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.

Things continue to accelerate in the Conversational AI market.

Omilia is raising $67M in a Series B. This comes six years after its Series A in May 2020. During that period, its live ARR has grown more than 10x to over $60M, representing roughly 45% CAGR.

The long-standing player with deep roots in voice solutions has operated somewhat under the radar, despite being featured in the latest Forrester Wave, Gartner Magic Quadrant, and IDC MarketScape for Conversational AI.

Founded in 2002 as a Nuance value-added reseller, Omilia pivoted to a product business in 2008 and has since implemented several large-scale deployments at Capital One, Discover, RBC, Taco Bell, DWP, and PSEG.

The funding also comes with two notable hires: Nick Delis as Chief Revenue Officer and Ryan Kam as Chief Marketing Officer. That signals an ambition to step out of the shadows and compete more aggressively in a market that is heating up quickly.

Interesting development in the conversational AI space.

Yellow.ai announced it is going public via a SPAC merger with Bluerock Acquisition Corp.

As part of the process, it provided several interesting data points: revenue of $34M and enterprise value of $300M.

The most notable aspect is the company’s strategy.

Yellow.ai sees the BPO market as a major growth opportunity. The company plans to use the $200M raised through the transaction to acquire and roll up BPOs, transforming them into AI-native operations powered by its platform.

That ambition mirrors the direction companies like Crescendo have started to pursue.

At the same time, incumbent BPOs are not standing still and investing aggressively in building their own technology platforms. In its latest quarterly results, TP highlighted the growing traction of its TP.ai platform, with 1,100 AI projects now in production and 270 launched in the last quarter.

With Yellow.ai becoming public, we’ll gain much greater visibility into the transformation of the BPO market.

The investor presentation is worth a read: here

Earlier this month, Zoom made a bold move by acquiring sales intelligence specialist Common Room.

The acquisition surprised many, myself included, even though I was aware of Zoom’s growing ambitions in sales and revenue technology.

Common Room was founded in 2020 as a community intelligence platform. It raised more than $50M across three funding rounds and has approximately 180 employees, suggesting annual recurring revenue around $20M.

The company captures buying and engagement signals across public and community ecosystems, including GitHub, Reddit, Discord, Slack communities, and, of course, LinkedIn. While it initially focused on helping companies uncover the “dark funnel,” Common Room evolved into a broader sales intelligence platform centered on a CDP-like data foundation. It unifies first-, second-, and third-party data into what it calls a customer journey graph, with capabilities including identity resolution, waterfall enrichment, and signal aggregation.

Common Room allows users to layer workflow logic or AI agents on top of its graph to score both individuals and companies, and orchestrate targeted sales plays. The resulting intelligence can be consumed through Common Room’s own seller interface, pushed into engagement platforms, or exposed to AI agents through MCP.

Zoom formally entered the SalesTech market in April 2022 with the launch of Zoom IQ for Sales, its conversation intelligence solution. I say “formally” because Zoom already had footholds in sales workflows through Zoom Meetings and Zoom Contact Center. In September 2023, the company rebranded Zoom IQ for Sales as Zoom Revenue Accelerator, later expanding it into an umbrella for its sales solution, adding capabilities such as deal intelligence, coaching, next-best actions, and automated CRM updates.

Zoom has architected its sales offering on top of a comprehensive communications platform. To understand the full breadth of its portfolio, you need to look beyond Zoom Revenue Accelerator and include platform capabilities such as Zoom Contact Center with its built-in auto dialer, Zoom Virtual Agent, and more.

Combined, the broader Zoom platform, Zoom Revenue Accelerator, and Common Room provide strong coverage across the revenue stack. Using my new Revenue Blueprint, I mapped the full breadth of the Zoom portfolio’s sales capabilities.

Zoom is now a company to watch in the SalesTech market, across both B2B and B2C. The Common Room acquisition adds a solid sales intelligence layer to its broader communications and revenue platform and propels it into the group of SalesTech consolidators.

