The Scene: When a Winter Storm Shuts Down an Airline’s Operations
Imagine it’s 6:00 AM on a winter morning. A massive storm sweeps across the country, grounding hundreds of flights and stranding thousands of passengers.
Present scenario (The response of AI-enhanced contact centers):
The airline’s system automatically rebooks passengers and pushes out app notifications. For simple one-leg trips, it works. However, for anyone with a connecting flight, special service request, or a family itinerary, the app falls short. Passengers still need to call in, wait on hold, or stand in line at an airport counter to sort out the details. Stress builds, lines grow, and the airline’s net promoter score (NPS) falls.
Future scenario (The response of AI-native contact centers):
When passengers tap the “Call Us” button in the app, they don’t reach an interactive voice response (IVR) system or sit on hold. Instead, they’re instantly connected to an agentic AI-powered voice agent that speaks naturally in their preferred language. The AI already knows their booking details, identifies the disruption, and offers complete options:
– Rebook across multiple carriers if needed
– Apply loyalty points to upgrades or alternative routes
– Arrange hotel or meal vouchers
– Push updated boarding passes directly to their wallet
For most passengers, the issue is fully resolved conversationally and instantly without waiting in line or speaking to a human. These AI-powered voice agents deliver the immediacy customers increasingly expect from contact centers.
Behind the scenes, resolution supervisors proactively monitor live AI sessions, provide guidance, tag edge cases, or take over instantly if empathy or complex judgment is required. Their mission isn’t to handle the backlog themselves but to ensure autonomous resolution happens at scale, with zero compromise in customer experience—the hallmark of an autonomous contact center.
By midday, the backlog is gone. Customers are not just rebooked — they’re impressed.
While this example comes from travel, the same transformation applies across banking, healthcare, insurance, retail, and utilities,wherever customers need fast, accurate, and empathetic service, AI-native contact centers can change the game.
AI-native vs AI-enhanced: The Difference
Customer experience (CX) leaders often wonder: “Aren’t we already using AI in the contact center?”
The truth is it depends on how you’re using it. If AI is just an add-on feature, you’re AI-enhanced; if AI is the foundation your operations are built on, you’re AI-native.
Here are some characteristics of each:
AI-enhanced Contact Centers
- Legacy contact center as a service (CCaaS) or on-premises platform with AI add-ons.
- Humans still handle most of the volume.
- AI assists by suggesting actions and summarizing calls but doesn’t close the loop.
- Gains are incremental: better reporting, reduction in average handling time (AHT).
- Designed for AI to act as the primary agent, autonomously resolving 90–95% of interactions.
- Agentic AI performs the same steps that a human agent would, albeit faster, more accurately, and in multiple languages, while delivering a seamless CX.
- Humans no longer wait for calls to be routed. Supervisors proactively monitor and guide AI-led interactions in real time, stepping in only when needed to ensure maximum resolution and excellent customer outcomes.
- Built for proactive engagement, multi-step workflows, and continuous learning.
Why the Shift Is Accelerating
- Customer Expectations Demand It
Customers expect instant resolution, even in high-stakes situations. In the AI-native model, rebooking a complex itinerary or resolving a billing issue happens in seconds — not hours — with no compromise in personalization.
- Economics Are Unmatched
Outcome-based pricing replaces seat-based billing. You pay for resolved interactions, not idle time. Advances in contact center automation and outcome-based commercial models mean that, with high containment rates, cost per resolution drops by half while CX scores rise.
- The Talent Equation Has Changed
According to Gartner, by 2029, agentic AI will be able to resolve 80% of common customer service issues autonomously, without needing human involvement. The preferred model, hence, seems to be moving towards fewer, highly skilled resolution supervisors and CX specialists ensuring AI delivers both operational efficiency and exceptional customer experiences.
The Power of Agentic AI + Human Intelligence
Agentic AI is autonomous, context-aware, and goal-driven:
- It detects intent and executes multi-step workflows (for example, cancellations, rebookings, and the issuance of vouchers).
- It retrieves, updates, and validates data in real time.
- It uses voice AI with natural, human-like intonation, instant translation, and multilingual fluency to serve diverse customer bases without delay.
Human Intelligence keeps the system sharp and trustworthy:
- Resolution supervisors oversee AI interactions in real time, providing corrections, adjusting strategy, or taking over when necessary.
