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.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Data foundation

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

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

Targeting & segmentation

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

Prospecting

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

Appointment-driven inbound

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

Deals

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

Tracking & Insights

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

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

First, data platforms are taking a back seat. While data is the undisputed foundation of any AI deployment, most sales organizations show limited concern for the data platform they are actually putting together. I expected data platforms to move to center stage in 2025 and become a key influencer of technology decisions. That has not happened. Sales and operations teams are instead prioritizing specific AI use cases they can deploy, with little scrutiny of the embedded data platform bundled with the chosen solution. Only the most data-mature organizations push vendors to connect to their data warehouse, and even then, the discussion rarely goes further.

Second, while organizations consistently complain about solution stack complexity and tool sprawl, many AI deployments, particularly agents, focus on stitching together existing stacks. Tool consolidation has been a long-standing aspiration for several years. Until last year, it was constrained by the limited maturity of integrated solutions. Fast forward to 2025, revenue platforms such as Apollo, Clari-Salesloft, Gong, Outreach, and ZoomInfo, followed by new entrants and CRM providers, have not only significantly matured but have also leveraged AI to accelerate their product roadmaps even further.

I was surprised to see this “overlay” approach gaining such traction. It is somewhat understandable, though: most organizations are targeting specific use cases for their initial AI deployments, and agents excel at simple workflow automation. The overlay approach delivers a double win. It allows sales organizations to finally do things they long wanted, such as automating CRM data entry or surfacing priority leads to engage with, while also maximizing their existing technology investments.

Another factor explaining these paradoxes is that the SalesTech industry remains overindexed on serving SaaS and technology companies. This segment is dominated by startups and scale-ups that prioritize speed and action over well-designed technology stacks.

I have come to terms with these two paradoxes, which are likely to persist into 2026. The irony is that recognition is growing around the need for a middle layer between data and AI agents to unify datasets, with context graphs emerging as the next frontier for AI effectiveness. The pendulum will likely swing back over time, refocusing attention on building a unified data foundation and simplifying sales stacks around one of the emerging revenue suites.

At NRF 2026, Google Cloud made two announcements worth unpacking. They mark an important milestone for the search giant and signal concierge agents coming of age.

First, Gemini Enterprise for Customer Experience (CX). It is an agentic framework that enables businesses to build shopping agents that behave like concierges, managing the end-to-end commerce experience from discovery through post-purchase support.

Agentic AI took digital commerce by storm in 2025. It made inroads, replacing rigid, rule-based chatbots and chat systems that too often connect customers to outsourced agents who lack context and knowledge to address customer questions. Offering agentic concierges was a key driver of Sierra’s rapid trajectory, helping it cross the $100M ARR milestone so quickly. In the B2B world, autonomous SDRs enjoy a similar momentum.

Probably the most important aspect of these agentic concierges is that they enable new experiences that erase the historical divide between pre- and post-sales. This is among the first examples of AI enabling things that were not previously possible. Before AI, businesses had to segment experiences by departmental boundaries, exposing customers to the structure of their organization.

As part of Google’s effort to bring shopping and customer service experiences together, Gemini Enterprise for CX integrates with Agent Assist to support hand-offs to human agents, providing real-time coaching, next-best-action guidance, and training.

Surprisingly, the announcement made no mention of Google Customer Engagement Suite. Separately, Five9 introduced an integrated solution that combines its CX Platform with Gemini Enterprise for CX.

Google also unveiled a new standard, the Universal Commerce Protocol (UCP), which it has open-sourced. It is the company’s response to OpenAI’s Agentic Commerce Protocol (ACP), also released in open source in September 2025.

UCP supports the full shopping journey by integrating with e-commerce and payment systems. The Native path enables customers to use the AI Mode in Google Search or the Gemini app, what Google refers to as its “surfaces,” while the Embedded path allows merchants to deploy their own shopping agents. UCP goes far beyond replacing site scraping to cover discovery, pricing, checkout, payment, and post-purchase services. Google has already secured support from 20 commerce ecosystem leaders, including Shopify, Target, and Walmart.

These announcements signal not only Google going on the offensive to redefine digital commerce, but also the coming of age of agentic commerce. For concierge agents, which are today focused on handling informational inquiries, this clearly charts the path toward expanding into fully transactional interactions.

