Yet another Salesforce acquisition. The buying spree shows no sign of slowing down.

This time, Salesforce is buying Cresta.

Salesforce hasn’t disclosed the price, but the $2.7 billion figure reported by The Information would put the deal in the same price range as its acquisition of Fin, which was valued at $3.6 billion.

Founded in 2017, Cresta initially focused on real-time guidance and coaching for human agents. Over time, and with more than $270 million in funding, it expanded its offering into a conversational and agentic AI platform covering the full spectrum of customer self-service and employee assistance use cases.

Its platform combines two complementary capabilities:

• Opera, its AI orchestration engine, enables enterprises to build and deploy AI agents.

• Cresta Insights, its conversation intelligence solution, analyzes customer interactions, employee behavior, and conversation outcomes to identify patterns and opportunities for improvement.

Cresta really took off in 2025, crossing the $100 million ARR milestone in April 2026.

Cresta’s suite overlaps with Salesforce Agentforce, raising questions about how the two platforms will ultimately come together. We will know more after the acquisition closes. But Salesforce’s announcement already provides clues about what it values most: voice agents, conversation intelligence, and real-time assistance for human agents.

Beyond expanding Salesforce’s voice AI capabilities, Cresta brings something particularly valuable: the ability to understand what happens inside customer conversations, identify the drivers of outcomes, and use those insights to improve both human and AI-driven interactions.

This is where I see the biggest upside for Salesforce. Cresta’s conversation intelligence capabilities could significantly enrich the customer context available through Data 360 and address the shortcomings of its Customer Signals Intelligence and Feedback Management offerings. More importantly, they could help Salesforce demonstrate the value of consolidating enterprise data in Data 360 by extracting richer, actionable insights from it. As Salesforce pushes enterprises to bring more of their data into Data 360, the ability to turn that data into valuable intelligence will be critical to its monetization strategy.

This should be viewed in the broader context of the convergence of Voice of the Customer (VoC) / feedback management and Conversation Intelligence. Bringing together what customers report in surveys and other feedback channels with what they actually say and do during interactions, and correlating both with business outcomes, represents one of the next frontiers in CX analytics.

At NiCE’s Industry Analyst Summit, CEO Scott Russell set the tone with a Hawaiian word: Huliau, meaning a turning point or moment of profound change. The word reflected the location, but, more importantly, captured the inflection point at which the CX industry finds itself.

As Scott put it: “Brands no longer own the customer journey. The customer’s AI agent does.” Personal agents were a key theme in Chief AI Officer Phil Heltewig’s presentation. Their growing capabilities and accelerating adoption are fundamentally changing how consumers interact with brands.

At NiCE World, Phil had already warned us about the impending explosion of AI assistants reaching out to brands and interacting on consumers’ behalf. The recent launch of Muse shows this is no longer a distant possibility but a rapidly emerging reality, bringing two major challenges for brands.

First, the volume challenge. As consumers increasingly delegate interactions to their personal agents, brands could face a massive surge in interaction volumes. Meeting this demand will require brands to accelerate the deployment of their own AI agents and ensure they can interact effectively with consumers’ personal agents. This is why NiCE joined Meta’s consortium for the Personal Agent Protocol earlier this month, reaffirming its commitment to staying at the forefront of this important trend.

Second, the disintermediation challenge. As personal agents take over a growing share of customer interactions, brands risk losing direct access to consumers. Brands will need to become more proactive. This will, in particular, entail identifying critical moments and seizing them to engage customers through human interactions when they can make the greatest difference.

This makes effective collaboration between AI and human agents even more critical. Brands will need to combine the scalability of AI with the ability to bring humans into interactions when they can make the greatest difference. NiCE’s ambition is to eliminate the friction of traditional handoffs by bringing AI and human agents together as collaborators rather than treating them as separate resources. Its new Agent Workspace embodies this vision, bringing AI directly into the flow of conversations and enabling human agents to assist AI behind the scenes or join the interaction to create a three-way conversation between AI, humans, and customers. Similarly, NiCE monitoring, quality management and workforce planning capabilities now span both human and AI agents.

President of CX Product & Technology Jeff Comstock put NiCE’s trajectory over the last 12 months into perspective. Last summer’s Cognigy acquisition put the company at the forefront of the agentic revolution. Cognigy now sits at the core of the NiCE platform while remaining a best-of-breed Conversational AI solution. One platform, two adoption paths.

The product demos helped me connect the dots between several innovations shaping NiCE’s path forward and redefining what a CX platform can be. I want to highlight two.

Agentic Analytics builds on the NiCE data platform, bringing together data across CX operations. It has evolved into a signal engine that continuously analyzes interactions, workflows, and outcomes to identify patterns and surface recommendations across the CX value chain, from automation opportunities and knowledge gaps to broken handoffs, journey optimization, and churn prevention. At the summit, analysts had the opportunity to build their own AI agents. Agentic Analytics streamlined the starting point and guided us through the process.

Agentic Engagement Plane (AEP), unveiled at NiCE World, is a vendor-agnostic orchestration layer connecting channels with human and AI agents. It works with NiCE Cognigy and third-party AI agents, as well as human agents using NiCE CXone or other vendors’ contact centers. It coordinates interaction resolution based on intent and rules, leveraging Shared Cases and Experience Memory to maintain context and continuity across interactions.