Zoom has the ingredients to become a meaningful player in the evolving revenue technology landscape. Capturing this opportunity will require it to bring together its portfolio of capabilities, deepen its functionality across key SalesTech categories, and expand its go-to-market motion to engage a broader set of buyers beyond its traditional communications and IT audience.

Verint held its annual customer and partner conference, Engage, in June.

The company was taken private in August last year and later combined with Calabrio. The merger closed in February, and Engage, held four months later, provided the first opportunity for the new Verint to unveil its refreshed visual identity and outline its direction.

Dave Rhodes addressed concerns from its two large workforce management (WFM) customer bases upfront, reassuring them that both Calabrio WFM and Verint WFM will continue to be supported, with no forced migrations.

At the same time, Verint is integrating Calabrio’s best-of-breed technology components into its CX automation platform.

The central message from the new Verint is its renewed commitment to an outcome-driven go-to-market (GTM) strategy. Verint continues to focus conversations on the business outcomes its 50 bots can deliver, rather than on technology or agentic capabilities. Its approach is incremental and allows enterprises to layer bots onto any existing contact center infrastructure. At a time when agentic narratives dominate, Verint is choosing to zig toward business outcomes while the market zags toward technology narratives.

To support this outcome-driven strategy, the company introduced a risk-free trial last year: if ROI is not demonstrated within the first 90 days, customers pay nothing. ROI calculators are embedded directly into the platform. Verint has also introduced workshops to demonstrate the art of the possible with its bots and identify priority use cases.

BT showcased a compelling example of this outcome-driven approach at the event. Anthony Cass detailed a phased deployment that scaled from 450 to 4,500 sales agents. Using three bots, Coaching, Wrap-up, and CX Scoring, BT reported improvements in real-time agent guidance, cross-sell and upsell, onboarding, and churn reduction. Cross-selling alone drove a $9M increase in annual revenue.

To reinforce this strategy, Verint is making substantial investments in GTM enablement and methodology. The goal is to equip teams to shift customer conversations from technology discussions toward measurable business outcomes.

On the product front, Verint Intelligent Virtual Assistant (IVA) deserves mention, as the company’s self-service offering is often overshadowed by its broader automation portfolio. It has now surpassed the 100-customer mark.

Among the product announcements, two stood out.

The first is Agent Factory, a control layer for its bots that enables customization and orchestration. It allows enterprises to orchestrate workflows across bots and humans, for example, combining post-interaction summarization with a proactive follow-up when customer sentiment indicates significant frustration.

Verint also unveiled a Desktop Intelligence product that uses AI to analyze screen captures, understanding both conversation outcomes and what actually happened during the interaction. This new layer of insight is a meaningful step toward unlocking the value of more than 20 years of interaction data. The solution can also be used to validate that what an agent says is aligned with what is entered into back-end systems.

After a somewhat quiet period, Verint reminded the market that it is a force to be reckoned with. The breadth of its automation portfolio, combined with a differentiated outcome-based go-to-market, gives Verint a distinctive position in a market increasingly centered around agentic AI.

Founded seven years ago, Warmly, has reached $7.5M ARR with 400 customers. It has seen accelerating penetration within the HubSpot ecosystem, which has grown to represent more than half of its customer base in just two years.

At the core of Warmly, is website visitor intelligence, a capability now claimed by many vendors as a checkbox feature, but with widely varying degrees of accuracy and coverage.

Warmly, has built a strong reputation here, claiming ~60% company identification and ~45% person-level identification. But HubSpot already had a strong foundation in this area through its Clearbit acquisition.

The differentiation in Warmly, is not the visitor identification, but what is done with it.

Where Warmly, shines is its ability to operationalize these signals into real-time GTM workflows. After intent scoring, it can trigger actions across the revenue stack, including Slack alerts, CRM updates, lead routing, sales task creation, AI chat engagement, and ad audience activation.

