How Smarter Metrics Can Unlock AI’s Potential in Healthcare

Med City News

Artificial intelligence is no longer a futuristic concept in healthcare. It’s here and it is already being integrated into everyday operations, including streamlining pharmacovigilance workflows(Opens in a new window), analyzing unstructured data, predicting operational disruptions and personalizing care delivery. Yet, despite growing investment and adoption, organizations still struggle to define what AI success looks like.

Too often, executives 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 built on trust, detailed clinical judgment and human insight, the value of AI must extend beyond automation. Instead of asking, “How many hours did we save?” consider “What milestones or accomplishments did we achieve with that time?” Did AI help safety teams detect risks faster? Did it enable care managers to intervene earlier? Did it allow analysts to uncover a pattern that would have otherwise gone unnoticed?

Financial ROI is still vital, but in healthcare, the bigger return is often human. This includes the ability to make better decisions, establish stronger teams and ultimately improve patient outcomes.

Redefining AI success across the organization

AI improves more than performance — it boosts morale. A 2025 survey(Opens in a new window) found that 82% of employees using AI said it helped them deliver better work while 58% reported reduced stress. When organizations track employee sentiment, adoption rates and retention, they gain real insight into how AI supports, not strains, the workforce. Beyond employee engagement, AI enhances analytical accuracy and reduces risk in high-stakes areas like safety, diagnostics and population health. In these areas, even small errors can lead to serious consequences. By standardizing how teams collect and assess data, AI reduces variability and flags inconsistencies early. In pharmacovigilance, this leads to more consistent narratives and stronger signal detection. Cleaner data enables faster, more informed decisions across the healthcare ecosystem.

AI also drives measurable operational efficiency. When deployed effectively, AI reduces manual workloads, eliminates redundancies and accelerates timelines. Many pharmaceutical and provider organizations now use AI to process safety case data, triage patient records and generate regulatory reports. These applications shorten cycle times and help teams meet critical deadlines. In fact, Deloitte reported that the internal rate of return on pharma AI investments(Opens in a new window) rose from 1.2% to 4.1% in 2023, signaling a growing alignment between technology investments and business strategy.

Success demands more than metrics

Tracking outcomes is critical, but it’s not the only part of the equation. The most successful organizations recognize that AI’s impact depends on how they implement, support and govern these systems. To move from adoption to transformation, healthcare leaders should focus on three strategic priorities.

Invest in culture, not just tools: Even the best AI tools can fall short without the right support. Top-down mandates often fail to gain traction. Instead, leading organizations form cross-functional teams to define requirements, pilot solutions and redefine workflows. They identify internal champions, or “super users,” who bridge knowledge gaps, train peers and help teams see AI as an asset rather than a disruptive threat. Transparency matters too. Building explainability into dashboards, documenting AI decision pathways and engaging regulatory teams early helps align efforts with frameworks like the EMA’s good pharmacovigilance practices and other guidance. Trust fuels adoption, and trust starts with clarity and engagement.
Use AI to focus user time: AI’s true value lies in its ability to strengthen — not sideline — human expertise. Automating repetitive tasks like initial case review or documentation frees skilled professionals to focus on complex, high-stakes decisions. In safety reviews, for example, AI-generated narratives give experts more time to evaluate emerging signals and high-risk cases. AI enhances judgment and precision when used to sharpen human insight.
Prepare for regulatory scrutiny: As AI systems become more embedded in clinical and operational workflows, regulatory expectations are rising. Agencies now require detailed documentation of how systems operate, who can override outputs and how decisions are logged. These aren’t just technical concerns — they are enterprise-level responsibilities. Healthcare leaders must establish audit trails, test for bias and uphold transparency. Ultimately, responsible governance will define the difference between tools that scale and tools that stall.
Let AI strengthen, not cut corners

Healthcare doesn’t reward shortcuts. It rewards outcomes. AI will play a critical role in shaping those outcomes — but only if leaders measure what truly matters. Operational gains are important. So is governance. But the best AI implementations elevate expertise, improve quality and create space for innovation.

The organizations that redefine success will be the ones that turn AI from a technological investment into a lasting strategic advantage