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

In a world where operational complexity is ballooning and labor costs are escalating, traditional cost-reduction levers such as Lean, Six Sigma and manual optimization are reaching their limits.

Today, we see more leaders recognize the new reality.

To truly want to move the needle on cost, we can’t just improve the process. The industry must rethink the engine itself. That means embracing artificial intelligence (AI) and intelligent automation as the new “cost engine,” powering faster and more sustainable savings.

Driving change across all areas of health care

Payers can reduce up to 35% of the cost of operations through automation and the use of AI. The opportunities to augment clinical and administrative workers through virtual advisers and faster access to company policies and knowledge are a start, but rather than relying solely on manual reviews and rule-based systems that are prone to delays and errors, payers can layer in predictive analytics and natural language workflows.

Health care organizations are reimagining how they take in, distribute and complete their work, increasingly using a blend of automation and AI to reduce costs, improve consistency of complex decisions and reduce cycle times by up to 30% to 50%. AI and automation are not imaginary; these tools change how payers accomplish their work and provide new levers with which they can effectively compete in the market.

The financial impact? Potential savings running to millions, realized within months by simply making smarter, faster decisions. This kind of impact isn’t limited to payers. Pharmaceutical firms are also adapting. Pfizer has implemented an AI-driven clinical data management system across 100-plus studies, including vaccine trials. Their system, trained on medical literature such as PubMed texts and real-world evidence, significantly reduced coding time by 50% assisting medical coders.

In clinical trials, AI and real-world data (RWD) are making studies faster and more inclusive. For instance, a U.K.-based health technology company launched a clinical trial initiative targeting individuals aged 65 years and older. By utilizing AI and RWD collected from home health care visits, the company reduced hospitalizations in older cohorts by up to 70%, saving the U.K. National Health Service over $1 billion per year.

Providers have an equally compelling opportunity. Consider a hospital network deploying AI-driven denial prevention models trained on thousands of past claims. Paired with smart coding assistants for medical billing teams, such technology could reduce denial rates within a few months. AI didn’t replace the team; it made them more effective, freeing them from chasing rejections, so they could focus on high-value cases that needed human judgment.

Another area where AI shines is supply chain optimization. With machine learning powering demand forecasts, hospitals are better equipped to predict supply needs, preventing both overstocking and shortages. A standout example is Apollo Hospitals in India, which adopted AI-driven inventory tracking across its hospital network. By predicting demand surges for critical drugs and surgical equipment, they reduced emergency stockouts by 50% while lowering excess inventory costs.

Several health care technology firms have AI solutions increasingly in their road maps as they race to take advantage of the game-changing capabilities that AI and automation can bring to the solutions they offer to the health care and life science markets.

Across each sector, there is an increased investment in data and cloud-based architectures to fuel the cost reductions possible through AI and automation. Creating efficient, affordable and reliable architectures becomes the foundation for the innovative solutions being demanded by the business.

Rethinking traditional cost reduction

But the real key to success here isn’t just the technology, but how organizations choose to deploy it. To capture the gains, a three-phase implementation road map called Assess, Automate and Optimize is vital.

1. Assessment: Understanding where the most significant inefficiencies lie, quantifying the potential value and identifying the areas where AI can have the most immediate impact.
2. Automation: Targeted automation, rolling out capabilities in phases, validating performance and ensuring the workflows are well integrated with existing systems.
3. Optimization: Continuous refinement of data accuracy and retraining AI models for better predictive power helps streamline workflows and ensure sustained performance. It allows for integrated user feedback at this stage and aligns systems with evolving compliance and clinical goals.

But the road to value isn’t always smooth. Take fragmented data as an example. Many health care systems still operate in silos, where billing data reside in one platform, clinical data in another and supply chain data in an entirely different system. AI thrives on clean, connected data. Without them, even the best models will underperform.

That’s why addressing data fragmentation is crucial. Studies show that over 80% of health care leaders believe that RWD-backed trials are more representative than traditional approaches. With 74% of drug companies planning to invest in real-world evidence-backed research solutions, it is evident that integrating diverse data sources is becoming a strategic priority.

Another critical factor that must be considered is change management. AI often meets resistance because people worry their jobs are being threatened, so they become skeptical and resistant to change. That’s why successful implementations always involve early and transparent communication and a true agile-at-scale approach that involves the business throughout the program. Often, the goal isn’t to replace but to evolve the workforce and make it more efficient. With a mix of automation and generative and agentic AI, workers will shift to supervise intelligent systems, manage exceptions and focus on higher-order tasks.

Compliance requirements will continue to evolve, and AI governance will become another business-driven structure that will be required in these organizations. Transparent models, explainable decision trees, explorable logs and rigorous monitoring frameworks ensure that automated decisions are not only fast but also traceable. Error rates caused by manual processes are bound to go down after automation because machines, unlike humans, don’t get tired or distracted, but they do need to be trained and supervised.

Beginning with a plan

A major question looming over us is, “Where do we start?”

It’s simple. Start where pain and the potential intersect. Pick a function where inefficiencies are dragging down performance and where clean data are available. Prove value quickly. Then expand. For instance, integrating AI-powered virtual assistants could be the starting point. These assistants can handle patient queries and schedule appointments. This helps ease staff workload and improve patient engagement while maintaining operational efficiency.

Ultimately, more than cost savings, what is most exciting is the shift in mindset that AI is enabling across the industry. Health care organizations are starting to reimagine what is possible. AI allows for a democratization of knowledge and insights and new ways to unlock the potential in your associates and your business operations. Capital that was once locked in inefficient operations can be redirected toward innovation and patient experience.

Perhaps most importantly, there’s a sense that control is returning to the system. In an environment where so much feels unpredictable, AI offers a way to respond faster, smarter and with greater precision.

It is still early in this journey. But the direction is clear. AI isn’t just a tool; it’s the new cost engine, and for those willing to embrace it, the payoff is just a step away.

John Edwards is senior vice president at Citius Healthcare Consulting, the consulting arm of CitiusTech.