NiCE held its annual conference in Orlando earlier this month.

The event was rebranded as NiCE World, reflecting both the company brand identity introduced a year ago and its broader ambition in the CX space. The event was structured around a show floor designed to facilitate interactions among the 3,000 customers, partners, and employees in attendance.

Under Scott Russell, NiCE has re-centered its GTM around partners, especially global systems integrators (GSIs). Partners now drive ~70% of net-new enterprise logos, with GSIs contributing 14% of ACV, roughly 3x their contribution a year ago. In international markets, close to 90% of the company’s business is partner-led via resale or co-sell.

Scott Russell reasserted the company’s position in the CX AI space with his characteristic conviction, stressing that AI is built in, not bolt on, and emphasizing the importance of a unified platform and its ability to scale.

NiCE reaffirmed Cognigy’s contact center neutrality. NiCE’s strategy is to offer multiple entry points into its suite, CCaaS (contact center), workforce management, or Cognigy’s conversational AI platform.

Nine months after the Cognigy acquisition closed, the integration is effectively complete. Cognigy now has access to NiCE’s proprietary datasets, including vast volumes of historical conversational data. These datasets include highly valuable human-handled interactions, typically the most complex and business-critical conversations, providing Cognigy with a strategic differentiator over other pure-play conversational AI providers. Cognigy has also become the agentic and conversational AI layer of CXone, bringing its best-of-breed capabilities into the NiCE platform.

The event showcased learnings from large-scale AI deployments. One point that caught my attention was that several customers explained they chose to tackle the most complex problems first with AI, rather than starting with simpler use cases, as has traditionally been the adoption pattern. This suggests new levels of confidence in the technology.

Philipp Heltewig offered a compelling example of Cognigy handling over 80% of the 20 million customer conversations with Lufthansa during a one-week strike, including cancellations and flight re-arrangements. He also warned of the imminent rise of consumer-facing conversational agents, urging enterprises to prepare for a significant increase in AI-driven traffic. These examples illustrate the technology’s progress, including instances of AI outperforming humans or managing situations humans could not.

Finally, Jeff Comstock and the product team provided a private preview of several new products and capabilities worth discussing:

• Agentic Engagement Plane is a new layer that allows AI and human agents to operate as a unified workforce. It uses the concept of engagement paths to define the resources that can handle conversations across NiCE and third-party conversational AI and CCaaS platforms. It builds on a global view of the workforce and supports diverse human-in-the-loop models, including full takeover, silent assist, and mid-flight takeover.

• It is supported by the next evolution of Experience Memory, which maintains customer identification and authentication, conversation context, and journey history, including reasoning and decision traces.

• Experience Optimization is evolving into Agentic Analytics spanning conversation, workflow, and outcome data. It leverages a set of domain-specific AI agents to analyze this dataset to identify new automation opportunities and improve existing ones. It draws on a new Screen Intelligence capability to understand everything that happened.

• NiCE is working on a redesigned agent workspace to weave AI agents into the conversation stream, whether handling the conversation, providing recommendations, or requiring human validation.

• AgentForge is a new capability that generates AI agents from artifacts such as documents or specifications.

• Guardian AI is a new governance and control layer that monitors both human and AI agents to enforce guardrails and policies and surface risks.

These building blocks demonstrate NiCE’s continued advancement toward managing customer conversations and resolution workflows across the full span of customer engagement through a unified human and AI workforce.

Last week, Zendesk held its annual user conference, Relate, alongside an Analyst Day. The event marked the culmination of a broad set of organic and non-organic developments that have re-shaped Zendesk’s product footprint into a comprehensive service suite spanning both customer and employee service, with AI embedded natively across the platform rather than dependent on third-party capabilities.

To complete its suite and accelerate its roadmap, the company has become a serial acquirer, snapping up eight companies in the last three years: Cleverly (AI triage and assistance), Tymeshift (workforce management), Klaus (quality management), Ultimate (customer service automation), Local Measure (contact center), HyperArc (GenAI-powered analytics), Unleash (enterprise search), and Forethought (agentic AI).

At the event, Zendesk showcased its Resolution Platform, now organized around four core solutions, each serving as an entry point into the broader suite:

1. Customer Service: Zendesk’s historical core solution.

2. Employee Service: Formally launched as a standalone solution last year and now expanded with IT asset management (ITAM) capabilities.

3. Contact Center: Added through the Local Measure acquisition, the solution powered by Amazon Connect Customer is now fully integrated into the Zendesk platform. The offering includes Zendesk’s agent workspace and reporting capabilities, leveraging the HyperArc acquisition. Zendesk shared that it has added more than 100 new customers since the acquisition.

4. AI Agents: Built on the Forethought acquisition, the solution remains available for non-Zendesk customer service and contact center environments while being integrated into the Zendesk Resolution Platform.

Relate’s theme was the autonomous service workforce, underpinned by a vision of AI and human agents working together, with AI agents spanning both pre-built, highly specialized agents and custom-built agents created through its newly introduced Agent Builder.

Deon Nicholas and Sami Ghoche, co-founders of Forethought, detailed one of its key technologies, the self-improving loop, which now underpins Zendesk’s new Resolution Learning Loop. The loop begins with AI analyzing historical data to surface knowledge gaps and identify automation opportunities, detecting patterns that can be translated into either knowledge base content or agent-driven workflows. It then evaluates conversations across multiple dimensions using customer feedback signals, structured topics, and QA performance metrics such as churn risk. Based on these insights, the system generates and refines agents, which are validated through simulation-based test suites and then deployed via controlled A/B testing in production. Today, these stages are AI-driven with human approval gates for reviewing recommendations, validating test outcomes, and approving production rollouts. The new AI Studio is positioned as the control layer that will progressively automate this loop end-to-end, enabling increasingly autonomous agent improvement.

Zendesk also provided interesting details around its data platform. Unlike many CRM platforms that require custom objects and workflows to accommodate customer-specific business processes, Zendesk has been able to preserve a consistent core data model for the ticket lifecycle and ticket-to-contact relationship. This provides a much more readable context layer for AI and agents.

CEO Tom Eggemeier has emphasized Zendesk’s AI ambitions, sharing progress through AI annual recurring revenue (ARR), which he stresses includes only revenue from AI products. The company reached $250M last quarter and now expects to exit the current fiscal year, ending January 2027, at $400M.

In the backdrop of traditional product category boundaries coming down and with so many vendor options available for AI, differentiation was a recurring theme in discussions. These conversations are often centered on AI capabilities and the data that feeds them. What stood out is that when asked why they are with Zendesk on their AI journey, customers consistently pointed to its people, both in terms of expertise and a genuine commitment to making them successful.

