What is AI for Contact Center?

AI for the contact center refers to the technologies and processes of leveraging artificial intelligence to automate contact center support and knowledge management tasks, as well as to augment and assist human agents (e.g. contact center agents, branch staff, field service teams) in customer interactions. The goal is to deliver effective, efficient, and consistent problem resolution (often on the first contact) along with personalized customer service and advice.

Common AI technologies used in contact centers include Natural Language Processing (NLP), Machine Learning (ML), Case-Based Reasoning, conversational Generative AI, and agentic AI, among others. To harness these effectively, businesses need a trusted AI knowledge hub to orchestrate AI and manage knowledge centrally—ensuring the right AI is used and the right knowledge delivered for the right situation, through the right channel (voice or digital) in the right tone.

Below are some of the key AI technologies in contact center environments and what they do:

• Machine Learning (ML) and Natural Language Understanding (NLU): ML is a subfield of AI that gives computers “the ability to learn without explicitly being programmed” (Source: MIT). NLU is the capability for a system to understand natural language input (spoken or written), infer the user’s intent, and respond intelligently in real time — for example, transcribing a customer’s voice call and interpreting their issue or sentiment.
• Generative AI (Gen AI): Generative AI describes algorithms (such as ChatGPT) that can be used to create new content including text, images, audio, code, simulations, and videos. Recent breakthroughs in this field have the potential to drastically change the way we approach content creation (Source: McKinsey). In a contact center, generative AI can draft responses for agents, summarize long emails or call transcripts, and even power conversational self-service bots that generate answers from knowledge bases.
• Conversational AI: Conversational AI is a set of technologies (including NLU and Case-Based Reasoning) that automates conversations with customers via chatbots or voice bots, and augments live agents with turn-by-turn guidance. For example, AI can guide a contact center agent on their desktop with the next best question to ask or the next best action to take during a customer call. Conversational generative AI can also drive customer self-service interactions (when strict compliance isn’t critical), enabling AI-powered contact center self-service through chat or IVR.
• Agentic AI: Agentic AI refers to AI technology that can autonomously execute multi-step tasks and make decisions to achieve specific goals on behalf of the customer. In practice, AI agents can independently perform transactions and coordinate across systems without human intervention. For instance, an AI agent could automatically issue a refund or credit to a customer’s account, generate and email a return shipping label, reschedule a delivery by interfacing with a logistics system, update an order or subscription in real time, or even book an appointment by coordinating calendars. Rather than just telling customers how to solve their problem (or handing them off to a human), these autonomous AI agents actually resolve the issue end-to-end, treating each customer query as a workflow that the AI completes across the service ecosystem.
• Case-Based Reasoning (CBR): This AI approach looks at past cases (historical issues and their resolutions) to resolve new cases by analogy. Much like a doctor diagnosing an illness by recalling similar cases, or a support expert recommending a solution based on prior incidents, CBR helps suggest proven resolutions or next steps to agents and customers by drawing on what worked in the past.

And much more. These and other AI technologies (like predictive analytics, speech recognition, etc.) form the toolkit for an AI-powered contact center when properly integrated.

Applied correctly, AI for contact centers can automate and improve service processes end-to-end. Here are common use-cases in contact center operations:

