How Agentic AI Can Change Customer Support & Cut Costs

Despite being a $25+ billion market, today's customer support still pulls at straws with fundamental issues that frustrate both customers and businesses. 

I’ve had the amazing experience of scaling with freshworks. Back then it was just Freshdesk, an up and coming company that found a space that Zendesk and Intercom couldn’t fill.

Today, we’ve come a long way from traditional support systems.

We’re at a day and age where AI is finally good enough to start interfacing with humans in the customer support scene.

The Current State of Customer Support: An Industry Ripe for Disruption

The typical customer support journey today is riddled with friction points:

• Repetitive information sharing: Customers explaining their issues multiple times across different channels
• Disjointed experiences: Starting in chat, moving to email, ending on phone calls with no context carried over
• Long wait times: Support agents scrambling to find relevant information while customers grow increasingly frustrated
• Limited self-service options: Basic FAQs that fail to address complex or nuanced issues
• Escalation bottlenecks: Simple issues consuming valuable human agent time

These pain points have two BIG issues.

1. They are frustrating for customers
2. They're enormously expensive for businesses. 

The average cost of a customer service call ranges from $2.70 and $5.60.

Of course this cost varies depending on factors like industry, call handling time, agent skills, and the value of each interaction.

Just for reference, a typical B2B company spends 8% of its revenue on customer service operations.

‍

The Traditional solutions have tried to address these challenges through a patchwork of technologies:

1. Rule-based chatbots: Great for handling FAQs but quickly break down with complex queries
2. NLP-powered chatbots: More sophisticated but still lack true contextual understanding
3. Human agents: Essential but expensive and often overwhelmed by volume

Agentic AI Entering is a Major Shift in Customer Support

Agentic AI is a fundamental evolution beyond traditional support automation. 

Unlike standard chatbots or even generative AI tools, agentic AI systems don't just respond to queries, they go ahead and take action to resolve issues autonomously.

Key Capabilities That Transform Support Operations

1. Memory & Contextual Understanding

How it works:

• Stores and recalls previous conversations and customer history
• Employs Retrieval-Augmented Generation (RAG) to pull real-time answers from knowledge bases, CRMs, helpdesks, and product documentation
• Continuously updates its knowledge as new data and customer trends emerge

Impact:

• Eliminates the need for customers to repeat themselves
• Creates genuinely personalized, human-like interactions
• Maintains context across multiple touchpoints and channels

"Unlike traditional AI that relies on pre-programmed instructions, agentic AI can make decisions and take actions based on its understanding of the environment and its goals."

‍

2. Autonomous Action Through AI Agents

How it works:

• Integrates directly with backend systems including CRM platforms, payment processors, and IT tools
• Can execute actions like processing refunds, updating account information, or resetting passwords
• Collaborates with specialized knowledge-based agents in AI to handle domain-specific issues

Impact:

• Resolves issues up to 50% faster without human intervention
• Reduces the volume of tickets requiring human attention by 60-80%
• Enables true 24/7 support capacity without increasing headcount

3. Intelligent Human-AI Collaboration

How it works:

• Recognizes when issues exceed its capabilities and requires human expertise
• Summarizes complex customer issues before transferring to human agents
• Assists human agents in real-time with AI-generated recommendations
• Learns from human agent actions to improve future performance

Impact:

• Optimizes workforce allocation with AI handling routine queries
• Reduces average handling time by 40% through intelligent assistance
• Improves first-contact resolution rates through better agent support

Real-World Results - Agentic AI Use Cases Transforming Customer Support

In my Freshworks days, I often heard from clients that about 40% of their tickets were just customers asking simple questions that were snowballing into hundreds of human hours. 

A decade ago, ticketing systems helped, but it was like pushing a boulder uphill. Today, Intelligent ticket deflection is a must for retention and growth. Yet, some companies still manually process every ticket.