I attended the TP Immersive conference earlier in May.

With Jorge Amar recently appointed as CEO, it was a timely opportunity to take the pulse of an industry at a crossroads. In February, he took the helm of the company founded 50 years ago by industry legend Daniel Julien, succeeding him.

Jorge came from McKinsey, where he was already working on TP’s strategic transformation before taking the CEO role. He stepped in with a clear conviction: TP sits at the center of the workforce transformation AI is about to unleash.

BPOs are first in line for AI disruption. A significant portion of their revenue still comes from the “butts and seats” model built around entry-level roles that AI agents are poised to replace. Under mounting pressure, BPOs are reshaping their business profiles by diversifying into higher-value services, moving up the value chain through more end-to-end process ownership, and shifting toward outcome-based models.

TP operates a large workforce and has to handle significant turnover. Over time, that has made it highly proficient at hiring, onboarding, and continuous training. This creates a foundation for transitioning workers into the new roles that Agentic AI will inevitably create.

Interestingly, a growing number of businesses struggling to realize the expected financial returns from their AI initiatives are turning to the BPO giant to take over functions outright, including the people, with the charter to optimize them.

BPOs are also “project factories.” They manage many projects and, with slim margins, profitability depends on disciplined execution, particularly around timing and delivery. TP is no exception. It worked on more than 500 AI projects last quarter alone, building deep expertise across both the technical and change management dimensions.

Last year, TP introduced TP.ai FAB, a platform designed to support its vision of humans and AI working together.

TP.ai FAB is built on an open framework that integrates with clients’ existing environments and incorporates technologies from selected partners, including Ema, Kore.ai, Parloa, and Sanas.

At the event, TP shared how it built five agentic solutions on top of this framework:

• FAB Assist delivers a complete work environment for human agents, combining AI assistance, KPI-driven coaching, and Sanas voice experience improvements

• FAB Connect provides an agentic customer self-service solution with escalation to human agents

• FAB Operate optimizes traffic orchestration across contact centers and agentic self-service based on customer intents and desired outcomes

• FAB Collect for collections and FAB Growth for sales are end-to-end solutions designed to maximize revenue outcomes through AI.

These solutions position TP to take on more outcome-driven engagements. One challenge, however, is that in multi-provider enterprise environments, the technology stack is often dictated by the customer.

Jorge explained that this transformation requires a fundamentally different conversation with customers. Success depends on the ability to deliver meaningful insight into what businesses should consider. Over the years, TP has built deep expertise across industries and business processes that it now needs to fully harness to earn that strategic seat at the table.

TP’s position at the intersection of technology, people, and large-scale delivery expertise gives it the right to participate in the emerging service market disruption. Key to its success will be the ability to realize definitional outcome-based projects within its customer base.

On the heels of its 1Q26 earnings call, Twilio hosted its Analyst Summit and Signal conference, where it unveiled new products alongside a new positioning.

Before getting there, it is worth pausing on the company’s remarkable turnaround. Once a market darling, Twilio exited 2022 with over $1B in losses, $1B in debt, and gross margins below 50%, entering a period of significant restructuring and strategic reset. Early 2024, Khozema Shipchandler, previously the company’s CFO, succeeded founder Jeff Lawson as CEO.

The financial turnaround was largely achieved in 2024, but it came at the cost of six consecutive quarters of stagnant growth, which led markets to question the durability of the long-term opportunity. In early 2025, Twilio shared a double-digit growth ambition. The latest quarterly results, 20% year-over-year growth after five consecutive quarters of sustained double-digit expansion, were rewarded with a sharp increase in market valuation, setting the stage for what the company is now positioning as its next chapter.

The company has spent the past couple of years unifying its portfolio, including its SendGrid (email) and Segment (CDP) acquisitions, an effort that culminated at Signal with the unveiling of a new Console giving developers a unified entry point across its products.

The next act for the company is to become the infrastructure for the agentic era, enabling continuous customer engagement with humans and AI. At Signal, it launched its conversation layer, adding three new building blocks to Conversation Relay:

Conversation Orchestrator enables continuous conversations across channels with both human agents and AI agents. It enables seamless channel switching, multimodal interactions, AI-human handoffs, and automated outreach for follow-up or notification.

Conversation Memory adds persistent context across interactions. It aggregates customer traits (profiles), identifiers, observations (signals extracted from conversations, such as behavior, sentiment, and preferences that can be manipulated semantically), and interaction summaries.

While leveraging Segment technologies, Conversation Memory is CDP-agnostic and can also connect to any CDPs, CRMs, and data warehouses. For AI agents, Twilio can further enrich context with knowledge through its Enterprise Knowledge ingestion module.

Conversation Intelligence lets you take actions in real-time on live conversation streams. It features a framework that uses language operators. These operators, either from Twilio or custom-built, include surfacing contextual knowledge recommendations, delivering real-time guidance, enabling intelligent routing and next-best actions, monitoring script adherence, and detecting sentiment and conversational signals.

Conversation Intelligence differs from Conversational Intelligence, Twilio’s post-conversation analytics solution, which is now evolving into a new product, Conversation Insights, that will combine the analysis of structured and unstructured data.

The conversation layer also includes Agent Connect, which integrates these building blocks into agentic platforms while abstracting away conversational complexities such as streaming and turn-taking. It supports OpenAI, Microsoft Azure, AWS Bedrock, Anthropic, and LangChain/LangGraph. It’s open-sourced and can be adapted to other frameworks.

Twilio has refocused on APIs and building blocks. That shift has reignited momentum with ISVs, a business that had been hindered by Twilio’s earlier ambitions toward packaged applications. In January 2025, Twilio disclosed that ISVs represented roughly a quarter of revenue, with the segment consistently outgrowing the broader business since then.

Serving both enterprises and ISVs across organizations ranging from SMBs to the world’s largest companies creates inherent positioning challenges. Twilio addresses the diversity of needs across these segments by marketing a collection of modules. While it makes it easy for builders to pick what they need, it can take some stepping back to see where the platform is heading.

Twilio is reshaping itself into an infrastructure provider for customer engagement in the AI world. Looking across its moves, three pillars are taking shape, each rooted in foundational requirements for customer engagement in the agentic era. First, conversation management with its newly unveiled layer. Second context, with Segment getting not only modularized but also embedded in other building blocks. Third identity and trust, with Twilio extending its authentication capabilities through its Stytch acquisition to support AI agents and expanding into compliance and fraud detection.

On April 23, AWS acquired NLX, and for a company with a strong build DNA, any acquisition is intriguing. Over 20 years, the cloud hyperscaler has made only eight that I could identify.

