Announced at the company’s CX Summit in Nashville, the new capabilities span routing, quality management, analytics, and digital engagement, tying them more closely together to help organizations extract greater value from AI at scale.

As many enterprises attempt to take AI from pilot projects into day-to-day operations, fragmentation continues to slow progress.

Disconnected data, inconsistent reporting, and standalone AI experiments often make it difficult to achieve the continuous improvement leaders expect.

Five9’s latest releases aim to combat these challenges by treating AI not as an add-on but as the connective layer running across the environment.

Five9 Chief Product Officer Ajay Awatramani framed the shift as a more fundamental rethinking of how AI should function inside the contact center:

“Our Agentic CX vision is about creating systems that don’t just respond but also help teams better understand and anticipate customer needs.”

“With these innovations, AI moves from the periphery to the core of the contact center – linking data, people, and processes into a system more closely embedded with contact center operations in ways intended to support continuous learning, adaptation, and more efficient and meaningful customer experiences.”

So, let’s take a closer look at Five9’s newest features.

Four New Innovations in the Genius AI Suite

Agentic Quality Management (AQM)

Five9’s new quality management approach is designed to evaluate up to 100% of customer interactions.

The model focuses on generating insights that can inform routing decisions, coaching programs, and ongoing performance improvement.

Rather than operating as a separate analysis tool, AQM feeds data directly into other parts of the Genius AI ecosystem.

Genius Routing

This updated routing engine uses defined customer attributes, agent skills, and proficiency levels to determine the best match for each interaction

Because it can draw on real-time inputs from AI performance systems and self-service applications, it becomes easier for organizations to deliver faster resolutions and more personalized experiences.

OneVUE Analytics

OneVUE builds on Aceyus VUE technology but packages it in a more approachable, self-service analytics experience. Users can assemble custom dashboards with flexible metrics that cover both traditional CCaaS operations and multi-vendor environments.

It is built with AI-ready reporting in mind, giving teams a clearer view of the KPIs shaping customer interactions.

Adaptive Digital Engagement

The digital engagement update introduces a no-code Dynamic Web Messenger Configurator, allowing teams to launch or adjust webchat experiences instantly.

Five9 also revealed a new partnership with Meta, enabling native WhatsApp connectivity with template support, broadcasts, and AI Agents. The broader goal is to give organizations modular digital channels that plug easily into Five9’s AI layer.

Early Customer Feedback

Northwestern Mutual is among the customers already experimenting with the new capabilities.

Eric Schanno, the company’s Principal Solutions Engineer, explained how leveraging Five9 had allowed Northwestern Mutual to “move faster from insight to action.”

“We are very excited about the potential of Five9 AQM to help us elevate coaching across 100% of interactions. And OneVUE gives us a single source of truth for the metrics that matter.

“The combination is raising the bar on performance and helps deliver more consistent, high-quality experiences for our customers.”

A Step Toward Fully Agentic CX

Five9’s message seems to be that the next stage of CX transformation isn’t just about adopting AI tools, but weaving AI into the operational fabric of the contact center.

These Genius AI enhancements move the platform further in that direction, giving organizations a more unified foundation for delivering adaptive, insight-driven customer experiences.

More News from Five9

Afiniti has teamed up with Five9 to bring its AI Pairing technology into the Five9 Intelligent Cloud Contact Center, signalling a step forward for more sophisticated, behind-the-scenes AI in CX.

Rather than placing AI directly in front of customers, Afiniti analyses behavioural and contextual signals in the background to match each caller with the agent most likely to deliver a successful outcome. In effect, it acts as a matchmaking engine for the contact center.

IDC’s new MarketScape points to rapid momentum in Europe’s CCaaS sector, forecasting growth from $1.5BN in 2024 to $3.7BN by 2029 – a 20 percent CAGR.

Five9 is highlighted as a market leader, backed by its AI-driven platform, solid compliance credentials, and strategy tailored to European requirements.

The cloud-based software company announced its third-quarter earnings report on Monday, revealing strong results across the board. 

This has led to ongoing investments into current and future AI products and initiatives. 

“We remain on track to exceed the $100MN in ARR from new products by the end of 2025,” said RingCentral CEO, Vladimir Shmunis, adding:

“Adoption of our new AI-led products is broad-based across various customer cohorts, from small businesses to large enterprises.

“Our GSP partners are also beginning to sell these new offerings, expanding our reach and accelerating adoption.” 

This expansion has led the company to roll out multiple AI product releases this week, increasing its customer base by branching out into emerging AI trends. The launches are aimed at enhancing communication experiences for both customers and agents.

This has resulted from an R&D spend of $125MN into its new AI portfolio. The company is already seeing consistent profitable growth, in the hopes of exceeding the year-end ARR target. 

RingWEM Adds AI Workforce Tools to Cloud Contact Center

On Monday, RingCentral released its latest AI product, RingWEM, an AI-powered workforce engagement management suite designed to enhance its native cloud contact center, RingCX. 

The suite offers four capabilities to strengthen customer experience across agent performance, customer satisfaction, and operational efficiencies by using AI-powered insights: 

AI Quality Management: Designed to give human agents the skills to improve their overall performance, the quality management tool will use personalized customer quality criteria to evaluate and provide extensive insight and feedback on customer calls. 

The tool is used to analyze and observe full customer interactions and agent workflows, delivering focused guidance for reskilling. 

Furthermore, the tool offers AI-based coaching recommendations to improve agent expertise, allowing enterprises to improve their workforce by viewing their agents’ performance analytics, common communication themes and advise them on next steps using the provided data-driven advice. 