Here are more examples of how Agentic AI is driving higher-quality, more efficient customer experiences across various industries:

1. Proactive Issue Resolution

• AI can predict and address potential issues before they escalate into major problems. By monitoring customer data and identifying patterns, AI can trigger proactive interventions, such as sending alerts or providing solutions before customers even realize there is an issue.
• Example: An internet service provider uses AI to monitor network performance and detect potential disruptions. If the AI predicts a service outage, it automatically informs affected customers and suggests troubleshooting steps, minimizing the impact on the customer experience.
• Impact: Reduced contact volumes, increased customer satisfaction through proactive communication, and enhanced brand loyalty.

2. Sentiment Analysis and Emotional Intelligence

• AI can gauge customer sentiment and emotions during interactions, allowing agents to respond more empathetically and appropriately. This emotional intelligence enhances the overall customer experience and helps build stronger customer relationships.
• Example: An insurance company could leverage Zoom Contact Centre and Zoom AI Companion to analyze the tone and sentiment of customer interactions. If the AI detects frustration or dissatisfaction, it alerts supervisors to intervene and provide additional support, ensuring that customers feel heard and valued.
• Impact: Quicker conflict resolution, enhanced customer satisfaction, and reduced escalations.

3. Automated Quality Assurance

• AI can automate the quality assurance process by monitoring and analyzing customer-agent interactions. It can evaluate the performance of agents, provide feedback, and identify areas for improvement, ensuring that the quality of customer service remains high.
• Example: A contact centre QA team implements Zoom Quality Management to automatically review calls and transcripts. The AI assesses agent performance based on predefined criteria, such as adherence to scripts and customer satisfaction scores, providing actionable insights to enhance training programs, and offering the ability to manage non-compliant interactions by exception.
• Impact: Increased compliance, reduced risk, and significant cost savings by automating QA.

4. Enhanced Self-Service Options

• AI-driven self-service channels empower customers to find answers and resolve issues independently. These channels utilize natural language processing (NLP) to understand customer queries and provide relevant information from knowledge bases or FAQs.
• Example: A financial services company might offer an AI-powered self-service platform where customers can check account balances, transfer funds, and get answers to common questions. The AI continuously learns from customer interactions to improve the accuracy and relevance of its responses.
• Impact: Increased operational efficiency, reduced wait times, and improved customer experience for routine inquiries.

5. Handling Multimodal Conversations Across Channels

• Agentic AI can now handle multimodal support, where it shifts seamlessly across channels (like phone, chat, email, and social media) in a single conversation.
• Example: A retail company might offer an AI that helps a customer who begins an inquiry on chat, then switches to a phone call, and finally needs follow-up through email. The AI retains the conversation history and context, making it appear as one continuous interaction, regardless of the contact channel.
• Impact: Enhanced customer experience through seamless multichannel support, reduced friction, and higher resolution rates.

6. Autonomous Agent Training and Performance Improvement

• Agentic AI can also function as a virtual coach for customer service agents. It can monitor interactions, provide feedback in real-time, and even suggest alternative responses and content for difficult questions.
• Example: In a tech support environment, an AI-driven coaching tool could observe an agent struggling to assist a customer and offer scripted responses, troubleshooting steps, or escalate the issue if needed. Over time, this helps agents become more efficient and effective.
• Impact: Enhanced agent performance, continuous skill development, and reduced onboarding times for new agents.

7. Dynamic Personalization in Customer Interactions

• Agentic AI can analyze vast amounts of customer data to deliver personalized experiences. By understanding individual preferences, purchase history, and behavior patterns, AI can tailor recommendations and solutions to each customer’s unique needs. This level of personalization enhances customer satisfaction and loyalty.
• Example: An AI assistant in an online retail setting can not only suggest products based on a user’s preferences but also consider real-time context. If a customer has an upcoming anniversary, the AI could suggest personalized gift options with expedited delivery options and a gift-wrapping service.
• Impact: Increased upselling and cross-selling opportunities, a highly personalized customer experience, and improved conversion rates.