So, who is NLX?

Founded by two brothers, Andrei and Vlad Papancea, in 2018, NLX was born out of Andrei’s experience building conversational applications and the recurring effort required to navigate technology cycles, iterate on experiences, and expand the use cases these applications can support.

This led to the creation of Canvas, NLX’s conversation builder, designed to be to conversational application design what Figma is to UI design. The platform was built to insulate application creators from the complexities of channels, communication infrastructures, and AI technologies.

The obvious question: don’t all conversational AI platforms, including Amazon Connect, already offer a no-code builder?

While almost all conversational AI platforms feature no-code development environments, most remain fairly low-level and require significant technical expertise. NLX created a platform for business users that minimizes dependencies on developers.

Its building blocks abstract application logic across channels, enabling teams to compose multi-modal experiences that guide users to the right modality at each stage of a workflow.

NLX Canvas enables teams to rapidly iterate and expand use cases, a critical success factor for deploying and scaling conversational experiences that today often depends on forward-deployed engineers.

NLX has remained focused on large enterprises with complex requirements, spanning customer experience and other enterprise use cases. Its customer roster includes Comcast, Red Bull, Saks Fifth Avenue, and Toyota.

NLX’s vision aligns closely with Amazon Connect’s mission to create ready-to-use solutions. It already extends AWS Lex Flow Builder and Amazon Connect Flows.

Now part of Amazon Connect Customer, NLX will focus on customer service use cases on the hyperscaler CX platform, while support for other platforms becomes customer-driven.

At this stage of conversational AI adoption, many enterprises are prioritizing customer self-service and selecting best-of-breed platforms. NLX enhances Amazon Connect’s ability to compete in these self-service modernization initiatives.

At its What’s Next with AWS event, the cloud hyperscaler made several announcements, including a particularly ambitious one: the expansion of Amazon Connect into a family of agentic business solutions, which I want to unpack.

You knew Amazon Connect as contact center software. Reset that view: it now represents a family of agentic solutions that package AWS cloud services into ready-to-use offerings.

This announcement carries real strategic weight. A few years ago, a rumor circulated that a team inside AWS was making the case for acquiring HubSpot, sparking debate about the company’s developer DNA and its ability to move up the stack into business applications.

Today, AWS is providing a clear answer to that question. It is not only building business applications, but also using agentic AI to deliver a new class of solutions.

The infrastructure giant has built a $1B business with Amazon Connect, its CCaaS solution. Using Connect as the umbrella brand for its solution portfolio signals a clear intent: replicate the same playbook that drove Amazon Connect’s success.

Colleen Aubrey, Senior Vice President of Applied AI Solutions, outlined the AWS approach: it leverages Amazon’s operational expertise, targets enduring business problems its parent could not solve with commercial software, and infuses talent from Amazon to bring that expertise into AWS. Proprietary “secret sauce” built from these in-house solutions is packaged into AI agents that function as teammates.

In addition to Amazon Connect Customer, the renamed CX/CCaaS solution, the portfolio includes three additional offerings.

Amazon Connect Talent is an AI-powered hiring solution built on Amazon’s experience of hiring 250,000 associates each year for peak season. It turns job requirements into structured, skills-based assessments and AI-led voice interviews that candidates can take on their own terms. It applies science-backed scoring and evaluation models to rank candidates and improve matching quality at scale.

Amazon Connect Health builds on Amazon Pharmacy and One Medical. The goal is straightforward: reduce administrative and documentation work from providers, a huge issue in care delivery. It uses agentic capabilities across the patient journey, starting with scheduling and intake to handle appointment booking, insurance checks, and reminders to reduce no-shows. During the visit, it transcribes patient conversations in real time, surfaces clinical context and coding suggestions, and generates draft clinical documentation for provider review. Every recommendation is explainable, with supporting rationale available to clinicians. Post-visit workflows, including follow-ups and medication adherence tracking, are also automated.

Amazon Connect Decisions is the agentic evolution of AWS Supply Chain, addressing supply chain issues for manufactured goods. It features two AI agents, one handling the demand side and the other the supply side, that proactively surface issues and generate options. A third agent assists human decision-making, including the option to incorporate best practices from top-performing planners. The system is powered by proprietary prediction models developed from Amazon’s experience managing hundreds of thousands of SKUs across its retail and logistics footprint.

Discussing his December prediction that we will soon live in a world with over one billion agents, CEO Matt Garman highlighted that agentic AI is starting to enable things that weren’t possible before. Two in particular: the ability to be given a desired outcome and run the end-to-end workflow toward its realization, and the ability to free up time for humans. The Amazon Connect portfolio is aiming at delivering on that vision.

What I find most promising in AWS’s approach is that, unconstrained by any application heritage, it can tackle end-to-end processes without boundaries, exactly the scope agents are built for. It will be fascinating to watch it mature and grow.

SoundHound AI announced earlier this week its acquisition of LivePerson.

The price stands out. SoundHound AI is snapping up LivePerson for an equity value of $43M while gaining access to $74M of its cash. Including LivePerson’s remaining debt obligations brings the implied enterprise value to roughly $250M.

Not that long ago, LivePerson crossed the $500M revenue mark. For a company once valued at $4.7B, the road here is worth revisiting.

Founded in 1995 as a web chat pioneer, LivePerson successfully weathered the early 2000s internet bubble burst. It found renewed growth with proactive chat following its 2006 acquisition of Proficient Systems. At a time when most brands struggled to deliver effective chat experiences, the ability to engage high-value customers based on available agent capacity proved transformative. The company then expanded into a broader digital customer service platform, reaching $250M in revenue by 2015 before hitting a plateau.

Then came COVID, which boosted revenue 2x, pushing it past the $500M mark in 2022. LivePerson followed with an aggressive growth push, investing heavily in go-to-market and expanding into conversational AI through the acquisitions of BotCentral, Conversable, e-bot7, and VoiceBase, funding them by increasing debt significantly, from $200M to $750M.

2022 marked the peak. By then, contact center players had caught up on digital capabilities, and LivePerson’s digital channel edge had eroded. Its provocative market positioning, declaring the death of contact centers and aiming to eradicate 1-800 numbers, backfired: businesses still needed to support voice and increasingly valued integrated voice and digital experiences over standalone digital customer service solutions.

Revenues declined to $240M in 2025, driven by customer churn. Founder Robert LoCascio was replaced in January 2024 by John Sabino, who reduced debt to around $400M, still a high level relative to its revenues. Revenues continue to decline, expected to land around $200M for the full year. This exit should come as no surprise to industry observers.

