AI Customer Service Agent: How Agentic AI Improves Customer Service

Customer service representative on the phone

Introduction: The Customer Service Revolution Has Arrived

Every business leader has experienced the same tension: customer inquiry volumes are rising, expectations are at an all-time high, and support teams are already stretched thin. Meanwhile, consumers expect instant, accurate, and personalized assistance at any time, whether that's via chat, email, or voice.

This is no longer a future challenge. It is today's reality.

The answer is increasingly clear: customer service AI agents powered by artificial intelligence, capable of automating routine interactions while helping organizations improve customer satisfaction and reduce costs. But not the rule-based chatbot of the early 2010s. We are now in the era of agentic AI, autonomous, intelligent systems capable of understanding intent, accessing context across multiple platforms, handling complex workflows, automate responses, and delivering resolutions end-to-end, without human intervention.

Across industries, companies are increasingly adopting AI customer service agents to improve customer experience, handle growing customer inquiries, and support customer service operations without continuously expanding their support team.

The numbers speak for themselves. The global AI customer service market reached $12.06 billion in 2024 and is projected to hit $15.12 billion in 2026, growing at a staggering CAGR of 25.8% through 2030. According to Gartner, by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs. The transformation is not coming, it is already underway.

What are AI Agents for customer service?

Before diving into impact, it's important to understand what distinguishes agentic AI from traditional customer support automation. A conventional chatbot follows a script. It can handle surface-level inquiries, but the moment a conversation becomes complex — a billing dispute, a technical support case, a multi-step return request, it fails, escalates, and frustrates.

An agentic AI customer service agent is fundamentally different. AI customer service agents are virtual assistants that can interact with your customers and support your service operations. They rely on machine learning and natural language processing (NLP) to handle a wide range of tasks, from answering simple questions and repetitive tasks to resolving complex issues — even multitasking. It:

Understands customer intent using advanced natural language processing (NLP) and large language models (LLMs)
Accesses real-time data from CRM systems, knowledge bases, and helpdesk platforms to analyze customer data and understand customer intent.
Takes action autonomously across multiple channels - chat, email, voice, without waiting for human approval
Learns continuously from every customer interaction it handles
Escalates intelligently to human agents when context demands empathy or judgment

According to Zendesk's 2025 CX Trends Report, which surveyed over 10,000 consumers and business leaders across 22 countries, companies are rapidly embracing AI copilots and autonomous agents as they move toward what analysts are calling "autonomous service models". These are not pilots anymore, they are enterprise-scale deployments enhancing support experience and boosting customer satisfaction.

Why Traditional Customer Support Can No Longer Keep Up

The pressure on customer service teams has reached critical mass. Zendesk projects that customer interactions will increase fivefold by 2027, yet two-thirds of CX leaders expect to manage this with fewer resources. This is not a sustainable model. Modern customer support automation helps businesses manage growing ticket volumes, improve response quality, and automatically respond to common customer questions.

The cost is equally stark. Every unresolved ticket, every long queue, every missed inquiry erodes customer trust and brand loyalty. Long wait times across digital channels can negatively affect customer sentiment and damage overall brand perception. More than 50% of consumers say a bad service interaction can ruin their entire day, and in a competitive market, that translates directly to churn.

The solution lies in redefining the role of the AI customer service agent, not as a replacement for human empathy, but as a force multiplier for your support team to increase efficiency.

McKinsey's 2025 State of AI report confirms that 23% of organizations are already scaling agentic AI systems within at least one business function, with an additional 39% actively experimenting. The adoption curve is accelerating, and customer support is at the centre of it.

The Business Case: What Agentic AI Delivers

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The ROI of deploying an agentic AI customer service agent is no longer theoretical. Across industries, the data consistently points to the same outcomes.

As per Zendesk's 2024 data, 3x faster response times and 40% lower support costs for businesses using AI in support operations (7)
38% reduction in first response time when AI automation and suggested replies are leveraged (8)
20–40% call containment, 25–35% lower cost per contact, and 10–20 point CSAT gains for organizations implementing agentic systems correctly (9)
90% of CX Trendsetters report positive returns on AI tools deployed for agents (3) These improvements help support teams resolve customer issues faster while maintaining high levels of customer satisfaction.
AI agents can already deflect over 45% of incoming customer queries — with retail and travel companies exceeding 50% deflection rates (1)

Critically, 51% of consumers now say they prefer interacting with bots over human agents when they need immediate service. And more than 60% of agents say they could perform their jobs better if they had access to more data to personalize interactions (10) . The customer is ready. The question is whether businesses are.

