Overcoming the Hurdles of Integrating AI into Customer Service

Imagine offering a hyper-personalized digital assistant for every customer. It remembers their history, preferences, likes & dislikes, current context, and goals from each interaction. This personalization allows customers to self-serve their requests without the company hiring an entire customer service department to fulfill this level of personalization and 24/7 attention. An effectively integrated AI system does this all while achieving a deflection and containment rate of over 90%.

Forecasting suggests AI will boost company productivity by up to 40% by 2035. Automating customer support with AI reduces costs , improves response times, and increases customer satisfaction. So how do you implement AI  into your customer service team? In what ways does this technology disrupt and improve your current systems and outcomes? And how are you going to turn AI into a winning strategy?

Building a Strategy from Scratch

Think big, start small, deliver value, and scale fast. This philosophy works best when trying to prove and expand a new technology within an enterprise as you see it deliver value. For example, if you are using an intelligent assistant for your e-commerce business, maybe you want to start with simpler use cases like order status and order changes, followed by taking sales orders and helping customers find what they want before moving on to complex use cases like returns, refunds, and replacements. 

Returns, refunds, and replacements involve many validation and approval steps. The earlier use cases lay the foundation for simpler tasks. When you move to more complex tasks, the AI brain is deeply integrated into the transactional and analytical systems.

Benefits to Consider when Integrating AI into Customer Service

When implementing an AI assistant, a 10 to 15% reduction in human agents should be met within a year. By the second year, that number should go up to 30%, eventually maxing out at about 70-80% over time. For example, at 6000 agents with a 30% reduction, an enterprise could decrease to 4000 agents in that first year, which is a massive change. 

Other areas are important to consider when bringing AI into customer service.

1. Improve Response Times

Human teams generally dislike being available for evenings, nights, and weekends. This is a great place to start implementing AI because having an AI assistant to back up the team will allow for total 24/7 coverage in all time zones.

Customers can self-service anytime on any device if your AI is optimized for multichannel and omnichannel performance. This empowers customers to have more control over their experience. You can also set up automated emails to notify human agents if a client didn’t reach a conclusion in their steps with an AI assistant. This way, nothing gets dropped.

2. Personalize Experiences

AI can analyze customer data and provide personalized recommendations because it remembers a user’s history, preferences, likes/dislikes, the current context of their problem(s), and any goals they would like to see as outcomes.

An AI assistant should be taught to behave and interact in ways that express the company’s culture around customer service. This way, when customers interact with an Ai assistant, their relationship with the company grows.

3. Reactive to Responsive to Proactive Conversations

This hyper-personalized digital assistant can respond to individual customer requests 24/7 and contact customers (who opted-in) contextually to remind them and help them with tasks. For example, it can reach out to the customer two weeks before their Mom’s birthday and ask them if they want to do something special or do the same thing they did last year since the customer’s mom loved it.

Hyper-Personalized AI assistants help companies move from Reactive to Responsive to Proactive customer service. This is how companies can offer a ‘Concierge’ service for each customer that automatically scales for millions of customers knowing individual customers’ preferences, needs, and goals. Truly serving a customer of one at scale where every customer feels the entire company exists to serve them.

How to Effectively Integrate AI into Customer Service

This is where you start small. A group of tasks to achieve a business objective becomes its own use case. Build a foundation of APIs and Ontologies with specific business rules used by all subsequent use cases while allowing for exponential growth further down the line.

Here are four key steps when implementing an AI solution in customer service:

1.  Identify high-value areas of your business – mission-critical, expensive, frequent requests where you’ll benefit most from automation.
2. Train the AI model on use cases with the same information every agent receives.
3. Integrate the AI-powered solution across all applicable use cases.
4. Continuously monitor, evaluate, and refine the AI solution analytics and KPIs for higher effectiveness.

Multichannel Is Not Omnichannel

For ideal customer service, an AI agent must be implemented in voice and text through phones, mobile apps, SMS, and websites. Here, multichannel integration is necessary to drive the most value so customers experience consistent service however they access support. But it’s important to go one step further.

Omnichannel integration means that regardless of what channel a customer uses and the stage of the interaction with an AI assistant, the user should be able to pick up the same conversation from the last step on an alternate channel. If they call in and finish at step three because they have to get off the phone, they can pick up in text at step three, and the AI assistant behaves just like an agent knowing the context and the goals of the earlier communication before they proceed.

Practice Makes Perfect

For a  use case such as, “I want to know the status of my order,” it may take a week to train the model. First, it has to verify that you are a customer and have an order. If you have multiple orders, it needs to identify which particular order you are referring to. Once you have enough Ontologies and APIs in place for this one use case, new use cases, such as “I want to change my order,” will take anywhere from a few hours to a  few days.

“Enterprise Brain” is created through this method and used as a foundation for all use cases, making it easier and faster to implement subsequent use cases while ensuring reliability, accuracy, consistency, and scrutability in every customer interaction.

It’s important to mention that creating extra AI assistants to work on different use cases causes more issues. Each bot is siloed, resulting in a lack of accuracy, reliability, and consistency. Having a centralized enterprise brain is always best.

Develop Once and Deploy It Everywhere

An enterprise must be prepared to give its AI brain access to various APIs to change customer-related and employee-related information. For instance, If a customer needs to change the address in an already placed order, there needs to be an API that gives access to that order and its address and allows the AI assistant to make the change. Delays of implementation occur if this isn’t done.

Ideally, as you deploy the AI solution across operations, the assistant should reside behind your cloud firewall. This ensures your enterprise has full control over data privacy, security, access, and retention.

Continuous Refinement Is Key For Higher Effectiveness

Over time, the deflection and containment rates should increase as the brain gets smarter in handling more complex tasks. Each use case provides Ontologies and APIs that compound with the existing infrastructure, making the assistant exponentially effective in dealing with higher levels of complex customer requests.

In Conclusion

By following these steps, businesses can effectively integrate AI into customer service, improving response times, reducing costs, and increasing customer satisfaction. The key is identifying the areas where AI can add value, building a foundation of APIs and Ontologies, and continuously monitoring and refining the solution for higher effectiveness over time. Although the initial stages can be a lot of work, the exponential outcomes pay endless dividends.