Omnichannel service AI blueprint

AI assist that makes the agent faster without removing accountability.

The target is a governed service-orchestration layer across voice, chat, secure messages, portal events, CRM, and QA. AI retrieves source-backed context, drafts the next action, stages CRM updates, and keeps the agent in control.

Outcome Faster resolution with source-backed answers.

Agents see policy, claim, eligibility, and document context in one service flow.

Control Human approval before customer-facing action.

Unsupported, sensitive, or low-confidence paths fail closed to manual service.

Evidence Every suggestion leaves replayable proof.

Transcript, citations, tool calls, edits, CRM writeback, QA, and escalation reasons.

Status Spanish flows are ready for deployment.

Claims call center expansion is in progress and release movement is pending Vinod approval.

Run service-test view Review control evidence CCAI roadmap priority

Service intelligence cockpit Intake -> context -> approval -> evidence

01 channel Voice, chat, secure message, portal event

Normalize each interaction into a single service event.

02 identity Member, broker, group, consent, role

Scope data access before retrieval or action.

WHPS service intelligence layer

A governed orchestration layer that connects source-system context, retrieval, agent decisions, CRM disposition, QA scoring, and operational telemetry.

RAG citations

Policy guardrails

CRM staging

QA replay

PHI boundary

Mainframe facade

03 decision Agent-approved answer or escalation

Customer-facing work remains supervised and auditable.

04 evidence Transcript, citation, tool call, CRM record

Every service action becomes a reusable proof asset.

CCAI roadmap priority | executive roadmap

Contact Center AI portfolio priorities are sequenced around claims, quality, after-call work, group integration, self-service, and analytics.

The roadmap separates near-term delivery from discovery and future-state planning. Priority 1 is claims IVR and data exchange; Priority 2 is VOC quality automation; Priority 3 is ET log creation; Priority 4 is group contact center integration. Self-service/BPaaS and Phase 3 analytics remain future-state design lanes.

Priority 1 Claims

Ops Portal API and data exchange review is the next control point before testing.

Priority 2 VOC QA

Discovery started with AI team and business SME for quality scoring automation.

Priority 3 ET logs

Agent/member transcripts support ET log creation with Verint dependency risk.

Priority 4 Group AI

SME and BA alignment are required before platform integration can advance.

Roadmap area	Description / background	Status	Owner	Target	Actions
Claims Contact Center AIPriority 1 · PMO 3684	Voice IVR for Claims Contact Center covering eligibility/benefits, claims status, and claims payment.	Risk: complexity	IT: Ali, Rashid PMO: Divya, Edwin OPS: Lynnette, Arup	Target Aug 2026	Review Ops Portal APIs and data exchange; testing follows. BRD is in progress with daily meetings to define requirements.
Contact Center VOC AI Agent for QAPriority 2 · PMO 3636/3938	AI-assisted call quality scoring for Voice IVR and agents, aligned to scoring worksheets and procedures for 100% call audits.	Discovery	IT: Ali, Rashid PMO: Divya, Edwin OPS: Linish, Arup	Target Sep 2026	Design discussions started with AI team and business SME. Phase 1 focuses on CCAI Voice IVR checklist guidelines; Phase 2 adds voice call with screen capture.
CCAI Agent Assist ET Log CreationPriority 3 · PMO 2022	Use agent/member voice transcripts to create ET logs and reduce after-call work by 30 seconds.	Risk: complexity / Verint dependency	IT: Ali PMO: Divya OPS: Arup	Target Oct 2026	Design discussions underway. Key risks are the 48-hour KPI to post ET logs to SLP for caller follow-up and inability to guarantee 100% log creation.
Group Contact Center AIPriority 4 · PMO 4014	Voice assist AI agent for current/future Group platform covering eligibility, enrollment, tier structure, updates, billing, payments, and renewals.	Risk: timeline / integration	IT: Sam, Rashid PMO: Divya, Edwin OPS: Linish, Arup	TBD	SME and BA need to be aligned. Clarify new platform integration and GroupLink ownership, then determine cost/benefit on 4 FTE headcount.
Self-Service / BPaaS Call CenterIndividual and Group	Discovery with Strategy Office for WHPS; researching future web-based contact center model via Cisco.	Yet to start	IT: Sam, Mike PMO: Elizabeth, Edwin STRAT: Amika, Dani	TBD	Document future-state direction and use cases for POC using internal and external sources.
Contact Center AI Phase 3Analytics and sentiment	Analytics and reporting, sentiment analysis, and additional agent-assist features for predictive help systems.	Yet to start	IT: Sam, Rashid PMO: Divya, Edwin	TBD	Hold strategic design discussions on platform changes required to support these functions.