Additional Benefits of Agentic AI in Customer Service

Beyond cost reduction and faster response times, agentic AI delivers several long-term advantages for customer support teams.

Enhanced Efficiency: AI customer service agents can manage multiple customer interactions simultaneously, reducing ticket backlogs and helping support teams increase efficiency without sacrificing service quality.
Improved Customer Satisfaction: By delivering accurate and personalized responses, AI agents help improve customer satisfaction and strengthen customer relationships. Access to customer history, preferences, and prior interactions allows for more contextual support experiences.
24/7 Support Availability: Unlike traditional support models, AI agents provide assistance at any time, ensuring customers receive support outside standard business hours and across global time zones.
Scalability: As customer inquiry volumes grow, AI agents can scale without requiring proportional increases in staffing levels. This allows businesses to expand support coverage while maintaining consistent service quality.
Data-Driven Insights: AI agents generate valuable insights from customer interactions, helping organizations identify trends, monitor customer sentiment, optimize workflows, and improve service operations.
Consistency and Accuracy: By following predefined governance policies and accessing approved knowledge sources, AI agents can provide consistent responses that reinforce trust and brand reliability.

Four Real-World Use Cases: Agentic AI in Customer Support

1. 🏦 Klarna: Autonomous Resolution at Scale

Swedish fintech giant Klarna is perhaps the most cited example of agentic AI transforming customer service. Klarna's AI agent handled the equivalent workload of 853 human employees, generating $60 million in savings by Q3 2025 (11) . The AI system managed customer inquiries related to payments, disputes, and account management, end-to-end, without human intervention for the majority of interactions. For a company operating across multiple countries and channels, this wasn't just a cost play: it was a strategic imperative to scale support without scaling headcount. The AI system was able to handle repetitive customer requests while maintaining consistent service delivery.

Key outcome: Massive volume handling with reduced operational costs and maintained service quality.

2. 📡 Verizon: AI Agents Empowering 28,000 Service Representatives

In January 2025, Verizon rolled out a Google-powered agentic AI platform to support its 28,000 customer service representatives. Rather than replacing agents, the agentic system worked alongside human teams, surfacing real-time data, providing next-best-action recommendations, and delivering contextual insights mid-conversation. The result? A nearly 40% increase in service-led sales and significantly reduced call handling times.

Verizon's deployment highlights a critical truth about agentic AI in customer support: the most effective implementations don't pit AI against human agents — they amplify them. When agents have access to the right data and context at the right moment, every customer interaction becomes an opportunity.

Key outcome: Boosted sales performance and faster resolution times through human-AI collaboration.

3. 🛠️ Salesforce: Eating Its Own Cooking with Agentforce

In 2024, Salesforce deployed its own agentic AI platform, Agentforce, directly onto its customer support site, making it the world's largest agentic AI deployment (13) . The project was a real-world stress test: if agentic AI could handle the complexity of Salesforce's own enterprise customer support, it could handle anything. Engineers revamped the interface to differentiate the experience from a conventional chatbot, integrating unified data access across the organization's systems. The result was a measurable improvement in ticket resolution rates and a reduction in the volume requiring human escalation.

This case underlines a key principle: data quality and unified context are foundational. AI agents perform only as well as the data they can access. A fragmented CRM or siloed knowledge base will hobble even the most sophisticated agentic platform.

Key outcome: Scalable, autonomous support with enterprise-grade data integration.

4. 💳 Esusu: AI-Powered CX Transformation in Financial Services

Esusu, a financial services company helping renters improve their credit scores, rapidly transformed its CX operations using Zendesk's AI agents. As a high-growth company, Esusu faced the classic scaling challenge: customer inquiries were growing faster than the team could hire. By deploying Zendesk AI agents with customizable conversation workflows extending across chat and email channels, Esusu was able to handle a significant portion of routine inquiries autonomously, freeing its human agents to focus on complex, high-value interactions that required empathy and deeper context.

For financial services companies, where security, trust, and accuracy are non-negotiable, this case demonstrates that agentic AI can deliver both speed and compliance when properly deployed.

Key outcome: Scalable growth without proportional headcount growth, with maintained security and quality standards.

Designing the Right Human-AI Balance

Image of a succesful casual business woman using laptop during meeting

One of the most important insights from the data is that agentic AI and human agents are not adversaries, they are partners.

