How businesses deploy, govern, and profit from agentic voice AI
Executive answer
Agentic voice AI is already delivering measurable value in active deployments. Among leaders with live voice AI agents who were asked to rate their returns, 82% report positive ROI, citing cost reductions, time savings, and the ability to redirect human staff to more complex work. Among the 191 active deployments whose operating model was described, 92% operate with high or partial autonomy. More than a third of leaders say voice AI agents can sometimes be a better option for high-stakes conversations compared with chat-based AI or humans, challenging the assumption that difficult customer interactions should always default to a person. Forty-four percent of non-users are actively planning to invest, while others are weighing the risks of moving too quickly against the possibility of falling behind competitors.
What is agentic voice AI?
Agentic voice AI is voice-based AI that leads conversations and makes decisions on its own — acting on calls rather than just transcribing them.
Agentic voice AI is already delivering measurable value in active deployments. Among leaders with live voice AI agents who were asked to rate their returns, 82% report positive ROI, citing cost reductions, time savings, and the ability to redirect human staff to more complex work.
Businesses are also handing agents meaningful responsibility. Among the 191 active deployments whose operating model was described, 92% operate with high or partial autonomy. At the same time, businesses are defining where agents can operate independently and where humans should step in.
Those boundaries are not fixed. More than a third of leaders say voice AI agents can sometimes be a better option for high-stakes conversations compared with chat-based AI or humans, challenging the assumption that difficult customer interactions should always default to a person.
For businesses that have yet to deploy agentic voice AI, the pressure to make those decisions is growing. Forty-four percent of non-users are actively planning to invest, while others are weighing the risks of moving too quickly against the possibility of falling behind competitors.
For more than 25 years, we have worked on one problem: making business conversations work reliably at scale. Over that time, the role of the conversation has changed. What began primarily as a way to connect people has become a source of data, intelligence, and increasingly, action.
This research shows just how far that shift has progressed: the conversation is becoming a place where the work itself gets done. Leaders are no longer asking whether a voice AI agent can hold a call. They are asking how much traditionally human-led work the agent can take on, where humans still need to step in, and what proof they need before handing over greater responsibility.
Three findings put numbers on how consequential that boundary question has become:
Real-time speech raises the stakes for AI agents because conversations unfold with little room for delay or correction. Agents have to understand speech, navigate interruptions, maintain context, and increasingly act on what happens in the conversation.
Voice is where human intent becomes action. With agentic AI, a conversation sets an agent in motion to reason, act, and deliver an outcome.
The insights that follow show businesses working through where these boundaries sit. Active deployments are delivering measurable returns, while voice AI agents are independently taking on meaningful amounts of work. At the same time, organizations are defining where people should step in, weighing the customer and brand consequences of getting those decisions wrong, and reconsidering assumptions about which interactions are inherently better handled by a person.
We're already seeing that operating model take shape with our own customers: deep autonomy inside a defined lane, a designed handoff to a person, and a record of every conversation. We hope this research helps you benchmark your organization's standing as you make those decisions.
The RingCentral Team · ringcentral.com
Voice has long been a critical channel for business decisions. Across phone calls and video meetings, people align to answer questions, strategize, and solve problems.
Agentic voice AI embeds automation at the origin of voice itself: it can lead conversations and make decisions, easing the human workload while automatically feeding the conversation data flywheel.
To understand how organizations approach adopting and deploying AI voice agents, G2 interviewed 359 professionals across industries and business sizes who say voice is an important business channel. Company sizes ranged from small businesses to large enterprises, letting us see where organizational scale changes the calculus around trust, governance, and further investment.
This study is evidence of the efficiency and rich qualitative data agents bring to voice-driven workflows: G2 used agentic voice AI to conduct the primary research cited in this report — believed to be the first study of its kind on agentic voice AI conducted entirely by agentic voice AI. G2 Interviews agents spent over 86 hours interviewing study respondents one-on-one, condensing into weeks what would have taken a human team months of coordinated outreach, scheduling, and data processing to complete. The result is one of the latest sets of voice AI statistics drawn directly from practitioner interviews, fielded in July—August 2026.