Again, HubSpot already offers a Customer Agent that can be configured to qualify inbound leads. In its most recent earnings call, the company actually reported 2x adoption growth, reaching 2,500 customers and roughly doubling usage. However, this agent is primarily oriented toward customer service and support scenarios.

Warmly,’s approach is more purpose-built and modular. A TAM Agent identifies and enriches new prospects and records them in systems of record, a Signal Agent monitors website engagement and buying intent, and an Inbound Agent captures and converts visitors in real time by triggering the appropriate workflows, with all three working in concert. The result is not just enriched visitor data, but coordinated GTM execution across sales and marketing systems.

This acquisition mirrors the one of Qualified by Salesforce: the same core use case around inbound engagement and demand capture, and a similar approach based on specialized, purpose-built agents.

It also reflects HubSpot’s continued move towards building an integrated revenue stack.

Genesys announced it is acquiring Pinkfish.

Pinkfish is a relatively new, 30-month-old workflow orchestration platform. Despite its founders’ roots in customer service and contact centers, Pinkfish has so far targeted enterprise workflow automation broadly rather than CX specifically.

It can be viewed as a modern, AI-native evolution of automation platforms such as UiPath or ServiceNow, with a strong emphasis on simplifying and democratizing process automation through natural language and AI. Workflows are built from nodes/steps that can be deterministic, agentic, or involve tool calls. The company lists 500 integrations and 25,000 MCP tools.

Pinkfish applies agentic capabilities primarily at the step level to create workflows or agents using natural language, dynamically discover and select tools, and execute simple tasks. This enables a hybrid model where deterministic workflows can leverage AI-driven step execution. The ability to define workflows or steps through natural language is making process automation more accessible to business users.

Genesys has been aggressively expanding its agentic capabilities. It introduced its Agentic Virtual Agent earlier this year, featuring Large Action Models (LAMs) via its partnership with Scaled Cognition alongside traditional LLM-based approaches. LAMs aim to reduce hallucinations, in particular through curated training datasets, including synthetic data and simulated actions, which improve the accuracy of tool use and action execution. Pinkfish extends these capabilities by enabling self-service requests to be executed and fulfilled across enterprise systems.

Perhaps more significantly, this acquisition marks a meaningful step into back-office customer interaction fulfillment and end-to-end process execution. Genesys already had Work Item and Case Management capabilities, and the press announcement explicitly links the acquisition to the latter for long-running processes. The signal is clear: Genesys isn’t just orchestrating customer experiences anymore. It wants to own a larger share of CX workflow execution.

AI-related layoffs and workforce reductions continue to dominate headlines, with contact center employment often cited as being most exposed to AI-driven disruption.

Indeed, Agentic AI is reaching a level of capability where it can absorb a large share of first-level and transactional service interactions. Recent research highlights the scale of potential exposure: Anthropic found customer service roles to be among the most exposed to AI, with up to 70% of tasks potentially handled by AI. Similarly, Goldman Sachs estimated that a significant share of customer service roles are exposed to AI-driven automation.

The US Bureau of Labor Statistics (BLS) recently published its annual employment-by-occupation report, offering a timely opportunity to assess the state of the contact center and customer service workforce.

For the second year in a row, employment of Customer Service Representatives has declined by 5%. The label Customer Service Representatives is somewhat misleading, as it encompasses only a fraction of customer service roles. Another category, Computer Support Specialists, points in the opposite direction. Last year, employment grew 2% after two years of 1-2% decline. This was a surprise, given that customer support automation is one of the most mature use cases for AI.

Combining the two categories shows a 7.5% contraction over the past three years. While Gartner’s 2023 prediction that AI will drive a 20-30% reduction in customer service and support agents by 2026 has not materialized, these numbers signal a structural decline.

But they only capture a fraction — 75% and declining — of the broader customer-facing workforce using customer communication technologies across the customer lifecycle. They do not capture the expansion of customer engagement activities, both in volume and across a broader set of roles across the enterprise.