The Zendesk 2025 CX Trends Report notes that 67% of consumers say traits such as creativity, empathy, and friendliness in AI agents are important and lead to better outcomes. Customers are not just looking for speed, they want quality interactions that feel human. AI handles the routine; humans handle the exceptional.

This means the optimal customer service architecture today is a layered model:

AI agents resolve routine tasks and inquiries autonomously- FAQs, order tracking, account updates, billing questions.
AI copilots assist human agents in real time with suggested responses, and contextual data.
Human agents focus on complex tasks, complaint resolution, and emotionally sensitive conversations

This is the model that 90% of CX Trendsetters are already implementing, and seeing measurable returns from

The Road Ahead: Omnichannel, Proactive, and Always On

The next evolution in agentic AI customer service is already taking shape. Businesses are moving toward fully omnichannel agentic deployments, where AI agents handle voice, chat, email, and social media simultaneously, maintaining context across every channel and every interaction. Future contact center environments will rely on intelligent AI systems capable of maintaining context across every conversation and customer interaction.

52% of executives globally report their organizations have already deployed AI agents , and 88% of senior executives plan to increase AI-related budgets in the next 12 months driven by agentic AI's ROI .

The message from the market is unambiguous: agentic AI customer service agents are no longer optional. They are the foundation upon which competitive, scalable, and trust-building customer experiences are built.

Conclusion: The Real Question Isn't Whether. It's How.

The debate about whether to deploy AI in customer service is over. The data closed it. The question every business leader should now be asking is more specific: how do we deploy it well?

That means three things. First, get your data in order, AI agents perform only as well as the systems they can access. A fragmented CRM or siloed knowledge base will handicap even the most sophisticated platform. Second, design for collaboration, not replacement. The strongest implementations pair autonomous AI with empowered human agents, each doing what they do best. Third, treat it as infrastructure, not a project because customer expectations are only moving in one direction. Check out the best 6 AI Agent platform for Customer service in 2026.

The AI customer service agent has moved far beyond the chatbot era. Agentic AI today is capable of resolving complex customer inquiries, taking real actions across enterprise systems, and delivering personalized experiences at a scale and speed no human team can match alone. These AI customer support agents can automate responses, improve efficiency, and deliver personalized experiences at scale.

From Klarna's $60 million in savings to Verizon's 40% sales uplift , the evidence is compelling and the business case is clear. Companies that invest in agentic AI now, with a strong focus on data quality, omnichannel integration, and human-AI collaboration, will not just reduce costs. They will build the kind of customer trust and loyalty that defines market leadership.

Sources

https://www.ringly.io/blog/ai-customer-service-statistics-2026 https://www.gartner.com/en/newsroom/pres[...]sues-without-human-intervention-by-20290 https://www.zendesk.com/newsroom/articles/2025-cx-trends-report/ https://www.eesel.ai/blog/best-ai-chatbo[...]for-zendesk-in-2025-features-and-pricing https://cxtrends.zendesk.com/https://www.usepylon.com/blog/50-customer-support-statistics-trends-for-2025https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai https://cosupport.ai/articles/customer-service-trendshttps://sqmagazine.co.uk/zendesk-statistics/ https://www.nice.com/agentic-ai/roi-of-agentic-ai-in-customer-experiencehttps://www.zendesk.com/blog/ai-customer-service-statistics/https://aimonk.com/agentic-ai-examples-enterprise-roi-case-studies/https://www.searchunify.com/resource-cen[...]mer-support-building-a-data-backed-case/ https://www.salesforce.com/eu/blog/lessons-in-roi-agentic-ai-deployment/ https://www.zendesk.com/blog/zip2-ai-summit-2024/https://www.googlecloudpresscorner.com/2[...]Unlocking-a-New-Wave-of-Business-Value,1 https://onereach.ai/blog/agentic-ai-adoption-rates-roi-market-trends/

FAQS

What's the fundamental difference between Microsoft Copilot Studio and Konverso for ITSM?

Copilot Studio is a horizontal builder — it can create AI agents and virtual agents across a wide range of business applications, but it arrives without any enterprise automation logic built in. Every service management workflow, every integration, every compliance requirement must be assembled by your team. It is a machine learning and natural language processing toolkit, not a finished product.

Konverso is a vertical conversational AI platform purpose-built for ITSM. It arrives with pre-built EasyVista, ServiceNow, and Jira, native Microsoft Teams support, and agentic automation capabilities that are deployed and handling tickets within weeks — no coding required. The key distinction is straightforward: Copilot Studio is a tool for building workflow automation from scratch; Konverso is a tool for deploying it immediately.

