contributors
Zach Berger
Healthcare Leader, Government Programs
January 15, 2026 • 5 Min Read
As Medicaid work requirements expand, payers are facing a compliance challenge that manual systems simply can’t handle. By 2027, more than 20 million enrollees across 41 states will be subject to monthly verification. Yet most states and many payers still rely on people, paper, and disconnected systems to manage this process. The result is rising administrative costs, unnecessary disenrollment of eligible members, frustrated providers, and growing regulatory risk. AI-driven automation is the only way to make verification work at scale without harming the very members these programs are meant to serve.
What’s Changed
The scale of Medicaid work requirements has changed dramatically. What once involved smaller pilots now affects millions of people every single month. This surge in volume alone would strain any manual process. At the same time, most states and payers don’t have the technical infrastructure or talent to quickly build advanced automation on their own.
Regulators are also paying closer attention. CMS has made program integrity, accurate reporting, and audit readiness top priorities. Errors that might once have gone unnoticed are now more likely to trigger penalties or corrective action.
And the burden isn’t just on payers. Providers are increasingly pulled into the process, spending time on exemption documentation and paperwork instead of patient care. Every new manual step in verification adds friction across the system.
Industry Implications
For payers, relying on manual verification means higher operating costs, slower processing, and greater risk of compliance failures. Small mistakes like missing documents, delayed reviews, or data entry errors can snowball into audit findings or financial penalties.
For providers, verification requirements translate into more forms, more follow-up, and more time spent away from clinical work. Over time, this administrative drag weakens already strained provider networks.
And for members, the consequences are the most serious. When verification is slow or error-prone, people who are compliant can still lose coverage. Disenrollment driven by paperwork problems rather than eligibility is not just inefficient; it undermines trust in the system.
The AI Opportunity
Verification at this scale cannot succeed without AI. It has to be built directly into the process from the start.
With AI embedded in verification workflows, payers can move from reactive processing to proactive management. Employment can be validated across payroll and workforce systems in near real time. Exemptions can be identified automatically from documentation. Patterns that suggest fraud or reporting errors can be flagged early, before they become compliance issues. And members who are at risk of losing coverage can be identified in time for outreach and support.
AI-enabled verification makes the system strong enough to handle scale, flexible enough to adapt to policy changes, and reliable enough to protect coverage for millions of people.
The AI-Driven Verification Framework
An effective verification model starts with intelligent automation that can pull data from multiple sources and validate work hours without manual intervention. Natural language processing allows exemption documents that are often unstructured and inconsistent to be read, classified, and routed correctly. Machine learning models can analyze reporting patterns to detect anomalies that may indicate fraud or errors.
Just as important, these tools must be embedded into existing eligibility platforms and state reporting portals. While AI does heavy lifting, people still play a role. Human review is essential for edge cases, appeals, and oversight, supported by strong Machine Learning Operations (MLOps) practices that ensure models are tested for bias, accuracy, and audit readiness
Risks of Inaction
If verification remains manual, costs will keep rising while accuracy falls. Payers will struggle to keep up, and administrative budgets will balloon. Members will continue to lose coverage not because they are ineligible, but because systems fail them. Providers will face even heavier paperwork burdens. And regulators will respond with penalties and increased scrutiny.
The real risk is systemic. When manual processes collapse under scale, everyone loses—payers, providers, and especially members who depend on stable access to care.
Call to Action
AI is no longer a “nice to have” for Medicaid work requirement compliance. It is the only scalable path forward.
RGP helps payers embed AI into verification workflows so they can meet CMS requirements, reduce disenrollment risk, ease provider burden, and build governance frameworks that ensure fairness and audit readiness.
Partner with RGP to make verification work—not just for compliance, but for the people it’s meant to serve. Contact one of our experts today to learn more.