Conversational AI for enterprise
Enterprises use conversational AI to handle a growing volume of customer conversations without a proportional increase in headcount. Some start with simple, scripted flows and expand from there. Others move straight to AI agents that can understand a request, decide what to do, and take action, whether that means resolving an issue, scheduling an appointment, or looping in a human agent with full context. Dialpad supports enterprises throughout that path. Talk to our team about how organizations are putting conversational and agentic AI to work across customer experience.
What conversational AI enables for the enterprise
Conversational AI uses machine learning and natural language processing to understand a wider range of customer questions than a traditional, menu-based chatbot, and to respond in a more natural, human-sounding way. For enterprises, that can mean handling higher volumes of customer inquiries, supporting more channels, and keeping hiring and expansion costs down as demand grows.
Where conversational AI creates the most value is in how it's deployed. A tool that can only answer questions still leaves customers waiting on a human agent for anything that requires action. That's the gap agentic AI is designed to close, and it's the focus of the rest of this page.
The enterprise conversational AI journey: from scripted to agentic
Not every enterprise is starting from the same place, and that's fine. Conversational AI capabilities generally fall along a spectrum, and Dialpad can meet teams along it.
Scripted flows and rules-based bots
Many customer service teams start here, and for good reason: an enterprise chatbot built on a defined decision tree can be a fast, low-risk way to automate common, well-understood requests, like sharing store hours or routing a call to the right department. The tradeoff is that these flows can only handle the specific paths they were built for.
Conversational AI
The next step is a system that can understand a wider range of phrasing and intent, hold context across a conversation, and pull answers from structured and unstructured knowledge sources, like help center articles or past customer conversations, rather than a fixed script. This is where enterprises often see a meaningful jump in self-service resolution and customer satisfaction.
Agentic AI
The furthest point on the spectrum, and the area Dialpad is heavily focused on, is agentic AI: systems that don't just generate a response, but understand a goal, make a decision, and take action inside connected business systems. A Dialpad AI Agent can look up an order status, verify a customer's identity, reschedule an appointment, or escalate to a human agent with the full conversation history attached, without needing a human to manually complete each step.
Enterprises don't need to choose one point on this spectrum and stay there. Many run rules-based flows for some use cases while piloting AI Agents for others, and expand as those pilots prove out.
What makes Dialpad's approach different
A full-stack agentic platform
Conversational AI, contact center, and AI Agents run on one platform, so context isn't lost when a conversation moves from an AI Agent to a human agent. Teams can also see performance across all three in a single set of analytics rather than piecing it together across separate tools.
Hybrid by design
Human intervention is treated as a feature of the system, not a fallback. Dialpad AI Agents can handle what they're configured to handle, then hand off to a human agent when a conversation calls for it, along with the context that agent needs to pick up where the AI left off.
Data-driven and no-code by default
Setting up an AI Agent doesn't require a data science team. Skills are configured through guided settings and natural-language instructions, and admins can test an agent before publishing it. This can shorten time to value and make it easier to see where an agent is working well and where it may need adjustment.
Built for enterprise governance and trust
Enterprise deployments of conversational and agentic AI typically come with real governance requirements, and Dialpad's platform is built with those in mind.
Data isolation: Customer data is segregated within Dialpad's multi-tenant architecture.
PII redaction: Sensitive information can be automatically masked in logs and transcripts.
Guardian: An AI safety layer is designed to monitor for and help prevent abuse, hallucinations, and security vulnerabilities within agentic workflows.
Compliance: Dialpad's platform is built to align with standards including SOC 2, HIPAA, and GDPR. For the most current details on certifications and compliance scope, visit the Dialpad Trust Center.
Skills, integrations, and vertical templates
Dialpad AI Agents connect to the systems enterprises already use for customer-facing work, including Salesforce, Calendly, ServiceNow, Google Calendar, Zendesk, Microsoft Outlook, HubSpot, and AthenaHealth. Common skills include knowledge retrieval, scheduling, order status lookup, identity verification, and feedback collection, and teams can configure custom integrations for systems specific to their business.
Vertical templates are available for industries with common, well-defined support and service workflows, including healthcare, legal, sports, and insurance, giving teams a starting point they can adapt rather than building from a blank canvas.
Seamless handoff between AI and human agents
When a conversation needs a human, whether a customer explicitly asks for one or the situation calls for it, Dialpad AI Agents can escalate directly into Dialpad Support for contact centers with context carried forward: reason for the interaction, what's already been attempted, sentiment, and any information the customer has already provided. The human agent isn't starting over, and the customer isn't repeating themselves.
Learn more about enterprise conversational AI
See how conversational AI and Dialpad AI Agents work together to support customer experience at scale.
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Dialpad AI Agents
Déployez des agents vocaux et numériques autonomes qui agissent et déclenchent des workflows automatisés.
Dialpad Support
Une soloution d''assistance et de centre d'appel hébergée dans le cloud pour guarantir une expérience client exceptionelle.
Enterprise conversational AI FAQs
Not quite. A traditional chatbot answers using prebuilt prompts and rules-based conversation flows, which can work well for narrow, well-defined use cases but has a limited range of what it can handle. Conversational AI uses machine learning and natural language processing to understand more varied phrasing and can provide more context-aware responses. Agentic AI goes a step further and can take action on a customer's behalf rather than just responding. Many enterprises use some combination of all three depending on the use case.
Machine learning is the study and implementation of algorithms that can recognize patterns and improve outputs based on training data. It underpins how machine learning in customer service shows up day to day, from intent recognition to response suggestions to sentiment analysis. Natural language processing (NLP) refers to natural language understanding (NLU) and natural language generation (NLG), and it's what allows conversational AI to interpret human language and generate responses in kind.
Conversational AI can help automate routine, repetitive parts of the customer experience, freeing human agents to focus on conversations that need empathy, judgment, or specialized knowledge. When paired with agentic capabilities, it can also complete some tasks directly, such as looking up order status or rescheduling an appointment, rather than only providing information.
Dialpad's approach treats human agents as a core part of the system rather than a fallback to be minimized. Agentic AI can handle a meaningful share of routine, well-defined requests, but many situations, particularly ones involving nuance, empathy, or complex judgment, are still best handled by a person. The goal is for AI and human agents to work from the same context, not for one to replace the other.
They're related but distinct. Conversational AI refers to systems designed to hold natural language conversations with people. Generative AI is a broader category of systems that can generate content, including but not limited to conversational responses, such as summaries, images, or recommended next steps.