Why AI agents, and Agentic Voice AI, are shaping the next phase of enterprise work
AI has been embedded in enterprise products and workflows for years, powering automation, recommendations, analytics, and customer engagement behind the scenes. More recently, advances in generative AI have made these capabilities far more visible in everyday work. AI now drafts content, summarizes meetings, supports customer interactions, and surfaces insights across business applications.
Many AI capabilities still live within individual tools or functions. Each delivers value where it’s deployed, but there is a larger opportunity ahead: connecting these capabilities so work can move fluidly across teams, systems, and processes. This shift from isolated intelligence to coordinated systems is where AI agents come into focus. Unlike task-based automation, AI agents are designed to operate across workflows, coordinating steps, passing context, and collaborating alongside people and other agents. With the proper governance in place, agents become dependable contributors to how work gets done, not just features embedded inside applications.
As organizations move toward orchestration, conversation becomes a critical input.
Work today spans multiple conversational channels, from calls and voice to video to chat, each playing a distinct role across customer-facing and employee experiences. Together, these channels capture intent, nuance, and exceptions that rarely appear in structured fields or dashboards. Within this omnichannel environment, voice stands out as the richest and most complex conversational signal, particularly in high-stakes customer interactions. Spoken conversations carry real-time intent, emotion, and ambiguity that other channels often abstract or lose.
“Voice captures intent and decision-making in real time, especially in moments where outcomes matter most. Agentic Voice AI is how we connect human conversation to automated execution at scale.”
Carson Hostetter VP & General Manager, AI and CX Solutions RingCentral
This is why RingCentral believes Agentic Voice AI will play a foundational role within orchestrated AI systems. By listening to and interpreting voice interactions, AI agents can ask clarifying questions, adapt in real time, and transform live conversations into structured context that workflows and systems can act on, bridging the gap between how people communicate and how work actually gets done. While AI adoption is already widespread and organizations are beginning to see meaningful impact, the next phase of AI will not be defined by how many intelligent tools organizations deploy. It will be determined by how effectively these systems work together at scale across channels, processes, and teams to power people, operations, and entire organizations.
About the study
RingCentral partnered with Opinium Research on a comprehensive study of business decision-makers in the United States and the United Kingdom.
The study surveyed 2,000 IT, HR, and CX leaders at the manager level and above across retail, technology, healthcare, legal, and financial services. Unless otherwise specified, findings reflect responses from decision-makers considering both customer-facing (CX) and employee-facing (EX) use cases within their organizations. Respondents represent a mix of small businesses, midmarket organizations, and large enterprises.
Fielded in Q4 2025, the research examines:
- How AI is being used across organizations today
- Where AI agents (digital workers) are gaining traction
- Which barriers are limiting broader integration and scale
The findings reflect a market in transition, where AI adoption is accelerating and early value is emerging, while organizational structures, workflows, and governance models continue to evolve to support a long-term, positive impact.
Terms and concepts
Agentic AI
An operational model for AI in which agents can take initiative, make decisions within defined parameters, coordinate work, and operate across workflows. Unlike traditional AI, which typically optimizes individual tasks or features, agentic AI emphasizes autonomy, context sharing, and system-level orchestration.
AI agents (digital workers)
Autonomous, software-based workers designed to execute tasks, follow multi-step workflows, and collaborate with people or other agents across systems. Throughout this report, “AI agents” and “digital workers” refer to the same concept.
AI maturity / AI agent maturity
A measure of deployment stage rather than technical sophistication. In this report, maturity is defined by survey stages: not exploring, exploring, piloting, deploying, or fully embedded.
Workflow readiness
The degree to which existing processes, systems, and roles can support AI agent execution.
Orchestration
The coordination layer that enables AI agents, systems, and humans to share context, manage handoffs, and move work end-to-end across workflows, tools, and communication channels.
Conversational data
Human exchanges, spoken or written, that contain intent, nuance, constraints, and reasoning.
Agentic Voice AI
A class of agentic AI focused on spoken interaction, enabling systems to interpret voice conversations, understand conversational context, ask clarifying questions, and convert live dialogue into structured inputs that workflows and systems can act on.
Trust and governance
Policies and oversight models that ensure AI operates transparently, predictably, and safely.
Deployment turbulence
Operational instability that occurs when AI rollout outpaces workflow readiness, integration, or governance.
Integration
Operational instability that occurs when AI rollout outpaces workflow readiness, integration, or governance.
Fragmentation
Siloed data, tools, and workflows that limit an organization’s ability to coordinate AI systems and scale impact across entire processes.
Context passing
The ability for agents and systems to transfer meaning so that workflows don’t reset between steps.