Our Take
AI is a tool for a working system, not a substitute for redesigning one that is fundamentally broken.
Why it matters
Healthcare providers and payers continue to invest in AI-driven claims solutions expecting them to solve operational chaos. This story cuts through the hype by naming the real bottleneck: process design, not computational power.
Do this week
Before piloting any new claims AI, audit your claims process for structural inefficiencies independent of technology—missing data standards, unclear authorization rules, and manual handoffs—and fix those first, or the tool will amplify the problems.
The Claims Problem Has No Technical Fix
AJMC's analysis challenges the assumption that artificial intelligence can solve healthcare's chronically broken claims-processing paradigm. The piece argues that the fundamental issue is not computational capacity or algorithmic sophistication. Instead, the root causes are organizational: unclear authorization requirements, fragmented data standards, manual routing, and workflows designed around legacy constraints rather than patient or provider needs.
Vendors and health systems have deployed machine learning to claims adjudication, denial prediction, and automated routing. None of these interventions have materially reduced claim processing timelines or denial rates at scale because they operate on top of a system architecture that was never designed for efficiency.
Automation Amplifies Bad Design
When organizations apply AI to a broken process, they do not fix the process. They accelerate it. A misconfigured authorization rule that rejects valid claims gets rejected faster. A data standard that omits critical clinical information gets propagated more consistently. The result is a faster, more reliable failure.
Healthcare providers and payers continue to budget for AI solutions under the assumption that better algorithms will close the gap. What they actually need is process redesign: standardized data fields, clear decision logic, automated escalation, and human-in-the-loop review at the right decision points. These are organizational, not technical, problems.
The economic stakes are real. Claims represent cash flow. Denials and delays compound across thousands of transactions. Vendors benefit from selling tools; systems benefit only if they first establish what a claims process should actually accomplish.
Start with Process, Then Tool Selection
If you are evaluating claims AI, begin by mapping your current state without any technology overlay. Document where claims get stuck, who makes decisions, what information is available at each step, and what rules actually govern approval. Interview the teams who handle exceptions and denials.
Only after you have named the broken pieces should you evaluate tooling. The question is not "What can AI do?" but "What does our process need to do, and where does tooling help?" Many healthcare organizations have discovered that 40 percent of their claims issues were resolvable through clearer authorization rules and data quality standards before any model touched the system.
This does not mean AI has no role. It means the role is much narrower than vendors suggest, and much more dependent on having a clear target process first.