When customers reach out for support, all they want is helpful answers and a positive experience. But, traditional chatbots fall short. They deliver scripted answers, fail to understand the customer's intent, and lack personalization, ultimately driving customers away.

With the rise of generative AI, customer experience leaders have realized the real value of personalized customer support using GenAI-powered chatbots. That’s because  Large  Language Models (LLMs), natural language processing (NLP), and machine learning, generative AI chatbots could interpret user intent, pull insights from vast datasets, and craft tailored responses in real-time. This has been a fundamental transformation for customer success teams in terms of how they could support and engage with customers.

Today, we're witnessing the next leap forward: the evolution of generative AI —AI agents. AI chatbots are evolving into autonomous systems capable of understanding complex requests and taking independent actions to fulfill customer needs. They can analyze patterns across customer interactions, identify opportunities for meaningful engagement, and execute multi-step processes without constant human oversight.

In this guide, we'll discuss the challenges in engaging customers and how AI agents go further than AI chatbots in solving customer engagement challenges with multiple real-world and meaningful use cases. 

Let’s dive in.

Business leaders spend a fortune on customer engagement and retention activities, yet many struggle to build genuine customer relationships. Why is this so?

Several challenges undermine these efforts:

Rising customer expectations

Today, customers demand fast, almost instantaneous responses, and your response time shapes their decision-making. Want proof? 78% of B2B customers purchase from the vendor that responds first. 

But reality tells a different story. Research shows that the average response time is 12 hours and, in some cases, longer than 8 days. 

Lack of personalization

Customers are more likely to engage with companies that initiate a personalized interaction than generic ones.

However, according to a  Statista survey, 56% of senior marketing executives say that delivering real-time personalization is one of the biggest challenges in creating effective customer experiences.

Lack of consistency 

Service quality shouldn’t depend on which agent or channel a customer uses. But, this is often the case in organizations where major customer support functions are handled manually. 

For example, a customer may explain an issue via chat, then call later, get connected to a new agent, and have to repeat everything. This kind of inconsistency creates friction. 

Statistics show that 85% of consumers go out of their way to switch to a company that offers better customer service.

Low scope for scalability

As your business grows, so do your support queries. But scaling support isn’t as simple as hiring more agents. Training new employees takes time, costs money, and doesn’t always guarantee efficiency. In the meantime, overworked agents struggle to keep up, make mistakes, and cause service quality to drop.

Both AI chatbots and AI agents improve customer experiences by offering 24/7 availability, quicker responses, and reduced support workloads. However, AI chatbots operate primarily within the boundaries of their training data and serve an assistive role without autonomous reasoning or decision-making capabilities. 

In contrast, AI agents independently reason, adapt to real-time information, and proactively perform tasks to address customer challenges.

Here’s how AI agents deliver greater efficiency and engagement:

24/7 availability

A Zendesk report shows that 51% of consumers prefer interacting with chatbots over humans when they want immediate service.

Generative AI chatbots handle simple queries like "What's your cancellation policy?" But when it comes to actually canceling an order, they often fall short, forcing customers to either contact support or figure it out themselves.

AI agents answer the customer’s question and independently perform the required action. For example, if a customer wants to cancel an order, the AI agent retrieves the order details, requests confirmation, and processes the cancellation immediately without friction. 

Reduce downtime

AI chatbots effectively understand user intent but do not have the reasoning or decision-making capabilities to identify what requests are most urgent. 

In this case, AI agents first attempt to auto-resolve queries. If no workaround is found, they categorize and prioritize tickets based on urgency, customer impact, and issue complexity and escalate to the human agent best equipped to handle these requests. 

This helps customers get the right support from the start and helps support teams resolve queries faster. 

Reduced organizational costs

Compared to AI chatbots, AI agents drive even more savings by independently managing complex interactions, significantly reducing the need for additional customer support staff, training expenses, and manual follow-ups. 

For example, AI agents can autonomously handle order returns, troubleshooting, or customer onboarding, enabling businesses to scale without expanding their support teams.

Better context management

Generative AI chatbots can retain contexts only from recent interactions. But, AI agents have advanced memory, which enables them to maintain context across multiple interactions and channels.  

For instance, if a customer discusses an issue via email and later follows up on WhatsApp, the AI agent immediately recalls the entire conversation history, proactively acknowledges previous interactions, and continues to resolve the problem without asking the customer to repeat details.

Better personalization and retention

A Deloitte study shows that brands that excel at personalization are 71% more likely to report improved customer loyalty.

The personalization offered by AI chatbots relies solely on previously trained data, which can quickly become outdated. As a result, the interactions fail to resonate with current customer preferences.

On the other hand, AI agents learn and adapt from each user interaction and regularly monitor knowledge bases for new customer information and product details so that customer teams stay updated. The result? A smoother, more personalized experience that keeps customers coming back.

Customer engagement chatbots with agentic AI independently handle tasks that earlier required more human effort, time, and cost. 

We’ve discussed some of the use cases of how you can leverage agentic AI in customer engagement:

Auto-resolve repetitive queries

Your customer support team is often overwhelmed by repetitive queries like, "Where’s my package?", "How can I upgrade my subscription?" or "I forgot my password." With AI agents, you can automate such queries completely. 

For example, if a customer asks, “Where’s my package?” the AI agent independently makes API calls to your order management system, verifies the shipping status, identifies delays, contacts the courier service directly, and updates the customer with the latest tracking details.