In addition to an estimated 120 enterprise customers, SoundHound is getting a digital customer service solution with best-in-class messaging capabilities and a conversational AI platform that earned a Contender position in Forrester’s most recent Conversational AI Platforms for Customer Service Wave.

The press announcement offered little detail on SoundHound AI’s vision. We should expect it when the merger closes in the second half of 2026.

This marks SoundHound AI’s third large acquisition, with $500M as its next revenue milestone and a 2027 revenue target of at least $350M to $400M.

The real work lies ahead: streamlining four conversational AI technology streams and building a strategy that brings together its self-service and human-assisted messaging channel solutions.

2025 marked a pivotal shift for customer-facing conversational AI (CAI).

Last October, I published an industry landscape featuring 650 players and highlighted how difficult it was to break through. For years, the market was defined by the three hyperscalers, AWS, Google, and Microsoft, alongside a small cohort of challengers struggling to break through the $100M ceiling and a long tail of sub-$20M players. CRM and CCaaS providers either built solutions using hyperscaler building blocks or partnered with specialists, yet made limited inroads.

In 2025, the lay of the land was redrawn.

A handful of pure-play providers have now crossed the $100M threshold. Kore.ai was the first, based on my research, to break that ceiling, followed by three others:

• Uniphore, which indicated last October alongside its Series F that it was well past $200M, with scale accelerated by acquisitions.

• SoundHound AI, benefiting from the acquisitions of Amelia and Interactions, ended last year at $170M in revenue.

• Sierra reported reaching $150M in ARR last December.

2 players emerged from the customer support automation corner: Zendesk, which reached $100M in AI ARR last year, and Fin by Intercom, whose CEO Eoghan McCabe disclosed earlier this month that it was about to pass $100M ARR.

Closely behind, a solid cohort of followers above $50M is well-positioned to cross the $100M ARR mark soon, including Ada, Cresta, LivePerson, Observe.AI, Parloa, and Yellow.ai.

ElevenLabs, coming from the Voice AI space, entered the market. While it is difficult to isolate the CAI portion of its $330M ARR last year, it is likely material, and I would not be surprised to see it in this group.

CCaaS providers that relied on partners or hyperscalers are now building or acquiring their way into proprietary CAI offerings. They are likely the largest contributors to NICE AI and Self-Service ARR ($328M in 2025), Genesys Cloud AI ARR (estimated at around $300M in 2025), and Five9 AI ARR ($100M last year).

CRM players have also been aggressively putting together their own CAI offerings, positioning Oracle (Digital Assistant), Salesforce (Agentforce), SAP (CAI/Joule), and ServiceNow (Now Assist) as significant players. We should also include IBM (Watson Assistant) in this group.

Three engines are driving this growth.

First, the incredible pace of progress across three key technologies—GenAI, Agentic AI, and Voice AI.

Second, CAI is enabling new use cases that go beyond what was previously possible and are setting a new benchmark for customer experiences. Three are now mature solutions consistently delivering tangible results: customer support automation, concierge agents unifying sales and service into a continuous assisted experience, and autonomous agents qualifying and processing contact or demo requests, with others gaining traction in proactive service, nurturing and re-engagement, and back-office customer service fulfillment.

Third, businesses are ready to modernize their digital front door. Self-service has consistently topped the priority list for customer service organizations for over a year. A similar trend is emerging in sales to manage incoming appointment and demo requests.

Adoption is accelerating as technology redefines the art of the possible in customer experience. Large CAI players are beginning to establish themselves, coming from the various corners of this vibrant market.

This article will be controversial.

I keep seeing posts about AI-driven layoffs that don’t match what I hear in direct conversations. The recent Citrini Research memo pushed me to write this.

I do not conduct quantitative research, but I review reputable studies. What they consistently show is that overall hiring has leveled off and that layoffs have remained limited, depending on the sectors. For example, retail jobs are declining and openings for white-collar roles, particularly in finance, have dropped.

On a recent podcast, Peter Cappelli, professor at Wharton, offered a grounded perspective. Gartner analyzed 1.15 million job eliminations reported between July 1, 2025, and December 31, 2025, and found that fewer than 1% were due to AI.

There is significant AI washing, with layoffs attributed to AI even when the true drivers are different. It is time to stop.

Zooming in on the technology sector, here is what I have been observing over the past few quarters:

• Many CFOs moved early in 2025 to curb hiring and boost productivity in response to rising economic uncertainty.

• Big Tech layoffs have been dominating the headlines and represent the closest casualty of AI. They are driven by three factors: post-pandemic overhiring corrections, growth slowdowns in large, mature product lines, and cost-cutting to fund their massive AI infrastructure investments.

• The restructuring of X under Elon Musk set a precedent. The company went from roughly 7,500 employees before the takeover to fewer than 3,000 (-60%). Revenues fell from $4.4 billion in 2022 to an estimated $2.9 billion (-34%). That reset reinforced a narrative that many software organizations could operate with far fewer employees.

• VCs are gravitating toward new startups with high revenue-per-employee potential, setting an aspirational $1M target.

• Beyond macroeconomic pressures, the SaaS sector faces sharper investor scrutiny. For years, it operated with deep pockets to grow, while efficiency took a back seat. As growth rates normalize and AI raises questions about long-term potential, investors now expect businesses to drive decisively toward profitability. This has driven, and continues to drive, workforce reductions.

Gartner and Forrester’s latest forecasts align:

• In October 2025, Gartner projected AI’s net impact on global jobs to remain neutral through 2026, emphasizing workforce transformation rather than outright displacement.

• Looking toward a longer time horizon, Forrester anticipates that automation and AI could account for about 6% of job losses in the United States, equating to roughly 10.4 million roles by 2030.

As 2026 unfolds, I am seeing another market shift. In 2025, the top expectation from AI was headcount reduction. Today, it is shifting to a secondary consideration as enterprises embrace AI as an inevitable way of operating.

AI will unquestionably impact the job market, at minimum driving a massive redefinition of roles. Mid- and long-term predictions remain uncertain, and AI washing only clouds the picture.

Salesforce Agentforce Contact Center announcement marks an industry-defining moment. It was the topic of many discussions at Enterprise Connect, allowing me to share a deeper analysis.

The convergence of CRM and CCaaS is not new. I wrote an article a few years ago outlining the moves from adjacent categories into the contact center. The trend is accelerating, with five prominent CRM providers now offering CCaaS: FreshWorks, Microsoft, Salesforce, Zendesk, and Zoho.

Three powerful forces are driving this evolution:

• First, data in general, and customer service interactions in particular, are critical in an AI-driven world. Salesforce was among the first vendors to recognize this and aggressively capture these conversations in its Data360 platform.

