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.

11 years ago, I created an Inside Sales Technology Landscape. While widely known as a map of categories and companies in the space, it was originally designed as a blueprint for architecting a software stack supporting inside sales.

The framework proved remarkably durable. I expanded it over the years to cover the full sales software landscape. It held up through a decade of innovation... up until agentic AI emerged.

The need for a reset became clear when I published its latest edition last November. So when Seth Marrs reached out to discuss assembling a blueprint to guide the design of a revenue stack, I seized the opportunity to leverage his insights to build it.

Data and AI now enable organizations to segment and target their market with laser precision, on a continuously updated basis:

• Define ideal customer profiles (ICP)

• Build a full, enriched total addressable market (TAM)

• Map buyers and buying committees to expand each target account with the relevant contacts

• Segment markets with far greater granularity

• Identify and aggregate signals that indicate emerging demand or timing

• Build scoring models combining fit, segment, and signals to prioritize accounts and contacts, and trigger the right sales and marketing plays

This data-driven market definition and prioritization foundation becomes the backbone of both sales and marketing, driving tighter alignment. It hinges on acquiring and enriching company, contact, and signal data, and combining it with your proprietary data.

AI is the catalyst for organizations to rethink their sales processes, shifting focus to end-to-end Jobs to Be Done (JTBD). We’ve become addicted to adding a new point solution for every sales engagement “play,” creating a patchwork of tools with poorly-connected mini-workflows. The result is constant context switching and friction at every handoff, ultimately impeding execution.

We can group the core JTBDs into the following:

• Enable sellers and buyers

• Build awareness & seed demand

• Prospect through personalized outreach

• Orchestrate deal pursuit through close

• Capture & convert inbound demand

• Execute high-velocity outbound calling & lead distribution

• Sell with and through partners

• Structure & execute deals

• Retain, renew, and expand customers

Not all of these apply to every organization, and relevance depends on your sales motions—inbound vs. outbound, targeted prospecting vs. high-velocity sales, partner-led models (sell-through vs. sell-with), account development, and product-led sales. These JTBDs can be mapped to the software categories that enable them.

The fragmentation of sales stacks, combined with persistent disconnects between marketing and sales systems, has driven a shift: from application-level reporting and analytics to a cross-stack instrumentation layer that captures engagement and sales activity and delivers a single source of truth for forecasting, pipeline, and performance management.

Agentic AI is inserting itself into the stack as a foundational layer that can execute workflows and act on them. It relies on a system of context that aggregates all the information required to execute its missions. It sits alongside legacy workflow automation and data orchestration technologies.

In this construct, systems of record sit at the base of the stack as the systems of truth for core data elements, providing context for AI.

It’s impossible to provide full color and detail in a single post. In the coming weeks, I will continue to unpack this new framework and share examples of how you can use it to map existing providers and your as-is and to-be stacks.

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.

Last month, Apollo announced its acquisition of Pocus.

Founded in 2021, Pocus was one of the first Product-Led Growth (PLG) platforms —and one of the last standing.

Its initial value proposition was to aggregate product signals and apply AI to score and trigger actions that drive playbooks. These workflows spanned the full lifecycle of product-qualified leads (PQLs), driving adoption and usage, monetization, and timely sales-led engagement. The company has since expanded its focus to a broader set of sales motions. It brings best-in-class capabilities to Apollo to unify signals and prioritize next-best actions, supporting its move upmarket.

The acquisition prompted a look back at the fate of the once red-hot PLG-CRM category.

Fueled by COVID, product-led growth became the buzz in 2022. Software managing PQLs emerged as a category investors believed would become a durable segment of the sales stack. Today, most of these players have wound down or shut down. Brendan Short mapped what became of each one. The few that pivoted found exits through acquisition: Endgame by HighTouch, Madkudu by HG Insights, and now Pocus by Apollo.

What happened to PLG-CRM echoes the 2018 absorption of predictive analytics into the broader stack: vendors were challenged to scale, and the category dissolved the same way. Mike Cabot’s earlier article offers insight into what happened at the time.

Over the years, I’ve watched pure analytics applications struggle when not connected to specific actions, both measurable and high-impact. The pattern has been consistent enough to shape a conviction: predictive and scoring models need to be part of a broader, action-oriented system to reach their full potential.

What’s clear today is that scoring has never mattered more, and the market is catching up to that reality. As data sources and signals multiply, the ability to make sense of them and surface the right next-best action is becoming a critical piece of the modern revenue stack. That makes predictive models the cornerstone of any logic that triggers signal-based plays or recommends next-best actions. That’s why scoring is hot again.

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.

FrontSpin announced it was joining TitanX. Created by serial entrepreneur Mansour Salame, FrontSpin is a sales dialer and engagement platform for B2B businesses, recognized for its ease of use, rapid onboarding, and tight Salesforce integration.

Founded by Joey Gilkey, TitanX is a relatively new player in the B2B salestech space. It originated from Apex Revenue, an outsourced outbound SDR company. In 2024, Apex acquired the sales intelligence firm Phone Ready Leads and pivoted to pioneer a new category it calls phone intent. By July 2025, just a year after its formal launch, TitanX announced it had surpassed $5M ARR.

Think of phone intent as behavioral analytics that predict the likelihood of a call being answered. It leverages 46 signals and counting, collected from multiple sources, including telcos and consumer databases. TitanX uses these signals to build a graph of business buyers and their associated phone numbers, and to predict which numbers are active and most likely to engage with a cold call.