Beyond ITSM, Konverso also supports AI agents for Sales, Marketing, and Knowledge Management, with HubSpot and Google integrations included to help teams enhance productivity across the entire business lifecycle.

Can enterprises automate ITSM tasks without coding?

Yes — but only if the platform was designed for intelligent automation from the start.

Konverso is a no-code platform. IT teams can configure, extend, and maintain agents to handle repetitive tasks — password resets, access requests, incident triage, ticket routing — without any software engineering involvement. This is what makes the Week 2–3 deployment timeline realistic. The virtual agent is chat-ready and deployed into your existing service management environment with minimal setup.

Copilot Studio, by contrast, requires developer resource to build the workflow automation logic, connect to ITSM systems, and ensure business processes are correctly mapped before a single task is handled autonomously. That gap in approach is what separates a two-week rollout from an eighteen-month project.

How critical are pre-built integrations to ITSM automation success?

They are often what separates a deployed system from a stalled pilot.

Effective enterprise automation for ITSM requires real-time access to EasyVista, ServiceNow, Jira, your knowledge base, and your identity systems. Without native connectors, your team spends months on API management and authentication orchestration across the digital transformation stack — before a single repetitive task is deflected. Pre-built integrations eliminate that overhead. Custom-built integrations, by contrast, are a recurring cost that compounds every time a connected system updates throughout its lifecycle.

Konverso's integrations are production-ready on day one. They have been built, tested, and deployed across enterprise environments, which is why L1 ticket deflection begins in weeks rather than quarters.

What causes most in-house AI agent builds to stall?

The same four issues appear consistently across organisations that attempt in-house intelligent automation:

Data readiness — Building a conversational AI platform on Copilot Studio requires clean, structured, well-labelled data across every connected system. Most enterprises have data spread across legacy environments in inconsistent formats, and preparing it consumes 60–80% of project time before any agentic automation logic is written.
Specialist headcount — Production-grade ITSM automation requires LLM specialists, and ITSM domain experts working in parallel. Most IT teams don't have all , and the cost of assembling them often exceeds platform pricing within year one.
Integration complexity — Each connector to EasyVista, ServiceNow, Jira, or a CRM needs to be custom-built, secured, tested, and maintained. Scope expands significantly once real enterprise applications are involved.
Governance and compliance — Audit trails, role-based access policies, and multilingual support are routinely deferred to "phase two." Ensuring these are in place from day one requires additional engineering cycles that rarely fit the original plan.

Is building always the wrong choice for enterprise automation?

No — and it is worth being direct about this.

If your organisation has a team of machine learning engineers already on payroll, a 24-month runway, and a service management use case so proprietary that no existing platform can address it, building may be the right goal. The efficiency gains from internal development are real when the engineering capacity already exists to ensure delivery.

For most IT organisations, however, those conditions do not apply. The typical evaluation is happening under a 12-month mandate to demonstrate ROI, with a lean team and a service desk that cannot absorb an 18-month development horizon. For that majority, the evidence is consistent: purchased intelligent automation platforms succeed twice as often as internal builds, deploy faster, and begin deflecting repetitive tasks from day one — which is precisely the digital transformation outcome most IT leaders have been asked to deliver.

How do I choose the right AI automation solution for my service desk?

Most IT leaders evaluating automation solutions start by comparing features across agent platforms — looking at which bot handles the most use cases, which conversational AI platform has the best natural language processing, which agent builder offers the most flexibility. That is a reasonable starting point, but it is not where the decision should end.

The more important question is: what does your team need to deploy intelligent automation in a live service desk environment, and how quickly do you need it working?

If your priority is speed to value, the choice becomes straightforward. Enterprise grade automation solutions that are purpose-built for ITSM — like Konverso — come pre-loaded with agentic automation workflows, machine learning models already trained on service desk contexts, and connectors to ServiceNow, Jira, and BMC that are deployed and tested out of the box. Your team does not need to configure a general-purpose agent builder from scratch or spend months preparing unstructured data before the platform can handle a single repetitive task. Workflow automation starts in weeks, not quarters.

If your priority is maximum control, a horizontal agent builder like Copilot Studio gives you more flexibility.

Bertrand Lafforgue

Bertrand has co-founded Konverso where he leads strategy, sales, partnerships and business development. Konverso has developed a vertical chatbot powered by artificial intelligence to augment the Service Desk team to better support millions of employees everyday.