Generative AI is the part of "AI in customer service" that produces new text or speech rather than just classifying or predicting. It writes a draft reply, summarizes a call, or holds a conversation with a customer, drawing on patterns it learned during training to generate a response that fits the situation in front of it.

That is a different job from machine learning, which tends to work in the background, scoring, routing, and flagging based on patterns in past data. Most modern customer service platforms, including Dialpad's, use generative AI and machine learning together. Machine learning decides what is happening in a conversation, and generative AI decides what to say or write next.

Where generative AI shows up in customer service

Generative AI has moved well past simple chatbots. Common applications include:

• Drafting responses: Generative AI can produce a first-pass reply to a customer message, which an agent can send as-is or edit before it goes out.

• Summarizing calls and conversations: Instead of an agent typing notes after every interaction, generative AI can produce a summary, capture the outcome, and flag any follow-up items.

• Holding a conversation directly with a customer: On chat and increasingly on voice, generative AI can carry a multi-turn conversation, answer questions, and complete simple tasks such as checking an order status.

• Synthesizing knowledge: Rather than surfacing a list of help articles, generative AI can pull relevant information from multiple sources and write a direct answer to what the customer or agent actually asked.

• Coaching in the moment: During a live call, generative AI can suggest phrasing or a next step to an agent, based on what similar top-performing conversations have looked like.

Why the model behind it matters

Not all generative AI is built the same way, and the difference shows up in how well it understands your customers.

Many generative AI tools in customer service are built on top of large, general-purpose language models trained mostly on public, broad text from across the internet. Those models can be highly capable, but they were not trained specifically on how your customers talk, what your products do, or what a good resolution looks like in your industry.

Dialpad's own generative AI model, DialpadGPT, was built differently. It was built by Dialpad's team of AI researchers and engineers, and trained on an extensive selection of business conversations, rather than relying only on broad, publicly available text. That gives it a more direct connection to how real customer service conversations actually unfold, including the tone, phrasing, and context that a general-purpose model trained on the open internet would not have seen. DialpadGPT powers features like AI Recaps and AI Playbooks, so the same underlying model is generating summaries, guiding live conversations, and drafting responses across the platform rather than switching between disconnected tools.

This is also why "generative AI" is not a single feature you can evaluate in isolation. The value depends on what the model was trained on and how it will be applied in your customer service operations.

What to look for in generative AI for customer service

A few questions can help clarify how well a given implementation is suited to customer service, rather than relying on general capabilities alone:

• Was the model trained or fine-tuned on real customer service conversations, or only on general internet text?

• Does it have access to your company's own knowledge base, policies, and product details, so its answers are grounded rather than guessed?

• Can a human agent see and edit what it drafts before it reaches a customer, particularly for sensitive or high-stakes conversations?

• Does it work across both voice and digital channels, or only one?

• Is there a clear answer for how customer data is handled and whether it is used to train models shared across other companies?

Generative AI does not replace human agents in customer service, and the strongest implementations tend to pair it with clear escalation paths rather than trying to automate every interaction. Starting with a narrow, well-defined use case, such as call summaries or draft replies, and measuring the before-and-after impact tends to build trust in the technology faster than a broad rollout on day one.