Salesforce is acquiring Qualified.

This is big news.

Qualified is widely viewed as one of the first to crack the autonomous SDR space.

But wait, Salesforce announced its own SDR Agent in August 2024 and brought it to market in October 2024 as one of its earliest agents.

So why did Salesforce feel compelled to acquire Qualified?

Three reasons stand out.

1. AI works well for converting inbound leads. Multiple vendors have proven the model from different angles, whether through autonomous SDR platforms built from the ground up, conversational commerce or concierge agents, or conversion automation. It’s a perfect AI first step for many businesses.

2. In inbound exploration and inquiry scenarios, AI is redefining the experience well beyond appointment setting. It fuses service, assistance, and sales, and is rapidly setting a new benchmark for brands engaging prospective buyers.

3. The challenge is tuning solutions to specific use cases. That is the hard part, and Qualified has excelled there. Bill Patterson, EVP of Corporate Strategy at Salesforce, had a front-row seat as a Board Observer at Qualified.

This acquisition should give pause to companies pursuing general-purpose
agents.

Congrats to both companies!

In part one, we explored why modernizing sales operations has become a top priority for B2C businesses. This second segment focuses on the foundational building blocks that enable varied sales motions and buyer journeys.

Lead conversion

Converting inbound leads hinges on effective distribution strategies. While contact center-style routing remains common, many sales organizations achieve superior results through targeted approaches: ensuring equitable access to high-quality leads, rewarding top performers with priority allocation, or leveraging AI to match each lead with the seller most likely to close.

In B2C environments, tracking inbound inquiries from advertising campaigns, including TV, Google, and social media, is critical for measuring performance and ROI. Complexity rises in franchise models such as automotive retail or insurance, where brands generate leads centrally and distribute them to dealers or agencies. It further escalates when leads originate from lead generation providers and are shared across multiple businesses, making speed of response paramount. “Speed-to-lead” is also critical for transactional sales, where the first seller to engage typically wins. This competitive imperative requires real-time lead prioritization and, when immediate follow-up is not possible, placement into re-engagement campaigns.

Meeting these requirements necessitates robust call and lead tracking, lead scoring capabilities, advanced opportunity-to-seller pairing algorithms, dialers with instant callback functionality, and multi-channel campaign management.

Digital journeys

Websites and social networks can no longer be left unattended. Relying solely on self-service is insufficient because buyers expect guidance as they navigate complex decisions. Traditional chat solutions often fail, with qualified associates unavailable or response times too slow.

Conversational commerce and AI agents are now acting as concierges to deliver near-human interactions, letting prospects explore on their terms while providing instant assistance for any question. When a customer signals readiness to buy, the interaction can be handed off to a human seller, scheduled for an appointment, or directed to a targeted outbound follow-up, keeping the buying experience fluid.

Sales Engagement

Voice remains the most effective channel for closing sales, with dialers forming the backbone of outbound operations. Advanced dialer techniques—power, progressive, or predictive—enable B2C teams to maximize the time sellers spend with prospects and customers on the phone. These modes leverage answering machine detection (AMD) technologies.

However, dialers alone are insufficient. Modern outbound operations require support for sophisticated call flows such as dropping personalized voicemails, bridging calls from inside sellers to field sellers or closers, and orchestrating multi-channel engagement sequences that combine SMS, messaging, email, and voice. Orchestrating these steps across channels depends on powerful workflow tools.

Outbound selling has grown increasingly sophisticated, and no dialer can operate effectively without a campaign manager. Sales campaign managers extend far beyond segmentation. They enforce compliance by scrubbing calling lists for Do Not Call registrants, opt-outs, and known litigators, while managing follow-up cadences in accordance with evolving privacy and prospecting regulations.

Unlike marketing campaigns with virtually unlimited sends, sales campaigns operate within a finite pool of sellers. A sales campaign manager adds essential nuance by optimizing the human capital, including balancing multiple campaigns competing for the same sellers, sequencing digital and human touches, and selecting the optimal time to call.