AI Workforce Management: Used to improve quality in customer service, planning for potential challenges, and overall efficiency, this tool combines precise data forecasting and resourceful scheduling to align staff with probable tasks to tackle targeted demand. 

By using precise data forecasting, the AI workforce management tool can use algorithms to analyze past and current data trends and business drivers to predict the likelihood of contact quantities, allowing time for agents to tackle abnormal spikes before they occur. 

With intelligent scheduling, the workforce tool can generate actionable schedules for agents to follow that include agent preferences, adjustable service level actions, and business requirements to keep agents on track with tasks. 

These can be modified to fit irregular changes in working conditions to ensure that service levels stay the same to avoid customer friction. 

Additionally, customer agent supervisors can run probable scenarios to analyze the effectiveness of staff models and company changes before they are implemented. 

AI Interaction Analytics: This tool provides enterprises with high-level insights into customer satisfaction with interactions, compiling data taken from surveys and summaries from the interactions themselves to address negative customer experiences. 

AI Interaction Analytics can dissect customer conversations through voice tone, language preferences, and patterns in speech to assess satisfaction. 

The tool can use this and other conversations to further analyze key customer trends and issues as a whole, allowing businesses to proactively address these concerns before escalation. 

Screen Recording: Similar to the AI Quality Management capability, this tool allows supervisors to evaluate customer-agent interactions by collectively linking calls and screen recordings for a wider range of information into quality of conversation and workflow efficiency. 

These tools can be utilized to address underlying issues with agent performance and customer satisfaction and elevate operations in contact centers to deliver smarter service. 

RingCentral Debuts Agentic Voice AI Suite

RingCentral has also released its agentic voice AI communications suite, encompassing three tools that enhance communication experiences across the lifespan of each customer interaction. 

AI Receptionist (AIR): Before a conversation begins, this tool ensures that calls are not left unanswered. 

Using the voice AI ability to interact with customers, this AI agent can comprehend a customer’s reason for calling, answer questions, hand off real-time interactions to agents with summarized caller context, and identify and log potential opportunities that may require a human agent to follow up. These can help to avoid customer friction and repeated information. 

For scheduling interactions, AIR provides multi-calendar support across a company to integrate employee schedules and harmonize teams. 

Sales opportunities are collected and stored for future use in Salesforce, HubSpot, or with AIR’s own database. 

AIR can also be used on any SIP-based phone, allowing AI customer handling to be dealt with across the cloud, any premises, or hybrid setups. 

Brian Tucker, Chief Digital Officer at Televero Health, is a customer of RingCentral’s AIR tool. 

He said, “Using RingCentral’s AI Receptionist, the results are undeniable. We saw our monthly appointments increase 14 percent in the first four months, an increase in monthly revenue of over $200,000. 

“That kind of growth and return on investment is exactly what we need.”

AI Virtual Assistant (AVA): During a conversation, AVA can provide an agent with real-time assistance across customer interactions by implementing four key capabilities: 

• Real-Time Calls and Meeting Summaries: Identify the relevant information, questions, and actionable tasks during the span of a call or meeting, generating summaries and highlight reels to allow agents to keep track of the interaction’s objective during the call. 
• AI Writer to Create and Translate Communications: This capability can draft, edit, and translate conversations in multiple languages, allowing for seamless and customer-focused messaging. 
• Multi-Use Assistance Across Workflows: By adapting to a user’s communication method, via phone, text, or chat, this tool can provide intelligent prompts and relevant actions for each task. 
• Product Adoption and Feature Discovery: AVA can advise discovery and management methods to improve RingCentral’s overall customer enterprise experience. 

Kira Makagon, President & COO, RingCentral, explained the value of AVA in an enterprise workflow: “By putting trusted voice intelligence at employees’ fingertips, AVA makes work more productive and empowering,

“AVA is your personal virtual assistant that enables you to work smarter, faster, and more efficiently.”

AI Conversation Expert (ACE): After a conversation, ACE steps in to offer evaluated business insights from these interactions and adds it into one analytics and insights layer for a simplified outlook. 

It provides real-time insight into current customer satisfaction, trends in revenue, and overall agent team performance, giving context to performance data to allow leaders to act quickly. 

When requested, ACE can turn compound data into written summaries, recommend actions and examples of improvement, and be used an interactive interface to allow leaders to inquire related queries with instant results. 

Zach Jecklin, Chief Information Officer at Echo Global Logistics, and customer of ACE, uses the tool for improving company knowledge on customer calls and data trends. 

“AI Conversation Expert provides us with the detailed coaching for individual calls, and the dashboard connects the dots by rolling up all that data into a clear, concise view of the major trends impacting the entire business,” Jecklin said.  

“We used to have call data. Now, we have business intelligence. It’s that simple.”

RingCentral Pairs New AI Tools with Solid Growth

RingCentral has launched the new AI tools in conjunction with the announcement of its strong third earnings quarter. 

During its earnings call, the company reported a total revenue result of $639MN, seeing a growth of 5 percent from the previous year. 

Subscription revenue also increased, rising by 6 percent to $616MN, with a 23 percent rise of $130MN in free cash flow, which it intends to increase during the rest of the year.

AI-generated voice attacks, imposter calls, and IVR mining have created a new kind of inbound threat that’s invisible to traditional fraud systems and surprisingly effective against undertrained agents.

The inbound voice channel, still a mainstay for financial services, healthcare, and government, has lagged behind digital channels in security evolution, Mike Schinnerer, Vice President of Product Management at Transaction Network Services (TNS), told CX Today in an interview.

