AI and A/R Automation in Healthcare: Six Reality Checks Behind the Hype

Healthcare revenue cycle management (RCM) leaders are hearing big promises around "zero-touch claims," fully autonomous accounts receivable (A/R), “instant denial resolution,” and even “artificial intelligence (AI) will replace staff.” The reality is that most hospitals and health systems are still struggling with payer portals, fragmented data, growing denials, and aging A/R backlogs that directly impact cash flow and cost to collect.

AI and automation are reshaping A/R in healthcare, but not the way the headlines suggest. Below are six reality checks to help healthcare organizations cut through the hype and build a practical roadmap for AI-driven A/R automation.

1. Hype: "Fully autonomous A/R is right around the corner."
   Reality: A hybrid structure of human plus automation brings the real value.

   In practice, AI and automation are not replacing A/R teams; they’re reshaping how those teams work. AI is a force multiplier that handles repetitive work, rules-based tasks, while people are reassigned to high-value work that requires judgment and a focus on exception handling and governance, utilizing their human judgment.

   Where autonomous A/R and basic automation work well today:

   • Bots and robotic process automation (RPA) for repetitive, rules-based tasks such as claim status checks, portal navigation, and data movement.
   • Intelligent automation to route and prioritize accounts based on risk, value, or aging.
   • Humans making nuanced decisions, resolving complex denials, interpreting payer behavior, and continuously improving models and workflows.

   What this means for RCM leaders: The most successful organizations are designing hybrid models where AI and digital workers handle repetitive tasks, while human teams focus on judgment-intensive work and oversee outcomes.

2. Hype: "Zero-touch claims will fix everything."
   Reality: Some workflows are ready for straight-through processing. Many aren’t.

   In claims submission, healthcare is close to “zero touch” in limited and standardized areas. Electronic claim submission is highly automated. However, once you address attachments, payer-specific rules, and portal-based workflows, manual effort increases.

   Where accounts receivable automation is working well today:

   • Pre-submission scrubbing to catch missing fields and obvious coding errors.
   • Automated claim status via APIs or bots instead of manual portal checks.
   • Payment posting and underpayment identification using contract models and rules.

   Where full accounts receivable automation still struggles:

   • Highly variable payer rules
   • Inconsistent use of denial reason codes
   • Non-standard attachment and documentation requirements

   What this means for RCM leaders: Aim for "maximum appropriate automation" at each step, which safely accelerates cash, rather than an unrealistic promise of "no human touch anywhere."

3. Hype: "Zero-touch claims will fix everything."
   Reality: Some workflows are ready for straight-through processing. Many aren’t.

   In claims submission, healthcare is close to “zero touch” in limited and standardized areas. Electronic claim submission is highly automated. However, once you address attachments, payer-specific rules, and portal-based workflows, manual effort increases.

   Where accounts receivable automation is working well today:

   • Pre-submission scrubbing to catch missing fields and obvious coding errors.
   • Automated claim status via APIs or bots instead of manual portal checks.
   • Payment posting and underpayment identification using contract models and rules.

   Where full accounts receivable automation still struggles:

   • Highly variable payer rules
   • Inconsistent use of denial reason codes
   • Non-standard attachment and documentation requirements

   What this means for RCM leaders: Aim for "maximum appropriate automation” at each step, which safely accelerates cash, rather than an unrealistic promise of “no human touch anywhere."

4. Hype: "More AI automatically means better results."
   Reality: If your processes and data are messy, AI just accelerates the chaos.

   Before you deploy advanced AI, ask three simple questions about the use in A/R:

   1. Is the process standardized? If multiple people handle the same denial in different ways, it is not ready to be automated.
   2. Is the task truly repetitive and rules-based? High-volume, frequent, rule-driven tasks are ideal. If every account requires fresh judgment, start with process design before automation.
   3. Are inputs digital and reliable? If teams still rely on paper, fax or unstructured notes, build the digital foundation first or be prepared to invest in strong data extraction and normalization.

