Health plans have been later to the AI party than other parts of the healthcare industry but that’s changing quickly. AI at health plans is moving from hype to practical, impactful implementations. Leaders who have spent decades in both clinical practice and payer operations are seeing a clear pattern: AI is most valuable when it targets real operational bottlenecks, simplifies previously manual workflows, and makes high-value clinical and quality processes more efficient. The impact is not when it attempts to replace the human judgment and empathy that is essential in healthcare.
Across Medicare Advantage, Medicaid, and commercial plans, several themes are emerging about where AI is delivering meaningful results today and where its biggest potential lies over the next 3–5 years.
1. AI Works Best When It Improves Efficiency at Scale
Whether in care management, prior authorization, quality reporting, or provider documentation, AI’s most successful early wins come from eliminating tedious, repetitive tasks and accelerating processes that already have clear rules.
Examples of high-impact efficiency gains
- Automated summarization & dictation: Clinicians and case managers gain back hours per week through AI-powered chart summaries, dictation tools, and pre-visit prep.
- Faster prior authorization reviews: AI can rapidly map requests against state and federal regulations, speeding up approvals (especially important for Medicaid plans).
- Structured data extraction: Plans use AI to pull relevant clinical information from unstructured notes for HEDIS, Stars, and risk adjustment.
These improvements don’t replace the clinician or care manager. They make their work more effective and reduce the administrative load that drives burnout.
2. Quality, Stars, and HEDIS Are Becoming Prime Targets for AI Innovation
Quality teams are among the most overextended groups in any health plan. AI offers a direct way to remove friction in closing gaps, forecasting performance, and driving member interventions.
Where AI is gaining momentum in quality
- Near-real-time gap identification across all members and measures
- Reducing unnecessary outreach by predicting which members are unlikely to close gaps without help
- Aggregating clinical data automatically from disparate providers
- Improving member experience which is especially critical as CAHPS weighting increases
Plans are especially focused on AI that can translate fractured, unstructured data into actionable insights, allowing them to deploy resources more strategically.
3. Fraud, Waste, and Abuse (FWA) Is Becoming an AI Priority Area
FWA is an area where AI can outperform humans by identifying patterns that aren’t obvious through manual review.
Emerging AI use cases in FWA include:
- Detecting abnormal billing patterns
- Identifying outlier providers or services
- Learning from historical fraud cases to catch new ones faster
As Medicaid and Medicare oversight intensifies, plans are increasingly prioritizing this capability.
4. Provider Organizations Benefit from Predictive and Proactive Engagement
On the provider side, AI is showing strong results in:
- Predicting deterioration in patients with CHF, COPD, diabetes, and other chronic conditions
- Remote monitoring triage to determine which patients need immediate outreach
- Post-discharge follow-up, where AI-driven calling agents or workflows dramatically improve 3-day follow-up rates
- Documentation accuracy and pre-visit planning
For clinicians, the value is simple: less time searching for information, more time caring for patients.
5. Successful AI Adoption Requires Clear Governance and a Problem-First Mindset
Organizations that succeed with AI share a few traits:
Core components of strong AI governance
- A cross-functional governance council (IT, data, clinical, quality, compliance)
- Clear policies for data security, regulatory monitoring, and model validation
- A framework for evaluating internal build vs. vendor partnerships
- Continuous QA testing to ensure accuracy before scaling
Common adoption pitfalls
| Pitfall | Better Practice |
| Starting with broad, unfocused pilots | Start with one high-ROI use case and prove value first |
| Buying large suites of AI tools | Choose modular solutions that allow incremental adoption |
| Expecting AI to replace staff | Use AI to augment teams and redirect human work to higher-value tasks |
| Pulling funding from core quality programs | Keep Stars/HEDIS budgets intact—AI should enhance, not replace, those efforts |
Plans that take a thoughtful, governed approach avoid wasted spend and build confidence internally.
6. The Next 3–5 Years: What Will Become Standard for Health Plans
Both clinical and operational leaders anticipate a major shift in foundational AI capabilities. The following will likely become standard:
Predicted baseline capabilities by 2028
| Capability | Expected Impact |
| Automated Stars & HEDIS engines continuously monitoring member gaps | Near-real-time quality performance; more precise interventions |
| AI-driven member engagement through voice agents & multilingual chat | Higher CAHPS and better experience for diverse populations |
| Advanced FWA detection models | Faster prevention and significant cost savings |
| Predictive member stratification | Clearer high-touch vs low-touch segmentation |
| Integrated home-based care monitoring | Member-centered care that unifies labs, specialists, and home health data |
| AI powering appeals, grievances & administrative work | Faster processing and more consistent quality |
This evolution will redefine payer operations—making AI as essential as claims processing systems are today.
7. Where Plans Should Invest Now
To stay competitive, health plans should prioritize investment in:
High-ROI starting points
- Member services AI (proven to outperform human CSAT in several plans)
- Quality and Stars automation
- FWA analytics
- Predictive models for care management
- AI governance infrastructure
Foundational data capabilities
Plans still struggling with ADT feeds, HIE integration, or fragmented EMRs should address these gaps early—AI is only as powerful as the data feeding it.
Final Takeaway
AI is not a future concept—it is already transforming core health plan operations. But the biggest breakthroughs come when AI is paired with strong governance, thoughtful prioritization, and a commitment to enhancing (not replacing) the human side of healthcare.
Health plans that build these capabilities now will be the ones that deliver better quality scores, lower administrative burden, more personalized member experiences, and ultimately, stronger financial performance—well ahead of those who wait.
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Lilac Software is building agentic AI capabilities into our platform to help health plans automate complex operational workflows.
If you’re interested in learning how these systems can transform your plan’s operational efficiency, reach out here to start a conversation with the Lilac team.