AI in Healthcare Revenue Cycle Management: Trends & Opportunities to 2026

Key takeaways:

• Nearly two-thirds of healthcare providers use AI in their revenue cycle management processes.
• Eligibility verification and patient access are top use cases, but providers remain cautious about leveraging AI for critical decision-making.
• Data privacy, security, accuracy and cost still present the biggest barriers to AI adoption.
• Solutions like Patient Access Curator™ (PAC) and AI Advantage™ are helping health systems prevent and reduce claim denials, leading to increased reimbursements.

The potential for artificial intelligence (AI) in healthcare revenue cycle management is clear — fewer denials, faster reimbursements and more efficient workflows. New Experian Health data shows provider confidence in AI is steadily growing, but many providers still have reservations about how and where to introduce AI into their RCM workflows.

AI is being used by only 63% of providers, but primarily for lower-risk tasks, such as data analysis and automation, due to concerns about its accuracy and security. Despite these reservations, there are bright spots. From preventing claim denials to automating patient billing, AI and automation are already helping many healthcare organizations improve operations, boost financial performance and deliver better patient experiences.

This article examines what providers need to know about bringing AI technology into their revenue cycle.

What role does AI have in healthcare revenue cycle management?

Artificial intelligence (AI) plays a transformative and rapidly expanding role in today’s healthcare revenue cycle management. New Experian Health data shows that 63% of providers have already introduced AI in some capacity to their workflows, but only 15% have fully integrated AI into standard RCM operations.

Manually managing complex non-clinical processes tends to slow reimbursement and strain provider resources. AI offers efficient solutions that are reshaping how providers manage key areas of the revenue cycle, such as medical billing, claims management and patient payments.

For many providers, AI’s main draw is its ability to deliver significant financial savings. The most recent CAQH index report suggests that switching from manual to electronic administrative transactions could save the industry at least $20 billion.

That’s a compelling prospect for revenue cycle leaders looking to do more, and faster, with fewer resources. Leveraging AI gives providers a head start in addressing rising costs, workforce challenges and ever-increasing volumes of data.

What are the benefits of using AI in healthcare RCM?

AI in revenue cycle management brings more than just financial savings. Providers also see broader operational benefits, including:

AI’s benefits extend to patients, too. By automating processes, eliminating errors and increasing transparency, AI and automation can help give patients financial clarity throughout their healthcare journey.

What are the top barriers to adopting AI for revenue cycle management?

A recent Experian Health survey found that half of healthcare leaders cite data privacy and security concerns as the biggest barrier to AI adoption. AI’s accuracy is another top concern, with 41% of providers saying it’s difficult to trust AI’s results fully. Another 31% of providers also cite cost as their top barrier to entry.

How is AI revolutionizing healthcare RCM?

Here’s a closer look at how AI is making a big impact in healthcare revenue cycle management.

Using AI to manage complex billing procedures

Medical billing errors cost healthcare organizations millions of dollars each week, and the problem is only getting worse. Experian Health’s 2025 State of Patient Access survey found that 56% of providers say patient information errors are a primary cause of claim denials. In the 2025 State of Claims survey, 54% of providers said claim errors were increasing, and 32% said inaccurate or incomplete patient data at intake drives denials.

Manual processes make managing the complexity of insurance plans, billing codes and patient payments nearly impossible. AI simplifies the task. For example, Patient Access Curator (PAC) uses AI-powered data capture technology, robotic process automation and machine learning to verify coverage and eligibility. This ensures accuracy throughout the billing cycle, reducing denials and accelerating collections.

Using AI to prevent claim denials

Claims can be denied for many reasons, but poor data consistently tops the list. Even so, nearly eight in ten providers say their organizations still rely on multiple solutions to collect the information needed for a claim submission and only 14% are currently using AI to reduce denials. AI helps providers buck the trend by improving data quality and using that data to improve claims management.

Experian Health’s AI Advantage™ analyzes patterns and flags issues before claims are submitted, using providers’ historical payment data together with Experian Health’s payer datasets. It continuously learns and adapts, so results continue to improve over time.

Using AI to reduce patient payment delays

The rise in high-deductible health plans is associated with a greater risk of missed patient payments. According to Experian Health data, 77% of patients said knowing what insurance covers before treatment is important. AI is vital for providers looking to help patients understand their financial responsibility early and avoid payment delays.

With solutions like Patient Access Curator, staff no longer need to sift through piles of patient data and payer websites to verify eligibility and get a clear picture of a patient’s insurance coverage. Instead, they can quickly gather the information needed to verify their insurance eligibility.

What logistical challenges and considerations should providers consider when implementing AI in RCM?

Understanding what AI technologies offer is an important first step for healthcare revenue cycle managers. However, successful implementation also calls for consideration of the practical challenges.

However, with careful planning and a trusted vendor, these challenges are manageable.

How can providers embrace AI for a smarter, more efficient RCM?

The benefits of AI in revenue cycle management are clear: more innovative, faster processes that free up staff time and reduce errors, resulting in much-needed financial gains.

To maximize AI usage, providers should begin by reviewing their organization’s key performance indicators and identifying areas where AI can add the most value. This should focus on points in the revenue cycle where large volumes of data are being processed, such as claims submissions or patient billing, which are common areas for inefficiencies and errors.

By taking a strategic, targeted approach, providers can find the right AI solutions to make the biggest impact – whether it’s through curating patient insurance information, improving claim accuracy or predicting denials. A trusted vendor like Experian Health can guide teams through AI setup and make sure it meets their needs.

The true value of AI, as demonstrated, is not a replacement for staff, but a powerful tool that serves as the intelligence layer to the revenue cycle, heightening people, processes and technology. At Experian Health’s High-Performance Summit (HPS), Jason Considine, President at Experian Health, noted:

“The future vision is clear: Technology must be used to amplify human expertise, not replace it. The revenue cycle has never been more complex. We have regulatory pressures mounting, the financial squeeze of declining reimbursements, and the constant pressure to do more with less.”

FAQs

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