With AI-generated transcripts and summaries, voice conversations serve as a primary site of decision-making and as the most significant input to sources of truth for record-keeping. The majority (62%) of leaders say they operationalize conversation data for decisions and process improvement — such as increasing first-call resolution or decreasing average handle time — rather than treating it as passive recordkeeping — the discipline conversation intelligence software is built around. In this way, conversations serve as the system of outcomes for businesses: humans on the calls drive the action, and AI quantifies and records the impact.
Data captured by voice agents in external channels quantifies how many live customer calls get answered and how many issues are resolved the first time, and fuels qualitative insights about customer intent and service quality. Leaders describe analyzing customer call transcripts to understand sentiment, extract important data to feed to CRM tools, and decide where humans in the loop are necessary.
Businesses operationalize call data in three main ways: storing transcripts for future use, cross-referencing conversation outcomes against point-of-sale or CRM records to link calls to business results, and using call data to build automated escalation rules for routing and performance analysis — the core workflows of conversation intelligence.
Nearly two-thirds (65%) of leaders cite voice as their organization's primary operational channel. Rather than relying on asynchronous emails or CRM notifications, colleagues and customers align in real time.
Voice of the respondent
Voice is crucial because, like I said, most of our communication internally is done with drivers who are driving a big, large vehicles. And it's not safe for them to be texting or chatting real-time. If we need an update, it's better to just call. They all have headsets, they all can go hands-free. You're most likely to get real-time status and not have to wait for a response... And when I say wait, I mean hours between them actually stopping.
Voice of the respondent
Voice channels [are] very important for us. We use it for customer contact. We also use it for meetings, on the phone, and it is mission critical for us.
For businesses that cite voice as a critical business channel, use of and interest in agentic voice AI is already high. Of the leaders we interviewed, over half (57%) have actively deployed voice agents across internal and external channels, and another 10% are in early-stage pilot phases with voice AI. Just under a third (32%) are still evaluating adoption.
Businesses start by assigning agents to the highest-volume, most repetitive, lowest-complexity tasks first, usually in external-facing channels. The most common use cases named are general customer support and service inquiries, followed by appointment scheduling, call routing and intake triage, and automated reminders and follow-ups — making AI customer support automation the default entry point for voice agents. Once agents prove themselves there, leaders look for places to expand their responsibilities.
Asked to rate their current ROI from voice AI agents, the vast majority of leaders with active agentic deployments reported positive returns (82%). Leaders attribute their voice AI ROI to cost reduction, time savings, and the ability to redirect human staff to more complex work.
Larger businesses tend to be more cautious around governance frameworks, trust in autonomous systems, and use cases, while also getting more mileage out of the data agents produce.
For example, a large majority (70%) of enterprises say voice AI is acceptable only with strong guardrails, verified accuracy, and handoff to a human, whereas only 42% of mid-sized organizations and 41% of small businesses cite the same stipulations.
At the same time, enterprises are further ahead in operationalizing conversational data for decision-making (73%) compared to mid-sized (50%) and small (48%) businesses.
Voice of the respondent
I'm very familiar with voice agents. About five years ago, our call center was like, real people making those calls. And then we kinda slowly shifted to where about half were agentic AI agents, and now we are getting to a point where it's all agentic AI. We don't have any actual people taking calls anymore.
Voice of the respondent
We're going to increase our investments, certainly, about a 20% increase each year. That's how we're planning our budget.
Almost every active deployment now runs with real independence. Of the 191 active users whose operating model was described, 92% say their agents operate with high or partial autonomy: 47% on high autonomy with a human ready to step in, 45% on partial autonomy inside a structured escalation system, and only 8% limited to simple tasks under close supervision. Asked what share of an assigned task their agents handle without human intervention, leaders put the average at 69%.
Tight scope is what makes the autonomy possible. Organizations assign agents narrowly defined jobs: booking appointments, routing calls, authenticating callers, running intake, and answering the questions customers ask hundreds of times a week.