• Understand query intent: AI can listen to or read what the customer says (the utterance) and infer the true intent behind the query, diagnosing the real problem beyond just the stated words. For example, Natural Language Understanding can interpret a caller’s spoken issue or a chat message to determine what the customer actually needs, even if they don’t phrase it perfectly.
• Customer self-service: Upon understanding the customer’s intent, AI can drive contact center self-service so customers can resolve issues themselves without agent involvement. This includes AI-powered virtual assistants and chatbots on digital channels and intelligent IVR systems on voice calls. By guiding customers through troubleshooting or answering FAQs, these AI systems deflect routine inquiries away from live agents and provide quick answers 24/7.
• Route the query: If self-service doesn’t fully solve the issue, AI can intelligently route the customer’s inquiry to the right queue or human agent. Using what it knows about the customer (intent, history, preferences), an AI routing system will transfer the call or chat to the best-suited agent or department. This smart routing improves response times and increases the likelihood that the issue is resolved effectively on the first try.
• Leverage context: AI doesn’t consider just the immediate question—it leverages contextual data to assist in service. This includes the customer’s past interactions (previous calls, chats, emails), purchase history, account status, and even their current sentiment or tone. By having a 360-degree view of the customer’s context, AI can recommend the next best action or personalized offer to the agent. For example, if a customer has called three times about a printer issue, the AI knows not to repeat basic troubleshooting and can escalate to a replacement offer faster, improving First Contact Resolution.
• Resolve the query: AI technologies such as conversational generative AI and case-based reasoning can provide real-time guidance to frontline staff to help them resolve customer issues correctly and ensure compliance with policies. The AI can present the agent with step-by-step solutions or answer suggestions on their desktop while they are on the call or chat, making even a novice agent as effective as your best expert. This boosts first-contact resolution rates and consistency, because every agent follows proven best practices and up-to-date knowledge.
• Analyze and improve: Continuous improvement is key to contact center excellence. AI can analyze customer interactions (call transcripts, chat logs, customer feedback) and knowledge base usage to identify trends and gaps. For instance, generative AI tools can mine thousands of recorded conversations to surface common pain points or reasons for repeat calls. These insights help managers optimize training, update knowledge articles, and streamline processes. AI analytics can also measure agent performance and customer satisfaction in real time, suggesting where to adjust—for example, highlighting if a certain issue is spiking in volume so you can proactively address it.

1. Reduced Service Costs

AI can significantly reduce contact center costs while simultaneously improving service quality. For example, eGain’s clients have achieved up to a 90% deflection of incoming inquiries to digital self-service, a 35% improvement in First Contact Resolution (FCR), a 15% reduction in Average Handle Time (AHT) for agents, and around 50% reduction in new agent training time (time-to-competency). By automating routine questions and empowering agents with instant knowledge, AI allows fewer issues to reach costly live support and shortens the length of those that do. Proactive AI capabilities can even preempt calls before they happen (for instance, by alerting customers about known issues or outages). In addition, knowledge automation with AI accelerates speed-to-value: Organizations have been able to speed up knowledge creation and curation tasks by 5× using the generative AI features of the eGain AssistGPT™ solution. All these efficiencies translate to lower operational costs in the contact center.

2. Improved Customer and Agent Experience

AI also elevates both the customer experience and the agent experience in the contact center. AI-driven personalization enables businesses to deliver tailored, context-aware experiences that boost customer satisfaction and loyalty. Customers feel understood when AI uses their data (securely and appropriately) to provide relevant solutions or offers. Meanwhile, agents benefit from AI assistance as much as customers do: Tools like the eGain AI Knowledge Hub™ deliver contextual knowledge and guided workflows right on the agent’s desktop during live interactions. This makes every agent as effective as the very best agents by ensuring they always have the correct answer or procedure at their fingertips. The guidance can even adapt to each agent’s skill level — for example, novice agents might be walked through a detailed step-by-step script for a complex troubleshooting call, whereas veteran agents get concise prompts or are allowed to skip steps they’re already comfortable with. The result is more confident, proficient agents, consistent answers and service quality across all channels, and ultimately happier customers.

3. 24/7 Availability

In today’s global market, customers expect support around the clock, regardless of time zone. AI-powered chatbots and virtual assistants make true 24/7 contact center availability possible. Whether it’s a late-night customer browsing your website or an account holder calling after business hours, an AI virtual assistant can handle inquiries instantly at any time. These bots can resolve simple issues or capture information for later follow-up, ensuring customers get immediate help when live agents aren’t available. Voice bots on the phone and chatbot assistants online can work in tandem with your human team to provide uninterrupted service. This not only improves customer satisfaction (they’re never left waiting until the next day for answers), but it also relieves agents from overnight shifts and repetitive FAQs, allowing the contact center to serve customers anytime, anywhere in a cost-effective way. AI Agents take service to the next level completing tasks for customers, based on their goals.

