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.

Earlier this month, I published a new revenue technology blueprint. I now want to start a series that takes a closer look at its key building blocks, starting with systems of record and the workflow and agentic layer.

Organizations run on systems of record. They capture data, embed business logic, and serve as the operational backbone of the business. It all started with CRM, which became the system of record for commercial relationships and pipeline management: accounts, contacts, opportunities, and deals.

The rigid CRM data model, centered on the lead-to-account-contact-opportunity lifecycle, quickly became a constraint for marketers. This gave rise to Marketing Automation Platforms (MAPs) such as Eloqua and Marketo. Built around a flatter, person-centric model, they became the systems of record for first-party lead data and campaign engagement.

The same rigidity later drove the emergence of Account-Based (ABX) platforms from vendors such as 6sense and Demandbase. They became the systems of record for account-level intent, signals, and engagement.

Similar limitations for post-sales motions led to the emergence of Customer Success Management (CSM) platforms from vendors such as Gainsight and Totango. They aggregate product usage and other account-level signals, becoming the systems of record for customer health and expansion by combining product, relationship, and commercial data.

The rise of product-led growth also spawned a new, albeit short-lived, category of Product-Led Sales (PLS) platforms from the likes of MadKudu and Pocus. They became systems of record for user-level product engagement, combining telemetry, user and account context, and third-party enrichment to support freemium-driven sales motions.

This proliferation of systems drove enterprises to build Data Warehouses (DWH) on platforms such as Databricks and Snowflake to create a unified view of their customers. These data warehouses became the systems of record for enterprise reporting and AI modeling, consolidating customer, account, and event data across the business.

Their analytics-oriented nature made them ill-suited for real-time marketing and sales engagement. This gap led to the emergence of Customer Data Platforms (CDPs) from providers such as Segment and Tealium, which became the systems of record for unified first-party events and signals, customer profiles, third-party attributes, and identity resolution.

More recently, two new platform categories have emerged. Customer Engagement Platforms (CEPs), such as Braze and Klaviyo, combine CDP capabilities with real-time customer engagement across channels, becoming systems of record for customer interactions and engagement orchestration. At the same time, Revenue Orchestration Platforms (ROPs), such as Gong and Outreach, have consolidated conversations, sales activities, and customer interactions, becoming systems of record for sales engagement and execution.

Today, most businesses rely on a combination of these systems. Taking inventory of your data estate is a critical first step in designing an integrated revenue stack. And when evaluating a provider, you need to understand whether it should serve as a system of record and, if not, how it integrates with existing systems while preserving their integrity.

This systems-of-record layer forms the foundation of the revenue stack.

Above it sits the workflow automation and agentic layer. Before the rise of agentic AI, workflow automation platforms helped synchronize data and orchestrate processes across systems of record and the broader revenue stack. Representative vendors include Zapier and newer players such as n8n.

AI agents represent the next evolution of workflow automation. Rather than following predefined rules, they can operate autonomously toward a goal. They can also replace some of the workflows embedded within the revenue applications that make up your stack.

Agents can be specialized or custom-built. While many practitioners are experimenting with building their own, most organizations should assume a mix of vendor-provided and custom agents.

Agents are now coming from all corners of the market:

• Foundation model and AI platform providers such as Anthropic, OpenAI, and cloud hyperscalers AWS, Google, and Microsoft.

• Workflow automation providers such as Make, n8n, and Zapier have added agent-building capabilities.

• Your systems of record providers have likely embedded agents directly into their applications.

• Large enterprise application providers such as HubSpot, Microsoft, Oracle, Salesforce, SAP, and ServiceNow are increasingly offering agentic platforms that extend beyond their software and can orchestrate work across a broad range of business and sales processes.

• Ultimately, every application across the revenue stack will embed agents.

• New agent-native platforms purpose-built for revenue teams, such as Lindy AI, Lyzr, Relevance AI, Rox, and Swan.

Before introducing new providers into your environment, start by inventorying the agentic capabilities already available from your existing strategic vendors.

While the debate over agent development strategy rages, from customizing prebuilt agents versus building from scratch to selecting the right platform, too little attention is being paid to the context that makes agents effective.

The middle box of the agentic layer is intended to rebalance that discussion and foster the design of the context cornerstone, assembling elements such as semantic definitions and connected datasets that enable effective agents. And when using the blueprint to map a vendor’s offering, elements such as context graphs and MCP should be captured there.

That’s all for this first double-click. Comments welcome!

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

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

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

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

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

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

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

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

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

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

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

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

I attended the TP Immersive conference earlier in May.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

So, who is NLX?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• Define ideal customer profiles (ICP)

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

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

• Segment markets with far greater granularity

• Identify and aggregate signals that indicate emerging demand or timing

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

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

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

We can group the core JTBDs into the following:

• Enable sellers and buyers

• Build awareness & seed demand

• Prospect through personalized outreach

• Orchestrate deal pursuit through close

• Capture & convert inbound demand

• Execute high-velocity outbound calling & lead distribution

• Sell with and through partners

• Structure & execute deals

• Retain, renew, and expand customers

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

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

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

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

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

SoundHound AI announced earlier this week its acquisition of LivePerson.

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

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

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

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

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

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

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

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

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

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

Last month, Apollo announced its acquisition of Pocus.

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

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

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

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

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

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

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

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

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

In 2025, the lay of the land was redrawn.

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

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

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

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

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

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

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

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

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

Three engines are driving this growth.

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

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

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

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

This article will be controversial.

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

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

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

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

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

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

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

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

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

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

Gartner and Forrester’s latest forecasts align:

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

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

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

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

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

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

Three powerful forces are driving this evolution:

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

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

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

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

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

• Routing engine and agent capacity model — Omni-Channel

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

• Knowledge Management and Self-Service Portals

• Workflow automation — Flow

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

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

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

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

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

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

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

• Analytics and reporting were also expanded.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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