Hospitals and insurers have long treated artificial intelligence as a back-office tool for processing claims after care is delivered. That is shifting fast. Insurers including Sun Life and Aetna are now deploying AI before treatment begins - identifying patients with high-cost conditions, recommending clinical trials, and guiding people toward specific care pathways before a single bill is generated.
Sun Life disclosed in its second-quarter 2026 earnings release that it is using AI-powered clinical navigation from Medzown, a precision medicine management company, to identify members diagnosed with cancer and other complex conditions and connect them to appropriate clinical trials "before high-cost claims escalate." The numbers behind the move are stark. Cancer costs an average of $252,000 per person per year; orthopedic and musculoskeletal conditions average $120,000, according to Sun Life's own 2026 high-cost claims report. Sun Life said its suite of AI and digital health tools, combined with other cost controls, saved the company and its self-insured employer clients more than $68 million in 2025.
"Employers who self-fund their health plans face immense financial risk when members develop complex, costly conditions," said Jennifer Collier, president of Health and Risk Solutions at Sun Life U.S., when the AI for Insurance partnership with Medzown was announced. "Medzown's clinical expertise and individualized support is uniquely suited to address both the human and financial sides of managing major health conditions."
Aetna automates both ends of the same process
Aetna, a CVS Health company, has embedded AI into how members navigate care and how claims get processed. Its Care Paths tool uses AI to give members personalized recommendations for health and wellness programs tailored to their conditions, and CVS is embedding a conversational AI assistant into the tool to guide members through those choices. On the claims side, Aetna launched a second-generation version of its Claims Assist Manager in May, an AI-powered platform that reduces processing time by more than 20% for complex claims requiring manual review, CVS Health said. The launch is part of a broader $20 billion multi-year investment in digital tools.
Together, the two tools show where AI for Insurance is going: AI that shapes what a member does before treatment, paired with AI that adjudicates what the insurer pays after it.
States draw a line at the coverage decision itself
That expansion has collided with a wave of new state regulation aimed squarely at how far insurers can let AI go. Seven states passed new laws in 2026 addressing AI's role in healthcare, with prior authorization the primary focus, according to a legislative review by Becker's Payer Issues. Alabama's SB 63 prohibits insurers from using AI as the sole basis for a coverage denial and requires disclosure when AI is used in review. Colorado's HB 1139 requires that AI-assisted utilization review decisions be based on a patient's individual clinical history rather than group data, and mandates that a licensed clinician personally review any medical-necessity denial before it takes effect.
Washington's SB 5395 goes further: only a licensed physician or health professional may deny a request on medical necessity grounds, AI cannot be the sole means used to deny, delay, or modify care, and insurers must report to the state's insurance commissioner what share of their prior authorization denials involved AI.
The pattern across nearly all of these laws is the same: insurers can use AI to flag, recommend, and pre-screen, but the moment a decision actually denies or delays a patient's care, a human must sign off. Whether that boundary holds as insurers push AI further upstream - into decisions that shape a patient's treatment options before a formal denial is ever issued - is yet to be seen.
Why this matters for insurance professionals
For claims managers, underwriters, and health plan product leads, the shift means two things. First, the job scope for AI oversight is broadening: you are no longer dealing only with post-claim adjudication tools but with pre-care recommendation engines that directly influence treatment choices. Second, state compliance is becoming a tighter constraint. If your organization uses AI to flag high-cost patients or recommend clinical trials, you need to audit where those systems interface with coverage decisions - and ensure there is a licensed clinician in the loop at the denial threshold, because several states now require it by law.
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