Top 7 Contact Center AI Solutions in 2024

Contact Center AI (CCAI) is technology that relies on natural language processing and generative AI to understand the intent behind customer queries and to synthesize responses from vast amounts of contact center data.

CCAI excels at repetitive, data-intensive tasks so that staff can focus on complex, qualitative aspects of customer service and operations. For example, contact center AI equips agents with contextually relevant answers in real time so customers don’t need to be placed on hold so often.

In quality assurance, CCAI frees humans to focus on tasks better suited to their individual judgment and discernment. For example, staff and managers can immediately address problematic calls that have been flagged by AI for specific issues, applying their problem-solving skills directly, rather than having to listen through several randomly selected calls to identify issues.

CCAI works best when it’s a collaborative partnership between humans and AI, allowing contact centers to scale customer service and operations at a fraction of the cost while ensuring that each interaction is handled with both speed and a personal touch.

Below, we cover the top seven contact center AI solutions, starting with Level AI.

1. Level AI

Next-Generation AI for Customer Service and Quality Management

Level AI uses semantic intelligence to analyze 100% of interactions and uncover intents expressed by customers in support conversations. This reveals insights that would normally require a great deal of human effort to detect (or may even get overlooked), as all conversations would need to be manually parsed for such insights.

In this section, we show you our contact center AI features for both customer service and operations.

Features for Customer Service

• Real-Time Agent Assist
• Real-Time Manager Assist

Features for Contact Center Operations and Management

• Customer Intent Recognition
• Sentiment Analysis
• InstaScore
• Auto Categorization and Summarization
• Voice of the Customer Insights
• InstaReview
• Flexible Reporting

Supporting Customer Service Reps with AI

Our real-time tools support agents during live calls by giving them the answers they need, at the moment they need them. We also provide managers with situational awareness around how conversations are going and how agents are performing, giving them ways to communicate directly with agents during calls.

Assisting Agents in Real Time

A major challenge of contact center agents is quickly finding accurate information while on a live call. If agents don’t immediately know the correct response, they sometimes put customers on hold to search or ask colleagues for help.

Manually looking up answers can be stressful and lead to escalations, and agents sometimes multitask to resolve issues, which lowers their performance.

We built Real-Time Agent Assist to get the right answers to agents as they need them. Our software analyzes conversations using Natural Language Processing (NLP) to detect the meanings behind words and anticipate the information the agent will need.

Our software proactively pulls together information from a variety of sources, as it detects moments in conversation, into one unified view for agents showing:

• A main feed of hints, flag warnings, and FAQs
• A resources section of recommended knowledge articles
• Chat with Your Knowledge Base (KB), offering advanced search
• Context cards containing past support interactions

All of the information corresponds to the topics currently being discussed, which are refreshed as the conversation changes.

Agents are encouraged to rate the information they receive by clicking on the thumbs up or down buttons to train the AI.

The information shown above is proactively displayed during a conversation, but oftentimes agents also need to do a search. We find that actively searching for information during conversations doesn’t always surface the exact answers needed, often because while on live calls, agents can find it hard to properly focus while scanning articles and doing follow-up searches.

We developed Chat with Your Knowledge Base (KB) to predict and suggest search queries relevant to the ongoing interaction, auto-filling the search bar in real-time based on the topics being discussed.

Chat with Your KB pulls related information from your knowledge base to anticipate what agents will search for. As with the other features of Real-Time Agent Assist, Chat with Your KB anticipates the knowledge an agent will need before they need it, greatly speeding up access to answers.

Monitoring Agent Conversations Live

Managing high-volume contact centers can be difficult because managers must oversee many agents and identify which conversations need their attention. This challenge is intensified by managers' limited time and the need to stay highly aware without becoming overwhelmed by the sheer number of interactions.

Managers typically want to know:

• How agents are doing on their current calls.
• Whether they need to step in and support an agent, and if so, is there a big enough opportunity to warrant their time.
• Whether they can turn around a flagged conversation by sharing advice with an agent.
• What insights can be understood from across all calls taken on a given day.

To address these questions, we built Real-Time Manager Assist to help managers decide which conversations to join and how to make better use of their limited time.

Level AI provides daily dashboards that display KPIs for ongoing or completed conversations in real time. For instance, our real-time dashboard auto-scores agents as they interact with customers.