Trust

A major barrier to AI adoption in contact centers is a lack of trust in the answers AI provides. Frontline agents and managers might worry: “Can I rely on the AI’s answer to be correct?” This concern is valid because generative AI may sometimes hallucinate (i.e. make up incorrect information) or give inconsistent answers to the same question. There’s also the classic “garbage in, garbage out” issue: if an AI is trained on unverified, outdated, or biased data, it will produce unreliable output. In a contact center, incorrect information can have serious consequences — from frustrated customers to compliance violations or legal liabilities (especially in regulated industries like financial services or healthcare). The solution is to layer AI on a trusted knowledge foundation and orchestrate both knowledge and AI centrally (in a hub architecture) so that the AI only draws from accurate, approved content. This is exactly the approach of the eGain AI Knowledge Hub, which integrates a reliable knowledge base with AI reasoning and generative capabilities. By using a unified hub, the AI’s answers are always based on vetted knowledge and business rules. As a result, agents and customers can trust the AI guidance, which in turn increases user adoption of the AI tools and drives greater business value.

Change Management

Introducing AI and new knowledge systems in a contact center isn’t just a technology challenge—it’s a people challenge. Key stakeholders like contact center agents, supervisors, knowledge authors, and subject-matter experts may be skeptical or resistant at first. They might worry that the new AI-powered system will be too controlling, reduce the need for their expertise, or even limit their career growth. It’s important to proactively manage these concerns through communication and training. Emphasize “what’s in it for them.” Show agents how AI will handle the drudge work (like repetitive questions and tedious data searches), freeing them to focus on more complex and fulfilling tasks that add more value. Highlight opportunities for career expansion: with AI handling basic queries, agents can upskill to support a broader range of products or services, take on higher-level customer inquiries, or even transition into consultative sales and Tier-2 support roles that leverage their expertise alongside AI tools. Rather than seeing AI as a threat, they should see it as an assistant that makes their job easier and helps them shine. By involving agents and experts in the AI rollout process, gathering their feedback, and celebrating quick wins, you can turn skeptics into advocates and ensure a smooth adoption of AI in your contact center.

Leading organizations have already reaped significant gains by infusing AI into their contact center operations. For example:

• Multinational Financial Services Provider: Improved First Contact Resolution by 36% and slashed new-agent training time by 40% by deploying an AI knowledge hub in the contact center (e.g. leveraging the eGain AI Knowledge Hub to unify knowledge and AI guidance for agents).
• Major Government Agency Contact Center: Deflected up to 70% of inbound calls to AI-powered self-service virtual assistants and reduced average case handling time by 25%, after implementing eGain’s AI knowledge hub. These knowledge-driven AI capabilities also boosted agent engagement to 92% (versus an industry benchmark of 67%) by relieving agents of repetitive queries and empowering them with better tools.
• Leading Health Insurance Company: Used AI-powered knowledge and process guidance to slash Average Handle Time and increase FCR, while reducing agent training time by 33%—even as over 2,000 agents went remote virtually overnight. The AI solution (powered by eGain) ensured agents had instant access to accurate answers and compliant scripts, helping this insurer meet all of its customer service KPIs and achieve a top-5 industry ranking in customer experience.

(These examples illustrate the real-world impact of AI in contact centers — from efficiency gains to improved customer satisfaction and employee morale.)

Have you ever bought a car without a test drive — or tried to evaluate it just by pushing a toy car on a table? Probably not. Similarly, acquiring an AI solution for your contact center shouldn’t be a leap of faith without proof. Why risk it with a limited toy sandbox or, worse, go live without a proper pilot? Instead, you can try AI in your own contact center environment first, with no risk.

eGain’s Innovation in 30 Days program is a no-charge, no-commitment production pilot (with our experts’ guidance) that lets you experience the value of AI for customer service and contact center operations in as little as one month. It’s a true “test drive” — you get to use our AI solutions with your own data, agents, and processes, in a controlled pilot, to see tangible results before you fully invest. Many of our enterprise clients have taken advantage of this approach to adopt our AI for contact center risk-free. Learn more about it here.

Want to talk to us about deploying AI for your contact center? Feel free to reach out — we’re happy to answer questions or help you get started. Contact us and let’s explore how to transform your contact center with AI.

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