Claims AI Priority 1 moves first because API and data exchange are prerequisites.

Claims Contact Center AI needs validated Ops Portal API contracts, data exchange patterns, BRD clarity, and test readiness before the team can move into delivery confidence. The current risk is complexity across eligibility/benefits, claims status, and claims payment workflows.

Quality automation VOC QA creates the path to scalable audit coverage.

The VOC QA lane should automate call scoring against approved worksheets and procedures. The phased approach starts with Voice IVR checklist guidelines, then adds voice call and screen-capture evidence for higher-fidelity QA review.

After-call work ET log creation targets measurable handle-time reduction.

ET log creation uses voice transcripts to draft logs and reduce after-call work by approximately 30 seconds. The lane needs a realistic control position on the 48-hour SLP posting KPI and on cases where 100% automated log creation cannot be guaranteed.

Group integration Group Contact Center AI depends on platform and SME alignment.

Group integration needs clarity on the current/future Group platform path, the GroupLink modernization interface, and accountable SMEs. The next executive decision is whether the expected 4 FTE headcount benefit justifies prioritization after the higher-priority AI efforts.

Self-service / BPaaS Future web-based contact center design should be treated as a strategy lane.

This lane should define future-state self-service and BPaaS contact center use cases for Individual and Group, including what belongs in Cisco/web contact center tooling versus WHPS-owned service orchestration, evidence, and domain services.

Phase 3 platform Analytics, sentiment, and predictive assist require platform design first.

Phase 3 capabilities should not be treated as isolated add-ons. Analytics, reporting, sentiment analysis, and predictive help need a platform decision for data capture, consent, retention, model evaluation, dashboarding, and operational feedback loops.

WHPS payer service strategy

Move HPS from transaction processing to an AI-enabled payer operating platform.

The Wipro HPS advantage is domain depth across sales, enrollment, billing administration, service, and member retention. The modernization strategy turns that operating knowledge into reusable platforms, governed AI assistance, source-backed service workflows, and measurable production evidence.

Why now ACA, EDE, payer service, and cost pressure require faster operating change.

What changes Service work becomes orchestrated across portals, CRM, core systems, and AI controls.

How it scales AI Factory delivery, reusable domain services, and evidence gates replace bespoke delivery.

01 domain platform

Rebuild around payer domains, not channels.

ServiceLink, BrokerLink, GroupLink, and Contact Center AI share identity, case context, document access, service APIs, audit logging, and operations telemetry.

02 AI with control

Use AI for service acceleration, not unbounded automation.

AI retrieves, summarizes, drafts, and stages actions. Human approval, policy controls, and source citations govern the customer-facing response.

03 legacy exit

Turn the mainframe into a managed migration source.

Legacy systems remain behind facades during transition while target services prove parity through replay, reconciliation, and dual-run evidence.

04 evidence model

Make operational proof the product of every workflow.

Each release produces trace logs, source citations, CRM writeback evidence, QA findings, support runbooks, rollback paths, and adoption telemetry.

In-depth operating analysis

The service strategy is a connected operating model, not a chatbot project.

Contact Center AI is the visible entry point, but the strategic value comes from connecting payer domain data, migration controls, service workflows, quality assurance, and AI SDLC release evidence into one production operating model.

Operating signal Agent effort is trapped in system navigation.

Modernization removes lookup burden by consolidating service context and source-backed response drafting.

Architecture signal Service events need a common contract.

Voice, chat, portal, broker, and group events should enter the same orchestration and evidence model.

Migration signal Legacy calls must be isolated before replacement.

The mainframe facade stabilizes consumers while domains migrate and certify target behavior.

Risk signal AI trust requires runtime evidence.

PHI handling, grounding, tool scope, agent edits, and QA outcomes must be logged and reviewable.

Value signal Service quality and cost reduction are linked.

Handle time, first-contact resolution, repeat contacts, QA defects, and knowledge gaps become managed signals.

Service model How agent work changes.

Agent work shifts from manual system lookup to supervised service orchestration. The desktop receives the normalized interaction, identity scope, retrieved sources, recommended response, and CRM disposition while the agent remains the approval authority.

Modernization path How service AI connects to platform migration.