We display an InstaScore that allows managers to quickly assess their agents’ performance against established rubrics:

Clicking on the metrics allows users to further explore the data behind any of our scores:

The daily dashboard aggregates data across all calls for a given day, giving managers an overview of trends in agent performance:

Our dashboards also include features for intervening in conversations, such as call whispering, which allows managers to provide real-time advice to agents, and the ability to proactively request an escalation through the dashboard.

These dashboards give managers a unified view of ongoing support conversations.

For those with limited time, we provide functionality to configure the system to send notifications, such as when a customer decides to close their account:

These notifications can be sent to Slack or Microsoft Teams and include detailed insights and recommended actions for promptly addressing the issue.

Leveraging AI for Advanced Quality Assurance

Level AI automates the evaluation of agents and conversations by providing a deep understanding of customer interactions. It identifies key issues, detects sentiment, and recognizes patterns and trends that drive business improvements.

Identifying Call Drivers

Resolving recurring issues is a major challenge for contact centers, but it’s difficult to address these issues without insight into why customers are reaching out for support in the first place.

Many organizations attempt to identify recurring issues manually by sampling a representative 1%–2% of all conversations. Unfortunately, this limited scope can result in trends and problems going unnoticed.

Contact center software typically identifies issues by detecting specific keywords. However, this approach often misses the context and nuance of customer interactions since keywords alone don’t fully capture the intent behind customer calls, leading to incomplete and inaccurate assessments.

To overcome these limitations, Level AI uses a semantic intelligence model to detect and highlight intent expressed in conversations. This is a more effective way of understanding customer interactions, as it considers the context and meaning behind words rather than just the words themselves.

An example of intent might be the desire to cancel a subscription. We associate a given intent with a set of example phrases signaling that intent, which users configure:

To define an intent, you just need a few example phrases. Our AI technology takes over from there — there’s no need to enter hundreds of keywords for a single intent.

The system also uses conversation tags to mark specific moments in conversations.

A conversation tag indicates a particular intent, and you can create custom tags tailored to your business. The system applies these tags to relevant phrases spoken or written by customers or agents.

Conversation tags help you identify common issues and intent across large volumes of conversations. They also pinpoint specific interactions where agents may need improvement or best practices can be highlighted.

Level AI tags conversations in other ways (e.g., it identifies different sentiments expressed in conversation) and all such tags in our system are searchable and can be reported against.

Analyzing Expressed Sentiments

Detecting sentiments has always been challenging, not least because:

• Variations in speech: Traditional technology has struggled with capturing variations in tone, pitch, and volume that convey different emotions, especially since they relied on keywords and basic statistical methods. Accents and dialects further complicate this issue.
• Contextual meanings: The meaning of words can shift depending on their context and position within a conversation.
• Degrees of emotion: Emotions can vary in intensity, ranging from mild annoyance to extreme irritation.
• Non-verbal cues: Non-verbal signals such as silences or long pauses can indicate a range of emotions like hesitation, contemplation, or discomfort. Accurately interpreting these cues is complex and often beyond the capability of traditional tools.

Because of these difficulties, other tools in the space typically define sentiments as either positive or negative, which often lacks the nuance needed for actionable insights.

Level AI goes beyond simple positive or negative labeling by identifying, tagging, and labeling specific emotions in conversation, including:

• Anger
• Disapproval
• Disappointment
• Worry
• Happiness
• Admiration
• Gratitude

Not only do we identify and tag occurrences of these emotions in interactions, but we also provide an overall Sentiment Score for each conversation, which ranges from 0–10. Our calculation of this aggregate metric reflects a customer’s overall experience with customer support during the conversation, and is based on a weighted average:

We weight sentiments expressed at the end of a conversation higher than at the beginning, since different sentiments may be expressed throughout a conversation.

The sentiment expressed at the end of a conversation usually gives a more realistic picture of the customer’s feelings towards the organization.

Auto Scoring Conversations

Typically, managers and QA analysts assessing an agent’s performance must listen to an entire call and manually grade agents based on certain rubrics.

Our software streamlines this process by analyzing all conversations and providing graded feedback, expressed as a percentage of a given rubric. We call this feedback an InstaScore, which allows managers and QA staff to quickly assess an agent’s performance at a glance.