First, we often assume a stable workload of interactions. In reality, while AI is absorbing a substantial share of interactions, volume and complexity continue to surge, offsetting much of the efficiency gains from automation:

• The scope of activities handled virtually keeps expanding; COVID demonstrated that there is effectively no ceiling to what can be done remotely.

• Processes are becoming more complex, driven in particular by mounting regulatory and compliance requirements.

• Digitization is spreading across more processes, which generate exceptions, adding to customer service workload. In its Inner Circle Guide to Customer Interaction Analytics, ContactBabel found that 80% of complaints reaching contact centers are caused by back-office failures.

• In parallel, the contact center’s managed communication infrastructure has extended its scope beyond traditional service into adjacent functions, including sales and service delivery, such as financial advisory or healthcare interactions.

• Finally, the looming rise of agents acting on behalf of consumers will drive interaction volumes to new highs.

In its AI Automation vs. Human Effort internal study, Zendesk found that automation was actually driving a significant increase in interaction volumes, with larger increases occurring at higher levels of automation. The study reported up to a 200% increase in volume at a 60% automation rate, suggesting that Jevons Paradox is at play in customer service.

Second, the occupational definitions we rely on are increasingly misaligned with how customer-facing work is structured:

• The contact center represents a relatively recent addition to customer service. Its structure reflects a traditional pattern of decomposing customer service work across the enterprise, mirroring Taylorism in early 20th-century manufacturing.

• It is a carved-out “front-end layer” for customer service, optimized for rapid responses through large pools of agents handling high-frequency inquiries, while other tasks are offloaded to specialized departments. Over time, these functions have become even more specialized, creating fragmented customer service experiences, longer resolution times, and friction across organizational boundaries.

• AI is not only becoming the “front door” to service, delivering instant responses, it is also assisting customer-facing associates with specialized tasks, enabling a reaggregation of jobs. Instead of funneling customer communications through the contact center to dispatch the work, a larger pool of the workforce will interact directly with customers.

Taken together, these dynamics suggest the contact center, as a distinct organizational construct, is likely to dissolve into a broader customer-facing operating model.

Rather than framing this shift as AI-driven job elimination, the focus should be on how customer engagement work is redistributed between AI and humans across the enterprise, and how that redistribution reshapes customer-facing roles.

A corollary of this transition is that many entry-level customer service roles that have traditionally served as an entry point into the workforce will disappear, and organizations will need to rethink pathways for employees entering the workforce.

NiCE held its annual conference in Orlando earlier this month.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Outreach held its annual user conference, Unleash, in early June

This year’s edition was a more intimate event, enabling deeper interactions with and among its larger customers and partners while reflecting the company’s steady move upmarket. It provided the perfect opportunity to take stock of the platform it has put together.

There are still parts of the market that do not yet fully appreciate the breadth of sales jobs to be done that it supports, from prospecting and deal management to account development, retention, coaching, and forecasting.

Here is a mapping of its suite’s capabilities to my new Revenue Blueprint:

Speakers from Cohesity, Databricks, Freshworks, OpenAI, SAP, ServiceNow, and SolarWinds shared their go-to-market (GTM) transformation journeys. Two things stood out: the scale of some of these deployments and the central role Outreach has played in enabling these transformations.

The foundation of the Outreach stack is a revenue data platform. The company was among the first vendors to expand its deep CRM integrations with two-way integrations to enterprise data warehouses. It consolidates engagement activity – emails, calls, meetings – along with first-party data from CRM, data warehouse, and website activity, third-party data and signals via its Smart Data Enrichment capability and Research Agent, as well as knowledge assets such as collateral and messaging guidelines.

The federated context can be used by its own agents or made available to third-party agentic platforms via its MCP server. At the event, it detailed its two-sided partnership with OpenAI, making its data, context, and agents available natively within ChatGPT and Codex.