Contact Center AI depends on the same modernization spine as the portal work: IAM, API gateway, CRM contracts, document services, mainframe facade, RAG, service-test replay, and observability. Legacy calls stay behind the facade until equivalent target services are certified.

Engineering proof What engineers inspect before release.

Each service scenario should carry test transcript, identity and consent checks, retrieval citations, policy decision, tool-call log, agent edit reason, CRM writeback record, QA score, exception handling, and rollback/runbook owner.

Five-zone architecture

Contact Center AI integrates omnichannel intake, conversational adapters, private model runtime, data, and security controls.

This is the professional architecture view from the source diagrams, translated into a target-state pattern: contact-center intake, conversational adapters, private/on-prem capable AI runtime, secure RAG, data integrations, and a zero-trust perimeter. The model layer remains replaceable behind the WHPS model gateway.

L1 system

Member interaction to AI answer, CRM, and evidence

flowchart LR
  subgraph Member["Omnichannel interaction layer"]
    M["Member / broker / group user"]
    VOICE["Voice Portal / IVR"]
    CHAT["Web chat"]
    MSG["Secure portal message"]
    EMAIL["Email / SMS handoff"]
    PORTAL["ServiceLink, BrokerLink, GroupLink event"]
    ROUTER["Contact center routing"]
    AUTH["Authentication and consent"]
  end
  subgraph Conversational["Conversational and channel adapters"]
    ADAPT["Channel adapter"]
    TEL["Telephony, chat, message, and portal integration"]
    APIGW["API Gateway"]
  end
  subgraph Runtime["Private AI runtime"]
    DGX["Private GPU / model runtime"]
    LLM["Model gateway adapter"]
    RAG["RAG retrieval"]
    COMP["Compliance guardrails"]
    NGINX["NGINX load balancing"]
  end
  subgraph Data["Data and integration layer"]
    DB2["Mainframe DB2"]
    CRM["CRM and business applications"]
    DOCS["Document repositories"]
    PIPE["Data pipeline"]
  end
  subgraph Security["Security perimeter"]
    ZT["Zero trust"]
    MON["Compliance and monitoring"]
    NET["Network security"]
  end
  M --> VOICE
  M --> CHAT
  M --> MSG
  M --> EMAIL
  M --> PORTAL
  VOICE --> ROUTER
  CHAT --> ROUTER
  MSG --> ROUTER
  EMAIL --> ROUTER
  PORTAL --> ROUTER
  ROUTER --> AUTH --> ADAPT
  ADAPT --> TEL --> APIGW --> NGINX --> DGX --> LLM
  LLM --> RAG --> DOCS
  LLM --> COMP --> CRM
  APIGW <--> DB2
  APIGW <--> CRM
  PIPE --> RAG
  ZT -.-> AUTH
  NET -.-> APIGW
  MON -.-> COMP
  MON -.-> CRM

Security and compliance architecture

Protected data stays inside governed boundaries while every AI decision leaves evidence.

The attached security and compliance materials strengthen the target pattern: private/on-prem capable model execution for regulated workloads, secure RAG, zero trust, role-based access, monitoring, audit logging, secure development practices, penetration testing, and compliance alignment. These are target controls and evidence expectations until production certifications are formally issued.

L3 control

AI Contact Center security and compliance control plane

flowchart LR
  subgraph Access["Access and identity"]
    CH["Omnichannel ingress"]
    WAF["WAF, TLS, bot controls"]
    IAM["SSO, MFA, RBAC, consent"]
  end
  subgraph Policy["AI policy enforcement"]
    CLASS["Data classification"]
    DLP["PII/PHI redaction"]
    PROMPT["Prompt and response firewall"]
    ACTION["Least-privilege action policy"]
  end
  subgraph Runtime["Private AI boundary"]
    GATE["Model gateway"]
    RUNTIME["Private model runtime"]
    RAG["Secure RAG and source citations"]
    TOOL["Read-only tool gateway"]
  end
  subgraph Systems["Enterprise systems"]
    CRM["CRM and case records"]
    API["Eligibility, claim, billing, document APIs"]
    DOC["Policy, plan, script repositories"]
    MF["Mainframe coexistence facade"]
  end
  subgraph Assurance["Assurance and compliance"]
    LOG["Immutable audit log"]
    SIEM["SIEM and 24/7 monitoring"]
    QA["QA, coaching, red-team replay"]
    XWALK["HIPAA, NIST 800-53, NIST AI RMF, SSDF, OWASP LLM, HITRUST/SOC evidence"]
  end
  CH --> WAF --> IAM --> CLASS --> DLP --> PROMPT --> ACTION --> GATE --> RUNTIME
  RUNTIME --> RAG --> DOC
  RUNTIME --> TOOL --> API
  TOOL --> CRM
  TOOL --> MF
  PROMPT --> LOG
  ACTION --> LOG
  RAG --> LOG
  TOOL --> LOG
  CRM --> LOG
  LOG --> SIEM
  LOG --> QA
  SIEM --> XWALK
  QA --> XWALK