Scott left us with a key takeaway: as AI becomes pervasive, accountability will be the next frontier. The challenge is no longer just building capable AI agents, but ensuring their actions remain governed, measurable, and traceable.

I’ve been asked many times for my take on this year’s Magic Quadrant. Here are a few thoughts.

Let me start by saying that I don’t buy for a minute the pay-to-play argument some people make. Having had extensive exposure to Gartner over the years, I have great respect for its rigorous methodology. The research is best appreciated alongside the companion Critical Capabilities report, which details the firm’s findings.

That said, I have also observed that Gartner’s frameworks can be rigid and slow to adapt to evolving markets and emerging categories. Gartner also tends to over-index on feedback from its large, predominantly North American enterprise client base.

Three main takeaways:

• Genesys and NiCE continue to pull away from the pack

• Amazon Connect Customer’s positioning and Microsoft Dynamics 365 Contact Center’s debut reflect the growing presence of hyperscalers in the market

• The list of notable mentions has swollen, and most arguably deserve a place on the Magic Quadrant, around Vonage’s position

Overall, no major surprises. But this Magic Quadrant raises a more fundamental question: does the CCaaS category still reflect the evolving market?

When core CX capabilities such as self-service and outbound are treated as optional, and data platforms remain peripheral to the evaluation despite their critical importance in the AI era, it may be time to revisit the category itself. New lenses are needed to assess the broader scope of emerging CX platforms.

I also believe Gartner’s definition places too much weight on built-in WFM. I continue to see contact centers with more than 250 seats favoring best-of-breed solutions.

Furthermore, there are now many ways to acquire a contact center solution: from a CX specialist, a hyperscaler, a CRM provider, or a unified communications (UC) provider. Twilio’s recent successes with its Flex SDK even suggest renewed interest in building contact centers.

To future-proof their contact center platforms, enterprises must assess not only the capabilities they need, but also the architectural approach best suited to their needs.

Yet current analyst frameworks, designed to rank vendors on a common set of criteria, are ill-equipped to account for different architectural options.

As the market evolves, the question is no longer simply which vendors lead the CCaaS market, but which platform model best supports an enterprise’s broader CX strategy.

I attended Fin Pioneer in early October.

Fin is an extraordinary story. Founded in 2011 as Intercom, the company initially focused on in-app messaging before expanding into customer service software. It crossed $200M in ARR in 2021, but growth later stalled.

When ChatGPT launched in November 2022, founder Eoghan McCabe had just returned to the CEO role. Seeing an opportunity, he bet the company on AI, launching Fin as a customer support AI agent in 2023 and redirecting most product resources toward this undertaking.

The bet paid off. By early 2026, Fin was approaching $100M in ARR. In May, Intercom rebranded the company as Fin, retaining Intercom as the name of its help desk software. A month later, Salesforce announced its acquisition of the combined business, which was a little over $300M in revenue, for $3.6B.

Following the acquisition’s close, Salesforce confirmed at Dreamforce that it would retain the Fin brand and product. It also plans to leverage Fin as a product-led growth (PLG) entry point into its broader portfolio.

Fin serves more than 12,000 customers, mostly SMBs, but also has some large deployments. Anthropic, for example, shared its experience at the event. Fin was adopted by Salesforce for its help center. Initially handling 5% of inquiries, Fin was ramped up to 40% of the volume handled by the juggernaut within a few weeks.

Fin started as a generative answer solution. The company has consistently reported the percentage of customer inquiries its AI can resolve across its customer base. That figure has now reached 76%. Along the way, Fin developed the proprietary Apex family of models, purpose-built for customer service.

CPO Paul Adams walked us through Fin’s evolution: from answering informational questions (Fin 1), to handling personalized queries and accessing enterprise systems (Fin 2), to resolving complex issues through structured procedures and simulations (Fin 3). The company also expanded Fin across interaction channels, adding support for email, voice, and Slack.

In April, it introduced Fin for Sales, an inbound sales solution that engages website visitors, answers product questions, guides prospects toward trials or purchases, qualifies leads, books meetings, and routes prospects to sales teams.

Fin 4, unveiled at the event, marks the next step. It extends Fin’s ability to tackle ambiguous, complex problems through reusable skills and expands the agent beyond simply responding to incoming queries. Fin can now proactively pursue outcomes such as onboarding, feature adoption, and retention.

Fin’s broader vision for customer operations revolves around three components:

• A Customer Agent that handles interactions across the customer lifecycle, spanning service, sales, and customer success. Inbound sales is already supported, while outbound sales remains part of the broader vision.

• An Operations Agent that manages and continuously optimizes the Customer Agent.

• A next-generation help desk, Intercom, redesigned for human-AI collaboration, including a rebuilt Copilot.

Operator, launched in May 2026, is the first embodiment of the Operations Agent. It analyzes customer service performance, identifies issues and knowledge gaps, and helps teams improve Fin. The longer-term vision is to make Operator autonomous and extend its operational capabilities beyond Fin itself.

Fin is pursuing an ambitious vision: orchestrating customer operations across not just service, but also sales and marketing. That vision is still taking shape, particularly on the sales and marketing side. Its track record of steadily expanding its agents’ capabilities makes it a credible one.