Privacy regulations and the FCC’s SHAKEN/STIR mandate have carriers flagging potential spam calls. Each carrier uses its own model, making number reputation management critical to any outbound strategy. These solutions continuously monitor phone number performance, assign premium numbers to high-value calls, and remediate or replace those flagged as spam. Brands are also adopting branded calling to reinforce trust and improve answer rates.

Managing the whole journey

Most businesses fail to follow up with leads that are not ready to buy. Conversational engagement transforms lead nurturing by making re-engagement possible at scale. Generative AI now mirrors human responses and can read a prospect’s emotional state. While humans are often pressed for time to convert a lead, AI adapts its pace to the prospect, engaging at their rhythm, answering questions, and adjusting to the buyer’s stage and concerns with precision. The most advanced AI agents are already opening new pathways to warm leads and maximizing conversion.

Onboarding new customers, while not strictly a sales activity, has become a critical step in the buying journey and can make or break a sale, in particular when it needs to go through an application process like a mortgage. Even when digitized, exceptions can derail the experience, making proactive assistance essential. For complex products like banking, effective onboarding combines step-by-step education with proactive guidance. While much can be automated, brands increasingly need sellers or associates involved at key, dynamically identified moments. Delivering a smooth onboarding experience depends on uncovering moments or exceptions that require outreach using customer signals and AI insights, supported by appointment-setting tools.

Many B2C sales have become bite-sized, making upselling essential to realize full customer value. Repeat and subscription sales make customer retention critical, with many such opportunities surfacing during service interactions. Conversation intelligence must be tuned to identify these revenue moments. Capitalizing on service-to-sale opportunities hinges on a next-best-action engine to surface them and real-time guidance systems that prompt sellers with recommendations tailored to customer emotions, objections, and concerns.

Tracking and optimization

Reps must be equipped with workspaces built for speed and precision. They need streamlined interfaces that surface critical information above the fold to minimize context switching, arranged in intuitive layouts that reduce cognitive load. With many B2C sales decided in the first encounter, real-time guidance and personalized coaching during conversations are essential, helping sellers make the most of the moment.

Measuring sales activities goes beyond tracking revenue. It requires visibility into productivity and the specific behaviors that drive performance. Every interaction must be captured and analyzed through quality monitoring, conversation intelligence, and performance management systems, with optional gamification to accelerate best-practice adoption and continuous improvement.

With a significant share of sales conversations originating from advertising and marketing campaigns, a closed feedback loop is also essential. Tracking and attribution must tie each engagement back to its source and often extend across business boundaries when leads are shared, giving organizations clear insight into which investments drive results and where to optimize.

An ecosystem in the making

Most B2C businesses still need only a subset of these capabilities, but the list is expanding as organizations that once relied on a single acquisition engine now run a mix of sales motions across their book of business. At the same time, they increasingly require technology tailored to B2C sales journeys and can no longer depend on generic B2B sales or contact center solutions. As organizations adopt more software and lean on best-of-breed capabilities, stitching together a stack of point solutions is starting to show its limits. This is driving demand for suites that simplify integration and unify how the various sales motions are executed.

A few B2B salestech vendors have been adapting their offerings for B2C in areas such as conversation intelligence, real-time guidance, role-play, coaching, and gamification. The most meaningful moves, however, are coming from UCaaS and CCaaS providers, along with pure-play specialists. Contact center vendors such as Five9, with its Acqueon acquisition, and NiCE, through its acquisitions of LiveVox and ContactEngine, are expanding their stacks. Several UCaaS vendors, including 8x8, Dialpad, RingCentral, and Zoom, have made sales a core use case and introduced sales-focused products.

B2C-focused providers are also broadening their platforms, whether from the outbound side with Conquer, Convoso, Readymode, and Regal, or from the inbound side with CallRail, CallTrackingMetrics, Convirza, and Invoca.

It is time to treat B2C sales as a distinct market with its own requirements.

In customer engagement, B2C sales stands as an outlier. Service, support, and B2B sales have modernized with technology, virtualizing large portions of their operations. B2C sales has not.

Digitization focused on e-commerce: web first, then social networks. In the US, e-commerce captures just 15 percent of retail sales. Physical stores, branches, and in-store channels still dominate. Live chat and social messaging only added minimal human assistance, leaving most online sales unassisted, effective mainly for simple, self-service products. Telesales and virtual sales remained limited to direct response and mass campaigns.