Data sovereignty boundary

Protected member data remains inside approved WHPS-controlled environments.
Model context is minimized, classified, redacted, and logged by policy.

Secure RAG

Answers must cite approved policy, plan, claim, script, or case sources.
Source freshness, retrieval score, and missing-source gaps are retained.

Zero-trust access

RBAC, MFA, consent, service mesh, certificate rotation, and least-privilege tool scopes.
Human approval gates block autonomous customer-facing action until controls mature.

Continuous assurance

24/7 monitoring, penetration testing, AI red-team replays, secure SDLC gates, and audit logging.
Evidence maps to HIPAA safeguards, NIST 800-53, NIST AI RMF, SSDF, OWASP LLM, SOC 2, HITRUST, and PCI where applicable.

Diagram depth levels

Contact Center AI transformation views by operating depth.

Use these views to explain the same capability to product owners, architects, engineers, QA, and operations without collapsing everything into one overloaded diagram.

L0 model

Service operating model

flowchart LR
  WORK["Customer service work"] --> ASSIST["AI assists agent"]
  ASSIST --> APPROVE["Agent approves response"]
  APPROVE --> RECORD["CRM records outcome"]
  RECORD --> QA["QA scores every interaction"]
  QA --> COACH["Coaching and knowledge updates"]
  COACH --> WORK
  APPROVE --> ESC["Escalation for sensitive or unsupported paths"]
  ESC --> QA

L1 containers

Contact Center AI containers

flowchart TB
  subgraph Channel["Omnichannel intake"]
    VOICE["Voice IVR"]
    CHAT["Web chat"]
    MSG["Secure message"]
    PORTAL["Portal event"]
    EMAIL["Email SMS handoff"]
    ROUTE["Queue routing"]
  end
  subgraph AgentLayer["Agent workspace"]
    DESKTOP["Agent desktop"]
    SUGGEST["Suggested response panel"]
    WRAP["Disposition and wrap-up"]
  end
  subgraph AI["AI services"]
    ASR["Transcript service"]
    ORCH["Conversation orchestrator"]
    GUARD["Guardrails and policy"]
    RAG["Retrieval service"]
  end
  subgraph Enterprise["Enterprise systems"]
    CRM["CRM"]
    CLAIM["Claim API"]
    ELIG["Eligibility API"]
    DOCS["Policy documents"]
  end
  subgraph Ops["Ops QA"]
    QA["QA scoring"]
    OBS["Observability"]
    KB["Knowledge backlog"]
  end
  VOICE --> ROUTE --> DESKTOP
  CHAT --> ROUTE
  MSG --> ROUTE
  PORTAL --> ROUTE
  EMAIL --> ROUTE
  DESKTOP --> ASR --> ORCH
  GUARD --> ORCH
  ORCH --> RAG --> DOCS
  ORCH --> CLAIM
  ORCH --> ELIG
  ORCH --> SUGGEST
  DESKTOP --> WRAP --> CRM
  CRM --> QA
  ORCH --> OBS
  QA --> KB

L2 ladder

Automation maturity ladder

flowchart LR
  A["Assist: retrieve and draft"] --> B["Supervise: agent approves next action"]
  B --> C["Constrain: policy controlled tool action"]
  C --> D["Automate: reversible low-risk workflow"]
  D --> E["Optimize: telemetry driven routing and coaching"]
  A --> A1["Evidence: citations and transcript"]
  B --> B1["Evidence: edit reason and approval"]
  C --> C1["Evidence: tool call and policy decision"]
  D --> D1["Evidence: rollback and exception handling"]
  E --> E1["Evidence: QA trend and knowledge update"]

Healthcare AI architecture

Speed, accuracy, empathy, and governance are separate layers.

The architecture is a layered service system rather than one large model answering calls: deterministic gateway handling for routine intents, real-time retrieval for accuracy, a healthcare-tuned language layer for phrasing, DGX/on-prem execution for protected workloads, and control-plane evidence for governance.