A few years ago, the Future of Work was a prominent research topic. While progress was made on working and collaborating from anywhere, the broader question of how work itself should evolve remains unanswered.

Meanwhile, enterprises have pursued successive transformation initiatives for customer-facing roles in sales and service, building change fatigue that new initiatives must now overcome.

Today, AI breakthroughs dominate the headlines, pushing change management and people-centered transformation down the agenda. But realizing AI’s full potential depends on engaging the workforce. It also requires addressing concerns about jobs and redefining how work should get done and how roles should evolve.

Against this backdrop, Centrical’s announcement of its Workforce Orchestration System caught my attention.

During my briefing ahead of the launch, Centrical Founder and CEO Gal Rimon shared three customer stories illustrating this challenge: a top-five US bank introducing new products, Verizon pursuing a strategic transformation, and Unilever transforming its workforce with AI. All three require translating business goals into changes in frontline employee behavior, a complex undertaking at scale.

Centrical offers performance management software for frontline teams across service and sales. Over the past 13 years, it has built an integrated suite to support workforce transformation, accumulating data and codifying best practices for teaching new skills and changing behaviors.

Its new Workforce Orchestration System builds on this foundation to translate business goals into comprehensive programs and coordinate execution across teams and systems.

The system works in three stages:

• Build translates business goals into the skills to develop, behaviors to change, KPIs, and program elements needed.

• Run configures Centrical and other systems to deliver the program, including learning, practice, coaching, and challenges.

• Learn (what Centrical calls Get Smarter) adjusts the program based on actual results, creating a learning loop that continuously improves performance.

Under the hood lies Centrical’s Frontline Codex, which combines two key building blocks: a context graph connecting business goals, KPIs, roles, program elements, employees, teams, and behavior changes; and a body of codified best practices.

SAP’s recent acquisition of TechWolf, a workforce intelligence specialist, points in the same direction. While the two offerings address different aspects of workforce transformation, both reflect a shared recognition that understanding workforce capabilities and managing how work and behavior evolve are essential to succeeding in the agentic AI era.

I have become increasingly passionate about bringing the transformation of work, its redistribution between AI and humans, and managing the associated evolution of roles to the top of the agenda. I look forward to seeing how enterprises put Centrical’s new Workforce Orchestration System to work and how it advances this important cause.

ApolloNext on September 30 provided the perfect opportunity to take stock of the company and how it has integrated its acquisition of Pocus. While I couldn’t attend, I was briefed beforehand and watched the presentations online.

CEO Matt Curl reaffirmed Apollo’s mission: “to make world-class go-to-market accessible to everyone.” This mission has driven Apollo’s transformation from a sales intelligence tool into one of the most comprehensive Go-To-Market (GTM) suites in the market. It addresses a key challenge for revenue organizations: the complexity of GTM stacks comprising multiple applications.

With the addition of Pocus’s customer data platform and scoring capabilities, Apollo has restructured its suite around three layers:

• Data connects Apollo’s own dataset of more than 240 million contacts with customers’ first-party data through integrations with CRM systems and data warehouses. It also brings in third-party data through waterfall enrichment from more than 20 vendor partners and custom signals gathered by its research agents.

• Intelligence combines the newly introduced Apollo Profiles, a unified data model that brings together first-party and third-party data into a comprehensive view of every person and account, with the Apollo GTM Harness. The latter turns contextual data into recommended next-best actions, leveraging Pocus’s decisioning capabilities.

• Execution enables sellers to act within Apollo Workspace or through other tools they use, including ChatGPT, Claude, and Perplexity, via Apollo Anywhere.

Apollo’s ambition is to combine these layers into a continuously learning AI GTM system.

At the event, Apollo announced two new products alongside its Intelligence Layer:

• Messaging OS expands Apollo into marketing campaigns, initially supporting email, with text messaging and WhatsApp planned next. It connects sales and marketing motions by allowing both to be triggered from the same audiences and signals. Campaigns can also push engaged contacts to Google Ads.

• Builder Studio lets users build and deploy custom agents and applications using natural language, with Apollo handling the underlying infrastructure and deployment into their production environment.

Apollo also aims to productize sales reps’ best practices through an AI-guided rep experience, with prioritized tasks, meeting briefings, call scripts, and automated follow-ups. Apollo emphasizes human review and control.

Using my new Revenue Blueprint, I mapped its GTM system capabilities:

Thanks to its freemium model, Apollo boasts more than 6 million users worldwide. In February, the company reported that it was approaching $200 million in ARR, with nearly 100,000 paying customers. Given its scale and presence in 100 Fortune 500 companies, it is somewhat surprising that Apollo wasn’t featured in any of the three major analyst evaluations: Forrester’s Wave for Revenue Orchestration Platforms for B2B, IDC’s MarketScape for Unified Revenue Orchestration Platforms, or Gartner’s Magic Quadrant for Revenue Action Orchestration.

Regardless, Apollo is a vendor worth putting on the radar. It’s time to stop thinking of Apollo as just a sales intelligence provider and recognize it as a comprehensive, integrated GTM platform.

