As much as generative AI has been in the news and industry publications, you’d be forgiven for thinking it’s the end-all-be-all for contact center AI. And there are generative AI use cases in the contact center to get excited about–from expanding the capabilities of chatbots to reducing after-call work for agents. However, AI is a broad field, and generative AI is not the only type of AI that contact centers benefit from.
If you’re a customer service leader trying to understand the different applications for AI in the contact center, start here. We’ll cover:
What AI terms do contact center leaders need to know?
Artificial intelligence (AI): A broad field of technology that deals with the ability of machines to solve complex problems. As the name suggests, it involves machines simulating human intelligence.
Machine learning: A branch of AI that involves using machines to “learn” or adapt to new data. Machine learning algorithms are designed to find new mathematical relationships within data sets and to solve the unknowns of a problem that is often not well defined. An example of machine learning in the contact center might be a model that predicts whether a customer is satisfied or dissatisfied with their service interaction (such as Creovai’s CSATai).
Artificial neural networks (ANNs): Part of machine learning models. Artificial neural networks work to find patterns and make decisions in a way similar to the human brain. ANNs can be trained to identify patterns in large data sets, with human supervisors correcting mistakes so that the model continues to “learn” and become more accurate.
Natural language processing (NLP): A branch of AI that uses machine learning to enable computers to understand written or spoken language. Some examples of natural language processing applications in the contact center include interactive voice response systems, chatbots, sentiment analysis, conversation intelligence, and real-time agent assistance.
Generative AI: A branch of AI based on machine learning and ANNs, generative AI creates new content based on provided data sets. While generative AI is still relatively new, 86% of contact center leaders say they are planning for GenAI investments. Example use cases include generating call summaries, augmenting chatbots, recommending next best actions to agents, and responding to questions or prompts about customer data.
Large language models (LLMs): A part of machine learning and AI specifically focused on processing and generating human language. LLMs aren’t necessarily a subset of generative AI but often are (you’ve likely heard the two phrases used together). They are trained on large data sets of text and are good for NLP tasks such as translation, summarization, and simulating human-like conversation.
Agentic AI: Connected and autonomous AI systems that can act independently to achieve specific goals. An example of agentic AI might be a chatbot that captures customer information and writes it back to a CRM, or AI agents that analyze what is being said during a call and provide recommended knowledge articles to human agents in real time.
How are contact center leaders using AI?
Conversation intelligence
Conversation intelligence is a technology that uses machine learning and natural language processing to analyze customer conversations and uncover trends and insights. Conversation intelligence helps contact centers identify areas for operational or product improvements, points of friction in the customer journey, agent training needs, signs of churn risk, and more so they can improve the agent and customer experience.
QA automation
One major benefit of AI in the contact center is its ability to automate repetitive and time-consuming tasks, freeing managers and agents to focus on higher-value activities. Quality assurance (QA) automation uses the same techniques as conversation intelligence to review 100% of customer conversations for objective QA criteria. This reduces the time managers spend on manual QA reviews and gives them greater visibility into agent performance. It also helps agents improve by giving them targeted insights into their performance and highlighting data-backed opportunities for improvement.
Real-time agent assistance
Real-time agent assistance often uses machine learning, natural language processing, and generative AI to guide agents through conversations with customers, ensuring the agent completes the appropriate steps and efficiently resolves the customer’s issue. Agents may see prompts on their screen with the next best action, a dynamic checklist that shows when they have completed the required steps, and recommended resources or information to share with the customer.
Predictive analytics
Predictive analytics uses AI to analyze large data sets and make predictions using mathematical relationships within the data. Example use cases include predicting hold times, predicting call volumes, and using customer data to route calls to the agents best suited to handle them. Predictive analytics can also help contact center leaders predict how customers would have rated their service interactions, even if they don’t complete a post-interaction survey. For example, Creovai analyzes customer conversations and uses AI to predict customer satisfaction, effort, and sentiment scores.
After-call work automation
After-call work typically includes summarizing calls, updating customer records, and taking other administrative actions to resolve customer issues. It’s vital, but it can also be time-consuming–and the more time agents spend on after-call work, the less time they have to spend assisting customers. Fortunately, AI can automate many common after-call work tasks. For example, AI solutions can automatically update customer information in a CRM, generate a call summary, and trigger criteria-based actions such as scheduling a follow-up call.
Chatbots
Chatbots, or virtual assistants, have become a popular application of AI in the contact center industry. They use natural language processing and machine learning algorithms to interpret customer queries and respond to them autonomously. With chatbots, contact centers can provide customers with quick and responsive service 24/7, improving customer satisfaction rates while reducing operational costs. Chatbots can also be customized for specific industries and use cases, such as product recommendations, account assistance, or scheduling appointments. Additionally, chatbot conversations can be analyzed using conversation intelligence software to identify areas for improvement.