For quality service purposes, this provides better sampling of agent conversations. By having an overview of each agent’s performance, you can use your time more effectively by focusing on conversations that require your attention, rather than having to listen to a few in their entirety upfront.

Categorizing and Summarizing Conversations

Manually assigning categories and subcategories to conversations can be time-consuming for agents and prone to errors, especially if they are tired or stressed after completing a call.

Generative AI excels at summarizing and categorizing customer interactions, and our software automatically classifies and summarizes completed conversations according to the issues and topics discussed therein.

We classify conversations using automatically generated categories or ones that you define, ensuring higher accuracy and saving agents the time and effort of doing this themselves.

When defining your own categories and subcategories, we provide suggestions and a list of “near miss” suggestions that may or may not pertain to your intended categories. By choosing or rejecting these suggestions, you train the AI to better recognize similar interactions in the future.

Our Smart Summary auto-summarizes the contents of support interactions, allowing you to quickly grasp important details without having to manually parse or listen to the entire conversation.

Getting Voice of the Customer (VoC) Insights

VoC data provides invaluable insights into the customer service experience, their expectations, and satisfaction, as well as how customers perceive your organization.

While this data is generally gathered from surveys, this process often encounters issues such as low response rates and biased responses. For example, customers with extreme experiences — either very positive or very negative — are more likely to respond, which can skew the data.

Our system extracts customer satisfaction and other insights directly from conversations, providing a highly accurate and detailed picture of how customers perceive you. We automatically gather standard metrics such as CSAT, customer effort, average handle time, and first contact resolution, without any effort on your part.

In addition to providing standard VoC metrics, Level AI proactively surfaces trends and patterns in customer interactions, including some that may be subtle and surprising.

As a theoretical example, it might identify potential upsell opportunities that were missed by agents. For instance, if customers frequently inquire about advanced features available only in higher-tier plans but agents do not consistently suggest upgrading, our platform can pinpoint these missed opportunities. This enables targeted coaching and training to improve conversion rates.

Reviewing Agent Performance

Reviewing calls to find ‘coachable moments’ is often the purview of QA staff and managers. Traditionally, these moments have been identified by listening to randomly selected samples and taking notes on agent performance.

This method is time-consuming, as staff must sift through calls that appear problematic, such as those with longer-than-usual durations. Occasionally, QA staff will select a one-time incident where the agent made a mistake (but quickly corrected it), but nonetheless call the agent out on it.

Level AI’s InstaReview streamlines this process by tagging coachable calls that warrant a closer look. It identifies these calls not only based on longer durations but also by high numbers of assists and specific negative metrics, such as negative moments or low customer satisfaction.

Level AI also also lets you to initiate and manage coaching sessions within our platform:

Aggregating Data for Flexible Reporting

Level AI’s QueryBuilder allows you to design custom reports by combining Level AI’s data — such as occurrences of specific sentiment tags and call duration — with information from external sources, including customer feedback platforms, sales databases, and your CRM.

Combining data in such a way allows you to ask questions like:

• How does customer sentiment correlate with the length of sales cycles?
• How do call durations impact the likelihood of upsells?
• Are there specific customer feedback trends that coincide with peaks in sales activities?

Our API supports the integration of various data types (e.g., JSON, CSV, XML), enabling you to ask comprehensive and holistic questions. The answers provide new insights, allowing you to proactively address customer inquiries and inform new training and coaching programs.

Level AI’s reports are typically displayed in intuitive dashboards:

Discover the Benefits of Level AI

Our advanced AI-driven platform provides deep and actionable insights into how your customers interact with your brand to help deliver a more personalized and satisfying customer experience.

Schedule a free demo to see how contact center AI can unlock the full potential of your customer support.

2. Google Contact Center AI

Google Contact Center AI (CCAI) is a suite of AI tools offered by Google Cloud that’s specifically designed for contact centers. CCAI works together with the CCAI platform, which provides the infrastructure and services on which CCAI runs.

CCAI provides the following modules:

• Contact Center AI Platform: Delivers the infrastructure, along with web and mobile SDKs, for embedding support experiences across multiple channels. It includes visual interactive voice response (IVR) for self-service and automatic routing to enhance operational efficiency.
• Dialogflow CX: Features virtual agents powered by generative AI to answer customer queries, along with a drag-and-drop visual flow builder for designing conversational interfaces.
• Agent Assist: Provides AI-driven answers to agents, enabling faster and more accurate customer service.
• CCAI Consulting: Google offers consulting services to help you set up and improve your virtual agents (Dialogflow CX) and overall platform.