- Prompt curators refine AI’s reasoning for new or complex scenarios.
- CX designers create journeys that blend AI’s speed with human empathy.
AI-native systems don’t eliminate humans; they elevate them. This collaboration forms the foundation of a new AI-powered customer experience model that is scalable, intelligent, and adaptive.
The result? Twice the customer experience at a third of the cost, with measurable improvements in customer satisfaction (CSAT), first-contact resolution, and NPS.
Challenges Enterprises Must Prepare For
While the benefits are undeniable, the shift isn’t plug-and-play. Key challenges include:
- Data Privacy and Compliance
AI-native systems must comply with the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA), to name a few. Masking of personally identifiable information (PII), role-based access, redaction, and audit trails are non-negotiable. A single misstep can cost millions in fines.
Hexaware’s AI-native platform includes built-in compliance frameworks, prompt firewalls, and hallucination detectors. - Bias and Ethical Considerations
LLMs may reflect training data bias, creating escalation errors or unfair treatment. Human-in-the-loop (HITL) supervision ensures fairness, preventing AI from drifting off course. - Customer Trust and Adoption
Customers are cautious, especially in healthcare, banking, or insurance. A Salesforce study found that 72% of customers consider it important to know whether they are communicating with an AI agent, indicating that transparency plays a significant role in building trust.Success depends on:
- Transparent communication (“You’re speaking with our AI agent trained to help you…”)
- Natural, fluent conversations (especially in voice)
- Seamless live handoff options
Also read: Generative AI in Customer Service: Going Beyond Traditional Chatbots
Integration, Vendor Strategy, and Practical Advice
Before jumping in to adopt an AI-native contact center, CX leaders should consider the following:
- Technology Stack Compatibility
Choose modular, CCaaS-agnostic, API-first solutions. AI should embed seamlessly into platforms like NICE, Genesys, Salesforce, or Zendesk and not require a costly rip-and-replace. - Start With the Right Use Cases
Begin with high-volume, low-complexity intents, such as order status, billing FAQs, and eligibility checks. These often achieve 60–85% containment in the first 90 days. - Reimagine Human Roles
Invest in training Human agents to evolve into AI supervisors, escalation specialists, and CX designers. Establish AI supervisory teams early to oversee, refine, and guide AI in production.
The Risk of Waiting
If you’re still using an AI-enhanced contact center, here’s the reality:
- Competitors are already transitioning to AI-native contact centers to deliver faster, more personalized service.
- Platform players and AI-first BPOs are redefining the economics.
- Once customers experience AI-native contact center solutions, they won’t tolerate legacy wait times again.
Your Next Move as a CX Leader
This isn’t just about a new tool—it’s about redefining your service model. Here’s how to start:
Step 1: Assess Readiness
Measure automation percentage, AHT, CSAT, and containment rate. Identify pain points in voice vs. digital.
Step 2: Identify AI-ready Use Cases
Target repetitive, data-retrievable interactions that don’t rely heavily on emotion or exception logic.
Step 3: Choose the Right Partner
Look for providers offering:
- Outcome-based pricing
- Live HITL capabilities
- Compliance guardrails
- Industry-specific AI agent packs
Step 4: Launch a 90-day Minimum Viable Product (MVP)
Prove value fast with real traffic, measurable ROI, and minimal disruption.
How Hexaware Can Help
Hexaware’s Tensai® AgentVerse for CX helps enterprises transition from AI-enhanced to AI-native contact centers without disruption.
Why Hexaware:
- Up to 90% containment across voice and digital
- Deployment in 6–8 weeks, not months
- No seat-based pricing—pay only per outcome
- Human intelligence console for real-time AI supervision
- Modular architecture that integrates with any CCaaS/CRM stack
Our Offerings Include:
- 90-day minimum viable deployment
- CX expansion packs (voice, chat, email)
- HITL enablement and training
- Fully managed services (AI agents + HITL roles)
Final Thoughts
AI-native contact centers aren’t just faster—they redefine what great service means in your market. With agentic AI, real-time multilingual voice, and proactive human oversight, you can deliver customer experiences that surpass competitors, set new standards, and become the benchmark for next-gen contact centers in the industry.
Curious what a 90-day AI-native pilot could look like? Write to us at marketing@hexaware.com for a demo.