Online, there is pressure to move away from passwords to multi-factor authentication, but inbound contact center engagements often still rely on knowledge-based authentication—the “What’s your mother’s maiden name?” routine—to verify callers.

“That’s pretty vulnerable,” Schinnerer said.

“It often ends up being an imposter talking to an agent. And those agents are sometimes easily fooled and manipulated… because they’re people too and they’re vulnerable to being misguided.”

The result is a perfect storm of human vulnerability and AI-enabled deception.

As AI voice synthesis becomes easier to deploy, bad actors are exploiting it to create lifelike caller voices that can pass basic screening. Some use interactive voice response (IVR) mining — repeatedly calling 1-800 numbers to study how an organization’s IPR system behaves, before launching a real attack.

“The intent of this initial call into the care support line is not necessarily to… steal money,” Schinnerer explained. “They’re just trying to do reconnaissance to understand where the vulnerabilities are.”

That’s the double-edged sword of AI in the contact center: it’s both the threat and the solution as enterprises deploy it for call routing to enhance customer experience. “That’s great, but let’s make sure that we’re not embracing a technology that has vulnerabilities,” Schinnerer said.

“It’s a new technology for the contact centers, so there is continual learning back and forth on how we can understand how they implement their AI agents, and then we can work with them to identify vulnerabilities. It’s almost like penetration testing and software code to help identify those moments,” Schinnerer said.

“The AI agent is new, it’s a good buzzword, but we’re still learning. Every cloud contact center, if you go into their marketplace, there’s [at least] five different service providers touting an AI voice or AI agent experience.

“We’re trying to evaluate the credibility of those and the vulnerabilities of those types of service providers, either in partnership with them or not.

“It’s new. It’s not going to be solved in the next three months. It’s going to take more than that to identify where those vulnerabilities are, and then to help the education of the enterprises using them on how to strengthen [their defenses] if there is an exposure or not.”

Fraudsters are now extending their reach into smaller companies, requiring them to be more alert.

For instance, in financial services, “ten years ago, a lot of the imposter fraud was attacking larger financial institutions,” Schinnerer said. “But we’re now seeing it trend into the regional banks. And those regional banks often have more vulnerabilities or exposure to risk, and… when these fraud events happen, they’re more impactful for them.”

For one, it creates an unsavory taste in the consumer’s mouth, and they might leave and go to a bigger brand that they feel is protecting them better, or they already have financial payouts and penalties.

From working with health care organizations on outbound calling, it is clear that businesses have to be tech savvy to navigate these increasingly sophisticated attacks, Schinnerer said.

“What we’re seeing when we engage the healthcare organizations is that they don’t necessarily have a sophisticated customer care service and platform that they’re using. So there’s a little bit of immaturity in the healthcare market on what calling platforms that they use, and a lot of them are calling from local numbers within their regions, but… it is a target.”

Brands also need to be aware of how imposters are carrying out multimodal attacks, using both voice and messaging to find vulnerabilities or use them in tandem, to see which one they can get through.

“We’re working with other partners who have more of a messaging background and messaging security, and seeing how we can complement one another with the data that they have and the data that we have, so that we can bring a multimodal solution to the market,” Schinnerer said.

From ‘Trust But Verify’ to ‘Never Trust, Always Verify’

To counter these threats, companies like TNS are pushing for a zero-trust approach to voice — treating every inbound and outbound call as potentially hostile until verified.

“A zero trust voice framework is gaining momentum. So we started shifting the conversations as we engage enterprises.

“Where before we were getting engaged with their support organization or their marketing organization for customer outreach, we started elevating these conversations to the CISO organizations, and those are the ones that understand and comprehend this ‘never trust, always verify’ approach, because that’s what they’ve implemented for their data security or their online security principles.”

“When we approach them and say we’re not going to trust any call into or out of your voice platform, because that’s the only way we can ensure that we remove fraud, they say, ‘Yeah, that makes perfect sense.’”

Ultimately, what we see is that there’s an ROI to it, there’s an arc. There’s an ROI from the customer satisfaction perspective, and then there’s an ROI because we can remove a lot of the fraud that originates from this exposure that they have, which is their voice network.

This zero-trust framework uses AI and network-level data to validate that calls come from the devices and networks they claim to — effectively creating multi-factor authentication for voice.

TNS monitors billions of calls daily for carriers and enterprises, which allows it to develop multi-factor authentication that is less reliant humans to authenticate callers.

“We can check [the carrier network] if the phone call is originating from the actual device,” Schinnerer explained. “If they’re on Verizon, we go ping Verizon — is this call active? We can do more deterministic type of interrogations. We can also do some AI voice detection, to see if this is not the actual caller’s voice.”

Filtering Fraud at the Edge

One of the biggest shifts in inbound voice protection is moving detection to the “edge”, using network attributes to identify suspicious callers.

“The goal is… to filter the bad actors and the imposters before they get to the agents,” Schinnerer said.

That way, customer service agents aren’t vulnerable or under pressure to resolve fraudulent calls, especially as high churn rates of contact center employees make it challenging for managers to keep their teams trained and vigilant.

This filtering relies on network intelligence—for example, seeing if one number has been calling multiple banks or trying to penetrate several financial institutions in a short time frame.

Crucially, these checks happen in real time. TNS’ Enterprise Voice Security suite also runs AI synthetic voice detection while a conversation is happening.

“We can do the detection within about 15 seconds and… we can alert the care agent that we think something’s going on here,” Schinnerer explained.

