Conversational AI for customer service

Customer service teams use the technology to enhance communications and offer real-time support to customers around the clock. It uses natural language processing (NLP) and machine learning (ML) to analyze human language and create human-like responses. Through this AI-powered technology, contact centers can offload simple inquiries and resolve issues, making for a better customer experience.

Conversational AI is part of a growing trend toward AI-driven customer service¹. By 2027, executives forecast a major shift toward fully autonomous automation, according to a recent IBM Institute for Business Value report. Executives surveyed anticipate a 53% increase in the use of AI to power personalized self-service for customers and a 47% enhancement in self-service call resolution by 2027.

This conversational AI technology can include chatbots, virtual assistants, AI assistants, AI agents, automation and generative AI. With these capabilities, human agents can focus on more complicated inquiries and let the conversational AI handle the routine customer queries². This immediacy can boost customer interactions and help customer service teams work more efficiently than ever before.

Conversational AI works by using NLP, ML and large language models (LLMs) technology to translate human conversations into a language understandable by machines. Once translated, the tool forms a reply based on information provided to it by a specific knowledge base.

It’s important to note that conversational AI technology is not the same across the board. The most effective and proficient AI tools are trained on billions of customer interactions³. The technology, if trained properly, can discern customer needs and enhance customer satisfaction. Furthermore, it can optimize workflows within contact centers and increase qualify of service overall. Conversational AI software keeps evolving as it continuously learns from each interaction.

NLP: This enables computers and digital devices to recognize, interpret and understand human language. There are two subcomponents, natural language understanding (NLU) and natural language generation (NLG). These tools make sense of the text and then converts it into a format understandable by humans.

ML: This technology uses algorithms trained on datasets to enable computers to imitate the way humans learn.

For example, a customer inputs a text query into the conversational AI software interface (app). After that, the NLP analyzes the user’s intent and generates a response based on the billions of interactions it was built on. As time goes on, the ML component of conversational AI is going to improve responses to customer inquiries and make them more accurate end-to-end.

Conversational AI platforms provide a consistent tone across customers, no matter what the inquiry might be. This feature ensures a uniform service experience for customers and as much attention for all.

The AI assistants and AI agents ability to handle large volumes of inquiries can help boost agent efficiency, which in turn can help improve customer satisfaction score (CSAT). This feature is a key metric from customer feedback surveys used to indicate how content a customer is with a service or product. AI-driven conversational tools can also boost resolution rate and shorten wait times for customers.

With conversational AI, customers can get support across multiple languages. Therefore, it ensures streamline customer interactions and fair customer engagement. This AI capability is like having a support team around the globe, available to answer customers at any time through text, SMS, app, social media and online.

A key benefit to conversational AI is that it doesn’t need training or configuration. Once the software is installed, it just needs to be connected to the organization’s content or knowledge base for it to absorb all customer data. This process could look like integration with an existing customer relationship management (CRM) system, e-commerce platforms and other business tools.

One of the main purposes of conversational AI is to provide customer support that is as human-like as possible and mirror a real support representative. Customers should be able to interact with the conversational AI solutions without knowing or feeling like the advice is coming from a robot.

Through conversational AI, customers can interact with a business across multiple channels either through a self-service interface like smartphones or at an in-store digital kiosk. With the omnichannel integration, the AI technology can carry over into other channels such as sales and marketing.

Conversational AI can come in many different types. The pricing and features are going to vary depending on which AI technology a business chooses and the use cases that technology is going to serve.

Choosing conversational AI software should depend on the nature of the business goals. A rule-based type is for simple, more straightforward needs. A generative AI type is more complex and can address a need for more personalization and creativity. Both can be used in a hybrid approach that combines the strengths of each to handle user interactions.

Modern AI chatbots now use NLU to discern the meaning of open-ended user input, overcoming anything from typographical errors to translation issues. Advanced AI tools then map that meaning to the specific “intent” the user wants the chatbot to act upon and use conversational AI to formulate an appropriate response.

A chatbot is a computer program that simulates human conversation with an end user through virtual agent technology (VAT). A traditional chatbot helps customers find quick answers and route them to the best department to handle their inquiry. Traditional chatbots have limited functions and are rule-based.

Through language processing, systems can respond to voice commands embedded in various devices. A few examples are Amazon Alexa, Google Assistant and Apple Siri, which can be used on smartphones, speakers, automobiles and headphones.

An AI agent or virtual assistant autonomously performs tasks by designing workflows with available tools and goes far beyond NLP. The agent can solve problems, interact with external environments and perform actions. AI agents can handle the unpredictable nature of customer conversations and answer more than just frequently asked questions (FAQs).

It is an automated phone system that enables callers to receive, provide information or make requests by using voice or menu inputs. Conversational AI can enhance this system by identifying why someone is calling a contact center and helping solve the specific request.

