In an industry where time and accuracy directly impact profitability, leveraging AI in the revenue cycle is no longer optional. When implemented correctly, AI in medical coding and billing helps providers:

However, without the right due diligence, providers risk losing control over accuracy, compliance, and revenue integrity to systems they don’t fully understand. This can result in hidden underpayments, audit-triggering overpayments, and processes that seem efficient on paper but actually increase rework and appeals. 

“Not all solutions or vendors are created equally,” said healthcare consultant Aimee Heckman. During a recent Tebra webinar, Getting paid: How to capture every dollar in 2026, Heckman outlined essential questions that providers and medical billers must ask to ensure your technology delivers a return on investment.

Before you bring AI capabilities into your medical billing and coding processes, consider these 10 points.

Does the AI medical coding and billing tool truly learn, or is it simply advanced automation? “What most people think of as AI, meaning tools that actually learn and improve with use, is still emerging in healthcare,” Heckman said. “True AI gets smarter over time. Automation, on the other hand, follows rules. It’s fast and efficient, but it doesn’t adapt or get smarter without direct input from a human being.”

Beyond generalities, ask why this tool fits your specific specialty and payer mix. How does the vendor support you when performance drops? Most importantly, ask for their roadmap: how is the software evolving to handle the regulatory pressures of 2026 and beyond?

Determine which decisions are fully automated and which ones require human sign-off. How often are humans required to validate AI-generated codes or modifiers? And for what reasons? Understanding the boundary between automation and manual oversight is key to managing your staffing expectations.

Look for systems that assign confidence scores to each code. Can you automatically route low-confidence cases to a certified coder? Ask what real-time guardrails are in place to prevent risky codes (e.g., unusual E/M level shifts, modifier spikes, or audit-sensitive codes) from submitting without review. How does the AI in medical coding and billing handle payer-specific rules and detect payer behavior changes?

If a mistake occurs, how quickly can the system detect it before it affects thousands of claims. A sophisticated AI tool in medical coding and billing should include a "kill switch" by payer or code family so you can pause automation for specific segments without stopping your entire workflow. How are denials, downcoding, and takebacks fed back into improving performance of the AI in medical coding and billing?

AI is only as good as its training data. Does the vendor’s data set reflect your specific specialty, site of service, and documentation style? If the data is outdated or irrelevant to your niche, the AI’s output will be unreliable.

Black box AI is a compliance liability. You should be able to see the rationale behind every code or modifier the system selects. Ensure you can reproduce this logic for compliance reviews and export decision data whenever necessary.

Beyond the software itself, check the security frameworks and certifications. Will the vendor sign a business associate agreement, and does it cover subcontractors? You also need to know its specific breach-response timeline should protected health information (PHI) be exposed?

Guardrails are essential to prevent the AI from upcoding to chase revenue or "downcoding" to avoid denials. Ask how the vendor prevents feedback loops that normalize payer-driven undercoding, which can add up to significant costs over time.

Vague promises of less work aren't enough. Which financial KPIs improved in live client deployments — and by how much? How does the AI affect downstream work like appeals and reprocessing? Ask the vendor to demonstrate results using your own data, says Heckman.

“Vendors rarely explain how their tools improve outcomes, often just claiming they make work easier. However, if a tool doesn't reduce denials, speed up revenue recovery, or lower admin costs, its value is questionable,” she adds.

As you explore AI in medical coding and billing, Heckman said not to settle for mediocrity. “Ask the hard questions and challenge vendors to explain how the tool works, what data it uses, and whether it’s built for the billing world,” she added. By asking the hard questions now, you ensure that your AI investment protects your revenue rather than putting it at risk.

Download this free one-page vendor evaluation checklist to get started and watch the free webinar recording for more tips and guidance.

Plus, learn more about how the right medical billing software can elevate your billing and promote financial sustainability.