“And within those 15-30 seconds, by that time, you haven’t really disclosed anything. You’re still trying to figure out why they’re calling, what the intention is, and you haven’t disclosed any PII information.”

This allows the enterprise to identify fake calls without disrupting the experience for legitimate customers.

“The thing we need to be mindful of is that we don’t ruin the experience for good actors,” Schinnerer said. “So we don’t want to make it too long on the phone to do the analysis and the determination before we send the good calls to the agents.”

Security as a Customer Service Differentiator

While adding new security layers can sound like friction, in many cases, it actually improves brand trust. “Ultimately, even if there’s a little delay getting [callers] to an agent, what we’re also seeing with institutions that we talk to, especially these downmarket ones, because they’re competing against the big brands… we help them position it to their consumers as a differentiator.”

Brands can highlight their strong security focus on protecting customers in their marketing campaigns, emphasizing how their fraud prevention measures set them apart, Schinnerer suggested. This messaging appears to resonate with customers, who express appreciation and trust towards companies prioritizing their safety.

This mindset also applies to buyer evaluations. “It’s really important to understand what impacts their business,” Schinnerer said.“First and foremost, understanding what really business outcomes you want and understanding what that spend and cost also might look like, because that is also part of that ROI assessment, and then it’s a matter of always trying to improve your implementation and your solution.”

What are you trying to accomplish? Are you trying to improve your agent efficiency? Are you trying to reduce your exposure, or reduce your out payment of fraud loss?

Because, as Schinnerer pointed out, there is no magic, hands-off answer:

“We’ve had inbound voice authentication for a little while now, and it’s been expensive… When you think of the fraud departments inside of those organizations, they were hopeful that it would be a silver bullet, in that if they can implement inbound voice authentication, it would remove the fraud, and it necessarily hasn’t, that’s just because, again, with the AI voice agents and other ways of bad actors always changing, it is not a set-and-forget type of solution.”

Enterprises have to stay on top of understanding the threat landscape and where new attacks are coming from. To do that, they need to “choose a partner that can see the whole picture, not just the inbound picture,” Schinnerer said.

As AI blurs the line between real and fake voices, inbound call security is no longer optional — it’s strategic. Enterprises need to start treating the phone channel like any other digital access point — zero trust, AI-augmented, and continuously verified.

To boost efficiency and improve service without putting AI directly in front of customers, some companies are quietly using it behind the scenes. This is where Afiniti comes in. When a customer calls a contact center, AI Pairing quietly analyzes behavioral and contextual data, like personality traits, communication styles, and even past interaction patterns, to figure out in real time which available service agent is most likely to handle that specific caller successfully.

If that sounds like matchmaking for call centers, that’s basically what it is.

The aim is smoother interactions, higher satisfaction rates, and stronger outcomes without the customer feeling like they’re “talking to a robot.”

Afiniti’s approach sidesteps that problem by keeping the AI out of the spotlight. The technology doesn’t replace human interaction; it enhances it. The customer still gets to speak with a live agent, just one who’s statistically more likely to connect with them on a human level.

Five9 customers can now access AI Pairing directly through the Five9 Intelligent CX Platform, or via the Five9 Marketplace, which allows them to deploy its capabilities within existing operations.

“By combining our AI-driven capabilities with the power of the Five9 Intelligent CX Platform, we are enabling enterprises to deliver smarter, more personalized customer experiences that drive measurable improvements to both revenue and operational performance,” said Eyal Brami, VP of Partnerships at Afiniti.

Unlocking Value with AI Behind the Scenes

The partnership reflects a broader shift in how organizations are thinking about AI in customer service. Rather than focusing solely on automation that can sometimes alienate customers, enterprises that are having success with AI tend to be those using it as a silent enabler, helping humans perform better.

It’s no secret that customers can be wary of AI. Chatbots that can’t answer questions and voice systems that misunderstand requests have made some people resistant to the idea of AI in customer service.

Surveys show that a vast majority of customers still prefer speaking with a human agent, especially when dealing with complex or emotionally charged issues. Many feel frustrated when they realize they’re interacting with a chatbot or an automated voice assistant. And if they are connected to an AI agent, they want it to be disclosed.

This kind of background AI, which operates quietly to support humans rather than replace them, is gaining traction. Instead of trying to make AI the star of the show, companies are finding value in letting it take on invisible, data-heavy tasks that make human service better.

In this way, organizations can use AI to enhance decision-making and increase efficiency while limiting customer frustration, maintaining the trust and empathy that define good service.

Afiniti says its patented AI Pairing technology has delivered more than $2.2BN in incremental annual value for global enterprises in industries like telecom, finance and healthcare.

The partnership with Five9 highlights a shift in how enterprises are thinking about AI, to enable satisfying interactions without customers realizing AI was involved.

Of course, some can offer a better voice platform, data strategy, and integration portfolio than others. Still, since the dawn of cloud-native solutions, the scope for differentiation has narrowed.  

As this trend accelerates, hyperscalers – including AWS, Google, and Microsoft – have crowded the space, sensing an opportunity.   

Meanwhile, tech providers in adjacent markets – such as CRM, UCaaS, and even CPaaS – have thrown their hats into the ring.  

However, few of the newer names to the marketplace have fared as well as Sprinklr.  

After it swept into the CCaaS space in 2022, Sprinklr soon picked up major clients, including BT, Deutsche Telekom, and EE. Meanwhile, it raced into the Forrester Wave for CCaaS, outranking many industry stalwarts.  

The report isolates Sprinklr’s chief advantage: its CCaaS solution sits on a broader platform.  