Identify and update the knowledge gap

AI agents constantly monitor customer interactions and your knowledge base to identify missing or outdated information. When a particular query is handled with inadequate responses, they proactively alert customer success managers and suggest relevant content updates. Managers need to simply review and approve the suggestions.

For example, if multiple customers report difficulty integrating third-party software due to unclear instructions, the AI agent notices this. It proactively flags this gap, recommends improved step-by-step documentation, and drafts updated support articles to explain the integration process clearly.

Automate ticketing and routing

AI agents help you categorize and prioritize support tickets based on urgency, complexity, and customer impact. 

For example, imagine two tickets arriving simultaneously: 

• One customer reports a software outage hindering their business operations

• Another customer asks about setting up a customized payment plan. 

The AI agent instantly recognizes the software outage as more urgent and quickly routes it to a specialized technical team for immediate attention. Meanwhile, the request about payment plans is categorized separately and sent to the appropriate support agent. 

Automate customer onboarding

You can automate end-to-end customer onboarding processes with AI agents by creating tailored plans for each customer.

AI agents execute tasks such as sending welcome messages, providing interactive product walkthroughs, promptly answering customer questions, sharing relevant product information, and conducting regular check-ins. 

This approach creates a positive first impression, saves your team valuable time, and speeds up onboarding.

Gather and analyze customer feedback

Most customers don’t fill out long feedback surveys, but they don’t mind answering a quick question in chat. AI agents help you collect quick customer feedback after resolving an issue or completing a purchase.

For example, the AI agent can ask a simple question like, "Did we solve your problem today?" or "How satisfied are you with your purchase?" Customers can quickly respond with a thumbs-up, thumbs-down, or short comments without much effort.

You can use this data to improve services, track customer satisfaction trends, and identify areas where support could be better without annoying customers with lengthy surveys.

With the advancements in AI, the market is flooded with plenty of tools offering to build chatbots with agentic AI capabilities for customer engagement. So, how do you separate the signal from the noise and choose the platform that best suits your enterprise needs? 

User interface

A chatbot should be easy to set up and manage, even for non-technical users. Look for a platform with a drag-and-drop builder and intuitive dashboard that allows you to customize workflows, responses, and conversation paths without coding expertise. A complex setup can slow down adoption and make updates difficult.

Integration with existing tools

The platform you choose should provide seamless integrations with your CRM, customer support platform, helpdesk, and internal databases to provide accurate, real-time responses.

Robust training methods

Pick a platform that allows you flexibility in selecting language models and provides multiple ways to train your chatbot effectively. It should support methods like uploading documents, URLs, specific web pages, and FAQ files. 

It should also enable you to create custom conversation flows and personalized responses for particular scenarios. This flexibility ensures that your chatbot provides accurate and valuable information.

Customizable workflows

The platform you choose should allow you to define workflows with flexibility. For example, while onboarding customers, you should be able to create personalized processes for each customer. 

Analytics and reporting capabilities

Your chatbot platform must include robust analytics and reporting tools. These insights help you clearly understand chatbot performance, identify common queries, measure customer satisfaction, and pinpoint areas needing improvement.

Multichannel deployment

Customers engage with businesses across websites, apps, social media, and messaging platforms. A chatbot should work across these channels while maintaining context.

Suppose a customer starts a query on your website. Later, they follow up via WhatsApp or Instagram. The chatbot should remember the context and continue the conversation instead of forcing the customer to repeat everything.

Today, customer engagement is all about creating personalized experiences for customers. PagerGPT helps you do just that by allowing you to build AI-powered chatbots that make support faster and more efficient.

With no coding required, you can quickly create, train, test, and deploy chatbots. You can train your chatbot using a wide range of data sources, including URLs, sub-pages, and uploaded files, ensuring it delivers accurate, context-aware responses. The small talk feature lets you fine-tune how your chatbot interacts, making conversations more natural.

PagerGPT integrates with your CRM, helpdesk, knowledge bases, and customer support platforms, enabling you to create custom workflows. Your chatbot can pull real-time data, update customer records, and execute tasks like processing refunds or booking appointments.

With our shared live inbox, support teams can manage customer conversations from multiple channels like WhatsApp, email, and Facebook in one place.

PagerGPT allows you to deploy chatbots across multiple channels, including WhatsApp, Facebook, your website, and more. This lets your customers reach you from their preferred channels, boosting engagement.

Book a demo now to see how PagerGPT can help you create a seamless customer experience and boost engagement.

How do you automate customer engagement?

You can automate customer engagement using AI chatbots with agentic AI capabilities. Key use cases include customer onboarding, processing refunds, canceling or tracking orders, identifying knowledge gaps, and personalizing recommendations. 

Can you use PagerGPT for free?

Yes, you can use PagerGPT for free and train your chatbot with up to 1 million characters and 100 sessions. However, the free version has limited customization options. 

To unlock the full potential of AI assistants, you can explore customizable pricing plans with advanced features.

Why do customers like chatbots?

Customers prefer AI chatbots because they provide instant, accurate, and personalized responses without long wait times. They eliminate the need for repeated explanations, offer a low-effort experience, and are available 24/7 across multiple channels.

How do AI chatbots help in dealing with customer queries faster?

AI chatbots speed up query resolution by understanding intent, pulling real-time data from integrated tools to provide accurate answers, and executing actions like tracking orders or processing refunds.

Will customer service chatbots replace human agents? 

No, AI chatbots will not replace human agents. Instead, they are evolving into more advanced systems having high agency that will empower customer support by autonomously executing repetitive but complex tasks, allowing agents to focus on more important issues.