Certain industries hold a tighter leash. Healthcare leaders report a lower average autonomy share (63%, versus 71% elsewhere) and are more likely to require governance and privacy controls, reflecting HIPAA obligations. These AI agent guardrails — narrow scope, human escalation, continuous oversight — align with the risk-management practices described in the National Institute of Standards and Technology (NIST) AI Risk Management Framework.
Of the 347 leaders whose escalation design was coded, 62% escalate calls to a human on complexity, uncertainty, or explicit prebuilt triggers; 31% escalate early for high-stakes, emotional, or judgment-heavy conversations; and 7% escalate only in limited exceptions.
Most leaders converge on the same model: agents run with high autonomy inside prescribed use cases, with humans in the loop ready to take over the complex or novel situations the agents cannot yet handle.
Voice of the respondent
The first thing that we do is we set operational boundaries...we call it an agent charter: what actions are allowed, what actions are required for confirmation, what is never allowed, what are some of the escalation triggers that we set up in place, and where do we set up the human handoff if there is a human handoff within the process of the workflow.
Voice of the respondent
We actively monitor the performance data as well as feedback from customers, and usually if a customer has a problem in their phone lines or customer service, they are able to talk to the AI and ask for a human representative.
The conversation around the risks of AI adoption tends to center security and governance as the biggest threats to businesses. For agentic voice AI, the focus has switched to brand harm. Of the 350 leaders who named concerns around implementation, 59% point to potential harm to their brand or customers as the primary risk, well ahead of privacy and governance (24%) and accuracy and reliability (17%). A voice agent speaks for the business in the moments customers care about most. For leaders, the AI customer experience is the asset at risk. Leaders describe the risk less as a technical failure than as a question of representation: whether the agent sounds like an extension of the company, not just whether it answers correctly.
Trust in an agent's ability to represent the brand well is built through performance monitoring. Businesses track quantitative performance data such as accuracy rates, resolution rates, and customer feedback, and support it with human judgment on how well agents do their jobs.
Agents also report on their own limitations through handoff triggers. When an agent recognizes it has reached the end of its ability to solve a caller's issue, it escalates to a human, and the system logs that handoff automatically. That built-in performance tracking record shows where the human in the loop should step in.
One underappreciated advantage compounds trust: unlike a human agent who may occasionally forget a required disclosure or procedural step, an agent can be trained to say it every time, without exception.
When measuring AI agent performance, leaders combine quantitative performance data, customer feedback, and human judgment. Leaders review accuracy and complexity case by case to decide when human involvement is needed.
Handoff triggers double as a performance record: every time an agent recognizes its own limit and escalates, the system logs it, giving leaders an automatic measure of where agents run out of road.
Voice of the respondent
I feel it's based on the combination of performance data and human judgment, because we kind of, like, look at accuracy or customers' feedback and then the complexity of the situation. We kind of, like, decide when human involvement is needed.
Voice of the respondent
Implementing [agentic] AI has had a highly positive impact on customer trust. Responses and consistent support have helped, but improving accuracy and handling complex issues are still important to maintain [it].
Voice AI agents hold a clear advantage over chat-based AI when the goal is a humanlike experience. Leaders like that voice agents can understand speech in real time, clock the meaning behind tone, detect when someone has finished a thought versus paused to draw breath, and hold the thread of the whole conversation while doing it.
In a tough conversation, agents interpret tone, convey empathy, and settle a situation before it escalates.
Common assumption puts humans ahead of AI in handling high-stakes, complex, or emotionally-charged conversations. But fewer than 1 in 10 leaders say they keep AI completely out of these kinds of interactions.
Furthermore, 35% of leaders argue voice AI agents are sometimes the better option compared to either chatbots or people, citing superior consistency and patience. This trust suggests that human staff might find themselves spending less time on calls with angry customers, not just those with simple requests a bot can handle.
Leaders describe agents that never rush a call to hit a metric, never match an angry customer's energy, and deliver the right disclosure at the right moment, every time.
Voice of the respondent
Agents, frankly, can spend as much time on the call as they want. They're not prompted to try to end the call quickly or move on to another call because they have goals, like a human may. They're not programmed to match the energy level of the customer, they're programmed to try to diffuse the situation, or remember when to say the right disclosure at the right time.