Over the last seven months, Five9 has undergone a major leadership overhaul. Half the executive team is new: Amit Mathradas, CEO; Niranjan Vijayaragavan, CTO; Rob Hornish, CSO; Jay Lee, CMO; and Sven Linsmaier, EVP Transformation and Strategy.

These new leaders bring experience from a diverse range of industries outside the contact center. This has created a fresh perspective on the business and triggered a review of many aspects of its operations.

Jay, who joined six months ago as Chief Marketing and Growth Officer, is bringing analytical discipline and rigor to the markets Five9 wants to pursue. He also wants to reposition the company around the conviction that interaction is not a cost of doing business, but an investment in the customer relationship, with the effort devoted to each aligned with its potential impact on customer lifetime value.

One year ago, at the same event, we were shown a demo of Five9 Voice AI agents. This year, the customer panel featured three customers that had successfully implemented them. Five9 has put a significant focus on modernizing the Inference IVA platform it acquired in 2020 and adapting it to an agentic world. The progress demonstrates Five9’s ability to execute and catch up quickly.

Customers also praised their trusted relationships with Five9 and the effectiveness of its delivery teams. This continues to be a key differentiator for the company. While AI vendors increasingly tout Forward Deployed Engineers, Five9 has long had a well-recognized model that covers both implementation and ongoing improvement. The company brings its domain expertise and acts as a trusted advisor, helping customers determine what is possible and providing prescriptive guidance.

The Google and Deloitte panel conveyed a similar message: trusted relationships are fueling successful partnerships. With system integrators and cloud marketplaces playing a greater role in the enterprise market, this ecosystem will become increasingly strategic. Today, about 50% of the company’s ARR flows through partners.

Shortly after joining, Amit made the bet that “people won’t go away” and that platforms combining AI and human work will win over time. Five9 wants to innovate how humans and AI work together, leading to “Humantic CX” as the new company motto.

Amit also strongly believes in openness. While it is a common claim across the industry, it appears to have played a decisive role in several of the company’s large wins.

The new executive team, comprising contact center and company veterans alongside leaders bringing perspectives from outside the traditional CX market, creates a promising combination. Amit and Niranjan in particular bring experience in workflow automation, which is becoming the next frontier as the industry shifts from managing interactions to resolving customer requests.

Within a few quarters, a new Five9 has started to emerge. The coming quarters should provide a clearer view of how this new leadership team is coming together and how its strategic directions translate into plans and execution in a rapidly evolving industry.

Zoom is making its revenue orchestration ambitions increasingly difficult to ignore.

When Zoom acquired Common Room in July, I described the move as an unexpected contender joining the race to consolidate the modern sales stack from the communications corner of the market.

At Dreamforce, Zoom took the next step and unveiled a broader vision for an AI-powered Revenue Operating System.

The company introduced Zoom Engage for multichannel outbound sequencing and Zoom Forecast for forecasting. Both are significant additions, underscoring the impressive pace at which Zoom continues to expand its suite.

Zoom is also pushing the envelope of what a unified sales stack can look like by combining sales execution and revenue intelligence with buyer intelligence and customer communications. The result is a broader proposition than traditional revenue orchestration, bringing together sales engagement, conversation intelligence, revenue intelligence, buyer intelligence, and customer communications.

The timing is notable. In September, IDC named Zoom a Leader in its inaugural MarketScape for Unified Revenue Orchestration Platforms, while Forrester recognized Zoom as a Strong Performer in its latest Revenue Orchestration Platforms Wave.

In just a few months, Zoom has made a name for itself in the crowded SalesTech market, moving well beyond its initial positioning around meetings and conversation intelligence.

2026 Dreamforce was different.

For the past several years, I haven’t attended the event in person, choosing instead to watch the keynotes online while spending time at more focused co-located events that offer a closer pulse on how businesses are approaching sales and service.

I regret not attending in person this year. It felt like a special edition.

In recent years, Dreamforce has been coming across as a showcase of increasingly polished demos, with presentations tightly scripted. This year, the tone was more human and relatable. Presenters were more natural and conversational, while the content placed greater emphasis on practical, pragmatic steps for putting technology to work.

Let’s unpack the main announcements.

Salesforce realigned its offer around seven agents built for specific jobs. This is a pivot from last year’s broader positioning around becoming an agentic enterprise, a vision that remains difficult for most businesses to execute at this juncture:

• Piper, inbound SDR

• Hunter, outbound BDR

• Casey and Fin, customer service

• Carter, product FAQs for online retailers

• Paige, IT and HR employee support

• Marshall, supply chain and back-office workflows

This reinforces a theme I have highlighted in my research and coverage of Salesforce’s acquisitions of Qualified, Momentum, and Fin: purpose-built agents are proving to be the most practical approach today.

Salesforce introduced AIforce, a new experience layer that brings Salesforce capabilities into the applications and environments where people and agents work, including Claude, Slack, and more to come. It was the star of the show. AIforce builds on what Salesforce originally announced at TDX 2026 as Headless 360. The underlying Headless Toolkit provides APIs, MCPs, CLIs, Skills, and other capabilities that decouple Salesforce’s data and business logic from its UI, allowing both humans and agents to access Salesforce capabilities from wherever they work.

