Executive summary Foreword Introduction Insights Conclusion Methodology

Insight 1

From conversations to outcomes for today's businesses

Insight 2

Agentic voice AI succeeds at high-volume tasks

Insight 3

Voice AI agents have significant autonomy, but they aren't being let loose

Insight 4

When voice AI represents the business, brand harm becomes the risk

Insight 5

Difficult conversations play to voice AI's strengths

Insight 6

Fear of falling behind competitors drives agentic voice AI adoption

Insight 1

From conversations to outcomes for today's businesses

66%

Nearly two-thirds (66%) of leaders cite voice as an important operational channel for their organization. Colleagues and customers use voice to align in real time, answer questions, resolve issues, and move work forward.

With AI-generated transcripts and summaries, what happens in those conversations can alsobecome a rich source of data and insights for the broader business. The majority (62%) ofleaders say they operationalize conversation data to inform decisions and processimprovements (such as increasing first-call resolution or reducing average handle time), ratherthan treating it as passive recordkeeping.

In this way, conversations are a key ingredient that helps businesses unlock more value: humans on the calls drive the action, while AI helps quantify and record the impact. Leaders describe putting that data to work in a range of ways: analyzing customer call transcripts to understand sentiment and service quality, connecting important information with CRM and analytics tools, tracking business and operational outcomes, and determining where human involvement is needed.

TRAINING AND COACHING HUMAN STAFF

"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."

— Digital Marketing, Moving

AUDITING FOR QUALITY AND CONSISTENCY

"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."

— Technology / AI Implementation, Data Management

FEEDING DATA INTO ANALYTICS & BI TOOLS

"We're using tools like the CRM platforms, analytic dashboard, database and management systems, and we also use AI monitoring tools to organize oranalyze and manage the whole conversational data."

— Senior Manager in Operations, Financial Services

IMPROVING WIN/LOSS OUTCOMES

"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."

— Marketing Team, Financial Services

How conversation data moves from record to action

Leaders describe conversation data serving different roles after a call: from creating asearchable record of what happened, to connecting conversations with CRM and businessoutcomes, to informing analytics, routing, and escalation.

This creates a more complete picture of what was said, what happened as a result of the conversation, and where the business may need to act next.

"Voice is crucial because, like I said, most of our communication internally is done with drivers who are driving 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."

— Operations Team, Transportation

"Voice channels [are] very important for us. We use it for customer contact. We also useit for meetings, on the phone, and it is mission critical for us."

— Project Management in IT, Banking

"There's a secondary application that is doing fraud [detection] and analytics... a separate system that'snot 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."

— Technology Professional, Financial Services

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Insight 2: Agentic voice AI succeeds at high-volume tasks

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