The Platform Advantage 

Sprinklr didn’t suddenly pivot into the CCaaS market when it entered three years ago. According to Amitabh Misra, Chief Technology Officer at Sprinklr, the move was part of a longer-term shift set out 15 years ago, when the company first aspired to build the world’s best Unified CXM (Customer Experience Management) platform. 

That platform also comprises social media management, voice of the customer (VoC), and conversational AI solutions that encapsulate its CXM vision.  

By cross-pollinating its platforms, Sprinklr leverages its broader platform to develop unique innovations, beyond what many pure-play CCaaS vendors can provide.  

That differentiator accelerated Sprinklr’s CCaaS mission, per Misra. “Customers were asking for AI capabilities and unified solutions rather than stitched-together point products,” he said.  

“Because our platform already combined all these capabilities — voice, digital, AI, and cloud — we could offer something unique: a 360° view of the customer across all channels, well ahead of traditional CCaaS players.”  

As service teams go further to share insights and align strategies with adjacent departments, that deeper customer view and broader product functionality set Sprinklr apart.  

A Deeper Dive Into Sprinklr’s Strategy 

Sprinklr’s VoC solution allows CX leaders to interact with their feedback. It pools insights and lets CX leaders ask questions such as: “How is my brand sentiment trending globally?” Or: “What are my customers currently most happy/upset with?” It then provides instant, contextual answers.  

Such a capability could transform contact center reporting. Imagine not only viewing static outputs, but being able to question that data, unpacking what’s driving numbers up and down, before making more contextual, targeted actions to improve key goals. That’s the future, and Sprinklr is building it.  

Also, consider Sprinklr’s conversational AI arm. Its team is switching much of its attention to the CCaaS marker, building AI agents that can independently handle tasks like transferring calls, taking inbound or outbound calls, and managing multi-step interactions.  

Additionally, they’re plotting out the future of hybrid human-AI contact center teams. Misra stated: 

“For example, when an AI agent struggles with a task, it can escalate to a human agent. Supervisors can also monitor how both AI and humans interact in real time, improving guardrails and learning loops.”  

“These systems will also be self-learning, constantly improving through feedback and supervision,” Misra concluded.  

With Sprinklr, the only vendor to feature in both the latest CCaaS and conversational AI Forrester Wave reports, it’s pulling these two realms together in exciting new ways.  

Yet, the provider isn’t only paving the future of contact center automation and reporting. It’s also rethinking the conventional CCaaS architecture.   

It’s Not All About Voice Anymore  

Most customer service solutions center in on a core voice platform and routing engine. Everything is built around that. Fitting workflows around the core is an afterthought. 

However, thanks to SaaS tech, ecosystems, partnership programs, and APIs, brands can plot out their ideal service experiences and key workflows before pulling in key operational systems, like voice.  

Sprinklr’s approach seizes this opportunity. It recognizes that voice remains essential. However, its Voice Connect solution is composable and reimagined for the modern AI era. 

As such, contact center teams can design their ideal journeys and mould the platform around it. That’s a significant step forward in an era where ACD systems are no longer the foundational contact center tech purchase.  

More from Sprinklr 

At its recent CX Unifiers event, Sprinklr made several more moves to unite the world’s contact centers, voice of the customer, and conversational AI. 

For more on its latest innovations and CCaaS vision, visit: sprinklr.com  

While the announcement got lost in the frenzy around the event, it’s significant in unifying CCaaS and CRM platforms. 

The integration, built through the Bring Your Own Channel for CCaaS Pilot program, embeds voice and digital channels from the Webex Contact Center into Salesforce Agentforce Service (formerly Service Cloud). 

As a result, every interaction can be managed directly inside Salesforce, with critical conversational data consolidated in Agentforce Service, and – in many cases – filtering into Data 360 (formerly Data Cloud). That’s powerful for boosting AI projects across the front office, with mutual customers also able to tap Webex AI agents and Agentfore. 

Other advantages include a simplified agent experience, as reps Alt+Tab less as they switch the systems, and reduced admin burden, with one central customer service platform.

Honing in on the first of those two advantages, especially, Vinod Muthukrishnan, VP & COO for Webex Customer Experience, told CX Today:

The two worlds have come together. Agents, on average, are looking at a dozen systems just to do their job, and we need to get out of their way and allow them to do what humans do best: lean in, pay attention, and give fabulous service. 

Alongside Cisco, AWS, Five9, and Genesys have announced similar integrations with Salesforce Agentforce Service.

Nevertheless, this is still significant, as Cisco rapidly expands its cloud contact center install base.

Much of that stems from its on-premise install base of large-scale, on-premise enterprises. Yet, it’s also winning customers outside of that, thanks to strong word-of-mouth swirling the Webex Contact Center.

Many of these customers and prospects will have installed Agentforce Service, with 60,000+ companies leveraging the app, making it the most widely-utilized CRM solution. 

As they plot out their plans around the app, Cisco can now these businesses a tighter integration alongside a reputation for doing “big” deployments well (per its recent Maersk case study).

Given this, the offering is an important integration for Cisco and a nice step forward for Salesforce as it builds the future of connected CCaaS-CRM systems.

As Matthew Kravitz, Product Manager at Salesforce, stated:

It’s wonderful that we’re able to take deep CRM experiences and open them up to Bring Your Own Channels, Bring Your Own Telephony… What’s included in this architecture, customers are going to be joyous about it.

The Webex Contact Center for Salesforce is now available for early access customers, along with general public availability opening in Q1 2026. 