According to their website, you must request a quote but they’ll give you $300 in free credits to start off with. They also offer a pricing calculator and state that you only pay for the cloud services that you use.

3. Qualtrics XM Platform

Qualtrics is a customer experience platform that provides bots and analytics for QA teams and service managers to better understand customer behavior, reduce churn, and improve agent productivity.

Qualtrics Frontline Care software is AI powered and ensures that customer reps have what they need to offer a good customer experience.

The software includes features for:

• Agent productivity: Provides insights and tools for driving continuous improvement through coaching dashboards and recommended actions.
• Interactive dashboards: Highlights coaching opportunities, along with your team’s strengths and weaknesses.
• Automatic scoring: Scores agent performance and interactions for quality management, helping to identify coaching opportunities while reducing operational costs.
• Call summarization: Summarizes key points from calls, saving agents time by eliminating the need to take notes after the call.

Pricing isn’t disclosed on their website and varies based on the modules a customer requests, such as XM for Customer Experience, Employee Experience, and Strategy & Research.

4. Observe.AI

Observe.AI is an AI-powered platform for contact centers that provides software for quality assurance teams. It analyzes interactions and provides insights, along with business analytics, to improve agent performance and ensure compliance.

The Generative AI suite provides:

• Agent Answers: Delivers real-time answers to agents during customer interactions by leveraging connected knowledge bases and AI.
• Auto Summary: Summarizes call topics automatically, eliminating the need for note-taking and reducing follow-up work after calls.
• Auto Coaching: Offers automatic stats and feedback to human agents for self-coaching and continuous improvement.

According to their website, you need to book a demo with their sales team to receive a price quote.

5. NICE

NICE CXone is a cloud-native customer experience platform that manages support interactions across a number of channels, such as voice and chat. It also addresses use cases for automatic call distribution, workforce optimization, and omnichannel routing.

The platform includes the following modules:

• Enlighten Copilot: For accomplishing time-consuming and repetitive workflows for agents. It also provides faster knowledge base answers.
• Enlighten Autopilot: Allows you to build self-service solutions for customers like chatbots using your knowledge base data.
• Enlighten Actions: Provides insights from your customer data to pinpoint opportunities for automation.
• Enlighten AI Complaint Management: Proactively identifies reputational and compliance risks, enabling you to address them promptly.

Pricing starts around $70 per user per month, depending on the modules you want to use. You can also get the entire suite of CXone products for around $250 per month.

6. Nextiva

Nextiva is a contact center platform that enhances agent productivity, improves first-contact resolution, and enables the design of outbound campaigns, such as proactively reaching out to customers with product updates.

The platform offers features that allow you to:

• Deploy virtual assistants as front-line customer care, allowing agents to handle cases that require human interaction.
• Forecast and staff shifts using workforce management and optimization capabilities.
• Equip customer reps with multi-channel conversational histories to provide greater context during customer engagements.
• Set call center thresholds to send notifications that alert agents and managers when call center traffic reaches predetermined limits.

According to the website, Nextiva offers several pricing tiers depending on team sizes and number of features. The lowest tier costs upwards of $20 per user per month with an annual subscription.

7. Dialpad

Dialpad is a voice-over-IP (VoIP) solution that enables voice calls, messaging, call routing, and organizing meetings. It also offers features for transcribing and analyzing team and customer conversations to ensure data security and compliance.

Dialpad’s features cater to:

• Teams: Enhancing efficiency with AI-powered note-taking, call summarization, and agent messaging.
• Support: Providing in-the-moment coaching, tracking agent performance, and scoring customer satisfaction.
• Sales: Offering live assists during interactions, agent scripts, and predictive, AI-generated action items to keep deals moving.

Pricing starts around $15 per user per month when billed annually and increases up to the enterprise plan. Costs vary based on the features used and any add-ons purchased. A free-trial is also available.

Start Your AI Journey Today

Level AI offers advanced conversational AI designed specifically for contact centers. Our platform automates the evaluation of support interactions and provides real-time insights and recommendations to your agents.

To get started, book a free demo to learn more about how Level AI’s contact center solution drives better business outcomes.