Recent innovations in image-recognition AI have led to new camera app technology. A camera app can use AI to capture sign language or other nonverbal cues to match with data from language models. And as a result, a business can assist customers with hearing loss and other ailments.

The use cases and examples of conversational AI in customer service are wide ranging, but three prominent examples are in banking, healthcare and telecommunications.

A bank handles a massive number of inquiries daily, from account balances and transaction histories to loan applications and fraud detection. Conversational AI can help manage these high volumes and respond to customers at all hours. Banking customers expect personalized experiences. Conversational AI can offer relevant services based on a customer’s transaction history or recommend financial products tailored to their needs.

IBM's finance team used IBM® watsonx Orchestrate® and IBM Apptio® Enterprise Business Management (EBM) to mitigate challenges around manual journal entries. The IBM finance team and the Jobotx team of IBM worked together to outline repetitive, manual steps of journal entry to the watsonx Orchestrate assistant. The team chose watsonx Orchestrate to streamline operations, make informed decisions with AI-powered insights and drive growth in a secure and scalable environment.

Patients frequently seek quick answers to common health concerns, medication reminders, appointment scheduling and basic medical advice. AI chatbots can provide these services round-the-clock. Therefore, they ensurethat the patients receive timely assistance regardless of office hours.

Conversational AI capabilities can also help manage patient records, schedule follow-ups and remind patients about preventive care, improving overall patient experience and health outcomes. By automating these tasks, healthcare staff can focus on more complex patient needs, enhancing the quality of care.

Humana, one of the largest insurance providers in the US, worked with IBM Watson® to update its outdated IVR system. The company—which offers Medicare supplements, health insurance, dental insurance, vision insurance and pharmacy coverage—worked with IBM to develop a conversational assistant that could streamline calls from administrative staff.

The telecommunications industry handles numerous customer inquiries including plan details, bill payments, service troubleshooting and technical support. With AI-powered chatbots, customers can get instant responses and address queries in real-time. Conversational AI also helps businesses proactively reach out to customers with personalized offers, usage alerts and service upgrades, enhancing overall customer engagement.

The role of AI for communication service providers (CSPs) is expected to surge upward, according to a report from the IBM Institute of Business Value. IBM, in partnership with GSMA Intelligence, surveyed 750 global network executives; the data reveals that CSPs expect an increase in traditional AI by 16%, and generative AI by almost 19%.

The first step in implementing a conversational AI strategy comes well before choosing the platform and tools. First, a business must know what it wants to gain by using said technology. For example, if a business wants to automate customer experiences it has to be specific about its objectives and problems it's looking to solve.

For example, a business wants to lower its operational costs and use only the human agents they have currently. An AI agent that can focus on basic questions and process more queries at a faster rate might be the best type of conversational AI to implement.

Simultaneous to the first step is analyzing data to ensure that the organization is making informed decisions about where conversational AI is going to be the most useful. Businesses should look for areas of high-volume or repetitive questions that are taking up unnecessary resources. Use the data to assess current workflows and figure out which conversational AI tool can improve the experience for both the customer and the human agent.

Building on the step above, a business should take a detailed look at existing infrastructure and current communication channels being used. The business should look for conversational AI tools that can be integrated easily with current systems and software. Once the current systems have been evaluated, the next step would be to secure support from stakeholders for the initiative.

Once there is support across the organization, then the next step is to consider how much implementation and deployment are going to cost. Smaller businesses might want to consider no-code software that is usable right out of the box. Larger businesses with a bigger budget might consider software that can be built to match the business needs. This process might take longer and require more resources.

A conversational AI software is an important decision for a business. This is why it’s important to choose the right platform provider with proven scalability and simple implementation. A business should understand implementation timelines and assess whether that time frame aligns with the business needs or not. The platform should also prioritize user privacy and data security. Some providers include IBM, Salesforce, Oracle, Zendesk and Amazon.

After the conversational AI software has been implemented, it is vital to collect data and customer feedback to evaluate how well the software is performing. The feedback is important for the business and the customer so that necessary adjustments can be made to improve the customer experience for all involved.

Ensure that the AI system communicates in a clear and concise manner. Users should easily understand the responses provided by the AI.

Personalize the interaction as much as possible. Use the customer's name, refer to their previous interactions, and provide solutions tailored to their specific needs or issues.

Don't wait for users to reach out. Proactively engage by sending updates, reminders or helpful tips based on their behavior or preferences.

When a query is beyond the AI's capabilities, ensure a smooth transition to a human agent. The handoff should be seamless, with all context of the conversation transferred accurately.

Regularly update and train your AI system with new data and feedback. This process helps improve accuracy, enhance understanding of user intent, and refine responses over time.