Contact Center AI:
29 Ways to Maximize Efficiency & Minimize Costs

AI has been used in contact centers for many years. In fact, MiaRec has been utilizing Machine Learning and Natural Language Processing for years to accurately transcribe call recordings, automatically extract keywords, and categorize calls by topics to humanize contact center data.

Despite the enormous impact Generative AI and other AI-based technologies will have on contact centers (for example, Gartner predicts that by 2025, “80% of customer service organizations will utilize generative AI to enhance agent productivity and customer experience”), many organizations struggle to envision exactly how they can benefit from this new technology.

In this resource, we will explain in non-techy terms what AI and related technologies are, and what you as a contact center manager have to know in order to confidently evaluate AI solutions for your organization. First, we outline 29 different use cases you should consider in your contact center.

What Are Generative AI & Conversational AI

Before we move on to how to use Artificial Intelligence in a contact center context, we have to define what Conversational and Generative AI are.

Generative AI

According to Gartner, Generative AI is the subset of Machine Learning that

“enables machines to learn from a representation of artifacts from data and models, and use it to generate brand-new, completely original artifacts that preserve a likeness to original data or models.

Generative AI can produce totally novel content (including text, images, video, audio, structures), computer code, synthetic data, workflows and models of physical objects. Generative AI also can be used in art, drug discovery or material design.”

In other words, Generative AI takes data inputs and generates new, original outputs based on those inputs, including writing text, composing music, creating artwork, evaluating new ideas, and much more.

In the Contact Center space, Generative AI technology is primarily used to generate summaries of meetings and telephone conversations, answer  questions using knowledge base, extract information from texts, interpret text inputs and generate human-like responses.

Conversational AI

Conversational AI systems include chatbots and virtual agents that are able to interact with humans in a form of dialogue using natural language.

Contact centers use Conversational AI solutions to build virtual agents, chatbots or real-time agent assist systems. Modern Conversational AI systems use Large Language Models to achieve phenomenal results in the understanding of human language. The goal is to improve customer service experiences, make interfaces more user friendly, and human-like.

Differences Between Conversational AI and Generative AI

Both Conversational AI and Generative AI terms can be spotted in a description of the same product, but these terms are not interchangeable.

• Conversational AI usually describes an end product that provides chatbot or virtual agent capabilities.
• Generative AI term describes a technology that very often can be used inside the Conversational AI products to perform some of its functions. For example, chatbots can use ChatGPT (Generative AI technology) to generate human-like responses to human inputs.

If you hear “Conversational AI”, then most probably you can assume “Generative AI” is used in such a product under the hood, if the product is modern.

But, if you hear “Generative AI”, it doesn’t mean the product has “Conversational AI” capabilities. Generative AI technology can be used in other areas, for example, journalists can use it to refine their written texts, artists can use it to generate new art, etc.

Generative AI plays a crucial role in improving customer experience and engagement in contact centers by providing personalized, efficient, and empathetic interactions.

Empower Self-Service through AI-powered Chatbots and Virtual Assistants

One of the maybe most straight-forward applications of Generative AI is the implementation of chatbots and virtual assistants for self-service. They enable customers to independently address common queries by typing in their question or concern into a chatbot or conversational interface. This allows support teams to focus on more complex issues, reducing hold times, lowering Average Handling Time (AHT), and increasing customer satisfaction.

Example: Amazon Lex Chatbot

Image: Lex chatbot on mobile

Example: HubSpot Chatbot, TalkDesk, Vonage

Image: TalkDesk chatbot with dashboard

Improving Self-Service Calling Experience

AI-powered Conversational Interactive Voice Response (IVR) uses Natural Language Processing (NLP) to ingest, analyze, and understand customer requests and then provide an automated response without the help of a live agent. It can create personalized experiences and, if needed, will direct customers to the appropriate self-service or agent channels based on their stated needs​​.

Example: GetVOIP, Teneo.ai, Five9 Intelligent Virtual Agent

Image: GetVOIP IVR call flow

Understanding Customer Sentiment

Another common use case for Conversational and Generative AI is analyzing customers' sentiments, moods, and emotions during interactions. This can be used live to gauge appropriate reactions, or be used post-call for quality management. For example, measuring how well an agent can navigate difficult conversations (angry customer = negative sentiment score) and bring them to a good outcome (happy customer = positive sentiment score) can give you real-world insights into an agent’s people skills and emotional intelligence. This helps to foresee and prevent escalations, improve satisfaction, and reduce churn rates​​.

Example: MiaRec Sentiment Analysis

Image: MiaRec's dashboard on sentiment analysis

Identify Potential Growth Opportunities

By analyzing conversation data, Generative AI can identify potential issues or growth/upsell opportunities (e.g., increased calls about shipping problems, calls requesting a feature that is not available) proactively. Essentially, AI mines your customer interactions to determine the interactive drivers, and surfaces these insights as trends or shifts in distribution.

Example: MiaRec Topic Analytics, Five9 Insights using OpenAI

Image: Miarec’s topic analytics dashboard

Automated Agent Evaluation and Call Scoring

Rather than manually evaluating a small fraction of calls at random, Generative AI can be used to automatically score 100% of your calls, providing a much more accurate representation of agent performance over time. These so-called Auto QA tools free up a tremendous amount of time every week that can be used in higher-value activities, such as focusing on calls flagged for follow-up or review.