B2C sales has relied on a patchwork of point solutions and technologies leveraged from call centers (e.g. dialers), B2B sales (e.g. conversation intelligence), and e-commerce (e.g. chat). It works for relatively simple engagement models using a few acquisition channels.

Many businesses have hit the ceiling of what unassisted e-commerce can achieve. Meanwhile, the rise of virtual selling in B2B and the COVID shift proved that far more B2C selling can occur virtually.

The next frontier enables sales of complex or high-stakes products like health insurance, financial plans, or kitchen remodeling, where financial or emotional factors make human guidance essential to address questions or provide reassurance.

Fast forward to today. The landscape is very different, with both the number and sophistication of buying journeys expanding significantly:

• Inbound inquiries arrive via any channel: web, chat, phone, or social networks. They originate directly from customers or through affiliated brands and third-party lead providers. Many require near-instant response, creating a speed-to-lead imperative.

• Outbound prospecting has become multi-channel, blending human calling and automated outreach via SMS and messaging, all under a web of privacy and do-not-call regulations.

• Nurturing allows leads that don’t convert immediately to be re-engaged in new ways through conversational engagement before being passed to human sellers.

• Onboarding has become critical. Sales of sophisticated products like mortgages or banking services are only complete once the application process finishes. They rely on digitized workflows with proactive exception handling. Step-by-step guidance maximizes adoption and product value, often requiring targeted seller involvement at key moments.

• Upselling opportunities arise during or after customer service interactions, requiring either equipped service reps or seamless transfers to sellers.

• Renewals and subscriptions drive B2C sales in bite-sized increments, where timely re-engagement drives revenue.

The result: most B2C businesses must now execute and optimize a broad range of sales motions for acquiring, upselling, and retaining customers.

Gaps in B2C sales technology became visible when COVID forced all industries to go virtual. While significant investments were made in e-commerce and B2B sales over the past decade, B2C sales had been left behind in the digitization movement.

This is why B2C businesses are now investing in modernizing their human-assisted sales motions and why revenue generation has become a priority for contact centers. McKinsey found that one-third of contact centers now prioritize revenue generation, up from less than 5% in 2016.

These developments have accelerated over the past few years. Vendors are sharpening their focus and/or expanding support for B2C sales motions. The momentum in Forrester’s 2024 inaugural Wave on real-time revenue execution platforms reinforces this trajectory. It signals the dawn of B2C sales software as its own category.

In the second part of this series, I will explore the requirements and core building blocks of a B2C sales system.

I’m excited to release the latest update of my SalesTech landscape.

This 11th edition was the most challenging to put together. It includes 2,130 participants, a modest 1% increase over last September’s version. By contrast, the 2024 landscape expanded by 34%.

The flat growth masks significant churn: more than 380 new entrants were offset by a similar number of exits and acquisitions. The influx of newcomers underscores how much AI has lowered entry barriers and how attractive sales and go-to-market use cases have become for its application. The exits and consolidations expose the other side of the equation: a saturated market crowded with fragmented solutions that are difficult to integrate, and vendors struggling to expand beyond the tech sector.

Agentic AI has made the landscape harder to analyze. Many vendors now call their products “agents,” even when they’re merely wrappers around commercial AI. The fate of these offerings remains uncertain and is already fueling a wave of pivots that are increasingly difficult to track.

This edition adds 4 new categories:

• RFP Automation, now a standalone category

• AI-Native CRM, discussed in a previous post

• Revenue Orchestration Platforms, which extend Sales Engagement to include sales activity capture, conversation, and deal intelligence, and recommendations/next-best actions

• AI Agent Platforms for Sales, enabling the creation of specialized agents that primarily handle non-customer-facing tasks

The landscape now tracks 60 categories, a number that illustrates the market’s fragmentation.

Unsurprisingly, Autonomous BDRs (+61%), Autonomous SDRs (+109%), and Conversational Commerce & Concierge Agents (+109%) are among the categories with the highest number of new entrants.

Sales Workflow Automation (+89%) is also experiencing significant growth, driven by the need to better stitch together fragmented stacks and the new AI-enabled ability to build automations.

Another category to watch is B2B Web-Scouted Data, formerly Internet Scouting & Search. AI has commoditized web scouting and scraping, enabling new entrants and allowing existing providers to expand their offerings.

