Dialpad has announced a series of new product capabilities aimed at helping enterprises move from AI experimentation to full-scale deployment, unveiling the updates at Enterprise Connect 2026.
The company introduced several enhancements to its Agentic AI Platform designed to help organisations identify the most impactful AI use cases, build AI agents more easily, and validate performance before launching them in production environments.
The goal, according to Dialpad, is to close what it calls the “AI execution gap” – the challenge of turning AI pilots into systems that deliver measurable business results at scale.
The announcement comes amid rapidly growing enterprise investment in AI-driven automation.
Dialpad cites industry estimates suggesting that 79 percent of companies adopted AI agent technology in 2025 alone, with spending on the technology projected to reach $155 billion by 2030.
Yet despite the surge in adoption, many organisations continue to struggle to move beyond early trials, with roughly half of AI agent projects remaining stuck at the pilot stage.
Craig Walker, CEO, Dialpad, said the company’s latest platform enhancements are designed to help organisations move past that barrier.
“Enterprises aren’t struggling with AI ambition – they’re struggling with AI execution,” Walker said.
“Billions have been spent on agentic AI, but too many projects stall before delivering real, measurable results.
“Our latest platform advancements eliminate the guesswork, helping organisations identify the right use cases, validate ROI before launch, and deploy AI agents that are safe, governed, and ready for production from day one.”
Identifying Where AI Can Deliver
A key focus of the announcement is helping organisations determine where AI agents can add the most value.
One of the newly introduced capabilities, called Skill Mining, analyses historical conversation data to identify friction points in customer interactions and highlight opportunities for automation.
By examining patterns across support calls, chats and other interactions, the tool is designed to help businesses prioritise the AI use cases most likely to deliver meaningful improvements to customer experience and operational efficiency.
Dialpad also introduced a feature known as Proving Ground, which allows organisations to test and optimise AI agent performance before deployment.
The tool is intended to simulate outcomes and validate expected returns on investment before AI agents are released into production environments.
In addition, the company has expanded its analytics capabilities to connect AI interactions with measurable business outcomes. These closed-loop analytics combine AI conversation data with contact centre performance metrics such as resolution rates, average handling time and customer satisfaction scores.
The objective is to provide enterprises with clearer visibility into the operational impact of AI-driven automation.
Hayley Sutherland, Research Manager for Conversational AI at International Data Corporation, said the ability to quantify potential outcomes before deployment is becoming increasingly important for enterprise AI initiatives.
“The real value for customers right now is moving beyond retrospective analytics to understanding the specific impact and resolution rates ahead of time. This empowers the critical decision-making that delivers real business value,” she said.
“By showing them the quantifiable ROI before deployment, you help them reduce failed AI pilots and maximise ideal business outcomes that are linked strongly to strategic – yet data-based – decisions.”
Building AI Agents Without Coding
Another significant part of the announcement is the introduction of Agent Studio, a no-code environment designed to allow organisations to create custom AI agents for both voice and digital channels.
Using a conversational interface, enterprises can build and configure AI agents without requiring specialised development resources. Agent Studio includes connectors and custom actions that allow AI agents to integrate with existing enterprise workflows, applications and security policies.
According to Dialpad, the tool allows organisations to deploy AI agents tailored to their own business processes, enabling teams to automate customer and employee interactions while maintaining compliance with enterprise governance standards.
The company says the approach allows businesses to activate their own operational data and workflows, ensuring that AI agents can respond effectively to real-world customer needs from the moment they are deployed.
Governance and Oversight Built Into the Platform
Alongside development tools and analytics capabilities, Dialpad also introduced a new governance layer designed to monitor and control AI interactions.
The feature, called Guardian, acts as a real-time safety supervisor that continuously monitors AI agent interactions. Its purpose is to reduce the risk of sensitive data exposure, ensure regulatory compliance and maintain operational oversight as organisations scale their use of AI.
By embedding governance directly into the lifecycle of AI agents – from training and deployment to ongoing optimisation – Dialpad aims to give enterprises greater confidence in deploying AI technologies across customer-facing operations.
AI Already Widely Used
Dialpad says its AI capabilities are already widely adopted among its contact centre customers. According to the company, 97 percent of its contact centre clients currently use AI features within the platform.
Those capabilities include real-time transcription, sentiment analysis, live coaching prompts and automated conversation summaries designed to assist human agents during customer interactions.
The company reports that its AI tools have generated more than 775 million AI Recaps and delivered more than 450 million AI-generated customer satisfaction scores to date.
Dialpad’s platform uses a multi-LLM architecture that combines several specialised models for speech recognition, intent detection and task execution. The system is designed to continuously learn from previous interactions, improving its accuracy and responsiveness over time.
With tools for identifying use cases, validating outcomes and governing AI deployments, the company is positioning its Agentic AI Platform as a pathway for enterprises seeking to move from early AI experimentation to fully integrated automation across their communications infrastructure.