Data 360 is now positioned as an agentic CDP. I commend Salesforce for calling a cat a cat and using CDP as the product descriptor. Many companies have become reluctant to use the term, making data layers increasingly nebulous at a time when they have become more critical than ever. Salesforce introduced the Agent Context Engine (ACE), a delineated context layer for AI agents, whether Salesforce or third-party.

Salesforce also announced Koa, a CRM reasoning model designed to help agents reason through complex, multi-step workflows and select the right tools to get work done. Koa is trained on a proprietary synthetic dataset modeled on enterprise knowledge from nearly three decades of CRM deployments.

The Service keynote focused on breaking down application silos and enabling a hybrid workforce, illustrated through the Hybrid Workforce Command Center. In its pre-event product adoption update, Salesforce highlighted the strong adoption of service AI, with service use cases driving five times more AWUs than other use cases. Interestingly, Fin and Casey, while functionally similar, coexist with different roles, with Fin focused on speed and Casey on complexity. Another key announcement was Agent Optimizer, an agent that analyzes conversations, identifies automation opportunities, and leverages existing capabilities, including Flows and Apex, to drive further automation.

Agentforce Contact Center is enjoying solid momentum, with 1,200 new customers since its March 2026 launch. The company has a large installed base already using Service Cloud and Omni-Channel Routing for digital channels, and flipping the switch on voice interactions seems compelling to many.

The Sales keynote focused on demonstrating Hunter and showing Momentum in action for automated conversation capture, insight extraction, CRM updates, and task creation.

Marc Benioff is adamant that enterprises want to consume AI primarily through packaged software rather than building their own solutions.

Salesforce sees a major opportunity in the 1 billion knowledge workers globally, with only 50 million using Salesforce today. It believes AI could potentially double software spend per knowledge worker by 2030, positioning knowledge workers as the foundation of a new software category. In this context, AIforce and Headless create new ways to bring Salesforce to more users and expand its footprint.

Headless is also an important monetization vector. Salesforce introduced a $50 Headless add-on package earlier this month, with full pricing details expected in November. It has also indicated that access to the Customer 360 Platform, whether through MCPs or APIs, will incur additional Flex Credits based on actual consumption.

Salesforce continues to move and adjust at remarkable speed, impressive for a $50B company.

At its annual user conference, Xperience 2026, Genesys unveiled an ambitious product vision that marks the beginning of its latest act, staking out a new market it intends to pursue.

After CTI, routing, omnichannel, and cloud contact centers, this fifth act harnesses agentic AI to move Genesys beyond interactions into resolution workflows.

The timing is notable. Genesys Cloud continues to grow at more than twice the market rate. Yet Genesys is making a significant bet on what comes next, expanding its ambition from managing customer interactions to orchestrating customer requests and desired outcomes.

The company has the financial strength to make that bet. Genesys Cloud ARR reached nearly $2.9B, up 30%; AI ARR hit $400M, up 60%; and first-half revenue grew 20%. Genesys also reported new financial metrics, including a 30% GAAP operating margin and more than $500M in free cash flow for the same period.

At the event, Genesys introduced several new products and presented a restructured product portfolio to execute on this vision.

Genesys Navigator is the first new product. It is a customer-facing agent that acts as a business front door, pulling together the relevant context, understanding customer intent, and routing the inquiry to the right “actor” to handle it, human or AI.

Genesys has purposefully divided the roles between Navigator and actors. Navigator uses a general-purpose model to understand why the customer is reaching out. Actors such as Genesys Agentic Virtual Agents (AVA) are focused on resolving the request. AVA uses Scaled Cognition’s new APT-2 Large Language Model (LAM), which Genesys says reduces hallucinations. It now also benefits from Pinkfish declarative workflows and MCP integrations that significantly broaden the scope of actions it can take.

Genesys Orchestrator is the second, and arguably most significant, new product announced. Orchestrator is context-aware and manages multi-step journeys toward desired outcomes. Steps are delegated to an actor: a human interaction or work item, outbound outreach, or any of the automations Genesys supports, including Guides, Workflows (Pinkfish), first-party agents such as AVA, or third-party agents via A2A.

Orchestrator is fundamentally a decisioning engine, not just a workflow and journey orchestration tool. It uses a newly introduced, proprietary specialized model, Kaleidoscope, which Genesys explained was created to make sense of policies, context, intents, and events to determine the desired outcome and the next best delegation towards that goal, strictly within the policies, guardrails, and scope defined by the enterprise. Genesys resolutely positions Orchestrator as autonomous. The ambition to rely entirely on autonomous decisions in a world of customer service where predictable and repeatable processes have traditionally reigned supreme is audacious.

Navigator is expected to reach general availability in Q4 FY27, followed by Orchestrator in Q1 FY28.

This ambitious undertaking rests on two essential capabilities: context and control. Genesys AI Control Plane centralizes governance and provides observability for these autonomous steps, while Genesys Contextual Intelligence maintains persistent context by connecting interaction signals, customer identity, history, business events, and journey data.

It felt like Genesys wants to break away from the traditional CCaaS market, almost as if it were willing to burn bridges with it.

Why make such a sweeping move now, when Genesys Cloud continues to perform so strongly?

My read is that Genesys is anticipating slower CCaaS market growth and intensifying competition from hyperscalers, CRM providers, and Conversational AI specialists, and is moving on the offensive to expand its addressable market.

