Agentic AI chatbot

AI chatbot for customer service

Give customers fast, accurate help around the clock. Dialpad Resolve, AI agents for voice and digital interactions, can answer questions and complete tasks on web chat, then bring in your team with full context when a person should step in.

What is an AI chatbot for customer service?

An AI chatbot for customer service is software that holds conversations with customers, answers their questions, and helps them get things done without waiting for a human agent. The most capable AI chatbots are agentic: instead of only pointing customers to an answer, they can take action, like booking an appointment, checking an order, or creating a support ticket, in the systems a business already uses.

Support chatbots typically sit on a company's website or in its app, where customers already look for help. They can cover routine requests at any hour, shorten queues, and give human agents more time for conversations that need judgment and empathy.

How an AI chatbot differs from a scripted support chatbot

Traditional support chatbots follow decision trees and keyword triggers. They work well for simple, predictable questions but can leave customers stuck when a request falls outside the script. An agentic AI chatbot understands what the customer is trying to accomplish and works toward resolving it.

	Scripted support chatbot	Agentic AI chatbot
Primary job	Answer common questions	Resolve requests end to end
How it works	Menus, keywords, and fixed flows	Reasons toward a goal using knowledge, skills, and guardrails
Systems access	Usually none, or read-only	Takes action in connected systems, like a CRM, help desk, or calendar
Handoff to a person	Often a cold transfer	Passes the conversation context to the human agent

The difference shows up most in the chatbot use cases that involve getting something done, like changing an order or booking a visit. Many support teams now use AI agents for customer service to handle routine work while people focus on exceptions.

How Dialpad Resolve works as an AI chatbot for customer service

Dialpad Resolve provides AI agents for voice and digital interactions that can answer questions, complete tasks, and work alongside your contact center team. Here's how it comes together.

Resolve requests in the systems you already use

Each AI agent uses skills, which define the tasks it can perform, like scheduling an appointment, looking up an order, or creating a ticket. Skills use connectors to take action in systems like Salesforce, HubSpot, Zendesk, ServiceNow, Google Calendar, and Microsoft Outlook, so the chatbot can complete a request instead of only describing how to do it.

Answer from knowledge that stays current

Teams connect the knowledge sources the AI agent should draw on, like an internal knowledge base, a public help center, documents, or any website. These sources sync automatically, so when your help articles change, the chatbot's answers can keep up.

Start with what customers ask most

Skill Mining analyzes contact center call transcripts to recommend skills, shows the share of calls each one could handle, and can pre-fill a new skill in one click. That helps teams decide which requests to automate first based on real conversations rather than guesswork.

Set it up in plain language, then test before launch

Teams build an AI agent by describing the company, what the agent should do, and how it should behave, including its tone. Guardrails set the limits, such as when the agent must ask for confirmation before acting and when it should escalate. Before publishing, teams can test the agent's responses and validate its setup.

Hand off to your team with full context

When a customer asks for a person, or a conversation needs one, the AI agent escalates into Dialpad Support for contact centers. The human agent sees the context, like the reason for contact and what the AI agent already tried, so the customer doesn't have to repeat themselves.

One AI agent for web chat and voice

When you create an AI agent, Dialpad sets up a web chat channel for it automatically, and the same agent can also be connected to voice channels. Whether a voicebot or a chatbot is better for tier 1 support often depends on where customers reach out; with one AI agent, the same knowledge and skills can serve both.

What can an AI chatbot for customer service handle?

An AI chatbot can take on many of the routine requests that fill a support queue. Common examples include:

Appointment scheduling: booking, rescheduling, and confirming appointments on a connected calendar.
Order status and changes: looking up orders and sharing updates, or starting a return when the order is eligible.
Support intake: collecting details, troubleshooting common issues, and creating tickets in a help desk like Zendesk or ServiceNow.
Lead qualification: asking qualifying questions, adding leads to a CRM, and routing them to the right rep.
Account questions: verifying a customer's identity before sharing account details or making changes.
After-hours coverage: helping customers outside business hours instead of sending them to voicemail or an inbox.

In every case, a clear path to a person matters. Customers can get frustrated when a chatbot can't help and won't let them reach someone, so effective setups pair automation with a fast, context-rich handoff.

Measure what your AI chatbot is doing

AI agent analytics show how conversations are going, including deflection, escalations, and customer feedback, so teams can spot knowledge gaps and decide what to improve next. Deflection on its own doesn't tell the whole story, though. To measure the ROI of an AI chatbot for customer service, compare results before and after launch on resolution, escalation reasons, customer satisfaction, and the time human agents spend on routine requests.

Because AI agent and human conversations happen on the same platform, the insight doesn't stop at the chatbot. Teams can use conversation intelligence and contact center analytics to develop a deeper understanding of their customers across every channel.

Security and data privacy for AI chatbots

When evaluating an AI chatbot for customer service, look at how it protects customer data, which actions it can take on its own, and how much control your team has over its behavior. With Dialpad Resolve, guardrails define what each AI agent is allowed to do, and Dialpad's security and privacy practices are documented in the Dialpad Trust Center.

What to consider before adding an AI chatbot to your website

A few questions can help you decide whether an AI chatbot is right for your team and how to set it up:

Do you need coverage outside business hours? An AI chatbot can help customers at any hour without staffing every time zone.
Are agents spending their day on repetitive requests? Routine questions and tasks are good candidates to automate, freeing agents for higher-value conversations.
Do you want to qualify leads automatically? A chatbot can ask qualifying questions and route prospects to the right rep.
Which systems does it need to act in? List the tools the chatbot should connect to, like your CRM, help desk, or scheduling system.
How will you measure success? Set goals for resolution and satisfaction, not deflection alone.
What happens when it can't help? Plan the handoff to a person before you launch.

Many of the same planning steps apply whether you're launching your first customer service chatbot or replacing a scripted one with an AI agent.

Why connect your AI chatbot to your customer experience platform

An AI chatbot can do more when it shares context with the rest of your customer operations, including your contact center, reporting, and customer data. Dialpad Resolve works with Dialpad's calling, messaging, and contact center products, so conversations handled by AI agents and by people can live in one place.

That shared foundation is what makes Dialpad the AI platform for customer experience: AI agents and human agents work from the same customer context, handoffs carry that context forward, and every interaction becomes operational insight your team can use.