Note: Some businesses are also testing out generative AI chatbots, which can be trained on open data sources or a business’s owned data to provide original, human-like responses to customer questions. However, due to the risk of a generative AI chatbot sharing off-brand or inaccurate information, businesses still need to exercise caution with this emerging use case.
Combining generative AI and machine learning in the contact center
As a contact center leader, you may be wondering whether you should be investing in generative AI or machine learning-based technology to increase efficiency and help your agents and customers. Each type of AI has its strengths, and each is best-suited to different use cases.
Generative AI is best for creating original outputs based on prompts and may be useful for generating call summaries, recommendations for agents, or insight highlights based on conversation data. However, there are a few caveats to keep in mind. One key consideration is the computing power necessary for processing prompts with varying degrees of complexity and context. The more context you put into a prompt, the more computational resources are needed to generate relevant responses–and the higher the cost to run the model. Additionally, due to the risk of a generative AI model generating inaccurate information, it’s important to have humans review outputs–especially outputs that will be shared publicly.
Contact center leaders should also keep in mind that large language models are not specifically trained for contact center applications. “There may be sufficient information in the training data to allow them to perform some tasks, but without being specifically trained for the task at hand, they will tend to underperform relative to more specifically trained machine learning models,” says Tom Shepherd, Senior Machine Learning and Analytics Engineer at Creovai. “It is, however, possible to combine the two, so that a machine learning model specifically trained for tasks like quality assurance or rating customer satisfaction can provide context to a generative AI model to produce a written response.”
Machine learning is best for rule-based, objective tasks. While machine learning models can’t be used for the same creative tasks as generative AI, they can increase efficiency and deliver valuable insights to the contact center. Use cases where machine learning shines include identifying specific call events or categories based on the words and phrases a customer or agent uses, completing objective fields in a QA scorecard, and predicting how customers would have rated their interactions.
Many contact center solutions now use both generative AI and machine learning models to perform different tasks and improve the overall experience for users. For example, Creovai uses machine learning for predictive scoring, applying categories to conversations, and QA automation. We use generative AI for automatic call summarization, breaking transcripts into easily-skimmable chapters, and answering questions that require contextual understanding of interactions.
AI to improve the agent and customer experience
As you evaluate different types of AI solutions for your contact center, keep your focus on the desired outcomes: increasing efficiency while improving the agent and customer experience. It’s not about adding technology for the sake of technology; it’s about choosing tools that will help your people succeed in their roles and ensure your customers’ needs are met. Achieving these outcomes will feed into long-term benefits for the entire company: lower employee turnover, greater customer satisfaction and loyalty, and greater customer lifetime value.
FAQs
Should I prioritize generative AI or machine learning for my contact center?
The answer depends on your specific use cases. Machine learning excels at rule-based, objective tasks like predicting customer satisfaction, automating QA scoring, and categorizing conversations. Generative AI is better for contextual analysis and summarization. The most successful implementations combine both technologies—using generative AI as a “conversation detective” that can summarize calls, answer questions about interactions, or conduct other contextual analyses, then layering on machine learning for predictive analytics and structured tasks that require a high degree of accuracy.
How should my contact center get started with implementing new AI technology?
Start by identifying your biggest operational pain points, such as lengthy after-call work, inconsistent QA processes, or lack of visibility into customer sentiment. Begin with one use case that addresses a specific business need rather than trying to implement multiple AI solutions simultaneously. Choose a solution that can provide quick ROI wins (such as reducing after-call work time by summarizing calls) and measure the impact. Getting that initial quick win builds confidence and can help you get buy-in for additional AI use cases.
How can I ensure AI solutions integrate with our existing contact center technology?
Before selecting any AI solution, audit your current tech stack including your CRM, data storage platforms, and CCaaS. Look for AI vendors that offer pre-built integrations with your existing platforms and APIs for custom connections. Prioritize solutions that can work with your current conversation data sources (voice, chat, email) without requiring significant infrastructure changes. Always request a technical integration review during the evaluation process.
What are the biggest risks when implementing AI in contact centers, and how do I mitigate them?
Key risks include AI generating inaccurate information, over-relying on automation without human oversight, and agent resistance to new technology. Mitigate these by implementing human review processes for AI-generated content, starting with AI as an assistive tool rather than full automation, and involving agents in the selection and training process. Establish clear guidelines for when agents should override AI recommendations and maintain transparency about how AI tools work.
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