What Stops You From Implementing Artificial Intelligence (AI) For Your Contact Center?

I recently booked a chimney cleaning through a home services app, selected a time slot, and paid. An hour before the appointment, a service person was assigned, and I shared my location via the app.

At the scheduled time, no one showed up. Later, the service person messaged saying he had a minor accident and couldn’t make it. I wished him well and asked if he could cancel the service but there was no response from him.

I tried canceling it myself, but the cancellation screen was unclear. So, I turned to the app’s chatbot. After sharing the situation, the bot responded with two options: [I’m unsafe] or [No, I’m fine]. I clicked “I’m fine,” and it looped back to the same question.

I realized the bot latched onto the word “accident” and wasn’t equipped to handle context. There was no option to input a real reason for canceling either.

My takeaway? The experience was frustrating, impersonal, and poorly designed highlighting how easy it is to get AI implementation wrong..

What Are the Possible Reasons for Not Getting the AI Implementation Right in Your Contact Center?

Investments

The costs involved in implementing AI include software, integration, and infrastructure. Even when you implement something as simple as a chatbot, you have to factor in the costs of integrating it with your existing infrastructure and training the staff to manage the technology.

Integration Complexity

Let us assume you have a legacy on-premise contact center system. Integrating your AI implementation with your contact center infrastructure is going to pose significant hurdles, especially for tools like predictive analytics and automated transcription services.

Data Sources

Let us assume you have multiple data sources – website, email, chat, SMS, bots, social, mobile app, inbound support, and customer conversations.

If they are scattered and remain in silos, then you are going to have a lot of difficulty in training the AI models, which will lead to poor performance.

Internal Resistance

What is the biggest competitor to change?

It is the status quo. It rings true in every situation, and people really don’t want to change.

When it comes to AI, the resistance becomes huge, as agents may fear that AI might replace them in the long run.

Compliance

How do you ensure compliance with regulations like GDPR, CCPA, and TCPA, as well as industry-specific standards with AI implementations?

This can be a showstopper in AI implementations, as some of the breaches may be irreversible.

Lack of Expertise

It is the fear of the unknown, and without AI specialists, organizations may find it difficult to take the first step toward AI.

Besides, any failure will be accentuated because of the fear of the unknown, and most often, organizations drop their initiatives.

Lack of Clarity

Most organizations don’t understand what AI can do for them and how they can use it to improve their business.

When it comes to contact centers, people look at AI as a luxury rather than a necessity.

While this remains the reality, there is a lot of noise around AI, and organizations genuinely want to look at implementing AI to ensure better business and customer experience.

Here are some statistics for us to look at:

Do you know that 80% of the CX leaders plan to increase their investments in contact center AI?

Do you know that 50% of the CX leaders feel that without AI, customer satisfaction will decrease, and productivity will decline?

If you don’t invest in AI technologies today, it will not only impact customer satisfaction, but it will also impact productivity, revenue generation, and customer lifetime value.

How Do You Go About Implementing AI Successfully by Removing the Barriers Mentioned Above?

Start Small

Identify an area where you can show the massive impact of AI implementation and begin there.

Let us look at an example.

An e-commerce company was facing high volumes of repetitive queries – order tracking and return policies.

They decided to take this use case and implemented an AI-driven chatbot.

Within weeks, the chatbot handled 60% of the customer queries, leaving the agents to handle more complex issues.

Over time, they refined the chatbot with additional data and improved customer satisfaction and efficiency.

Data Readiness and Integration

Imagine if your data sources are fragmented and scattered – how do you go about implementing AI?

Let me explain this with an example.

A telecom provider with customer data from various sources struggled with slow resolutions.

They wanted to see if AI could help them.

AI requires authentic data to work on. The very first thing they did was invest in an omnichannel contact center platform that brought visibility to all the data sources, and they integrated it with a CRM system.

Besides, they cleaned up all duplicate entries, and now they had a single view of their customer interactions and information.

Their AI model also had a single view of their cleaned data, which allowed it to route interactions based on customer history intelligently.

This brought down their average handling time by 30% while increasing customer satisfaction.

Educate and Train Your Teams

The biggest fear people have with any automation is that they will be replaced sooner rather than later.

However, data shows that most AI implementations have only complemented human efforts and not replaced them.

Here is an example.

A banking contact center implemented an AI virtual assistant to help agents during calls by providing instant answers to complex queries.

The agents were worried about being replaced.

Then, the bank put together a training session on how this virtual assistant is going to help them transform the way they offer customer experience, and the agents were reassured. After the training, they could easily understand how AI helped them reduce their workload and improve performance metrics.

Partner With Trusted Vendors

You have to choose the right vendor to ensure compliance, integration, and ongoing support.

Let us look at an example.

A healthcare contact center wanted to implement AI but was worried about handling sensitive patient data under HIPAA regulations.

They wanted an AI vendor who complied with HIPAA regulations. Once they found them, they implemented their appointment scheduling and follow-ups using AI.

This allowed them to reduce their no-shows to near zero and improved their follow-ups by 30%.

Your AI implementation has to be carefully thought through so that it has the maximum impact on your business.

Start with initiatives that solve your immediate problems. You should work with experts who make your life easier when complying with standards and regulations.

Ensure that you have a good data strategy and educate your employees to see AI as a collaborator and not as a competitor.

Every contact center solution should implement AI as both a strategic and tactical tool in their journey of providing exceptional customer experiences.