In a recent industry survey, 67% of practices said they believe AI will meaningfully reduce denial rates. Only 14% said they have actually adopted AI-driven tools in their billing operation today. That is a 53-point gap between belief and action.

That gap is not a story about technology. The technology is real and increasingly capable. The gap is a story about trust, and about what it takes for a practice to feel ready to put AI in the path of its revenue.

Why the Belief-to-Action Gap Exists

Most of the friction comes down to four concerns that show up repeatedly when practice managers and billing leaders evaluate AI tools:

• Audit trail. Practices need to be able to reconstruct what an AI tool did and why. If an AI agent submits a claim or appeals a denial autonomously, the team needs visibility into what decisions were made and what data drove them. Vague or opaque AI workflows do not pass that bar.
• Error ownership. When an AI tool makes a mistake, who is accountable? If the AI submits a wrong claim and the payer denies or recoups, the practice carries the financial impact. The contract and operational structure has to be clear about how errors are handled and corrected.
• Underlying data quality. AI tools make decisions based on the data they receive. If the underlying eligibility verification, claim status, or ERA data is incomplete or stale, the AI’s decisions inherit those errors at scale. The technology is downstream of the data quality.
• Human escalation path. Practices need confidence that when something falls outside the AI’s reliable operating range, a human escalation path exists and works. AI tools that are not designed with clear handoff points tend to fail in ways that are hard to diagnose.

What the 14% Got Right

The practices that have adopted AI for revenue cycle work are, in most cases, the ones that did three specific things differently:

• They evaluated the AI tool’s foundation, not just its features. An AI agent that sits on top of unreliable clearinghouse infrastructure is just an automated way to generate problems at scale. The 14% that adopted tend to have started by validating the underlying transaction infrastructure, then layered AI on top.
• They started narrow and expanded based on demonstrated outcomes. Successful adopters did not flip a switch on full AI automation. They started with a single workflow (often claim scrubbing or eligibility verification), measured the impact rigorously, and expanded only after the numbers held up.
• They negotiated explicit terms around error ownership and audit access. The 14% have contracts that spell out what the AI tool will and will not do, how errors are corrected, and what audit access the practice has to AI decisions. Vague terms got renegotiated or the deal got walked.

What ‘Responsible AI Adoption’ Actually Looks Like in 2026

For practices currently in the 53-point belief-to-action gap, the practical question is what a responsible adoption path looks like. A few elements:

• Start with the layer where AI has the clearest, most measurable impact: pre-submission claim scrubbing, payer-specific edit application, and first-pass acceptance optimization. The denial-reduction impact is the most direct, and the audit trail is easiest to maintain.
• Verify the underlying clearinghouse infrastructure first. If your current clearinghouse cannot give you reliable transaction logs, complete eligibility data, and clean ERA reconciliation, AI on top of that foundation will magnify the problems, not solve them.
• Insist on outcome-level metrics, not feature claims. The right question is not ‘does this tool use AI’ but ‘what is the measurable change in my first-pass acceptance rate, denial rate, and rework labor cost.’ If the vendor cannot demonstrate that with real-world data, the tool is not ready.
• Maintain human-in-the-loop for high-stakes decisions. AI scrubbing for routine submission is one thing. AI-driven appeals on high-dollar denied claims, AI-driven settlement decisions, and AI-driven prior authorization submissions on complex cases all warrant human review, at least at the current state of the technology.
• Build a clear escalation path. When the AI flags an ambiguous case, who reviews it? When the AI is wrong, how is the error corrected? Document these processes before the tool goes live, not after.

Where the Industry Is Going

The 67% belief number is not going to drop. As payers continue to deploy AI in their adjudication workflows (and as the WISeR pilot and CMS-0057-F reporting both demonstrate, they are), the case for AI on the provider side becomes stronger, not weaker. The gap will close. The question is whether practices close it on their own terms, with infrastructure and contracts they trust, or whether they close it under pressure and adopt tools that were not properly evaluated.

The 14% are not pioneers because they took risk. They are leaders because they took the time to evaluate AI tools the way every other infrastructure decision in healthcare should be evaluated: against the foundation underneath, the outcome metrics, the audit trail, and the people on the other end of the contract.

How HSC Approaches AI

Harris Secure Connect operates as the connectivity and infrastructure layer that AI tools depend on. Our Claims Correct AI scrubbing is built on top of 26 years of payer-edit depth and infrastructure investment. Where we offer AI capabilities, they are designed with the audit trail, error handling, and human escalation that practices need to actually trust them in production.

If you are currently in the 53-point belief-to-action gap and want a clearer picture of what responsible AI adoption looks like for your specific operation, our team is happy to walk through it.

Related Resources

• MGMA industry surveys on AI adoption
• HIMSS analysis of healthcare AI adoption
• HFMA AI in revenue cycle resources

If you are currently believing in AI but not yet ready to adopt, our team can help you work through what evaluation looks like for your specific operation. Reach out for a no-pressure conversation.