Conversational AI is booming—but complex. With 192% projected growth by 2031, CX leaders face pressure to adopt—but success isn’t guaranteed. Agent experience matters. AI tools like transcription and summarization often add work instead of removing it, according to new frontline research. Most brands still fall short on results. Only 11% of companies say they’re highly effective at using AI to deliver human-like conversations.
Conversational AI is having its breakout moment. But are contact center agents celebrating? Maybe not.
According to a July 4 Research and Markets report, the global conversational AI market is expected to grow from $17.05 billion in 2025 to $49.8 billion by 2031. That marks a growth projection of 192% overall from 2025 to 2031. And it has a compound annual growth rate of 24.7% projected through 2029.
The promise of AI isn’t speculative: According to CMSWire’s 2025 Digital Customer Experience Report, 46% of CX leaders already expect AI to significantly reshape digital customer experience within the next five years.
For customer experience (CX) leaders in the call center world, the implications seem clear: legacy IVR systems and basic chatbots may not compete with modern, generative AI-enabled conversational AI platforms. The 2025–2026 Conversational AI Solutions for the Enterprise report offers a detailed look at six enterprise-grade vendors—Cognigy, IntelePeer, SESTEK, Teneo, UJET and Verint Systems—across 170+ RFP-aligned criteria.
But is it all just a romanticized notion and nothing more? Where automation and human meet seamlessly for a new era of contact center agent experience?
Some industry reports tell a story of more sizzle than steak when it comes to conversational CX. More on that in a bit.
Table of Contents
Call Center AI Capabilities at a Glance
This table highlights key differentiators in enterprise-grade conversational AI platforms featured in the Research and Markets report.
CapabilityWhy It MattersVendors Supporting ItReal-Time Guidance (RTG)Delivers context-sensitive next-best actions to agents during live interactionsCognigy, Verint, TeneoAutomated Post-Interaction SummarizationReduces agent workload and improves handoffs between channelsUJET, Verint, IntelePeerMultimodal OrchestrationEnables seamless integration across voice, chat, and digital channelsCognigy, SESTEK, TeneoHuman-in-the-Loop EscalationEnsures automation flows can hand off to humans intelligently when neededAll vendors featuredLive TranslationSupports multilingual service delivery in real timeSESTEK, UJET
What’s resonating in call centers today? About 40% of organizations rank customer service automation — including chatbots — as the most useful generative AI application in CX today, making conversational AI platforms a natural next step, according to the CMSWire State of DCX report.
Related Article: What Is Conversational AI? More Than Just Chatbots
Agent Augmentation Becomes the Real Battleground
The conversational AI report features that what separates leaders from laggards isn’t just AI—it’s how that AI empowers humans. Agent-facing capabilities like real-time guidance, transcription translation and automated summaries are core differentiators in today’s conversational AI race. These features enable hybrid service models where automation and human touch coexist more seamlessly.
According to CMSWire’s 2025 Digital Customer Experience Report, 26% of organizations cite limited insights into customer needs or the customer journey as one of their top digital experience challenges — a key pain point that CAI platforms aim to address.
Is Conversational CX Delivering? A Reality Check
But what does this look like in execution? Just today, July 7, cloud communications platform provider Infobip released findings from a new study conducted by Harvard Business Review Analytic Services (HBR-AS) revealing a hard truth for conversational CX: while 93% of those surveyed from the HBR audience recognize the high importance of creating positive conversational experiences, only 36% believe their organization is highly effective at it. Further, just 11% report they’re highly effective using AI to deliver human-like conversations.
The majority struggle to translate investment into measurable outcomes—like reduced call handle time, satisfaction gains or consistent self-service deflection.
This highlights a critical risk: without a clear strategy, coherent metrics and continuous optimization, brands risk deploying glossy conversational AI tools that end up underperforming—despite hefty price tags and high expectations.
While everyone talks CX, almost no one delivers. When brands can’t deliver meaningful, human-like conversations, they don’t just lose efficiency – they lose trust. It’s time to rethink what customer experience really means in the AI era.
– Ben Lewis, VP Marketing and Growth at Infobip.