TitanX transforms a rep’s outbound math from 80 dials per day at a 5% connect rate, yielding 4 connects, to 50 dials per day at a 25% connect rate, its claimed average, resulting in 12 to 13 connects. TitanX backs that claim with a guarantee: if you are unable to get the 3x connect rate lift, they refund your pilot plus pay $10,000. The company has grown to over 300 customers and raised $27 million in a Series A in January.

Today, customers use TitanX to scrub calling lists of bad numbers and segment them by propensity to answer. With FrontSpin, TitanX gains both a dialer and a number reputation solution. Number reputation naturally extends its intelligence layer, while the dialer closes the loop on performance measurement.

The combination positions TitanX to own the full B2B outbound calling stack: predictive intelligence, number reputation, and performance-driven dialing in a single solution.

Salesforce has acquired Momentum, a salestech company, to extend its Agentforce platform.

Momentum positions itself as an AI GTM Data Orchestration platform, automating a wide range of activities, including sales activity capture, churn prediction, sentiment and feedback analysis, coaching, deal progression tracking, and clip creation from recorded calls and meetings.

It can integrate with existing conversation intelligence platforms or leverage its own capabilities.

Momentum is recognized for delivering these capabilities through use-case-specific agents that not only update systems but also act on their insights.

Momentum’s agents will complement and enrich Slackbot, a central element of Salesforce’s vision to streamline how users engage with its expanding suite of clouds and applications.

The acquisition addresses a chronic gap in conversation intelligence deployments: recordings, transcripts, summaries, and insights remain underutilized when action depends on humans to update the system.

Like Qualified, Momentum’s capabilities already exist within Salesforce. What differentiates them is packaging, anchored by a prescriptive, phased deployment methodology that begins with automated activity capture.

For customers who find the Salesforce Agentic Enterprise blueprint intimidating, this acquisition should offer a new, more approachable entry point.

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.

2025 marked a turning point for sales and revenue technology. For years, vendors and pundits predicted SalesTech consolidation, yet progress remained incremental. Unified go-to-market platforms have now matured and are beginning to replace fragmented point-solution stacks. AI has been instrumental in this shift, making it straightforward to layer agents onto solid data foundations and expand supported sales motions.

Against this backdrop, I’m launching a series to explore what the market’s consolidators have to offer through a jobs-to-be-done lens. I begin with Apollo, which introduced its all-in-one platform and agentic capabilities at its Next event last November.

Data foundation

Apollo maintains a proprietary dataset of 200 million accounts and contacts, including technographic details. Its network of 2 million contributors keeps contact data current and accurate. Its Deliverability Suite monitors emails and provides closed-loop feedback on address accuracy. For intent signals, it combines proprietary web crawling with data from Bombora and Foundry. It recently added waterfall enrichment, letting you control the order in which sources are queried.

Users can manage account and contact stages, as well as deals and tasks. All activities in Apollo, including emails, calls, and meetings, are captured automatically, along with recordings from meeting platforms and its own dialer. These objects can all be synced back to CRM. Apollo also includes a content center that ingests website content and other key assets to feed its messaging engine, enabling personalized communications.

Targeting & segmentation

Apollo Projects serves as the workbench for building lists and campaigns, guiding AI context gathering. The AI Assistant lets you describe your ICP in plain English and identify your total addressable market. You can refine it using custom attributes pulled from the web. Apollo can also find lookalikes based on selected accounts. You can then apply account scoring to prioritize your lists, and once finalized, source contacts with scoring models that assess relevance and likelihood to engage.

Prospecting

Apollo Engage lets you enroll your lists into prospecting sequences across email, LinkedIn tasks, and voice. Apollo AI Assistant supports account and contact research, sequence design, and messaging using the content center. The Apollo Deliverability Suite handles mailbox warm-up, rotation, and email verification. In November, Apollo added a parallel dialer with call guides, local presence, and voicemail drops, completing its sales engagement platform.

Appointment-driven inbound

Apollo manages inbound leads from your website, starting with visitor identification at both contact and account levels. It handles form enrichment, scoring, lead routing, and meeting scheduling. Apollo Workflows let you define a frictionless buyer experience end-to-end.

Deals

Apollo lets you manage deals and tasks directly in the platform. It features a unified, role-based workspace with access to all suite capabilities via the left-side navigation. Reps can also use a mobile app and a Chrome extension for prospecting. A meeting assistant supports pre-call research and post-call activities, with Apollo AI generating follow-up tasks and recommendations. Customizable pipeline views and a deal dashboard, including research, insights, interaction history, and tasks, complete its deal management module.

Tracking & Insights

Apollo automatically logs all outbound prospecting, inbound lead management, and deal activities. Call and meeting recordings are processed through its conversation intelligence module, letting you create playlists for onboarding and training and score these interactions to drive sales coaching. Analytics draw on this comprehensive data to track campaigns, outbound prospecting, inbound web engagement, seller performance, call and email effectiveness, and pipeline.

In 10 years, Apollo has built one of the most comprehensive go-to-market suites. At SaaStr Annual 2024, founder and CEO Tim Zheng reported over 80,000 paying customers. By May of last year, the company announced 2 million users and $150 million in annual recurring revenue, signaling strong traction in the mid-market. It’s time to reset perceptions of Apollo as only a data provider.