• Second, despite predictions that digital would dominate, voice remains a critical modality for customer interactions.

• Eventually, the complexities of voice and telecom have steadily declined since the advent of Voice over IP. While not eliminated, they have been significantly reduced, enabling more providers to include voice in their offerings.

Many saw Salesforce’s announcement as no surprise. Indeed, it had been in the making since Salesforce acquired Activa, a live chat provider, in 2010. Over the years, Salesforce assembled, through in-house development and a few tuck-in acquisitions, nearly all components required for a contact center:

• Agent desktop — Service Console or a unified workspace built with the Lightning Framework

• Routing engine and agent capacity model — Omni-Channel

• Digital Channels — email, SMS, chat, and messaging

• Knowledge Management and Self-Service Portals

• Workflow automation — Flow

• A comprehensive set of AI capabilities, now unified in Agentforce for conversation intelligence, customer self-service, agent assistance, and automated fulfillment of service requests

For voice — interactive voice response and call distribution — Salesforce leveraged partners with its OEM of Amazon Connect and deep integrations with 17+ BYO-Contact Center providers.

Until recently, Salesforce’s “surround” strategy enabled the San Francisco software giant to capture large chunks of the contact center market while partnering for voice and dodging the complexities of telephony and networks.

The 2024 acquisition of Voice AI company Tenyx signaled Salesforce’s willingness to enter the voice space. In October 2025, it launched Agentforce Voice agents, and now, with the addition of IVR and call distribution modules, it delivers a complete contact center solution:

• The new IVR enables creation of voice flows through the no-code Flow builder, connecting natively to Data360 and back-end systems via Mulesoft. Flows can include Agentforce Voice Agents.

• Call distribution to human agents uses Omni-Channel Routing with full context handoff, while customer service reps continue to use the existing Omnichannel utility within their unified workspace.

• The supervision console, recently modernized as part of BYO-Contact Center integrations, supports native voice calls.

• Analytics and reporting were also expanded.

• And of course, the solution integrates natively with Agentforce Service (CRM), Customer360 (customer profiles), Data360, and the Agentforce 360 agentic platform.

Salesforce has put together a telecommunications layer on its Hyperforce platform, working directly with telecom wholesalers and aggregators to provide voice and SMS connectivity and take the complexity out of procuring and setting up these channels. Building a robust network is a significant endeavor, and it will be interesting to watch how coverage expands across countries.

The Voice Contact Center add-on list price is set at $75 per user per month. It puts the full solution at $300 per user per month, using the minimum prerequisite Agentforce Service Enterprise edition ($175) and the Voice Contact Center and Digital Channel bundle ($125). Customers will likely layer in Data 360 and Agentforce to unlock the full value of the solution.

Salesforce began selling its contact center module last month and reported traction in the middle market, where its all-in-one approach resonates. Salesforce has a strong customer base in that segment with a solid cohort fully vested in its platform that should provide an initial runway. To fully deliver on the all-in-one promise, Salesforce will need to round out its solution with comprehensive outbound capabilities, quality management, and workforce management. It may also have to harness the Technology Solution Distribution/Technology Advisor (TSD/TA) channel, which increasingly commands access to this segment.

I expect Salesforce to rapidly go after the enterprise segment, its largest and most important market. Success there will hinge on delivering a rich feature set. While software development speeds continue to accelerate, adding all the features this segment expects will take time. AWS took three years to enter the CCaaS Gartner Magic Quadrant and another three to reach the leaders’ quadrant. Zoom entered the quadrant in two years. This provides a useful proxy for tracking progress.

Partners will be critical to Salesforce’s success in the space. The company can leverage its extensive ecosystem via strategic relationships with all global system integrators (GSIs) and many regional system integrators (RSIs). However, most already have established contact center practices on other platforms, and Salesforce will need to shoulder its way in to secure a foothold.

The 2023 collaboration announced by Genesys and Salesforce was well received by the market, resolving buyers’ prior confusion over which components to source from whom. It established an industry reference for allocating roles between the platforms: CCaaS handles IVR, voice routing, workforce, and quality management; CRM provides the unified agent workspace and consolidates customer, journey, and interaction history; digital channels remain the customer’s choice. Since then, players in both categories have added Agentic platforms, expanding areas of choice and overlap. With CCaaS also available with CRM, buyers will have to reassess where to draw the lines, which may elongate sales cycles.

Like many big market moves, this announcement helps put ongoing changes into perspective. It notably counters predictions of the impending death or irrelevance of contact centers. It also highlights the erosion of traditional CCaaS vendor moats. The center of gravity for CX solutions is rapidly shifting to the data platform and managing customer communications where they begin—the customer service front door.

ElevenLabs has quickly become one of the most talked-about players in voice AI. The company recently reported ending 2025 with more than $330M in annual recurring revenue (ARR).

Mati Staniszewski, the company’s founder, has candidly predicted the commoditization of voice AI and outlined how the company is moving up the value chain.

His January keynote at the London company event provided clear insights into their current initiatives.

The company now offers ElevenLabs Agents, a platform to build, deploy, and monitor AI agents. It includes a template for support.

The keynote demo showcased a customer service scenario where a voice AI agent assisted a rapidly growing new business owner on behalf of a government agency. The experience used natural voice and real-time interaction—ElevenLabs’ core strength—but went further. It was secure: the agent verified identity with a time-limited code before sharing sensitive information. It enabled seamless transfer to a human with full context. It demonstrated proactive engagement, with the agent calling the business owner about a startup grant, and personalization, referencing previously shared growth plans. It operated across multiple channels, including WhatsApp, web, text, and phone.

As with any AI demo, it shows well; production deployments are the real test. Three companies showcased their CX agent deployments using its platform: Klarna’s voice agents delivering first-line phone support to its 35 million US customers, Deliveroo’s agents supporting rider onboarding and restaurant setup, and Deutsche Telekom’s agents managing support inquiries via app and phone.

Eleven Labs also highlighted internal use cases, including two focused on sales: an SDR agent nurturing and qualifying leads, and an events agent following up with attendees for feedback.

ElevenLabs built its name on voice AI. It’s now a conversational AI player to watch.

While Salesforce captured attention with its Agentforce Contact Center announcement at Enterprise Connect, Zendesk announced its 7th acquisition, Forethought.

Founded seven years ago, Forethought is a pure-play customer support automation company.

It adds a team of over 140, according to LinkedIn, a material expansion to Zendesk’s AI and engineering talent.

Zendesk anticipates a boost to its roadmap through Forethought’s voice automation capabilities, complex workflow automation, and expanded access to back-end systems, including those without APIs, via browser-based automation.