Elsewhere, Salesforce continued its momentum in the CCaaS earlier this week by partnering with PWC as it launched an “Agentic AI-Powered Contact Center”. That is just one of many major announcements from Dreamforce 2025.

Meanwhile, Cisco made many more headlines at Webex ONE, including the release of an Auto-QA solution to monitor hybrid human-AI teams and a new AI Agent Studio.

For years, the contact center playbook focused on adding more channels, assuming that more touchpoints automatically improved experiences.

Yet, despite investments, many organizations found themselves facing fragmented journeys, silos, and duplicated efforts.

Enter CCaaS 3.0, the latest evolution in cloud contact center technology.

Unlike its predecessors, this iteration isn’t about piling on channels; it’s about creating connected, contextual interactions that can be resolved in real time.

In a discussion with CX Today, Zac Scalzi, Director of Sales at Cobrowse, summed the concept up nicely:

“A new model has emerged. CCaaS 3.0 is defined by AI assistance and rich contextual data, both for human and virtual agent-led interactions, which finally closes the gap between digital complexity and customer clarity.”

“Together, they form the new customer standard: real-time guidance, context-rich engagement, and seamless resolution.”

The Evolution of CCaaS

Like any major technology or platform that persevered, CCaaS has already undergone a few iterations.

However, while tech staples like the iPhone seem to have a new version every other month, the evolution of CCaaS has been more considered.

In its original format, CCaaS 1.0 was essentially responsible for moving voice and multichannel support into the cloud.

CCaaS 2.0, on the other hand, stitched together chat, email, and social channels. Yet, despite the improvements, it still relied heavily on integrations and often left customers repeating themselves.

CCaaS 3.0 is a different animal. The latest iteration is CRM-first, AI-enhanced, and designed to integrate powerful contextual tools like native cobrowsing as a core component.

This evolution reflects a broader shift: from simply offering more channels to creating an environment where every interaction is seamless, visible, and capable of resolution in the moment.

As one of the pioneers of CCaaS 3.0, Google Cloud Contact Center defines the latest version of the technology with the following four pillars:

• Omnichannel & Embedded Experience: Customers can move fluidly between voice, chat, email, and digital channels without losing context, while embedded experiences keep interactions within the apps and portals they already use.
• AI Automation & Continuous Optimization: AI-powered routing, self-service, and predictive engagement ensure issues are handled quickly, while analytics and conversational insights surface trends, sentiment, and opportunities for ongoing improvement.
• Agent Empowerment & Assist: Human agents gain real-time insights, contextual guidance, and suggested actions, allowing them to focus on resolution instead of gathering basic details.
• Rich Contextual Evidence: AI outcomes depend on the quality and breadth of data. By combining structured knowledge from support systems, real-time account information, and AI cobrowsing, the critical layer of visual context is added. This missing piece enables agentic AI systems to guide customers with confidence and deliver successful outcomes at scale.

Google’s blending of AI intelligence and visual context sets a new enterprise benchmark.

Why Omnichannel Alone Isn’t Enough

Although omnichannel systems have their merits and are a popular choice for many in the customer service and experience space, Scalzi argues that they are no longer enough to navigate today’s digital complexity.

In modern society, customers now face multi-step loan applications with identity verification, e-commerce checkouts juggling payments and promo codes, telecom and utility apps handling complex account issues, and insurance claims requiring documents and cross-references.

Scalzi explains how these processes can sometimes overwhelm people:

“Customers often get stuck partway through, not because they lack motivation, but because digital portals have grown intricate, layered, and sometimes overwhelming.”

Cobrowsing addresses this problem.

By allowing agents to step directly into a customer’s digital space, enterprises move support from reactive troubleshooting to proactive enablement.

In doing so, this helps users complete tasks in real time rather than just receiving verbal guidance.

The AI + Cobrowsing Advantage

Another advantage of CCaaS 3.0 is its ability to combine intelligence with visibility.

Google AI is key to delivering this. The solution powers self-service, predictive routing, automated summaries, and agent assistance.

When customers are stuck navigating your websites and apps, cobrowsing brings visual context into the interaction seamlessly.

“Without insight into what is actually happening inside digital portals, both human and virtual agents are left guessing.

“Cobrowse is a tool that provides rich context in real time to both human and virtual agents, and we’ve seen tremendous results. ” Scalzi explains.

CCaaS 3.0 fixes this gap, making context inseparable from every interaction.

The benefits extend beyond quicker resolution; guided experiences also support customer education and digital adoption, giving users the confidence to navigate complex channels independently.

Real Benefits for Enterprises

Outside of some of the more general features and benefits of CCaaS 3.0, the solution also delivers the following measurable outcomes:

• Digital Adoption: Guided interactions double as teaching moments, helping customers master previously confusing features.
• User Empowerment: Customers grow self-sufficient, reducing repeat support requests.
• Agent Efficiency: Agents resolve cases faster with deeper context and understanding.
• Operational Gains: Shorter handle times, fewer escalations, and less training overhead.
• Compliance: Sensitive information is masked or redacted in real time, meeting enterprise and regulatory standards.

Scalzi emphasizes the wider impact, stating:

“Google AI and native cobrowsing shift the contact center away from being a reactive cost center; instead, they establish it as a proactive engine driving customer loyalty and digital adoption.”

Redefining the Contact Center

In short, CCaaS 3.0 reimagines the contact center.

It is no longer just a call-answering hub; it has become the digital front door of customer relationships, where trust is built, journeys are supported, and brand experiences are defined.

The combination of AI and rich context from tools like cobrowsing, enables enterprises to move from reactive problem-solving to proactive engagement, setting a new standard where customers feel supported and confident every time they interact digitally.