Example: MiaRec Auto Call Scoring

MiaRec’s Auto Score Card

Adding Context to Automated Quality Scoring​​

For some questions like “Did the agent say their name?” the answer is simply a yes or a no, while answering questions like “Was the agent polite?” requires more context. While Generative AI can be used to automatically evaluate the call and gauge the level of politeness, it can also provide context as to why the answer might be “no” by including a quick statement about how the agent responded rudely to the customer’s request for a refund. This gives the supervisor enough context to dig deeper if needed.

Example: MiaRec Auto Call Scoring

Image: MiaRec's auto call scoring adding context

Identifying Agent Training Opportunities​​

Similarly to post-call coaching, Generative AI can identify opportunities for improvement and make individualized and unbiased suggestions for additional training based on the call evaluation to enhance the agent’s performance.

Example: MiaRec Auto Call Scoring

Image: MiaRec's auto call scoring identifying training opportunities

Alerting Supervisors to Issues​​ With Their Agents

As a contact center supervisor, you always want to know if something extraordinary happens with your agents. Maybe your agent is struggling with a personal crisis and comes to work sleep-deprived and in a foul mood, which he takes out on the customers he or she deals with. Generative AI can identify if swear words are being used, if the mood in the call suddenly goes icy (sentiment analysis), or if the customer gets angry. As a supervisor, you will be notified and can intervene as soon as possible.

Example: NICE Enlighten Actions, MiaRec Auto QA + Notifications

Image: NICE Enlighten Actions dashboard

Image: Example of an automated agent scorecard from MiaRec

Automating Compliance Management (Data Redaction)

Contact centers process an enormous amount of Personal Identifiable Information (PII) and payment card numbers on a daily basis. Those get stored in your call recordings and the associated transcripts. In most cases, you are obligated by regulatory compliance to redact them. Machine Learning and Named Entity Recognition (NER) can reliably identify and redact sensitive information from your calls and transcripts​​, increasing your compliance and decreasing your liability should you suffer a data breach.

Example: MiaRec Auto Redaction

Image: MiaRec’s auto redaction in a call transcript

1. What are the key benefits of implementing Generative AI-based solutions in our contact center?

Generative AI can enhance customer experience, improve agent efficiency, reduce response times, and provide valuable insights from customer interactions.

2. How will Generative AI integration impact our current workforce?

Generative AI is designed to augment the capabilities of your human workforce, not replace them. Think of Generative AI as something that makes you more efficient and takes care of all the tedious manual tasks. It can also assist agents with real-time recommendations and automate routine tasks, allowing them to focus on more complex customer needs.

3. What is the typical cost and ROI of implementing Generative AI solutions?

Costs can vary based on the scale and complexity of the implementation. However, the ROI often manifests itself in improved customer satisfaction, higher agent productivity, and reduced operational costs. We have created two ROI calculators for specific AI features that will give you a good indication of what to expect: the Auto Call Summary ROI Calculator and the Auto QA ROI Calculator.

4. What are the data privacy and security implications of using Generative AI?

Implementing Generative AI requires adherence to data privacy laws and regulations. It's crucial to ensure that the AI solutions are compliant with GDPR, CCPA, and other relevant data protection frameworks.

5. How can we ensure our Generative AI system remains up-to-date and effective?

It is important to understand that AI gets better over time as it learns and improves itself based on the data inputs it receives. Regular training and updates are therefore essential. This involves feeding the AI system with new data, adjusting algorithms based on performance analytics, and staying updated with the latest AI advancements.

6. Can Generative AI be integrated with our existing CRM and IT infrastructure?

Yes, most Generative AI solutions are designed to be compatible with existing CRM systems and IT infrastructure. However, a detailed assessment by your IT team is advisable to ensure seamless integration.

7. How does Generative AI handle complex or sensitive customer inquiries?

For complex or sensitive issues, Generative AI can flag these inquiries to be handled by human agents. It's also capable of learning from such interactions to handle similar future queries more effectively.

8. What training is required for our team to effectively use Generative AI tools?

Training varies depending on the complexity of the tool. It generally includes understanding the AI interface, learning how to interpret AI recommendations, and training on data input and system monitoring. For now, most AI use cases can be achieved without having to hire a data scientist. However, as your AI requirements and solutions evolve, you will need qualified personnel.

9. How do we measure the success of a Generative AI implementation in our contact center?

Success can be measured through various KPIs like customer satisfaction scores, average handling time, first call resolution rate, and overall cost savings.

10. What kind of customer experience improvements can we expect with Generative AI?

You can expect more personalized interactions, quicker resolution times, and enhanced self-service options, leading to an overall improved customer experience.

11. Are there any industry-specific considerations we should be aware of when implementing Generative AI?

Yes, different industries might have specific compliance, language, and customer interaction nuances. It's important to choose a Generative AI solution that can be tailored to meet these specific industry requirements.

12. How long does it typically take to implement Generative AI in a contact center?

The implementation timeline can vary, ranging from a few hours to several weeks or even months, depending on the scale of the project and the complexity of your existing systems.