Q2 2026 Trends Focus: AI in Healthcare

Key statistics on use of AI in healthcare

Sources for these statistics are available upon request.

Leveraging AI to improve healthcare cost and outcomes

AI is becoming embedded across the health benefits ecosystem, with significant implications for plan sponsors. AI tools are reshaping how participants engage with their health, how claims are processed and how vendors manage utilization and payment. Used thoughtfully, AI can improve participants’ experience, increase engagement in underutilized programs and support cost control. Used without appropriate governance, it risks accelerating cost growth by protecting intermediary margins rather than delivering savings to plan sponsors and participants.

How AI is reshaping participant communication and care navigation

One of AI’s most promising applications for plan sponsors is personalized health communication. Plan sponsors no longer need an authenticated, data-driven platform to provide relevant and personal information and guidance. Users can simply share what they want to share about themselves and their needs, and an AI tool will guide them intuitively (through simple questions and answers, in their preferred language, at their level of benefits literacy) to find or choose what’s right for them. This increases the user’s control and agency in deciding how their personal information is used by the plan sponsor, which in turn helps build trust and engagement.

Personalized messaging can improve awareness of preventive services, guide participants to appropriate care settings and encourage use of sponsored programs, such as care management, mental health support and condition-specific interventions. Improving engagement in programs that are already funded but underutilized can enhance the return on investment.

Participants are increasingly using AI themselves to navigate the healthcare system. Many rely on AI tools to interpret lab results, imaging reports and other clinical information that can be difficult to understand. Others use AI to organize questions, summarize recent test results and prepare for physician visits. When used appropriately, these tools can help participants arrive better informed, support more productive clinical conversations and improve adherence to care plans. For plan sponsors, this trend underscores the importance of ensuring that participant-facing AI tools are aligned with evidence-based guidance and clearly positioned as supplements to — not substitutes for — clinical judgment.

A growing group of vendors combines AI-driven navigation, primary care-centered models and clinician-supported These approaches support plan sponsor objectives such as steering participants to high-value care, improving primary care utilization, reducing avoidable emergency and specialty spending, and increasing benefit understanding without relying solely on higher deductibles or narrow networks.

Predictive analytics can reveal emerging patterns across a covered population, enabling earlier outreach and care management before conditions escalate into high-cost claims. Earlier intervention can improve outcomes and help moderate long-term cost trends, particularly for chronic and progressive conditions. These benefits are anticipated to be realized when AI insights are paired with human review and clear clinical pathways.

AI-enabled administration: claims efficiency and payment integrity

AI has the potential to improve claims processing efficiency and payment accuracy. Automation can reduce processing time and the administrative burden, while advanced analytics can help identify anomalous billing patterns and potential fraud. For self-funded plans, these capabilities may support stronger payment integrity and lower administrative costs. However, automation alone does not guarantee savings. Without transparency into how AI models operate and how decisions are reviewed, automation can just as easily accelerate overpayment as prevent it.

The risk becomes more pronounced when AI is deployed by intermediaries whose incentives are misaligned with plan sponsors. Hospitals are increasingly using AI-powered ambient listening and clinical documentation tools that convert clinician-patient conversations, labs and notes into highly detailed medical records. A March 2026 Blue Cross Blue Shield Association/Blue Health Intelligence study found that expanded documentation often captures additional diagnoses or comorbidities without corresponding changes in treatment, making patients appear more clinically complex on paper. As a result, approximately 20 percent of inpatient cost growth (within a 9 percent overall increase) was driven by coding intensity rather than more care delivered.

Strategies for AI adoption

Plan sponsors can harness AI to improve participant experience, increase engagement and support earlier, more effective care — while maintaining strong oversight. With intentional design and governance, AI can help plans manage costs and improve outcomes. Without it, AI risks becoming another driver of healthcare inflation — more sophisticated in execution, but no less misaligned with plan sponsor objectives.

Key strategies include:

• Vendor accountability and contracting. Plan sponsors should apply the same scrutiny to AI-enabled vendors that they apply to traditional vendors. Audit rights, data access and financial transparency are increasingly critical in an AI-enabled environment. Solutions that improve navigation, support informed decision-making and reduce waste can deliver meaningful value; tools that primarily optimize revenue capture without passing savings through to plan sponsors do not. Where AI is used, plan sponsors may want to:
  • Require AI transparency disclosures.
  • Add AI‑specific questions to RFPs and renewals.
  • Establish audit rights for AI‑assisted decisions.
  • Ensure human review pathways for high‑impact determinations.
  • Monitor denial rates, appeal outcomes and equity metrics.
  • Use data to confirm billing aligns with care delivered.
  • Tie performance guarantees to measurable outcomes, such as total cost of care, not just operational efficiency.
• Integration of AI in benefits administration. Common use cases include:
  • Participant self‑service and benefits navigation through AI‑enabled chat and search tools that answer common questions, with escalation to human support for complex or sensitive issues
  • Onboarding and enrollment support that provides guidance through plan selection, timely enrollment and key tasks
  • Open enrollment decision support to help participants identify “right‑fit” plan options based on demographics, utilization signals, preferences and risk tolerance
  • Year-round proactive outreach based on interaction patterns to trigger timely nudges or route cases to benefit teams before issues escalate