AI and Automation Drive Health Care Cost Savings and Efficiency

The new cost engine: How AI and automation are reshaping health care economics

Artificial intelligence (AI) is changing how health care operates by cutting costs and improving efficiency. Automation, predictive analytics, and real-world data are key tools helping clinical trials and operations run smarter and faster.

Traditional methods like Lean and Six Sigma are hitting limits in the face of growing operational complexity and rising labor costs. Now, health care leaders see that to achieve meaningful savings, the system itself must be rethought. AI and automation have become the new engines driving faster and more sustainable cost reductions.

Driving change across health care sectors

Payers can slash operational costs by as much as 35% using AI and automation. Virtual advisers and faster access to policies ease the burden on clinical and administrative staff. Instead of relying on slow, error-prone manual reviews, payers now apply predictive analytics and natural language workflows. This combined approach cuts cycle times by 30% to 50% and delivers millions in savings within months through smarter decision-making.

Pharmaceutical companies are also benefiting. For example, Pfizer’s AI-driven clinical data management system, used in over 100 studies including vaccine trials, cut coding time by 50%. AI-powered clinical trials, enhanced with real-world data, speed up studies and make them more inclusive. A UK health tech company used AI to reduce hospitalizations by 70% among seniors, saving the National Health Service over $1 billion annually.

Providers gain as well. Hospitals deploying AI-based denial prevention models and smart coding assistants are already lowering claim denial rates. AI frees medical billing teams from repetitive tasks, letting them focus on cases that need human judgment. Supply chains also improve with AI demand forecasts, preventing shortages and excess inventory. For instance, Apollo Hospitals in India cut emergency stockouts by 50% and trimmed inventory costs using AI-driven inventory tracking.

Health care technology companies are rapidly adding AI tools to their offerings. Investments in cloud infrastructure and data integration pave the way for scalable automation solutions that support cost reduction and operational reliability.

Rethinking traditional cost reduction

Success with AI isn’t just about technology; it depends on how organizations implement it. A three-phase approach—Assess, Automate, Optimize—is critical:

• Assessment: Identify inefficiencies, quantify potential improvements, and target areas where AI can deliver quick wins.
• Automation: Deploy automation in phases, validate results, and integrate workflows with existing systems.
• Optimization: Continuously enhance data quality, retrain models, and adapt workflows to maintain performance and compliance.

Data fragmentation poses a major challenge. Many systems keep billing, clinical, and supply chain data separate, limiting AI’s effectiveness. Clean, connected data is essential for strong AI models. Over 80% of health care leaders agree that real-world data makes clinical trials more representative. With nearly three-quarters of drug companies investing in real-world evidence solutions, integrating diverse data sources has become a strategic focus.

Change management is equally important. Resistance arises when staff fear job loss. Clear communication and involving teams early helps ease concerns. The goal is to evolve the workforce, not replace it. AI allows staff to supervise intelligent systems, handle exceptions, and focus on tasks requiring human expertise. Compliance and AI governance frameworks ensure automated decisions remain transparent and traceable.

Starting with a clear plan

Where to begin? Start at the intersection of pain points and data readiness. Choose a function with clear inefficiencies and reliable data. Demonstrate value quickly, then scale up. For example, AI-powered virtual assistants can manage patient queries and appointment scheduling, reducing staff workload and boosting patient engagement.

Beyond cost savings, AI is shifting how health care organizations think about their operations. It democratizes knowledge and insight, unlocking potential across teams and processes. Capital trapped in inefficient workflows can be redirected toward innovation and improving patient experience. In a sector often facing unpredictability, AI offers a way to respond faster and with greater precision.

AI is still in early stages within health care, but its role as a cost engine is clear. Organizations ready to adopt it will find valuable benefits just within reach.