Surprisingly, the industry has made only modest progress in delivering solutions for unified data repositories that integrate first-party data and federate third-party sources. While more categories now feature data aggregation, few players focus on data infrastructure capable of supporting all sales motions:

• A few AI native CRM players have emerged, and only the largest CRM vendors have expanded their data layers

• Account-based platforms remain largely focused on marketing campaigns and ads

• Revenue execution platforms have been building data aggregation layers, but that capability has taken a back seat in their positioning

• Once-hot categories like Product-Led Growth CRMs and Customer Data Platforms are shrinking

Interest has shifted to Clay-like workbenches that enable data manipulation. The result is a fragmented, hard-to-map landscape. I expect this to change as the need to manage all revenue motions from a single repository and feed AI drives renewed focus on unified data.

Inbound is poised for consolidation. It has historically been a patchwork of point solutions by channel or experience: appointment scheduling, live chat, conversational commerce, call tracking, call distribution, and lead routing. Some providers have already expanded into full qualification–routing–booking suites. After creating a mini category of autonomous SDRs and reinventing conversational commerce, AI will drive the unification of capabilities across channels, enabling seamless seller connections and streamlined appointment booking.

This edition seems to mark the end of one cycle and the beginning of another.

For a deeper discussion or to brief me on new offerings, schedule time during my office hours: here

Last week at Celebrate, Gong unveiled several new capabilities in its Revenue AI Platform, now rebranded as the AI Operating System for Revenue Teams.

The system is built around three layers — Revenue Graph, Intelligence, and Automation.

1. Revenue Graph forms the data foundation, connecting systems of record, conversations, signals from its partner ecosystem, and news from the open internet.

2. The Intelligence layer applies AI across this unified dataset, powering workflows and surfacing insights through the Cockpits Gong is building for its four key personas: sellers, frontline managers, RevOps, and Enablement.

3. The Automation layer, anchored by Gong Orchestrate, operationalizes revenue plays at scale.

The focus on sales enablement is notable. A few months ago, Gong introduced AI Trainer, an agent leveraging its data and intelligence to deliver targeted role-play for sellers. Gong is signaling broader ambitions in enablement, positioning it as a critical driver of the sales transformation initiatives its platform supports.

Gong reported strong momentum with 4,900 customers, including 800 on Forecast (+75% YoY) and 1,100 on Engage (+100% YoY).

A standout moment: Cisco shared its global rollout to 11,000 users, expanding at 2,000 per week toward full deployment across its 20,000-strong sales organization.

So when the concept of an AI-native CRM emerged, I met it with skepticism.

Yet the category, though still nascent, is gaining momentum among teams who want a CRM tailored to their business without having to assemble a complex GTM stack.

Early traction could be felt in the August funding rounds of Attio ($52M Series B) and Aurasell AI ($30M Seed). Other emerging players include Aero, Breakcold, Day AI, Lightfield, Reevo, SalesDesk (UnifiedCRM), Skarbe, toflow AI, and Zipteams.

So, what should you expect from an AI-native CRM?

1. A flexible data model that breaks the rigidity of the legacy lead/contact/opportunity framework, enabling custom objects and adaptable workflows without complex customization.

2. A unified data infrastructure with a graph combining structured data (people, accounts) and unstructured data (conversations, intent, documents).

3. Built-in dataset enrichment.

4. Automated activity capture (emails, calls, meetings, chats, documents, etc.), all summarized and semantically indexed.

5. A modern UX that allows users to interact through natural-language and conversational commands, with in-context coaching and next-best-action guidance.

6. Built-in pipeline management, sales engagement, and forecasting.

Of course, incumbent CRM players aren’t standing still and are racing to infuse these capabilities across their platforms.

Salesforce has woven Data Cloud across its portfolio to unify structured and unstructured data, with Customer 360 acting as the connective tissue between its clouds and MuleSoft managing third-party integrations. A broad set of AI features is available as add-on modules, with Agentforce agents simplifying access to them. On paper, it checks every box, but in practice, realizing the vision still demands stitching together multiple modules and complex integrations.