What Genesys unveiled at Xperience 2026 is a bold move. Following NiCE last year, Genesys is now setting its sights on resolution workflows, an increasingly contested market with CRM and other technology providers.

CCaaS players are all striving to redefine and expand their market. Many have adopted CX platform terminology without clearly defining how their newly minted CX platform differs from CCaaS. Genesys has taken a serious stab at answering that question.

At its Investor Forum in May, Atlassian revealed that its customer service business, delivered through its Service Collection bundle, had surpassed $1B in ARR and was growing 30% year over year.

It has been adopted by 65,000+ businesses using Jira for internal use cases, such as employee help desks and engineering work management, as well as external ones for customer service and support. Atlassian is seeing particularly strong traction with product- and technology-centric businesses, where tightly connecting customer support with product and engineering is central to the customer experience.

What I hadn’t appreciated is that more than 75% of Jira Service Management users are non-developers. That is notable because Jira has long been perceived as a service desk for developers and product teams.

With 150,000+ customers using Jira Cloud, Atlassian also has a substantial installed base to cross-sell Service Collection into, giving it a long runway for growth.

Atlassian introduced its Customer Service Management solution in April 2025. Built on Jira, it includes a customer-facing help center with a built-in knowledge base. Customers can submit service requests via email, chat, embedded product widgets, or forms on a support site, which Atlassian calls omnichannel intake.

Because Customer Service Management runs on the same platform as Jira Service Management, support teams can seamlessly escalate customer issues to product and engineering teams. Issues can also be connected to internal conversations across Slack, Microsoft Teams, and Zoom, bringing additional context into the resolution workflow.

This fits into a broader shift in Atlassian’s strategy. The company built its remarkable product-led growth around standalone products that were easy to adopt. In 2025, it pivoted toward Collections as a new go-to-market model. Collections bundle complementary apps and AI agents around a business function, using a common platform and shared Teamwork Graph to connect teams and provide richer context.

In October 2025, Atlassian introduced Service Collection, combining employee- and customer-facing service management, asset management, and Rovo to build AI agents that automate support workflows and assist humans. AI-assisted resolutions are priced at $1 each.

At Team ‘26, its annual user conference held in May, the company expanded that vision with several significant announcements:

• It announced support for messaging channels such as WhatsApp and SMS, as well as live chat agents with handoff to human agents.

• For voice, which has been supported via Amazon Connect, Atlassian announced native voice support with an AI agent front end and escalation to human agents.

• The company also unveiled Optimize, its solution for self-improving support. It will focus first on identifying knowledge gaps and updates and generating articles from human-resolved conversations, with Atlassian envisioning a future of proactive service that spots emerging issues before customers file a ticket.

Separately, Twilio highlighted Atlassian as a major customer on its 2Q26 earnings call, indicating that Atlassian will use its Flex SDK and Super Network to build and deliver omnichannel support within its customer service management app.

Atlassian is quietly assembling a serious customer service and contact center challenger. With $1B+ in Service Collection ARR, a large Jira Cloud installed base, and rapidly expanding omnichannel and AI capabilities, it has the distribution, platform, and capabilities to become a force the market needs to reckon with.

Earlier this month, I attended the KeyBanc Technology Leadership Forum.

What I love about this event is that it provides a broad perspective across the entire technology landscape.

One topic dominated this year’s agenda: data centers.

The scale of the buildout is extraordinary. The 14 largest publicly traded data-center operators are expected to spend nearly $750B in CapEx in 2026. But the buildout is increasingly constrained by five forces: the 5 Ps:

• Planet: climate and environmental constraints

• Policies: regulation and permitting

• Power: electricity and grid availability

• People: availability of skilled construction workers

• Pushback: growing resistance from local communities

What I found even more interesting was that these constraints are prompting a rethink of not just how quickly we can build computing capacity, but how much we actually need.

Furthermore, token economics, Chinese models, and open-weight models are forcing a closer look at the economics of inference and the architecture required to run AI.

I left the conference with five ideas to explore:

1. Deployed GPU capacity may be underutilized for inference.

2. Memory is becoming the constraint, forcing a rethink of how AI systems are architected.

3. Networks are becoming the critical connecting layer between storage and compute.

4. Frontier models aren’t always necessary. SLMs and open-weight models may often deliver the best economics and performance for specific tasks.

5. Today, we hear about agentic pilots using reasoning every step of the way, but many enterprise processes need repeatability more than reasoning, pointing toward hybrid workflows that combine deterministic automation with agentic reasoning where needed.

We are still figuring out what AI actually needs to scale. AI infrastructure constraints are forcing a rethink of the assumptions underlying the current AI scaling narrative.

Crescendo has introduced its Customer Experience Platform (CXP).

It is a major milestone for the company and, in many ways, a complete business pivot.

Crescendo is a relatively new entrant in CX, founded in late 2023 by industry veterans including Andy Lee, founder of Alorica; Anand Chandrasekaran (ex-Five9); Slava Zhakov (ex-Genesys); and Tod Famous (ex-Cisco, ex-Genesys).

Andy played a central role from the outset, particularly in shaping the vision of disrupting the BPO industry with a 100% outcome-based business model.