Strategically, Forethought brings a standalone customer support automation solution, enabling Zendesk to target companies currently using other providers’ support and ticketing platforms.

Zendesk has moved aggressively on AI, investing over $500 million across seven acquisitions in the last three years. It has also adopted the resolution-pricing model, which is transforming customer support.

After reaching $200 million in AI ARR last year, CEO Tom Eggemeier shared the growth targets for his company: $400 million in AI ARR by year-end and $1 billion by 2028.

On the heels of its fourth-quarter earnings, RingCentral hosted its industry analyst summit last week. Three key takeaways emerged.

The company continues to demonstrate strong execution. Over the past two years, it has adapted to a single-digit growth environment, reduced debt to $1.25 billion, grown free cash flow to over $500 million, and achieved GAAP profitability.

The summit was led by Kira Makagon, celebrating her first year as President and COO. Vlad Shmunis, founder and CEO, joined for the Q&A. The team, a mix of veterans, boomerangs, and newcomers, conveyed a strong sense of alignment across all functions.

RingCentral is all-in on AI and business communications, dedicating over 60% of its engineering investments to developing new AI-based products. Several have already launched and are enjoying strong adoption. This is worth celebrating, given the challenges many face with AI adoption.

AI Receptionist (AIR) is a concierge agent delivering a unified front door across departments. AIR handles appointment booking, lead capture and qualification, and transfers to a human based on detected intent. Introduced a year ago, it has reached over 8,000 customers. Last November, RingCentral launched its AI Virtual Assistant (AVA) to support human employees. Together with AI Conversation Expert (ACE), formerly RingSense, they form the company’s “three As”—AIR, AVA, ACE. Each represents a potential entry point for applying AI and, over time, can be combined into a virtuous automate-assist-automate loop.

Also last November, RingCentral launched a customer engagement bundle, extending its RingEX communication solution to provide contact center-like voice and SMS capabilities to roles that are not full-time contact center agents. In just a few months, it surpassed 1,000 customers, highlighting market demand to expand the CX discipline and eliminate friction in customer interactions across departments.

Introduced two years ago, RingCX, RingCentral’s homegrown contact center solution, was recently enhanced with RingWEM, which combines recording, quality, and workforce management following the CommunityWFM acquisition. It now serves 1,500 customers and likely accounts for a substantial portion of the $100 million in ARR attributed to new products.

The product presentations and demos demonstrated the care and precision that went into tailoring these solutions for their target markets. AI Receptionist and AI Virtual Assistant, in particular, are designed for zero-effort setup and flywheel-driven improvement, enabling distribution through a product-led motion.

On the go-to-market (GTM) front, RingCentral’s success comes from elevating discussions from technical infrastructure to business impact. The company has been developing an approach tailored to its target industries, with granular vertical definitions driving effectiveness—focusing on clinics rather than the broader healthcare sector, for example. This enables highly specific conversations on where its AI solutions can deliver impact. This evolving GTM is well received by partners, enabling the company to leverage its well-established global service provider and Technology Services Distributor (TSD)/Technology Advisor (TA) channels.

RingCentral increasingly uses the term business communications. While generic in meaning, for the company, it refers specifically to essential interactions between a business and its current or future customers. RingCentral doesn’t see AI replacing human-to-human communication; on the contrary, it observes these interactions increasing within its customer base. They include customer service inquiries with contact center agents, sales conversations, communications between patients and healthcare providers, and more. Traditionally, these were handled separately—contact center software for customer service and unified communications for internal and external messaging. RingCentral’s new portfolio aims at unifying them into a continuum while harnessing AI to drive impact.

RingCentral describes its third act as RingCentral 3.0, aiming to become the front door for both human and AI agent interactions. In a crowded market, the company emphasizes its robust, large-scale communication infrastructure—a foundation built over a quarter century. Its comprehensive business communications approach, coupled with disciplined execution and a targeted focus on industries and use cases, makes its strategy very promising.

I continue to get questions, particularly from investors, on whether existing CX providers still have a meaningful opportunity in an AI-driven world, amid what some are calling the SaaSpocalypse.

So, this is a good moment to take stock across the public CCaaS and CRM players and assess how AI is reshaping their business:

• Salesforce AgentForce Annual Recurring Revenue (ARR) reached $800M, up 169% year over year, representing 2% of total revenue.

• ServiceNow Now Assist Net New Annual Contract Value (NNACV) was $600M, growing at a 67% run rate, about 4.5% of total revenue.

• NiCE AI and Self-Service ARR reached $328M, growing at a 66% run rate, representing 13% of interaction management revenue.

• Genesys Cloud AI ARR surpassed $250M, growing at a 60% run rate, representing 10% of total revenue.

• Five9 Enterprise AI's annual run rate revenue crossed $100M, growing at a 50% run rate, representing 9% of revenue.

These datapoints represent different metrics and, so, should be taken with a grain of salt. They mix revenue and ARR, and forward growth rates are not always disclosed, but the numbers tell a consistent story.

Incumbents from all corners of the CX market are fully participating in the AI opportunity and generating material revenue from it.

This aligns with feedback from enterprise surveys, consistently identifying customer service as a top priority for AI deployment.

Of course, these figures are dwarfed by OpenAI’s $20B and Anthropic’s $5B ARR, but only a handful of mega AI players exist at this stage. AWS and Google do not break out AI revenue, which likely falls in similar ranges, and Microsoft’s 15 million paid Copilot seats put it at a comparable level. Beyond them, Databricks is estimated at $1.4B, with all other players below $1B.

Stories of the doomed fate of seat-based SaaS vendors are premature. Another misconception to discard is that CCaaS providers are poised to become irrelevant. Managing customer conversations at the company’s front door gives these providers a clear advantage.

Of course, incumbents are/will be challenged by new entrants, but that is a discussion for another post. For now, my call is to view this market through new lenses.

Instead of fixating on seat-based models, investors should focus on how these companies are driving incremental AI revenue, transforming their platforms for the AI paradigm, and securing their positioning as a strategic AI provider that enterprises rely on for mission-critical use cases, not just for commoditizing AI features.

Last week, Medallia held its Experience user conference.

One year after Mark Bishof took the helm and completely refreshed the leadership team, it was the right moment to take stock of how the company’s Act 3 is shaping up.

Founded in the early 2000s, Medallia took off in the mid-2010s as an enterprise feedback management and survey software provider. After becoming public in 2019, it expanded aggressively into multiple market adjacencies through a series of targeted acquisitions, including Strikedeck for customer success management, Zingle for customer messaging, Stella Connect for quality management, Voci Technologies for for speech analytics and conversation intelligence, Thunderhead for customer journey orchestration, and Mindful, formerly Virtual Hold, for callbacks. The 2021 acquisition by Thoma Bravo opened its latest chapter.

