Real AI, CX and automation use cases — filter by service, industry and platform.

Problem. High call volume for routine scheduling ties up patient-access agents and drives long queues.

Approach. Natural-language voice bot on Amazon Connect + Lex, integrated with EHR scheduling APIs and healthcare-grade auth.

Outcome. Self-service containment on scheduling calls with warm transfer to a human agent when clinical questions arise.

Problem. Claim-status calls flood member services and are highly repetitive.

Approach. Genesys + Kore.ai IVA fronting the claims platform with knowledge-grounded responses and secure auth.

Outcome. Members self-serve status, EOB questions and denial reasons; agents focus on appeals.

Problem. Complex product mix drives long AHT and inconsistent responses from newer agents.

Approach. NICE Enlighten + Azure OpenAI copilot with RAG over product, policy and compliance content.

Outcome. Next-best-action prompts, live compliance checks and auto-summary at wrap.

Problem. FNOL calls are long, structured and expensive; adjusters get incomplete intake data.

Approach. Five9 IVA + Kore.ai bot collects loss details, uploads photos, sets severity and books the adjuster.

Outcome. Structured, adjuster-ready FNOL packages delivered before the first human touch.

Problem. WISMO and returns account for the majority of contact volume during peak seasons.

Approach. Agentforce service agents backed by Commerce Cloud + OMS APIs; voice fallback via Amazon Connect.

Outcome. WISMO and returns fully deflected from live agents across web, app and voice.

Problem. Traditional QA samples 1–3% of calls, missing risk and coaching signal at scale.

Approach. AI QA scoring every voice and digital interaction with client-specific scorecards and dispute workflow.

Outcome. 100% coverage, faster coaching cycles and defensible client reporting.

Problem. Billing, coverage and payment-plan questions require agents to navigate 4–6 systems.

Approach. Grounded copilot surfaces balance, plan eligibility and script-safe payment options in real time.

Outcome. Faster resolution and consistent, compliance-safe messaging.

Problem. Member experience drivers are buried in unstructured call and chat data.

Approach. Enlighten + custom topic models surface Stars/CAHPS drivers and coaching moments.

Outcome. Targeted coaching against measurable Stars/CAHPS drivers.

Problem. Early-stage collections outreach is expensive and inconsistent with FDCPA/UDAAP rules.

Approach. Retell AI voice agent with policy-grounded scripts, hot transfer to human on hardship signals.

Outcome. More right-party contacts at lower cost, with full audit trail.

Problem. Prior auth is a multi-step, payer-specific workflow that consumes clinical and admin time.

Approach. Multi-agent system: intake agent, payer-rules agent, submission agent, follow-up agent — human-in-the-loop on approvals.

Outcome. Autonomous submission and status tracking; humans review exceptions only.

Problem. Periodic KYC refresh is manual, time-boxed and expensive across large customer books.

Approach. Agentforce + Bedrock agents pull entity data, screen adverse media, generate refresh packets and route for review.

Outcome. Analyst-ready refresh packets with reasoning trace; humans approve.

Problem. SMB submissions are low-margin and slow, forcing declines or long turnaround.

Approach. Underwriting agent extracts submission data, runs appetite checks, prices tiers and drafts quotes for underwriter approval.

Outcome. Same-day quotes on straight-through submissions with underwriter oversight.