In today’s fast-moving digital landscape, the message is clear: those who combine intelligence, context, and real-time guidance will set the benchmark for customer engagement.

CCaaS 3.0 isn’t just another contact center upgrade; it’s the blueprint for the future of customer experience.

You can find out more about Cobrowse’s CCaaS 3.0 philosophy and relationship with Google by visiting the website today.

First, he predicted human customer support will be “totally, totally gone”. Next, Altman suggested that he is “confident” contact center jobs will be the first to be replaced by AI.  

With leading AI voices making such predictions, AI experiments are ramping up, CIOs are paying more attention to the contact center, and leaders are honing in on deflection and containment rates.  

Yet, while these are helpful metrics, they’re not a strategy. A hyperfocus on these measures can lead to poor customer experiences. As Goodhart’s Law suggests, when a measure becomes a target, it ceases to become a good measure. 

In this case, by valuing automation above all else, contact centers risk high-value calls getting diverted away from agents. These may be make-or-break to customer loyalty or offer a significant upsell opportunity.  

As such, contact centers should be clear on why customers are contacting the business and the value each of those contacts has.  

Breaking Down Customer Demand 

Take the contact center’s most common contact drivers and consider which have the most value to the company and which have the most value to the customer.  

Those with low value to both are prime candidates to eliminate completely. Contact automation is a powerful solution here. However, by tracing back the root cause of the issue, through an analytics initiative, the service team can often remove the need to reach out to the business altogether. That’s pre-emptive customer support at its finest! 

For those with high value to the company or customer, look to simplify the process. Again, a virtual agent flow may be an effective solution. However, also consider opportunities for proactive customer service, detecting signals in back-end systems to automate the entire process without the customer lifting a finger.  

Finally, there are those calls of high value to the company and customer, where loyalty is won and lost. That’s where the human touch shines, and human reps will ideally be equipped and ready to deal with these common contacts as they reach them.  

Supporting Agents to Deliver in the Moments That Matter 

Handling high-value customer contacts well starts long before an agent picks up the phone. The experience should be carefully orchestrated.  

When an front-end generative virtual agent deciphers the customer intent, leveraging a LLM trained on business and customer specific data, it will ideally engage with the customer to take information from them that’s pertinent to the intent and helpful to the agent.  

That agent should also understand which channel is best to handle the incoming query and route it accordingly, bolstering the possibility of a successful outcome.  

From there, the contact reaches the agent, who should have all the necessary information at hand (and the coaching) to guide the customer through the contact.  

Along the way, generative agent assist tooling uses the LLM to synthesize the trained data set with the ongoing conversation and available customer metadata, which serves up helpful information at the moment of need, and support the agent in handling objections. As such, they can focus on listening to the customer, showing empathy, and building real connections.  

That vision may seem like a distant prospect for those still leveraging on-premise technology and first-generation cloud solutions. Yet, it’s one that some vendors are making a possibility every day.  

Work with a Partner That Gets It 

In 2015, UJET broke into the contact center technology market with a solution designed around the modern smartphone, enabling a multimodal experience and simplifying channel shift. Still, that differentiates its CCaaS platform.  

Since then, it has built out its solution further, leveraging a tight relationship with Google to apply the latest generative AI advancements to the contact center via virtual agents, agent-assistance, conversational intelligence, and much more.  

Its cloud contact center has resonated especially well in the midmarket, where companies are more agile and ready to embrace a strategy to best balance the strengths of human and AI agents.  

For more on its transformative contact center technologies, visit: ujet.cx  

Sean Taylor, CEO of Content Guru, was a judge and, as part of the evaluation, sat down with one of the finalists’ agents to watch how they worked.  

“They were taking a customer’s payment and had a stack of yellow Post-it notes on their desk,” said Taylor during a recent CX Today interview. “They typed the credit card details into one system, then quickly wrote them on a note.  

“When I asked why, they explained that the information also needed to go into another system, but they couldn’t do both at once.” 

Yet, imagine leaving the building with a pocket full of Post-it notes containing names, credit card numbers, expiry dates, and CVV codes… 

Needless to say, they didn’t win the competition. Nevertheless, the story illustrates just how difficult it is for many agents to switch between systems. 

For all the talk of a “single pane of glass,” a Deloitte study in the UK found that the average contact center agent still works with around 14 separate systems of record. 

Ultimately, that illustrates that there is no silver bullet. A closely considered omni-data strategy is instead the best path forward. 

What Is Omni-Data? 

Just as omnichannel communication brings together voice, email, RCS, WhatsApp, and video into one platform, omni-data brings all an organization’s data systems together. 

Whether it’s Salesforce, Microsoft Dynamics, or proprietary systems like Guidewire in insurance or Epic in healthcare, an omni-data strategy surfaces them in one place. 

Agents can push and pull information without resorting to Post-it notes or duplicate entries. 

Content Guru is leading the omni-data charge by converging its CCaaS solution with a first-party customer data platform (CDP), enabling that single data thread.    

“To me, that’s a no-brainer,” added Taylor. “Yet when we deliver it, customers often say, “Wow, that’s unique.”  

“Other vendors may have strong connectors into Salesforce or Dynamics, but they don’t address the full challenge of integrating data across systems.”  

Omni-Data and the Future of Customer Service  

Omni-data provides a single source of data for agents to push and pull from. Yet, that’s not only human agents; AI agents can also do so.  

Indeed, contact centers may orchestrate experiences where AI agents interact with customers while leveraging data from one a single layer, instead of pulling from a myriad of integrations, which require continuous, rigorous security checks. 