HubSpot has taken a different route, evolving its CRM into the newly minted Smart CRM, a unified repository for structured and unstructured data. It is gradually expanding customization, notably through custom objects and a more flexible schema design, while Data Hub now provides rich connectivity to third-party systems. The integration of Clearbit into the platform simplifies data enrichment and signal acquisition. Over time, HubSpot has embedded numerous AI features across the suite, which feel native to the user experience.

The AI-native CRM category is still in its early days, but alongside the moves of incumbents, it is already setting a new yardstick for modern GTM infrastructure. I’m curious to hear your perspective and whether other innovative providers deserve a spot on your radar.

HubSpot hosted its annual Inbound conference in San Francisco last month. I didn’t attend in person but joined the keynote and investor session remotely. The event offered several important takeaways worth highlighting.

On its path to $3B, the company continues to demonstrate steady execution and a strong ability to reinvent itself through market and technology shifts. CTO Dharmesh Shah keeps exploring and sharing the art of the possible with AI. The company added 20 new Breeze AI agents to its suite. More notably, at a time when AI adoption challenges dominate the conversation, HubSpot’s baked-in approach stands out as one driving usage.

The inventor of inbound marketing addressed the steady decline in organic traffic, warning businesses of a “traffic apocalypse.” It introduced a new playbook, the Loop, to respond to changing buying behaviors. Anchored in precise Ideal Customer Profiles and continuous monitoring of signals that provide context and indicate readiness to consider your products or services, the approach orchestrates multiple marketing and sales plays across channels, supported by highly tailored content.

While HubSpot continues to gain share downmarket with its all-in-one approach, it is also expanding upmarket, as more sizable businesses choose it for ease of use, unified customer context across marketing, sales, and service, and low total cost of ownership. The company reported that customers with $5K+ ARR now account for a third of revenues, up from 20% in 2021. I am seeing more competitive displacements where companies trade feature depth for a simpler, easier-to-manage CRM.

HubSpot unveiled a major CRM platform overhaul, embedding new capabilities rather than layering them on top. The CRM has evolved into a unified repository of structured and unstructured data, a mini Customer Data Platform that can connect to other repositories like Snowflake via Data Studio. It leverages Clearbit to enrich company and account data and HubSpot Data Quality to keep data clean, current, and enriched. Its newly named HubSpot Smart CRM adds several AI-Native CRM promises: auto-updating, automatic enrichment, and the ability to unlock unstructured data and conversations.

HubSpot is seeing pre- and post-sales customer journeys converge, making unified customer context central to its value proposition. Sales and Service Hubs continue to grow over 20% year over year and are becoming a meaningful share of revenues. While the company doesn’t break out ARR by hub, I was able to triangulate their trajectories. Sales alone is approaching $1B, driven by businesses seeking a unified salestech approach, a notable achievement, as few providers have achieved sales stack unification at this scale.

HubSpot has firmly established itself as a CRM powerhouse, continuing to stay at the forefront of key market shifts by swiftly adapting its platform to a fast-evolving market.

Fullcast just completed its third acquisition in 3 months.

After acquiring Commissionly, a UK-based commission management (ICM) provider in June, and Atrium (Acquired by Fullcast), an innovative performance management solution for line managers in August, Fullcast has now snapped up Ebsta (now part of Fullcast), another UK-based revenue intelligence and analytics company.

Founded in 2016 by Dharmesh Singh and Bala B., Fullcast was created to simplify complex sales planning. Both founders met at Salesforce, where they had to scale an offshore team in order to assemble and update the hundreds of custom reports required to support the company’s planning process—Dharmesh recalls sharing 1,500 spreadsheets with 400 managers—an experience that shaped their vision to automate and streamline the process.

Originally VC-backed, Fullcast was acquired in 2024 by Ryan Westwood and its co-founders, with the support of EPIC Ventures, ushering in ambitious growth plans.

These three acquisitions position Fullcast to assemble a complete sales performance management solution, with a functional footprint rivaling Anaplan, CaptivateIQ, Oracle, Varicent, and Xactly Corp.

On the heels of the Clari–Salesloft and Bigtincan-Showpad mergers, Fullcast’s moves also mark another milestone in investor-driven SalesTech consolidation...

On the heels of taking Bigtincan private in April, Vector Capital Management is now acquiring Showpad to merge the two companies.