Despite its promise, conversational AI often adds friction—this illustration shows how tasks like transcription and summarization can still burden human agents.Simpler Media Group
Related Article: Intuit Gets Conversational: AI Agents Tackle CRM, Finance and CX Tasks
What Agents Really Think About Conversational AI
Further, a July study titled “Customer Service Representative’s Perception of the AI Assistant” reveals how frontline agents experience AI in the contact center. While AI tools like real-time transcription and automated summaries are intended to save time, many agents say they often create extra work.
For example, transcripts were helpful when customers spoke quickly—but prone to errors when accents or call lengths increased. Summaries required rephrasing or trimming to meet documentation requirements. Emotion recognition? Most agents found it inaccurate and ignored it entirely.
The takeaway: Even well-intended AI features can disrupt workflows when they’re not calibrated to how agents actually work. For CX leaders, the lesson is clear—successful AI doesn’t just perform; it fits.
Enterprise Buyers Want More Than Flashy Demos
As for the Research and Markets report, that includes a side-by-side comparison of over 170 functional, technical and strategic questions. These include:
Generative AI feature maturity and model governance Omnichannel orchestration UX Post-call analytics and reporting depth Security, compliance and integrations with CDP, CRM and DXP platforms
CMSWire’s 2025 CDP Market Guide shows that nearly 70% of companies investing in conversational AI are simultaneously updating their customer data strategies. Deep integrations are no longer a nice-to-have—they’re table stakes.
Smart Takeaways for CX Decision-Makers
Map capabilities to CX maturity. Are you focused on better routing, next-best-action or full orchestration? Let this guide your shortlist. Don’t overlook orchestration design UX. Platforms that simplify flow building are the ones your teams will actually use. Usability trumps novelty. The most powerful generative AI features mean little if agents don’t trust or adopt them.
Not About Replacing Humans—It’s About Improving Conversations
Customers don’t care if their support comes from an AI or a person—they care if it’s fast, clear and doesn’t waste their time. The best conversational AI platforms today are not replacing humans; they’re making them better. And they’re doing so across multiple touchpoints, with a blend of automation and intelligence that finally feels seamless.
According to CMSWire’s 2025 DXP Market Guide, while many Digital Experience Platforms offer chatbot capabilities, the guide emphasizes the broader value of AI across the experience stack — from content generation and personalization to predictive analytics and workflow automation. Some platforms, like CoreMedia and Crownpeak, integrate conversational features as part of an orchestration suite, rather than as isolated tools.
As the conversational AI market accelerates, the question CX leaders must ask is simple: How will this solution transform our conversations—for customers and for agents? The right answer could set the tone for years to come.
Core Questions About Conversational AI in the Enterprise
Editor’s note: Key questions surrounding the role, limitations and strategic value of conversational AI platforms for customer experience leaders.
What is conversational AI and how is it used in the enterprise?
Conversational AI refers to technologies that enable machines to understand, process and respond to human language. In enterprise settings, it’s used in customer service, contact centers, IT help desks and employee support. These systems may include chatbots, voice assistants and AI-powered agent tools.
Learning OpportunitiesView all
What are the benefits of conversational AI in call centers?
Conversational AI can reduce handle times, enable 24/7 self-service and support agents through real-time transcription, sentiment analysis and automated summaries. When deployed effectively, it improves customer satisfaction while easing workload on support teams.
What challenges are companies facing with conversational AI?
Despite rising adoption, few companies feel they’ve mastered conversational AI. A July 2025 study by arXiv found that only 11% believe they’re highly effective at delivering human-like AI conversations. Agents often report friction—such as summary rework or inaccurate transcripts—when tools aren’t aligned to workflow needs.
How do successful organizations evaluate conversational AI platforms?
Leading CX teams prioritize vendor evaluation across criteria like generative AI maturity, integration with CDP/DXP/CRM platforms, orchestration UX, security and compliance. Tools that enable continuous improvement and real-time guidance tend to see higher adoption.
What role does conversational AI play in the larger CX tech stack?
While some treat conversational AI as a standalone chatbot, more mature organizations integrate it as part of a broader experience architecture—connecting data, journey orchestration and service design into a unified customer flow.