Yet, they won’t just drink from the hosepipe; they feed back into it. 

As such, contact centers can advance their conversation automation strategies, thinking not only about inbound but also outbound.  

After all, the future of customer service isn’t only reactive, it’s proactive, and even pre-emptive.  

With an omni-data strategy, contact centers can detect signals from various systems that indicate the customer is experiencing (or is about to experience) an issue. From there, an AI agent will trigger a corresponding resolution flow and communicate that with the affected customer.  

Here are four cross-industry examples of what that proactive flow may look like in practice. 

1. Proactive Servicing & Repairs (Automotive & Retail)

IoT devices can send signals into the omni-data layer from sensors hidden within products. That includes everything from transport to white goods. 

The devices help monitor product health, so if a car has low tire pressure or a washing machine is about to give up, they can trigger a flow that ends with an AI agent scheduling a repair with the affected customer. 

2. Proactive Pipe Management (Water) 

Don’t think of only first-party data sources as triggers for a proactive flow. Public sources can also feed into the omni-data layer.  

For instance, data from the weather forecast could filter into a water company’s omni-data layer. With that data, an AI agent can predict whether a water supply pipe will freeze and when it may unfreeze, rather than just telling the customer to schedule an engineer.

3. Proactive Network Updates (Telco)

Telcos can feed data from their Network Monitoring System (NMS) into the omni-data layer, with AI agents on alerts for an outage.  

When this occurs, they can proactively notify customers and send updates, keeping them in the loop, so affected customers don’t have to contact customer service. 

4. Proactive Journey Recoveries (Travel)

Plane and train delays often cause passengers to miss their connections. Yet, by plugging data from the Traffic Flow Management and CRM systems, companies can charge AI agents with rebooking customers on the next-best journey. 

As such, when the customer arrives or lands, they don’t have to queue for customer support. Instead, they receive an alert with their new booking or alternative options to consider.  

Get Ahead with Content Guru  

Content Guru entered the cloud contact center market in 2005, before “cloud software” was a term that many people had ever used.  

Its parent company, Redwood Technologies, subsidized the business for the first eight years because adoption was slow. But, Taylor and his team believed the cloud would matter. 

The same happened with AI. Content Guru invested years before the current boom, before COVID delayed adoption, but models like ChatGPT accelerated it again. 

“Our philosophy is to blend customer-driven innovation with long-term bets based on intuition and experience,” said Taylor. “Sometimes we’re early, which can be frustrating, but when the market catches up, we’re ready.” 

Its next bet is omni-data. Given its track record and status as a global CCaaS provider, who’d bet against the company this time around? 

For more on Content Guru’s contact center tech, visit:  www.contentguru.com  

Customers will continue to have full access to the Sell product until the shutdown date.

As Zendesk noted in a web post, the move will help channel all its attention toward transforming the customer service stack.

“This change allows us to focus even more deeply on what we do best – helping businesses like yours deliver exceptional customer and employee service,” noted Jennifer Chang, VP of Product Development Program Management and Operations at Zendesk.

By concentrating our efforts on service, we can move faster, innovate more boldly with AI, and build solutions that make a bigger impact for your teams and your customers.

To help customers make the transition, Zendesk has set up a native integration with the Pipedrive sales CRM so they can export their data from Sell.

Similarly to Zendesk Sell, the sales force automation solution helps customers visualize their pipeline, manage leads, and automate the sales process, while generating reports with AI in a single workspace.

Zendesk Is Primed to Go All In on Customer Service

Zendesk has a massive global user base that leverages its customer service CRM. But until recent years, it struggled to answer: what’s next?

In 2018, it expanded into providing a broader CRM platform by releasing Zendesk Sell, but never went all-in to channel Salesforce, Microsoft Dynamics, and others across all CX functions.

From there, it moved to acquire SurveyMonkey to expand into voice of the customer (VoC) solutions, but the deal ultimately broke down.

Shortly after, it went private and brought in former Genesys President Tom Eggemeier as CEO.

He then pulled Zendesk back to what it does best: customer service.

With now almost as many people who had been Genesys execs on its board as Genesys, it has since bought a cloud contact center platform, expanded into workforce engagement management (WEM), and launched its Resolution platform.

How Zendesk Is Reimagining Customer Service Experiences

Eggemeier shared his vision for the future of customer service in a recent interview with CX Today.

During the interview, he stated: “Five years from now, it’s definitely going to be more automated… I think it’s going to be more proactive. It’s going to be more personalized. And you’re going to start blurring these lines because everyone’s going to be focused on how do I create lifetime value, retention, and satisfaction with my customers, employees, and the businesses that I serve.”

Eggemeier acknowledged that “there is a little bit of a hype cycle” when it comes to integrating AI into contact center operations. But, he added that the technology is more than a passing fad. The CEO continued:

It is going to fundamentally change the way you provide customer service or employee service, whether it’s business or consumers. It is going to fundamentally change your work and the nature of work.

This will present challenges as voice is seen as the most natural interface for human communication and is at the forefront of AI advancement, especially with the rise of autonomous agentic workers in contact centers.

However, as AI-powered voice assistants take on more responsibilities, potentially even to the point of interacting with other digital agents on behalf of customers, questions arise around how they are monitored and how systems ensure accountability and human escalation when things go wrong.

“We’re preparing ourselves and our customers for that future and that eventuality today. So, rather than buying a point solution that is boxed out, we’re laying the foundations so that our customers can grow on the journey,” summarized Jonathan Barouch, VP & GM of Zendesk for Contact Center.