision-making pathways and participating in regulatory teams early can help align with frameworks such as EMA's good drug flow practices and other guidance. Trust fuel adoption, trust begins with clarity and engagement.
- Focus on user time using AI: The real value of AI lies in its ability to enhance (rather than edge) human expertise. Automating repetitive tasks, such as initial case review or documentation, allows skilled professionals to focus on complex, high-risk decisions. For example, in security reviews, AI-generated narratives provide experts with more time to evaluate emerging signals and high-risk cases. When used to enhance human insight, AI enhances judgment and accuracy.
- Prepare for supervision: Regulatory expectations are rising as AI systems become increasingly embedded in clinical and operational workflows. Now, agencies need detailed documentation to understand how the system works, who can override the output and how decisions are recorded. These are not only technical issues, but also enterprise-level responsibilities. Healthcare leaders must establish audit trails, test bias and maintain transparency. Ultimately, responsible governance will define the difference between tools that extend and stagnate.
Make AI stronger, not bend
Healthcare won't reward shortcuts. It rewards the results. AI will play a key role in shaping these results, but only if leaders measure what really matters. Operating income is important. The same is true for governance. However, the best AI implementation improves expertise, improves quality and creates space for innovation.
Redefining successful organizations will be organizations that transform AI from technology investments to lasting strategic advantages.
Photo: Ipuba, Getty Images
Updesh Dosanjh is responsible for developing IQVIA's overall strategy for AI and machine learning, as it relates to safety and drug protection. He has over 25 years of knowledge and experience in process and system management, development, implementation and operations in life sciences and other industries.
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