Crescendo initially took off with a concierge use case, combining human agents and AI to drive self-service improvement and adoption. In my first briefing with the company in 2024, Tod Famous explained that the outcome-based model drove the company to take ownership of knowledge management and build a closed-loop feedback mechanism with agents.

In October 2024, Crescendo acquired PartnerHero, a small BPO that brought its ARR to $50M. In April, the company announced it had crossed the $100M ARR mark.

What I learned in my briefing earlier this month is that the outcome-based model also pushed Crescendo to expand beyond self-service, develop a Forward Deployment Engineer (FDE)-based go-to-market model, and build a comprehensive CX stack. The objective: address disconnected technology stacks and drive continuous, proactive improvement.

Today’s announcement is a major one for the company and helps explain why Andy stepped into the CEO role last month.

It feels like Crescendo 2.0.

Crescendo Customer Experience Platform combines CCaaS, ticketing, workforce management, self-service, knowledge management, quality management, conversation intelligence, and Voice of the Customer (VoC) functionality, packaged as a set of specialized agents.

Two agents particularly stood out to me:

• Applied Insights works across Crescendo’s data estate to generate proactive recommendations.

• Optimization Agent translates those recommendations into system setup and configuration changes.

Together, they form the cornerstone of what Crescendo calls its governed self-improvement loop, designed to enable continuous improvement.

Crescendo has already started deploying its technology with customers, initially targeting the 50-500 seat market.

The company will continue serving its existing outcome-based customer base, leveraging Alorica’s talent pool.

In a crowded CX market, this is a bold move. It will be interesting to see how the strategy plays out.

TTEC announced last week that it is exploring strategic options for its TTEC Digital business.

The company intends to capitalize on the current valuation premium associated with AI, a business it has successfully grown. The decline in its BPO business this quarter and its debt level were also likely key factors behind the strategic review.

With revenues approaching $500 million, TTEC Digital is one of the few CX-specialized mid-size system integrators to have emerged from the transformation of the North American contact center channel over the past decade.

Its evolution illustrates how fundamentally the channel has changed.

In the pre-cloud era, VARs were an essential channel for implementing contact center solutions. Deployments involved substantial system integration, creating a lucrative services opportunity alongside product sales. Professional services could represent $2 for every dollar of product sold.

The cloud changed those economics. Vendors increasingly took large enterprise deployments directly, while cloud and more holistic solutions reduced the amount of integration required for deployment. Professional services attached to product sales fell to less than 50 cents per dollar, a decline that market growth could not offset.

The result was a fundamental reformatting of the channel. Many VARs were acquired or transitioned into Technology Advisors (TAs) served through Technology Solutions Distributors (TSDs). Those that remained evolved into specialized SIs focused on a technology ecosystem. A few exceptions, including Miratech, Servion, and of course TTEC Digital, have developed multi-vendor practices.

AWS’s entry into the contact center market accelerated another part of this evolution, driving Global System Integrators (GSIs) to establish contact center practices. Initially focused on deploying Amazon Connect, they quickly expanded to other platforms and into integrating contact centers with the broader enterprise fabric.

This created a new opportunity for SIs. Rather than simply deploying the contact center, the opportunity increasingly became connecting it to CRM and other enterprise applications, and implementing AI, now a top priority in CX.

TTEC Digital is a product of this transformation. It started with a traditional contact center deployment business focused on Avaya and Cisco. Today, it has become a multivendor CX, CRM, and AI business, with sizeable practices around Amazon Web Services (AWS), Cisco, Genesys, Google, Microsoft, NiCE, Pega, and Salesforce. It built these capabilities through several acquisitions, including Serendebyte, VoiceFoundry, and Avtex. It has also built a portfolio of solution assets around these platforms to complement products and accelerate deployments, while significantly expanding its managed services business, which now represents roughly two-thirds of its revenue.

TTEC Digital generated $104 million in revenue last quarter, down 8.5%. The decline reflects the fading of one-time on-premises license and product transactions and declining revenue from its two legacy practices.

It will be interesting to see how TTEC’s strategic review unfolds, and what it ultimately reveals about the future role and economics of specialized CX system integrators.

In this second installment, I continue unpacking my new Revenue Blueprint, double-clicking on two critical jobs-to-be-done:

• Defining and prioritizing target accounts and buyers

• Acquiring and enriching company and contact data and signals

The abundance of data and AI are transforming how companies define who they should sell to and when they should engage them.

It starts with defining and identifying your Ideal Customer Profile (ICP). AI can analyze your existing customer base to identify which customers are truly your best, looking beyond revenue contribution to dimensions such as lifetime value, including upsell and cross-sell potential, cost to serve, and other relevant factors. Rather than handpicking a few representative customers, you can analyze a much broader set of data points and then clean and enrich the attributes of these highest-value customers to uncover what makes them ideal. While parts of this process are still mostly done manually, specialized solutions such as AlignICP, HG Insights through its MadKudu acquisition, and Revic AI are emerging to support this process end-to-end.

From there, precise ICP definitions can be used to build a comprehensive account universe for your Total Addressable Market (TAM) or identify lookalike accounts. The next steps are to:

1. source contacts matching your target personas and covering the key members of the buying committee,

2. gather signals from both your own interactions and third-party sources across those accounts and contacts, and

3. layer on decisioning models to trigger sales and marketing actions.

