Artificial intelligence (AI) has now reached a crucial tipping point in the way U.S. healthcare plans carry out their operations. A study by Everest Group, supported by Sagility, found that 70% of health plans have introduced AI tools to streamline both administrative and clinical activities. Nevertheless, 84% of payer executives say that they have seen limited benefits from these implementations and just 10% have noticed measurable improvements in operations.
The fact that these systems are not aligned points to a fundamental error in the digital transformation of healthcare, namely the belief that simply adding automated tools to the scattered existing infrastructure will automate the current state of chaos instead of enabling operational efficiency.
Healthcare administrative spending in the U.S. is approximately $1 trillion annually, costs that result in part from manual processes, complex clinical reviews, and the lack of systems integration. Although technology frequently functions as a quick fix, achieving true operational efficiency involves some fundamental rework. Before introducing automated tools, health plans should re-engineer their workflows end-to-end to ensure the optimal use of AI. Only by restructuring workflows will health plans harness AI as an intelligence amplifier for human healthcare experts to implement specialized clinical judgment rather than replace it.
The Flaw of Overlaying Tech on Legacy Chaos
For many years the administrative and clinical operations of health plans have worked in isolation. The handling of claims, utilization management (UM), payment integrity, and member services have usually depended on separate platforms, manual transfers between them, and outdated rules engines.
When health plans try to modernize by adding AI models or robotic process automation (RPA) directly onto these disjointed systems, the fundamental problems still exist. Imagine an AI system that flags claims for investigation. If the underlying contract terms or prior authorization rules are wrong, the AI may identify thousands of claims that never needed review in the first place. Instead of saving time, claims staff are left sorting through unnecessary alerts and correcting avoidable errors.
To achieve a meaningful return on investment, healthcare operations must build a stronger operational foundation by standardizing workflows, improving data quality, and establishing consistent governance before deploying AI and automation at scale.
Indeed, AI code depends on health plans mapping the full lifecycle of a transaction, whether it’s a prior authorization request or a complicated inpatient claim. Identifying policy gaps, eliminating redundant hand-offs, and standardizing data inputs creates the necessary operational baseline. Technology can deliver true scalability and efficiency only on a clean, connected foundation.
Why AI Is an Intelligence Amplifier Paired with Domain Expertise
A related misconception in the field of healthcare technology involves the idea that AI is a solo solution that can solve complex problems entirely on its own. By extension, AI can replace human expertise entirely, including in complicated administrative and clinical processes. Yet, research reveals that 94% of payer executives stress the importance of having human oversight when introducing automated tools.
Healthcare operations depend on deep clinical, payment integrity, and operational knowledge that generic algorithms can’t replicate. Healthcare is by its nature complex, dynamic and full of high stakes. Although AI is good at processing large amounts of data, recognizing patterns and automating routine administrative work, it lacks the kind of nuanced reasoning needed for complex decisions. In clinical areas like UM, complex case management and claims appeals, full automation opens the door to substantial financial and regulatory risk.
Augmented intelligence is the best method of handling this complexity. Technology from this point of view amplifies intelligence for registered nurses, certified coders, and claims specialists. For example, generative AI tools help RNs by examining hundreds of pages of unstructured medical records, and clinical history and comparing the data with evidence-based protocols within minutes. RNs can then review the combined evidence to make the final clinical decision.
Instead of focusing on pure automation, organizations should redesign their operating models so AI and healthcare professionals work together as an orchestrated team. This human-in-the-loop approach greatly reduces manual review times without losing the important clinical judgement of experienced healthcare professionals. It changes frontline experts from administrative paper-chasers into valuable decision-makers.
Knocking Down Silos Across the Claims and Clinical Lifecycle
AI and automated decision engines can only deliver on their promise when fed clean, integrated data across the entire organization. For health plans to achieve sustainable financial and operational value from these technologies, they need to break down the barriers separating clinical oversight from financial settlement. Usually, functions like UM and payment integrity have worked as separate, reactive departments. Payment integrity in particular has been concerned with post-pay recovery, pursuing improper payments months after claims have been resolved.
Connecting operations across the entire claims lifecycle creates a continuous feedback loop that directly links workflow redesign to measurable clinical and financial outcomes:
- Prevent: By carrying out clinical interventions at the upstream level, agreeing on prior authorization, and validating benefit configurations, errors are prevented before adjudication occurs, improving first-pass accuracy.
- Detect: Before any payment is made, high-impact editing rules, pre-payment clinical reviews, and risk-scoring algorithms flag potential overpayments or coding anomalies, mitigating financial leakage.
- Resolve/Recover: Outlier claims are sent to specialized auditors and clinicians for a specific review, while normal claims go through without issue, reducing administrative burden and provider abrasion.
- Learn: The root causes are identified by systemically analyzing the findings from downstream audits, appeal outcomes, and payment trends, driving better decision-making.
- Prevent (continuous feedback): Insights gained from post-pay findings fed back into upstream configuration, clinical guidelines and adjudication rules to prevent repeated errors.
When clinical context, contract logic, and processing of claims work together as an integrated ecosystem powered by re-engineered workflows and domain-specific AI, health plans reduce the number of errors on the first submission, remove provider friction and cut down on administrative overhead.
The Path Forward Through Intelligent Operations
With narrow profit margins a common concern for health plans, along with changing CMS requirements and persistent clinical workforce shortages, technology ROI will become increasingly important.
To future-proof payer operations, the path forward is domain-led intelligent operations, a strategy that combines the use of purpose-built AI, re-engineered workflows, and licensed healthcare professionals who are familiar with the intricacies of clinical care and payment policy. By giving priority to workflow design, human-AI orchestration, and deep healthcare expertise, health plans will finally be able to bridge the AI ROI gap and secure both financial and operational resilience.
About Anand Biradar
Anand Biradar is the Chief Go-to-Market Strategy & Enterprise Transformation Officer at Sagility.