Mark discussed how his first 12 months focused on rebuilding a customer-centric culture and modernizing the platform, with a strong emphasis on text analytics. One of Medallia’s strategic uses of AI is to help users maximize its rich set of capabilities and remove the friction created by their sophistication, which can slow implementation and hinder full adoption. It unveiled Insights Assistant as a step up from Ask Athena in how users interact with the platform. It’s important to stress that, despite numerous acquisitions, Medallia has maintained a singular architecture, which, together with its ability to scale to meet the needs of the largest enterprises, continues to underpin this evolution. He briefly addressed financial performance for the fiscal year ended in January, pointing to business momentum and AI adoption, with more than 550 customers using its Frontline AI products.

The presence of large enterprise customers was notable. Even more significant were the stories they shared and the magnitude of their deployments. Bank of America, for example, collects data across more than 50 touchpoints through Medallia, which is used by 60,000 frontline employees in its consumer banking business.

Chief Strategy Officer Sid Banerjee urged customers to become change agents, a call to action reflecting the industry’s ongoing shift from traditional surveys to capturing a broader set of signals—including digital, contact center, physical, and franchise experiences—to drive meaningful transformations.

Experience management vendors are not immune to enterprises re-evaluating their SaaS investments in the AI era. Anecdotally, I heard that large enterprises are reaffirming their commitment to Medallia for two reasons: the platform is deeply embedded in their operations, and it can orchestrate workflows across complex organizations to share insights with customer-facing teams, celebrate successes, and assign corrective actions.

Medallia executives expressed strong conviction that combining signals not only generates insights exponentially but also establishes the foundation to link them to key outcomes—revenue, retention, operational inefficiencies, risk reduction, and regulatory compliance.

Text analytics is central to enabling this vision. Chief Product Officer Fabrice Martin explained that while AI can operate unsupervised to surface insights from massive datasets, Medallia has found that topics remain foundational: they ensure traceability by linking insights to actual experiences and enable consistent tracking over time to measure improvement. To support this, Medallia announced Smart Topic Builder, which helps identify topics, define the keywords and linguistic rules to track them, and audit their precision to validate the rules.

The scale of data Medallia collects and connects is impressive, positioning the platform to play a pivotal role for AI-driven insights, as highlighted by its strategic partnership with Ada announced last month. To fully seize this opportunity, Medallia and Experience Management providers more broadly will need to actively drive customers to place these platforms at the center of sales and customer service workflow design.

For the past decade, the software and SaaS sales playbook has revolved around a product demo followed by a pilot:

• Early-stage efforts would focus on securing a demo that provides a high-level view of the product and serves as a stepping stone to identify a small set of priority use cases tied to the customer’s most pressing business issues.

• Sellers would then guide the customer toward a tightly scoped pilot addressing one or two of those issues. The pilot would validate usability with a subset of users and confirm that the targeted workflows operate effectively within the customer’s operational context.

These pilots would typically be short and narrowly defined, handled by Customer Success Managers, with exit criteria framed as straightforward checklists, and failure rates kept low largely thanks to their limited scope.

Today, AI is reshaping the playing field.

AI demos are compelling and often impressive, but what comes after is a cliff. It is hard for prospective buyers to visualize what it actually takes to put AI to work in their own environment. It is equally challenging to define and measure success, set clear expectations for results, and fully grasp what ongoing operations and continuous improvement will require.

To address these questions, buyers are turning to pilots of a very different caliber: true trials. In its 2026 State of Business Buying report, Forrester found that more than 60% of B2B buyers include a trial in their purchase process.

One finding from the same study underscores the challenges of these pilots: half of the trials for $1+M projects result in either the project being halted or a vendor switch.

Vendors are responding by supplementing or replacing Customer Success Managers with forward-deployed engineers (FDEs). In many cases, FDEs continue beyond the initial trial to drive ongoing improvement and expansion.

Success also requires a methodology overhaul that addresses:

• Discovery of the customer environment

• Assessment of data readiness

• Managing potential employee resistance to change and upskilling

• Identifying practical measures of success

• Building and prioritizing the use case portfolio

• Managing the ongoing cycle of improvements

Eventually, success also requires stepping back to reassess which problems to pursue. Until last year, most companies would favor “safe” initial use cases with limited risk but modest upside. Today, there is greater readiness to apply AI to bigger problems that command higher upside. The good news is that more businesses are comfortable with measures of success that don’t mechanically translate into cost-cutting or revenue gain, but they do expect tangible improvement.

This isn’t a temporary adjustment. The combination of deeper technical involvement, comprehensive methodology, and sharper use case selection is setting a new standard for putting AI to work.

AWS turned re:Invent earlier this month into a platform to accelerate its Agentic AI strategy. The cloud leader has taken a fast-follower approach in the Agentic AI race, launching toolkits and frameworks designed for enterprise-scale deployment and adoption.

Amazon Connect, fresh off crossing the $1B ARR mark, centered its announcements on packaging AI agents for its contact center suite.

For human agents, it offers prebuilt AI agents that support Q&A, case summarization, task automation, real-time recommendations, automated note-taking, and message writing. In one year, AI-enhanced interactions doubled from 6 billion to 12 billion minutes over a 12-month period.

For self-service, Amazon Connect has expanded its capabilities with AI agents and the ability to combine deterministic and agentic workflows. It also introduced integration with AWS’s homegrown voice AI model, Nova Sonic, delivering near-human voice quality with low-latency speech-to-speech performance. Paired with its per-minute, all-inclusive AI pricing unveiled at Enterprise Connect, it underscores an aggressive move to modernize voice self-service IVRs. Notably, ElevenLabs cut its pricing by 50% two weeks later, signaling a broader industry push for voice AI adoption as we enter 2026.

Another significant feature introduced is Journeys, multi-step experiences that can be campaign-driven or event-triggered. Building on the proactive service vision unveiled at last year’s re:Invent, Journeys lets businesses engage customers interactively via voice, email, SMS, or WhatsApp. Proactive service has long been elusive for customer service organizations. With AI, it is now both feasible and essential as self-service and automation de-intermediate brands from customers. This positions Amazon Connect at the forefront of a major industry trend.

In 2024, AWS shifted the Amazon Connect positioning from a composable suite to an all-in-one solution, highlighting its out-of-the-box capabilities. This led to the renaming of individual components into capabilities. Contact Lens, for example, is being subsumed into Amazon Connect AI-powered conversational analytics. Reference architectures, previously featured in nearly every presentation, were largely absent from the 25+ Amazon Connect sessions.