Gateway inferencing

Route routine inquiries without invoking high-cost generation.
Keep deterministic service responses for bounded status, eligibility, and document-retrieval paths.

Real-time retrieval

Resolve answers from claims, benefits, plan rules, scripts, and service history.
Return citations and freshness checks before answer drafting.

Governance control plane

Monitor policy decisions, QA scores, model behavior, agent edits, and knowledge gaps.
Preserve prompt, source, approval, and CRM writeback evidence.

Real-time channel path

Support voice interruption, chat continuity, secure-message context, and portal handoff behavior.
Escalate sensitive or unsupported intents to trained agents.

Deep contact-center architecture

Agent desktop, AI orchestration, service APIs, CRM, and QA telemetry.

This diagram shows the target operating stack and the boundaries engineers must implement: channel events, identity, transcript processing, RAG, policy, tool calls, agent approval, CRM writeback, QA, and monitoring.

Runtime architecture

Contact Center AI resolution stack.

The architecture keeps the customer experience in the contact center while adding governed retrieval, response drafting, service API access, and QA automation.

Omnichannel and CRM flow

Channel ingress to governed response and QA evidence

Human escalation remains available at every step. Policy gates decide what the AI can see, suggest, write, and archive.

Channels

Voice, IVR, chat, secure message, portal event Metadata, transcript stream, intent, authentication state, routing context.

Queue routing Skill, language, sentiment, plan, urgency, and escalation policy.

Intelligence

Conversation orchestrator State machine, confidence threshold, tool policy, handoff trigger.

RAG retrieval Benefits docs, claim policy, scripts, employer plan knowledge, source citations.

Systems

CRM and case platform Member profile, prior contacts, tasks, notes, disposition, service history.

Core APIs Eligibility, claim status, enrollment, payment, document status, provider data.

Assurance

Response guardrails PII redaction, approved language, confidence, source validation, action limits.

QA automation Transcript scoring, compliance flags, coaching queue, knowledge gap capture.

Telemetry

Operational signals Quality, handle time, resolution, escalation, latency, cost, adoption, overrides.

Model monitoring Drift, hallucination, unsafe answer, fallback rate, source freshness.

Engineer one-through

How a member interaction becomes a resolved case.

Engineers should be able to follow each event, tool call, source, approval, and writeback from the first utterance, message, portal action, or agent task to the final QA record.

Detect Intent and identity

Capture transcript, verify member, classify service intent, attach plan context.

Retrieve Grounded context

Pull policy, plan, claim, and knowledge snippets with source IDs and freshness checks.

Decide Tool or human path

Apply confidence, policy, sentiment, and reversibility gates before suggesting action.

Respond Approved answer

Generate language with citations and redacted fields; agent approves or edits.

Document Case update

Write approved summary, disposition, task, resolution code, and escalation reason.

Learn QA feedback loop

Score transcript, route exceptions, update coaching, and add knowledge gaps to backlog.

Use-case ladder

Automation expands only after controls are proven.

Each service use case graduates through assist, supervised action, controlled automation, and continuous optimization only after evidence gates pass.

Claim status

Verify identity
Retrieve claim state
Explain pending requirement
Stage follow-up task

Enrollment and benefits

Confirm plan context
Summarize coverage rule
Mask sensitive fields
Escalate plan exceptions

Payment or document

Check service API result
Confirm customer preference
Stage CRM note
Archive QA artifact

Sensitive or unsupported

Detect policy mismatch
Suppress AI answer
Route to trained agent
Create backlog item

Value logic

Service quality creates the financial case.

Financial impact comes from fewer repeat contacts, faster knowledge retrieval, cleaner case notes, QA coverage, improved coaching, and reduced escalation friction.

Routine inquiries routed by gateway

60%

Response latency target

<200ms

Production accuracy target

95%+

Average handle-time reduction target

40%

Targets sourced from the WHPS Contact Center strategic positioning deck; final values require pilot validation and production telemetry.

External strategy signals

Reference points behind the WHPS approach.

The strategy aligns Wipro payer-domain positioning, ACA/EDE control expectations, enterprise contact-center AI patterns, and AI risk-management frameworks with the WHPS operating model.

Wipro HealthPlan Services Wipro PayerAI Wipro application modernization CMS Enhanced Direct Enrollment Google CCAI Platform docs NIST AI Risk Management Framework OWASP LLM Top 10