Today, Outreach boasts one of the richest integration ecosystems across CRM systems, data warehouses, and LLM platforms:

CEO Abhijit Mitra shared his conviction that architecture matters and outlined how its Agentic AI Platform for Revenue Teams turns myriad signals into coordinated action.

Outreach has embraced agentic AI, with a vision of agents as teammates that get things done. It has created a suite of specialized agents, notably its Revenue Agent, which builds on segmentation by layering enrichment and signals to assemble prioritized targets to pursue, and its Deal Agent, which keeps an accurate view of opportunities through automated activity capture, stage progression, summaries, and insights. Earlier this year, it added an Agent Studio to its agent portfolio, enabling customers to build their own. At the event, it announced both an agent marketplace and several new purpose-built agents.

By offering a broad set of focused agents, Outreach aims to accelerate agentic adoption across its customer base. It also introduced an AI Maturity Model for sales, enabling revenue organizations to assess their operations and prioritize their agentic progression.

Outreach serves sellers, managers (leaders), and RevOps via unified workspaces where they land on a “home” page that provides a global view of their book of business and prioritized actions. At the event, the company previewed expanded AI recommendations with new capabilities such as Deal Strategy for sellers or Win Paths, helping leaders identify the best options to reach their targets. The company is also making its capabilities available to users wherever they are, via Slack or its mobile application.

The event also provided an opportunity for Outreach to showcase Omni, its newly introduced conversational agent. Omni a natural language interface built on top of its data platform that leverages revenue context to allow users to ask business questions and receive both answers and recommendations.

Outreach is at an inflection point. The platform has come a long way since its debut as a sales engagement solution and now spans a much broader set of sales and revenue workflows.

Yet, the company does not enjoy its fair share of mind in the consolidating sales and revenue technology landscape. Combined with the lingering perception of being primarily an inside sales prospecting tool, this places the company in a position where it has to continuously articulate the full scope of its platform, which can make the overall value proposition somewhat overwhelming.

It is time for Outreach to streamline its positioning and tackle its brand perception more directly, reframing itself as an all-in-one platform for B2B sales.

Meta held its 2026 Conversations messaging conference in London earlier this month and made several significant announcements about its business messaging portfolio.

US-based observers often underestimate how pervasive WhatsApp has become, not just for consumers but for business engagement across the globe. WhatsApp's reach extends well beyond consumers: more than 1 million businesses use Meta's messaging and automation capabilities across WhatsApp and Messenger. In Brazil, WhatsApp is the primary channel consumers use for business communication, a pattern also reflected elsewhere: Air France exchanged 33 million messages with passengers over 12 months, making WhatsApp its #2 digital contact channel.

So far, Meta has been offering its Meta Business Platform directly to enterprises and through partners for small and midsize businesses. Meta Business Platform provides APIs to manage conversations, enabling use cases such as chatbots, marketing campaigns, and appointment scheduling.

At its London event, Meta made four announcements:

• Meta Business Agent is a ready-to-use customer engagement agent for SMBs, embedded across WhatsApp, Messenger, and Instagram, that handles conversations, including answering customer inquiries, qualifying leads, booking, taking payments, and escalating to humans.

• Meta AI Agent Platform targets large enterprises looking to build similar customer engagement use cases across Meta's three messaging channels, with the ability to apply customizations and connect to existing systems.

• Meta Business Agent Home is an application that lets businesses manage their messaging conversations at scale.

• Enhancements to WhatsApp Discovery that enable customers to find businesses directly inside WhatsApp.

While messaging remains promising, it has not yet been adopted as a broad-based business channel and has largely been confined to specific use cases. What is missing is enterprise support for a wider set of end-to-end sales and service experiences for these touchpoints to reach their potential. Meta has the market footprint to drive this shift. When it launched its Business AI program last March, I questioned whether it could finally crack enterprise sales and service. It increasingly looks like it can.