The Ten Jobs Health Plans Should Hand to AI — And Why Workflow Automation Won't Finish Them
Health plans are not short on automation. They are short on autonomy. Most payer AI programs today are a patchwork of point solutions bolted onto static, linear workflows — a bot that reads a fax, a model that scores a claim, a chatbot that answers a benefits question. Each delivers a local win. Together, they leave the plan's administrative core as brittle as before: rule-bound, exception-heavy, and dependent on people to move work between systems.
The regulatory clock makes that gap expensive. Under the CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F), impacted payers must return expedited prior authorization decisions within 72 hours and standard decisions within seven calendar days, with FHIR-based API requirements phasing in through 2027. Compressed decision windows cannot be absorbed by adding reviewers.
At Elevance Systems, we integrate data, AI, and automation into a unified digital core. Our strategy centers on adaptive, event-driven agentic architectures built for high autonomy, resilience, and precision. Below are the ten highest-value targets for AI inside a health plan — and what each one actually requires structurally.
Top 10 Goals for Health Plans
1. Automate prior authorization end-to-end
This is the clearest ROI entry point in payer operations. The objective is not faster manual review; it is auto-approving the clearly approvable and routing only genuine exceptions to clinical staff.
What it requires:
Goal-directed agents that gather clinical evidence across disparate sources, evaluate it against policy, and execute a decision — not a form-routing script.
2. Compress utilization management cycle time
According to a 2025 survey conducted by the National Association of Insurance Commissioners (NAIC), utilization management is already the most common AI application among health insurers, with 71% using or exploring it and 68% applying it to prior authorization approvals.
What it requires:
Iterative problem-solving. Evidence assembly is rarely one-pass; agents must recognize insufficient documentation, request it, and resume.
3. Deflect routine member contact to true self-service
Results are real where the architecture is sound. One multinational plan reached 88.8% intent recognition and 34.4% self-service containment after redesigning its contact center ecosystem. A Medicaid and Medicare plan automated 21% of requests and handled 18% of web traffic through self-service within two months of deployment.
What it requires:
Real-time context. Containment collapses when the agent cannot see eligibility, accumulators, and claim status at the moment of the conversation.
4. Raise first-contact resolution and cut handle time
AI-driven decision support improves call quality, reduces errors, and shortens call times while lifting first-time resolution.
What it requires:
Agents that act, not just suggest — completing the downstream transaction while the member is still on the line.
5. Prevent denials instead of litigating them
AI is now streamlining denials, coding, documentation, and appeals across both payers and providers. The strategic goal is upstream prevention.
What it requires:
Event-driven detection at submission, not batch review after adjudication.
6. Push straight-through claims adjudication higher
Claims adjudication is a primary AI focus area for insurers. Every point of auto-adjudication lift permanently removes fixed cost.
What it requires:
Distributed agent networks that resolve pended claims in parallel rather than queueing them behind a single orchestrator.
7. Strengthen fraud, waste, and abuse detection
According to a 2025 Survey for the National Association of Insurance Commissioners (NAIC), roughly half of insurers report using or exploring AI for claims fraud detection (50%) and provider fraud detection (51%).
What it requires:
Continuous monitoring with closed-loop feedback, so detection models automatically retrain on confirmed outcomes.
8. Fix provider data and directory accuracy
The value case for agentic AI rests on connecting fragmented data across clinical and administrative operations. Provider rosters, contracts, and directories are the most persistent example of that fragmentation.
What it requires:
A structured knowledge layer treated as infrastructure, not a downstream report.
9. Automate risk adjustment and chart review
AI-driven chart abstraction and coding gap identification support accurate risk capture as value-based arrangements expand.
What it requires:
High-integrity data provenance — every inference traceable to source documentation.
10. Scale care and disease management outreach
According to the National Association of Insurance Commissioners (NAIC), 61% of insurers use or are exploring AI in disease management programs. The goal is reaching the right member with the right intervention without adding headcount.
What it requires:
Autonomous prioritization that adapts as clinical signals change, not static campaign lists.
Why linear automation stalls at goal three
Reviewing the ten goals for health plans listed above, a clear-cut pattern emerges. Each one fails for the same reason: work stops moving when it hits an exception, and exceptions make up most of the cost. Rigid process orchestration assumes the path is known in advance. In payer operations, it almost never is.
The architectural argument for agentic systems rests on four capabilities.
Adaptive agentic frameworks.
Autonomous, goal-directed agents capable of dynamic decision-making, iterative problem-solving, and real-time execution across complex enterprise environments — agents that pursue an outcome rather than complete a step.
Decentralized intelligence and control.
Replacing rigid process orchestration with distributed agent networks that communicate, coordinate, and adapt autonomously to changing operational inputs. No central bottleneck to re-engineer every time a policy or payer rule changes.
High-integrity data ecosystems.
Secure, real-time context and structured knowledge layers that allow agentic systems to reason accurately while adhering to enterprise governance and compliance boundaries. Autonomy without governed data is not efficiency; it is exposure.
Self-healing infrastructure.
Continuous closed-loop feedback monitors system state, automates fault remediation, and optimizes performance without manual intervention—so throughput does not degrade quietly between releases.
Together, these four capabilities replace reactive processing with proactive, self-governing intelligence, maximizing operational efficiency, agility, and scale.
The governance line executives must draw first
Autonomy is not an excuse to remove judgment from coverage decisions.
AI can meaningfully reduce administrative burden and care delays by automating clearly approvable requests, improving documentation quality, and supporting appeals—but insurers and providers need stronger institutional governance to vet and monitor these tools. There is also a documented risk of escalating automated adjudication and automated appeals on either side of the payer-provider relationship.
The workable boundary is straightforward and should be set before deployment, not after:
Agents may approve autonomously within defined clinical and policy limits.
Adverse determinations remain a licensed clinician's decision.
Every agent action is logged, explainable, and auditable to its source evidence.
Model behavior is monitored continuously, with drift triggering human review.
Next Steps
Pick one closed loop, not ten pilots. Prior authorization intake through decision is the strongest candidate — the regulatory deadline forces the scope.
Instrument before you automate. If you cannot measure touches per authorization, auto-adjudication rate, and containment today, you cannot prove the gain.
Build the context layer once. Every one of the ten goals draws from the same eligibility, benefit, provider, and clinical context. Fund it as shared infrastructure.
Write the governance boundary into the architecture. The platform should enforce approval authority, audit trails, and escalation paths, not a policy memo.
Plans that treat agentic AI as a procurement decision will end up with more disconnected tools. Plans that treat it as an architectural decision will end up with a digital core that gets faster as volume grows.