Voice of the respondent
When you have a rowdy customer on the phone who's very disrespectful, I have employees that would quit after a couple weeks because they did not know how to handle the confrontation. Agentic voice can help take care of that. But because they don't have any emotion, it's not going to affect them.
We asked non-users whether they had plans to adopt agentic voice AI. A plurality (44%) are actively planning an investment in agents, with another 42% considering adoption without a commitment. Just 14% of non-users say they probably won't adopt agents. Agentic AI investment has become a when-not-if question for most non-users.
The appetite to adopt is driven by competitive pressure and mediated by a need for further evidence of ROI. That pressure tracks the record pace of enterprise AI investment documented in Stanford HAI’s AI Index Report. Asked whether underinvesting or overinvesting poses the greater business risk, non-users point to underinvesting and the danger of falling behind competitors.
Those considering an investment without committing do not necessarily doubt that the technology works; they want to see it improve before they buy. Many would like to start with a small pilot but worry that a half-measure could damage the brand and cost them customers, so they would rather wait for stronger evidence around safety and effectiveness.
The fence-sitters are waiting on evidence: stronger accuracy, safer deployments, and examples from businesses like theirs. Regulated industries, healthcare especially, cite compliance obligations as the reason to wait. The same pattern — adoption outpacing organizational readiness — runs through G2’s broader study, AI At Work: Adoption, Friction, and Workforce Redesign.
Voice of the respondent
Probably underinvesting, because [agentic voice AI] is going to take off, and we're going to have to stay competitive with other companies.
Voice of the respondent
I believe underinvesting [is the bigger risk], because the potentials are endless.
Unscripted answers from the interviews, grouped by the chapter they support. Filter by theme, then press play to hear each leader.
There's a secondary application that is doing fraud [detection] and analytics... a separate system that's not part of the [agentic] workflow, but it feeds into the back-end processes. That data is an indicator for certain voice calls that need to be escalated. Most of it is based on prebuilt triggers, and we're adding some of this fraud/sentiment analysis on the back end that we're hoping to feed through a different agent process as a watcher.
We are in the process of evaluating [voice AI] tools, testing them, prototyping them, with a small group of users, before we go very broadly, because if we go all in out of the gate and it doesn't work very well, it can hurt the brand significantly... could hurt the brand in some customers' eyes forever.
I very much like it when it's the AI agent's own feedback. And they surrender control to the human, flag something that it is not able to do. It's aware of its own limitations, and flags it to a human. That is definitely my preference that scales the most, that it's most automated.
Mostly, it's having AI be compliant with the framework that we have. In this case, HIPAA, HITRUST are the references we have to check. AI also has to be checked for security. We want to know who else has that information, where it's holding, who has access, and what kind of access.
I think adoption will be phased. We'd likely start with lower-risk, high-volume use cases. Measure the results, and then expand only if the technology can consistently deliver a better customer experience and operational efficiency.
It's usually a combination, but we're always ensuring that anything coming from agentic AI has a degree of human oversight to prevent any misinformation being presented throughout our company or to outside clients.
We're using tools like the CRM platforms, analytic dashboard, database and management systems, and we also use AI monitoring tools to organize or analyze and manage the whole conversational data.
Just being able to have the information and make the loan officers better at their jobs — help improve close rates and different performance metrics of the sales funnel.
We record our calls, we monitor the sentiment in the calls, and then we use those calls for training purposes to try to convert more customers into paying customers.
We use voice — we rely on voice a lot of the times — to audit ourselves and to see that we're keeping up with customer needs and satisfaction.
I'd rather us underinvest, make sure that it works, roll it out with a few pilot people, and then fully launch.
Agentic voice AI embeds automation at the origin of voice itself: it can lead conversations and make decisions, easing the human workload while automatically feeding the conversation data flywheel. Unlike traditional IVR menus or text-based chatbots, agentic voice AI leads the conversation and completes the task itself rather than following a fixed script.
Of the leaders we interviewed, over half (57%) have actively deployed voice agents across internal and external channels, and another 10% are in early-stage pilot phases with voice AI. Just under a third (32%) are still evaluating adoption.
