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Proof C Live confirmed

UnitedHealthcare

genAI companion for benefits navigation and care coordination

IndustryHealth & pharmaLeverRetentionFamilyConversationImplementationCustom AIStagepost-purchase
Pattern proven in 10 industries still untouched in Retail & e-commerce, CPG & D2C, Tech & SaaS +3 See the pattern map
6,5 millions
Members with access at launch (employer-sponsored plans), plus 160,000 on Medicare Advantage
"approximately 6.5 million members with employer-sponsored health plans" S1

UnitedHealthcare launched Avery in March 2026, a generative AI companion open to 6.5 million members and targeting 20.5 million by year-end, with 90% of users resolving their request without a human advocate.

Objective

Make using health insurance simpler for the member, by answering 24/7 in natural language on coverage, reimbursements, and costs, and easing the load on human advocates for requests the member can resolve alone.

The deployment

Avery is a generative AI companion launched by UnitedHealthcare on March 26, 2026. It answers in natural language, 24/7, on coverage, reimbursement details, cost estimates, appointment-booking help, and care options, and can call network providers to book an appointment on the member's behalf. It is accessible through the UnitedHealthcare app and myuhc.com. At launch, Avery is open to about 6.5 million members on employer-sponsored plans and 160,000 Medicare Advantage members, with a target of 20.5 million commercial, Medicare, and Medicaid members by year-end. UnitedHealthcare says that 90% of the time, members who use Avery did not need an advocate.

Results Proof C

6,5 millions
Members with access at launch (employer-sponsored plans), plus 160,000 on Medicare Advantage
"approximately 6.5 million members with employer-sponsored health plans" S1
20,5 millions
Year-end target, commercial, Medicare, and Medicaid members
"20.5 million commercial, Medicare and Medicaid members by the end of the year" S1
90% des cas
Resolution without a human advocate
"90% of the time, members who use Avery have not required assistance from an advocate" S1

The launch and volumes are documented by the official UnitedHealth Group release and by established press (Fierce Healthcare). The launch scope (6.5 million) is in production; the 20.5 million target is prospective. Press naming the brand plus a primary source = C, not A because it is not a financial result.

How it works

Documented architecture
prise de rendez-vousreponse en langage naturelescalade des cas complexes Membre UnitedHealthcare ApplicationUnitedHealthcare etmyuhc.com Compagnon d'IA generative Avery Benefices, claims etcouts du membre Prestataires du reseau Conseiller membre

The stack in detail

  • outil Avery Generative AI companion developed in-house by UnitedHealthcare: natural-language answers on benefits, reimbursements, and costs, and appointment booking by calling providers.
  • llm LLM d'Avery (modele exact non publie) UnitedHealthcare does not name the underlying generative model; the sources describe a custom/in-house generative AI.
  • plateforme Application UnitedHealthcare et myuhc.com Member's access surfaces to Avery, open to about 6.5 million members at launch.
  • infra Systemes de benefices et de claims UnitedHealthcare Plan data, reimbursement history, and cost reference that feed the personalized answers; without up-to-date access, the assistant refers the member to a human.

How it runs, concretely

For ops teams
CadenceReal time, available 24/7, with learning as interactions accrue
Operated byUnitedHealthcare's digital product team, backed by member advocates who take over on complex cases
  1. 1
    Member question Customer

    The member queries Avery through the app or myuhc.com about their coverage, a cost, or an appointment.

  2. 2
    Personalized answer AI

    Avery answers in natural language from the member's benefits and history.

  3. 3
    Action on the care journey AI

    If needed, Avery calls a network provider to book an appointment on the member's behalf.

  4. 4
    Escalation Member advocate

    Unresolved cases are passed to a human advocate (about 10% of cases).

  5. 5
    Learning Product team

    The interactions feed the improvement of the answers and the self-service.

The signal that drives it

The member's own benefits, reimbursement, and cost data. Without up-to-date access to the plan and the claims history, the assistant cannot give a reliable personalized answer and refers to a human.

How your customers perceive this type of use

Sourced studies

Les consommateurs n'acceptent pas les chatbots par defaut : 64% prefereraient que les entreprises n'utilisent pas d'IA dans leur service client (Gartner, 2024) et pres d'un utilisateur sur cinq du service client par IA n'en retire aucun benefice (Qualtrics, 2025). L'acceptation se construit sur trois conditions mesurees par Salesforce : savoir qu'on parle a une IA, pouvoir escalader vers un humain, comprendre la logique de l'agent.

64%
Consommateurs qui prefereraient que les entreprises n'utilisent pas d'IA dans leur service client (2024)
53%
Consommateurs qui envisageraient de passer a un concurrent s'ils apprenaient que l'entreprise prevoit d'utiliser l'IA pour le service client (2024)
pres de 75%
Consommateurs qui veulent savoir s'ils communiquent avec un agent IA (2024)

Acceptance conditions

  • Etre informe qu'on parle a une IA et non a un humain (pres de 75% le demandent, Salesforce 2024)
  • Un chemin d'escalade clair vers un agent humain (45% plus enclins a utiliser l'agent IA, Salesforce 2024)
  • Une logique de l'agent clairement expliquee (44% plus enclins, Salesforce 2024)

Red lines

  • Rendre l'humain injoignable : c'est la premiere inquietude des consommateurs sur l'IA dans le service client (Gartner 2024) et 50% craignent que l'IA les coupe du contact humain (Qualtrics 2025)
  • Remplacer le service client par l'IA sans alternative : 53% envisageraient de partir chez un concurrent (Gartner 2024)

Sources: Salesforce 2024 · Gartner 2024 · Qualtrics 2025

See full acceptance: by country, by use, by generation

How to replicate

Inference, not sourced

Data prerequisites

  • benefits and plan data per member
  • claims history
  • a reference set of costs and the provider network

Org prerequisites

  • a conversational product team
  • advocates for escalation
  • health compliance governance

Possible stack

  • an LLM with RAG over the plan data
  • conversational agent orchestration
  • telephony integration for appointment booking
Team to operate1 PM + 3-5 engineers (LLM/RAG, integrations) + 1 health compliance lawyer + member advocates for escalation

The plan, step by step

  1. Step 1
    Frame the data and compliance: map benefits, claims, and costs per member, set the health legal framework.Deliverable: An inventory of member data accessible in real time + a validated compliance dossier.
  2. Step 2
    Build the assistant on a narrow scope of frequent questions (coverage, reimbursements), in RAG over the plan data.Deliverable: An internal prototype tested by advocates on real cases.
  3. Step 3
    Connect human escalation and launch a closed pilot on a member segment.Deliverable: A pilot with self-service resolution rate and escalation rate measured.
  4. Step 4
    Add actions on the care journey (appointment booking, calling providers) with full logging.Deliverable: An assistant able to act, each action traced.
  5. Step 5
    Roll out progressively by line of contracts, tracking the use of advocates.Deliverable: A rollout with a self-service vs escalation dashboard by population.

First step: Reliably tie a member's benefits and claims data to a conversational assistant, with a clear human escalation.

Sources

  1. S1 UnitedHealthcare Introduces AI Companion Empowering People with Simpler Navigation, Personal Experience Primary unitedhealthgroup.com · 2026-03-26 · accessed 2026-07-11 archive pending
  2. S2 UnitedHealthcare launches Avery, a generative AI companion for members Established press fiercehealthcare.com · accessed 2026-07-11 archive pending