The composable suite remains a key strength of Amazon Connect, driving adoption among large enterprises and system integrators. At $1B ARR, expanding into the midmarket represents a logical growth path. This further aligns with Amazon Connect’s positioning within AWS Applied AI Solutions, a group with a core mission to democratize access to AWS’s AI services.

One related development is the OEM relationship with Zendesk. At the event, the customer and employee service specialist was named AWS Customer Experience Partner of the Year (Technology) – Global, and the companies announced a strategic collaboration agreement enabling the vendor to assemble a cohesive solution, Zendesk Contact Center, on top of its Resolution Platform. This zero-middleware solution extends Amazon Connect’s reach in the small and midmarket segments. Leveraging Zendesk’s positioning and track record of simplifying customer service is a smart move from AWS.

At re:Invent, AWS not only unveiled Amazon Connect’s agentic capabilities but also positioned its customer experience suite at the forefront of its strategy to drive adoption of its AI services.

Three months ago, Tod Famous challenged us to reconcile Genesys Cloud ARR growing nearly 35% year over year with a cloud contact center market growth rate of 12%.

The 12% market growth rate reflects the average of public companies. It aligns with the SaaS new normal and exceeds Gartner’s forward-looking 9% projection.

Genesys defines Cloud ARR as fiscal quarterly revenue, combining committed contracts and usage-based revenue, multiplied by four, making it a somewhat apples-to-oranges comparison.

The difference still feels significant. After Genesys reported third-quarter metrics with Cloud ARR approaching $2.4B, up more than 30% year over year, I received another wave of questions about the contact center market growth that pushed me to dig through all its announcements to estimate its revenue trajectory.

The exercise was not trivial: some announcements read like a treasure hunt, for example, “after exceeding $250M during the second quarter, Genesys Cloud AI ARR continued its strong momentum, with a year-over-year rate more than twice that of Genesys Cloud ARR and accelerating from the previous quarter.”

After some triangulation, I was able to piece together quarterly revenues by cloud, legacy products, and professional services. The attached chart shows my findings. Keep in mind, these are back-of-the-envelope estimates.

I find that the company’s last-quarter cloud revenues grew an impressive 33%. Turning the on-premises installed base into cloud customers has been driving a significant portion of growth. Genesys appears to be executing this exceptionally well, a remarkable performance given the angst its legacy product’s end-of-life announcement generated and the fate of other on-premises providers whose installed bases have plummeted. Total product revenue grew a solid 13%.

Reports of the end of contact center growth have been greatly exaggerated.

A little present before Christmas: the 8th edition of the CX landscape is out. I got it over the line before the end of the year.

The total number of participants grew by 12% since July 2024 to 1,300. CX remains a strategic and attractive market, and it delivers many of the top AI use cases. That growth should surprise no one.

Conversational AI and customer-facing agents drove the largest increase, with, in particular, a surge of new entrants in customer support automation and voice agents. The category is maturing fast. It was once a hyperscaler-led market with a long tail of small players. In 2025, several providers will cross $100M ARR, with a solid cohort over $50M.

Beyond customer self-service and agent augmentation, agents are emerging for workflow automation, driving the birth of an AI Agent Platform category.

Data platforms have made their coming out. For years, CRMs served as the reference customer data repository while other players positioned themselves as feeders of interaction details and dispositions into those systems of record. With AI, more providers now see the imperative for a comprehensive data foundation, consolidating interaction records, recordings, transcripts, and customer profiles into dedicated platforms.

This 8th edition may well be the last to use the current construct.

I created the first version in 2017 based on a few assumptions:

1. Self-service would become the front door for service.

2. The system of engagement would be contested between interaction management platforms, including CCaaS with a voice lineage, Digital Customer Service with a digital channel lineage, and CPaaS with a messaging lineage, and workflow platforms, whether CRM or BPM/low-code platforms.

3. Analytics would remain a distinct layer, combining best-of-breed applications and aggregation platforms.

This framework has remained remarkably stable, evolving only slightly over the years.

As I reflect on the changes of the past year, it’s time to consider structural changes.

• Data platforms are eclipsing interaction management and workflow software as the foundation of the CX stack.

• Above this, an agentic layer will emerge, supported by a monitoring and control layer.

• Self-service and human service, historically loosely coupled, will become tightly intertwined to enable new, collaborative motions.

• Resolution and fulfillment workflows will become an integral part of the stack.

• The CX stack will need to support not just service and support but also sales and retention, demanding new capabilities and accommodating these new roles.

• Work allocation, once focused on instant distribution of inbound inquiries, will change. Combine the new roles to support, a more diverse mix of real-time, asynchronous, and deferred/scheduled interactions, and the growth of outbound driven by proactive service enabled by AI, and the result is a different work allocation model.

But that’s for next year...

Five9 held its CX Summit last week in Nashville, introducing three new products. I wasn’t at the event, but was briefed on them.

Five9 Genius Routing - Most contact center routing today relies on rules. Low-code tools have simplified workflow design, letting practitioners visualize decision chains. But as rules compound, complexity can grow rapidly, making changes delicate and risking triggering unintended side effects.

AI routing is not new. It was pioneered by Afiniti and Mattersight, now part of NiCE, harnessing AI to pair interactions with agents based on goals such as maximizing sales or minimizing churn. While effective, practitioners have remained on the fence, concerned about the black-box nature of AI algorithms.

Today, the industry has grown accustomed to and comfortable using AI for optimization, setting the stage for its adoption in routing to take off. Five9 Genius Routing uses multi-criteria optimization with the ability to loosen criteria to ensure service levels are met. Its APIs pull inputs from performance management—agent profiles, proficiency levels—and self-service—intent, urgency, emotion—to make routing decisions in under 200 ms.

Five9 Agentic QM - Five9 stressed that the “A” in its AQM solution stands for Agentic, not Automated. It provides autoscoring with visibility into the “why” via Interaction Viewer. The solution operates in real time, autonomously selecting priority actions and surfacing them to supervisors.

CTO Jonathan Rosenberg emphasized the intelligent scoring baked into the solution, which requires sharp-precise prompting. It took four iterations of its prompting to reach the desired performance, leveraging its “Golden dataset” and capturing best practices.

Five9 OneVUE - a unified repository for CX data across Five9 and other systems. It leverages Aceyus technology, acquired two years ago, to ingest third-party data—Voice of the Customer, CRM, etc.—and combine it with Five9 Data Lake. The solution layers dashboards and analytics on top of this aggregated dataset.

Through product demos, Five9 made innovation the centerpiece of its keynote.