8. Efficient Call Routing

• Agentic AI can optimize call routing by analyzing the nature of incoming calls and directing them to the most appropriate agent or department. This ensures that customers are connected with the right person to resolve their issues efficiently.
• Example: A healthcare provider uses AI to route calls based on the patient’s symptoms and medical history. This intelligent routing reduces wait times and ensures that patients receive specialized assistance from the most qualified healthcare professionals.
• Impact: Better experience through reduced queuing and transferring, utilizing the right advisors for the right work, and improved efficiency.

Implementation: A Strategic Approach to Agentic Support

Successful deployment of agentic AI in customer support requires a strategic approach:

1. Start with High-Volume, Low-Complexity Issues

Begin by identifying support tickets that are both frequent and relatively straightforward. These "quick wins" demonstrate value while allowing the system to learn and improve.

2. Build a Comprehensive Knowledge Foundation

Agentic AI systems rely on high-quality data. Invest in organizing and structuring your support knowledge base, product documentation, and historical ticket data.

3. Define Clear Boundaries and Escalation Paths

Establish clear parameters for when issues should be escalated to human agents. This includes sensitive situations, complex problems, or high-value customers requiring special attention.

4. Implement Progressive Learning Cycles

Deploy your agentic system in phases, allowing it to learn from each interaction and continuously improve. Regular review cycles with human supervisors ensure quality and alignment.

5. Measure Impact Beyond Cost Savings

While cost reduction is important, also track customer satisfaction, resolution times, first-contact resolution rates, and agent satisfaction to capture the full value of your investment.

Case Study: Global SaaS Company Transforms Support with Agentic AI

Camping World hit a bit of a snag when they saw a huge spike in calls coming into their contact centers. With the extra volume, their team was getting overwhelmed, and any calls coming in after hours were either missed or pushed to the next day. This meant customer questions were left hanging, and sales opportunities were slipping through the cracks.

To solve this, they brought in IBM's cognitive AI tool and built an AI assistant called Arvee. This AI buddy took care of calls 24/7, answering customer questions without needing an agent to step in. And even when the agents did get involved, Arvee captured all the call details for them, no matter when the call came in.

Saurabh Shah, CIO of Camping World, said, “The agents love how easy it is to work with Arvee. It hands things off smoothly when needed, and having customer engagement stats right on the dashboard helps agents stay on top of things.”

The Results:

• 40% more customer engagement
  
  
• 33-second reduction in wait times
  
  
• 33% boost in agent efficiency

Beyond Chatbots and into Agentic Support

The evolution of customer support with agentic AI extends far beyond current capabilities. Near-future developments include:

Predictive Resolution

Agentic systems will identify potential issues before customers experience them, initiating proactive outreach and resolution.

Emotion-Aware Support

Advanced sentiment analysis will allow agentic AI to adapt its approach based on customer emotions, providing appropriate responses to frustration, confusion, or satisfaction.

Multimodal Support Experiences

Agentic AI will seamlessly integrate text, voice, and visual elements to provide rich, intuitive support experiences across devices and contexts.

Cross-Product Intelligence

Support agents in AI will understand relationships between different products and services, providing holistic assistance across a company's entire ecosystem.

The Human-AI Partnership in Modern Support

The real magic of Agentic AI in customer support is actually the Human Ops - AI Ops Integration. 

By handling some of the simpler stuff on its own and equipping agents with the right info at the right time, Agentic AI creates a whole new way of working. 

This is what the end results look like:

• Customers get quicker and more consistent support.
  
  
• Agents get to focus on the tricky, high-impact tasks.
  
  
• Businesses save money while seeing better results.

I always say, “The future of AI isn’t AI vs. humans—it’s AI + humans.” And nowhere does this show up more clearly than in customer support.

Companies that make this shift are saving costs, turning customer support into a powerful advantage that builds loyalty, boosts retention, and strategically driving growth for the long haul.

The question is, have you joined this movement yet?