   A practical decision framework for AI in revenue cycle:

   • If data is the main driver, it’s a good candidate for automation.
   • If expert knowledge is the main driver, keep humans primary and use AI to support, not replace, decisions.

   What this means for RCM leaders: Process discipline and data quality determine the ROI. AI will amplify whatever already exists, whether good or bad.

5. Hype: "GenAI will solve denial management on its own."
   Reality: Targeted, data-driven use cases are where AI is winning today.

   In denial management, the real gains in generative AI come from focused, measurable use cases, not abstract promises. GenAI performs best when there are clearly defined uses and humans can review outputs for accuracy and tone. Outcomes need to be continuously monitored and prompts and models require refinement.

   Examples that are working today in AI-driven denial management:

   • Next-best-action models that recommend whether to appeal, rebill, or adjust.
   • Propensity-to-overturn scores that rank denials by likelihood of recovery.
   • GenAI-assisted appeal drafting that generates first drafts for staff to review and refine.
   • Analytics and dashboards that show denial patterns by payer, denial type, diagnosis, and location, increasing the opportunity to prevent them.

   What this means for RCM leaders: Treat generative AI as a productivity engine for your denial team, not a fully autonomous denial robot.

6. Hype: "A single platform or vendor will fix our automation problems."
   Reality: Accounts receivable automation requires a roadmap, governance, and shared ownership.

   Tools and technology are important, but they are not a strategy. The most successful A/R automation programs treat AI and automation as an enterprise capability tied directly to financial performance, not as a siloed solution.

   High-performing healthcare organizations:

   • Use cross-functional teams comprised of staff within revenue cycle, IT, compliance, finance, and clinical stakeholders to own automation decisions.
   • Define success using CFO-level metrics, including A/R days, cost to collect, cash acceleration, write-offs, rather than just "tasks automated."
   • Pilot in one or two high-value scenarios to prove the return on investment and then scale.
   • Build a phased roadmap that evolves from basic automation to intelligent automation to carefully governed GenAI.

   The question is not "Which vendor has the flashiest demo?” but “Who can partner with us over multiple phases and share real outcome data, including where things didn’t work?"

   What this means for leaders: A partner involved over multiple phases and shares real outcome data will drive a roadmap designed for success.

7. Hype: "The biggest risk in accounts receivable automation is moving too slowly."
   Reality: The biggest risks are ungoverned models and unmeasured outcomes.

   While taking no action carries risk, especially as payers advance their own AI capabilities, it is crucial to develop a thoughtful plan for deploying automation.

   Common pitfalls in AI-driven A/R automation include:

   • Deploying AI models without clear accountability for decisions and outcomes.
   • Training on biased, incomplete, or error-filled historical data.
   • Failing to compare pre- and post-automation performance.
   • Ignoring legal, ethical, and compliance implications.

   Strong AI-driven A/R automation programs have:

   • Clear governance structures for model approval, monitoring, and change control.
   • Ongoing quality, bias, and accuracy monitoring.
   • Transparent metrics that tie automation back to financial and operational outcomes.
   • Human-in-the-loop checkpoints at critical decision points.

   What this means for leaders: AI needs to be auditable, explainable, and aligned with your financial, compliance, and operational risk tolerance.

Next Steps in AI and A/R Automation for Healthcare Leaders

AI and A/R automation are transforming the way health systems and RCM partners manage denials, follow-up, and cash flow. Revenue cycle management and finance leaders should focus on:

• Identifying several high-impact, data-rich processes as pilots, such as no-response claims, specific denial types, and/or underpayment review.
• Aligning IT, revenue cycle, and compliance around a shared automation roadmap.
• Selecting partners who can show proof of outcomes through hybrid models that combine automation, analytics, and human expertise deployed against clearly defined problems with measurable outcomes.
• Building a culture where staff use AI as an ally.

Watch our on-demand webinar, "Hype vs. Reality: Charting the Accounts Receivable Automation Journey," to hear real-world examples, lessons from other industries, and a practical roadmap to achieve results for your healthcare organization.