Everyone Has AI, But Not Everyone Seems Happy About It!

98% of contact centers today are powered by AI, but still, customers are not happy about their experiences.

Here’s the catch.

More than half of the contact centers are turning to AI to handle tougher, more emotionally charged customer conversations.

Does AI adoption make customer interactions feel robotic, frustrating, and impersonal?

This is at the heart of a new Calabrio research report, which surveyed 437 contact center managers across 13 countries.

The study points to an important finding, which states that it is no longer about using AI; it is about using AI thoughtfully.

What Are the Most Common Types of AI Implementations?

The most common implementations include chatbots, voicebots, scheduling systems, and analytics dashboards. Most organizations have started deploying these, but how thoughtfully they integrate them per the customer’s expectations is what makes them successful.

This shift in AI implementation, where you integrate them as a part of your operations, will turn every customer interaction into an opportunity and empower your agents to become problem solvers.

How Do These Common AI Implementations Work?

Chatbots

They handle routine queries like balance checks, order status, and FAQs.

Imagine a retail brand implementing a chatbot to address customer queries. A customer asks if a shirt is available in a particular size and color.

He will get real-time answers without having to wait or get transferred between extensions.

This is an excellent use case for chatbots.

However, when a chatbot can’t escalate properly or doesn’t understand the context of a repeat interaction, all hell will break loose.

Customers will have to repeat themselves, and trust will erode considerably.

Voice Bots

Voice bots can provide self-service through phone interactions instead of key presses. This is great for IVR flows, account lookups, and appointment scheduling and cancellations.

Imagine a healthcare provider using voice bots. They can use it to confirm test results and to schedule, reschedule, and cancel appointments.

How much of a workload would it reduce?

My guestimate is that it would reduce agent workloads easily by at least 35%.

However, you have to be wary of over-automation.

What if a customer calls you for nuanced issues like insurance disputes and emotional complaints?

They would expect you to show empathy, and not to repeat themselves.

Chatbot Analytics

Analytics will help you track chatbot metrics like drop-off rates, query types, and escalation paths.

Let me give you an example.

Imagine a telco using chatbot analytics. They understand that 70% of their queries were about bill breakdowns.

What do you do with this information?

They would add a visual bill explainer to the bot, and this would considerably reduce the chat volumes.

Do contact centers really act on this type of data?

Most gather data, but they don’t act on it. AI without learning is just automation and not intelligence.

Scheduling Tools

Scheduling tools use AI to forecast call volumes and automatically schedule agents based on demand patterns.

Let us assume you are a telecom company that uses AI scheduling to handle seasonal spikes like new phone launches and billing cycles.

The scheduling tool would analyze the historical data and predict when call volumes would peak.

It would then build agent schedules around those high-traffic times while also considering individual agent preferences, like preferred shifts or time off.

This would help reduce customer wait times during peak hours and drop agent absenteeism considerably.

However, if you focus only on efficiency, the scheduling AI might pack agents into every high-volume slot, which would cause the agents to break down in due course. You have to take into consideration break times, family schedules, and work-life balance while scheduling and ensure that it is humane.

Is the Expectation – Experience Gap Widening?

Today’s customers don’t want just faster service. They want relevant interactions.

If your bot does not connect context across channels and offers irrelevant responses, what would happen?

It would not just be ineffective, but it would be damaging to your brand.

Most customers expect to solve even complex issues with just one interaction, and a majority expect consistent experiences across all channels.

AI can certainly make a difference in bridging the expectation – experience gap, but only if it’s seamlessly integrated.

What Does a Good AI Implementation Really Mean for Contact Centers?

Agent Empowerment

AI should help the agents do their jobs better. For instance, AI will handle all the repetitive and routine queries, which will free up the agents to handle complex issues.

Strategic Escalation

Do you use AI to know when not to use AI? That’s what the best contact centers do. It should be the job of your AI to figure out when to escalate your interaction to a human agent based on the query, sentiment, and urgency.

This would considerably increase the CSAT scores.

Customer-Centricity

Design AI with empathy. Do you feel heard even when you know that your first interaction is with a machine? That’s how the workflow should be designed.

Act On Data

Don’t just collect data. Ensure that you act on all the metrics that you monitor and measure. Use chatbot analytics, voice analytics, and scheduling patterns to improve continuously.

Customers don’t care what tech you use. They care how you make them feel.

AI is not a silver bullet to all your customer service challenges. Contact centers that win with AI are those that integrate it strategically, not just technologically.

Every customer experience decision you make should be aligned with your customer needs and expectations, not just vanilla metrics and operational KPIs.

Ensure that the intelligence systems you implement work strategically with human agents.