Businesses start by assigning agents to the highest-volume, most repetitive, lowest-complexity tasks first, usually in external-facing channels. The most common use cases named are general customer support and service inquiries, followed by appointment scheduling, call routing and intake triage, and automated reminders and follow-ups. Once agents prove themselves there, leaders look for places to expand their responsibilities.
Asked to rate their current ROI from voice AI agents, the vast majority of leaders with active agentic deployments reported positive returns (82%). Leaders point to cost reduction, time savings, and the ability to redirect human staff to more complex work as the main benefits of using agentic voice AI.
Of the 191 active users whose operating model was described, 92% say their agents operate with high or partial autonomy: 47% on high autonomy with a human ready to step in, 45% on partial autonomy inside a structured escalation system, and only 8% limited to simple tasks under close supervision. Asked what share of an assigned task their agents handle without human intervention, leaders put the average at 69%.
Of the 347 leaders whose escalation design was coded, 62% escalate calls to a human on complexity, uncertainty, or explicit prebuilt triggers; 31% escalate early for high-stakes, emotional, or judgment-heavy conversations; and 7% escalate only in limited exceptions.
Of the 350 leaders who named concerns around implementation, 59% point to potential harm to their brand or customers as the primary risk, well ahead of privacy and governance (24%) and accuracy and reliability (17%). A voice agent speaks for the business in the moments customers care about most.
More than a third of leaders say voice AI agents can sometimes be a better option for high-stakes conversations compared with chat-based AI or humans, challenging the assumption that difficult customer interactions should always default to a person. Leaders describe agents that never rush a call to hit a metric, never match an angry customer's energy, and deliver the right disclosure at the right moment, every time.
We asked non-users whether they had plans to adopt agentic voice AI. A plurality (44%) are actively planning an investment in agents, with another 42% considering adoption without a commitment. Just 14% of non-users say they probably won't adopt agents.
Start where leaders start: high-volume, low-complexity calls with escalation designed in. Of the 350 leaders who named implementation concerns, 59% point to potential brand or customer harm as the primary risk, so successful deployments pair narrow agent scope with human handoff triggers for complex, emotional, or high-stakes conversations.
Deployed agents run inside deliberately narrow lanes: tightly scoped tasks, prebuilt escalation triggers, and a human ready to step in. Of the 347 leaders whose escalation design was coded, 62% escalate on complexity or uncertainty and 31% escalate early for high-stakes or emotional calls — autonomy is earned task by task.
The majority (62%) of leaders operationalize conversation data for decisions and process improvement rather than treating it as passive recordkeeping. The three most common workflows: storing transcripts for future use, cross-referencing call outcomes against point-of-sale and CRM records, and building automated escalation rules for routing and performance analysis.
For organizations that depend on voice as a dominant channel, agentic voice AI has moved past the pilot stage. More than half (57%) of businesses in this study have actively deployed voice agents, and among them, the technology is already paying for itself: 82% report a positive return on investment, driven by reduced headcount costs, faster response times, and staff redirected to higher-value work. Nearly all of these agents (92%) now operate with real autonomy, and businesses have learned to keep their brands and customers safe by fencing agents inside narrowly defined areas of responsibility rather than granting broad latitude.
Agents' ability to make decisions brings inherent risk that businesses can't ignore. Brand or customer harm is the most-cited risk with regard to agentic adoption, and organizations respond by building in guardrails, like limited task scope, escalation triggers, and human handoff points, as a prevention method. Caution around deploying voice agents is more pronounced among healthcare organizations, who report meaningfully lower autonomy and a higher governance bar than respondents in other industries. Even active, successful deployments haven't fully resolved this tension; the same organizations reporting strong ROI are, in many cases, the ones most deliberate about where they still keep a human in the loop.
This research cuts against the assumption that a human is always the safer choice. Over a third of leaders (35%) now say voice AI agents can outperform chat-based AI or people in high-stakes, complex, or emotionally charged conversations. Leaders describe agents that, unlike human staff, don't get frustrated, don't rush a call to hit a metric, and stay consistent no matter how upset a caller becomes. Non-users of agentic voice AI are largely moving toward adoption, citing the fear of falling behind competitors as a key driver. That said, many are waiting for more proof of agentic voice AI's value before they commit to investing.