Traditionally, this work was done across systems of record (f.e. CRM or Account-Based Platform) and sales intelligence platforms such as D&B and ZoomInfo. Clay has introduced a new approach that I characterize as a GTM Workbench, allowing you to aggregate and orchestrate data from multiple sources within a single environment. Many vendors now call these environments “studios.”

Signal-based selling has become a buzzword, and many vendors have poured every data point into the signals category. I have long advocated for a clear distinction between segmentation, which relies on static or slow-changing attributes, and signals, which are inherently dynamic. I also found signals are most valuable when aggregated and layered on top of your TAM, creating a dynamic view of which accounts and contacts warrant action. Vendors don’t often expose their underlying aggregation data platform, making this category difficult to track. Examples include Common Room (acquired by Zoom), Tapistro, and UserGems.

The final step is turning this data and signals into decisions about which accounts and contacts should be prioritized for enrollment in sales and marketing programs and campaigns. This can be done through AI-based scoring or workflow logic layered on top of data and signals, with the resulting decisions triggering campaigns and plays.

Taken together, these steps turn the job of defining and prioritizing target accounts and buyers into an end-to-end process spanning ICP definition, TAM construction, contact sourcing, signal aggregation, and activation.

It hinges on the ability to acquire and enrich company and contact data and signals.

Company and contact data providers typically offer a mix of company data (firmographics) and contact data (demographics). Specialists can also provide technology data (technographics), financial intelligence, and/or relationship intelligence.

Signal providers typically deliver some combination of company news, intent, job changes, job openings, and social and community monitoring.

Today, most providers draw on multiple sources to build their datasets. Understanding how that information is sourced, whether through in-house research and validation, purchased datasets, or the open internet using web search and other forms of scouting, is crucial. So is understanding whether providers maintain comprehensive coverage of specific segments, by industry, country, business size, or function, or are simply aggregating the data available to them. These distinctions matter because freshness, accuracy, and completeness can vary significantly depending on the sourcing model, with direct implications for building your TAM.

Given these variations across providers, multi-sourcing data has gained significant traction, particularly through waterfall enrichment. Businesses may also use scraping tools to complement or validate purchased data.

Embracing this shift has become critical for revenue teams in the age of AI. Rather than defining an ICP and target-account list periodically and working from a fixed set of assumptions, companies can now continuously refine who they should target, identify the right buyers, detect changes in buying propensity, and dynamically trigger sales and marketing campaigns and plays. The combination of abundant data, AI, and automation makes this possible at a scale and level of granularity that was previously impossible.

That concludes this second double-click. Comments welcome!

Five9’s Q2 results offer another valuable perspective on the CX market, while providing an important six-month checkpoint for new CEO Amit Mathradas as he reshapes the company’s strategy, priorities, and go-to-market.

Revenue reached $312M, beating estimates and growing 10% YoY, marking the third consecutive quarter of accelerating growth.

CCaaS revenue grew 7%, in line with the previous quarter, while AI revenue jumped 78% YoY to $39M, now representing 15% of total subscription revenue. Together, these two growth engines drove subscription revenue up 14% YoY.

In a market where investors are concerned about seat contraction, Five9 indicated that its concurrent seat count continues to grow at the same pace as CCaaS revenue.

The company also highlighted its push to accelerate the ramp of AI deals. It drove an expansion of professional services to support faster deployments, putting pressure on GAAP gross margin, which declined to 53.4%.

One win is worth highlighting: a $100M total contract value over five years with a Fortune 100 financial services provider. Sold through the Google Cloud Marketplace, the deal builds on Five9’s major investment in Google Cloud earlier this year.

Amit discussed the executive changes announced in late June in the context of the four priorities he outlined last quarter: building a performance-driven culture, optimizing operations, strengthening the core business, and winning in AI-powered customer experience:

• Niranjan Vijayaragavan joined as Chief Technology Officer to bring together Product Engineering, Product Management, AI Automation, and Architecture.

• Rob Hornish joined as Chief Sales Officer, bringing expertise in translating complex technology into measurable business outcomes.

• Sven Linsmaier became EVP of Transformation and Strategy, a newly created role responsible for enterprise strategy, corporate development, and the company’s highest-priority initiatives.

• These appointments followed the hiring of Jay Lee as Chief Marketing and Growth Officer in April, bringing an analytical background to the expanded CMO role and its mandate to chart the company’s growth path forward.

The diverse, non-CX backgrounds of these new executives reflect Amit’s conviction that the next phase of customer experience will require much more than contact center technologies, alongside a greater focus on business outcomes in Five9’s evolving go-to-market.

Amit also shared the early direction for Five9’s positioning: a voice-led enterprise CX platform focused on complex, regulated enterprises such as financial services, healthcare, and insurance. The company believes humans and AI need to come together to deliver what it calls “humantic CX,” with voice AI deployed as part of a broader CX platform rather than as a point solution.

In just a few months, Amit has reshaped the leadership team, accelerated the AI business, and begun repositioning the company around a broader vision of enterprise CX. The pace of change is notable, and the next few quarters should provide an important read on how this new strategy shapes Five9’s next chapter.