Businesses have stopped asking whether a voice agent can keep up during a hard conversation with a customer. They are now deciding how much of that conversation to hand over, and how fast.
This research draws on 359 in-depth interviews with business leaders representing a wide mix of roles, industries, and company sizes.
Respondents included business professionals across healthcare, manufacturing, retail and ecommerce, financial services, transportation, and legal. All participants were selected for their direct involvement in decisions about AI, software, and technology purchases, and all represent organizations that regard voice as an important or primary business channel and are considering, evaluating, or actively deploying agentic voice AI. Company sizes ranged from small businesses to large enterprises. Interviews were conducted in July–August 2026 with respondents based in the United States.
All interviews were conducted using G2 Interviews, an agentic voice AI platform, which carried out over 86 hours of one-on-one conversation over the course of this study. Respondents were interviewed on adoption maturity and rollout of agentic voice AI, the role voice plays as a channel and as a source of truth, channel preference in high-stakes customer interactions, brand risk and the conditions for trust, autonomy boundaries and human escalation design, and returns and future investment.
The analysis of 359 interview transcripts was conducted using AI for semantic understanding, with multi-iteration validation and cross-verification to ensure analysis quality. Reported percentages are recomputed directly from the coded interview data, and each transcript was independently reviewed by G2's AI Custom Research team to inform narrative, context, and clarity. Bases vary by question because the interview flow branched for current users and non-users. All analysis and conclusions are those of G2’s AI Custom Research team; RingCentral did not influence the findings.
Voice brings AI directly into the moments when customers and employees need answers and action. That makes it a useful proving ground for agentic AI, as businesses can see what happens when agents move beyond providing information and begin taking on work within the conversation.
The research shows that the shift is already underway. Many businesses are starting with high-volume, repeatable interactions where the task and expected outcome are relatively clear, including customer support and service, scheduling, call routing and intake, reminders, and follow-ups.
Among the 82% reporting positive returns, businesses cited cost savings, time savings, and the ability to redirect employees to more complex work.
Voice is also generating information that businesses can use after an individual interaction ends. Sixty-two percent of leaders said their organizations use conversation data to inform decisions or improve processes, whether measuring service quality or feeding insights into analytics systems to track outcomes.
“AI has reached an inflection point. The question is no longer whether businesses will invest, but where those investments will deliver real returns. Voice AI is emerging as one of the clearest answers because it sits where customer intent, action and business value converge. This research shows the market is crossing that threshold now, with businesses moving beyond pilots and putting voice AI to work with greater autonomy, measurable ROI and real operational impact.”
The research points to measurable returns in time, cost, and employee focus. Customers using RingCentral AI Receptionist share how those returns show up in their own operations.
After-hours coverageevery incoming call routed, day and night“[RingCentral AI Receptionist] controls all the calls coming in and directs them to where they need to go to each individual extension. Then after hours, it handles every single call that comes into the facility.”
Director of Compliance, Packaging and Logistics
30–40% → 80–90%call answer rate“Our call answer rate went up from 30 to 40% all the way up to 80 to 90%. The overall volume of calls that needed to be handled by a human came down considerably. Overall, we’re able to get through more of our customer requests than we were before.”
Director of Sales Operations, Electrical Supply
~30 hours a weekfreed up for the team“[RingCentral AI Receptionist] probably frees up thirty hours a week. It allows our team to focus on escalations.”
VP of Operations, Healthcare Management
No missed callscalls routed, transcribed and logged“The ability to route calls, to transcribe live calls and voicemails that are left, to ensure that no call is missed is incredibly valuable in terms of client satisfaction and also the ability to sign up new clients.”
Associate Attorney, Legal
Partner content supplied by RingCentral. Customer quotes come from a separate feedback study of RingCentral AI Receptionist customers, not from the 359 interviews in this report. Customer names and companies are withheld.
This report was prepared for RingCentral by G2 AI Custom Research.