The combination creates a $250M revenue enablement player, positioning it as the number three behind category behemoths Seismic and Highspot (each around $500M ARR by my estimate). The new entity will operate under the Showpad brand.

This merger also allows the combined company to distance itself from Allego and Mindtickle, which are closer to $150M ARR.

Revenue enablement has been the first—and hardest—hit by vendor consolidation. Spanning content creation, sharing, enablement, and personalization, the category is poised to play a bigger role as knowledge becomes a cornerstone of AI-driven sales and closed-loop analytics emerge as key enablers of transformation.

Coming just weeks after the announcement of Clari and Salesloft combining, this further suggests investors are taking the driver’s seat in consolidating the highly fragmented SalesTech category.

The Clari-Salesloft merger represents one of the most significant salestech transactions in recent years, and an important milestone for revenue orchestration.

This combination creates a revenue orchestration powerhouse with 2,300 employees serving 5,000 customers. By my estimates, the merged entities generated over $450M in ARR last year. Their technology portfolio spans conversation intelligence, deal intelligence, revenue intelligence, and sales engagement. It is backed by 10 billion revenue actions and 1 trillion data signals ingested, one of the largest datasets in the industry. However, this investor-driven merger faces significant integration challenges, particularly given substantial technology overlap between Clari and Salesloft.

Revenue orchestration is an emerging category blending conversation intelligence, deal intelligence, revenue intelligence, and sales engagement. Forrester coined the term in 2024, signaling its belief that the category would become a cornerstone of salestech consolidation by dedicating a Wave to it. Gartner echoed this trend with a similar label—Revenue Action Orchestration—though it has yet to release a Magic Quadrant or Market Guide.

Revenue orchestration is propelled by two forces. First, businesses want to simplify and streamline bloated salestech stacks. Second, alpha vendors—a term coined by Craig Rosenberg in 2022 for well-funded startups aggressively expanding beyond their original mini-categories—are driving consolidation.

The funding numbers make the latter imperative clear: Clari ($496M raised, $2.6B valuation), Gong ($584M raised, $7.25B valuation), and Outreach ($489M raised, $4.4B valuation) all need to create a market large enough to sustain their growth ambitions. While PE-owned, Salesloft faced similar pressure after Vista’s $2.3B majority acquisition in 2022, later merging with Drift, which Vista had acquired in 2021 for $1B.

Despite these forces, revenue orchestration has grown more slowly than expected.

The push to consolidate sales stacks has been talked about for years, but has remained a moving target. It mostly applies to tech companies with extensive salestech investments. Other industries are still in the early stages of adopting modern salestech technologies, and prefer to proceed one bite at a time.

In tech, consolidation has largely meant cutting software that didn’t directly impact revenue operations or consolidating vendors within the same category. Only in 2024 have we seen true consolidation projects emerge—like Reddit using Gong as the backbone of its sales transformation, SAP adopting Outreach across its various revenue workflows, or IBM expanding its deployment of Salesloft to its global sales organization.

The three largest players in the category—Gong, Outreach, and Salesloft—each hit a growth plateau around $300M ARR last year. While growth has resumed, the overall category lacks the tailwind needed to meet their ambitious targets.

Another challenge of the category is that only a handful of players—Aviso, Clari, Gong, Outreach, Revenue Grid, Revenue.io, and Salesloft—have assembled a suite covering its full gamut. CRM players—HubSpot, Microsoft, SAP, and Salesforce—have steadily added the capabilities needed to compete in the category. However, except for HubSpot, adoption of their integrated suites remains limited, primarily due to implementation complexity.

Could this merger be a catalyst to re-energize the category? I believe the real opportunity isn’t just consolidating salestech stacks in the tech sector but expanding into other industries. Together, Clari and Salesloft have the scale to pursue salestech’s next growth frontier.

It’s time to make RFP Automation its own category.

I was previously lumping it under Sales Content & Collaboration, but it no longer reflects the market situation.

AI and GenAI have transformed RFP response solutions from static libraries of snippets into intelligent systems that connect to multiple knowledge sources and generate precise, tailored answers. With over 50 players now in the space, the category is nearing $500M in revenue.

It’s striking that most salestech categories cater to sales and revenue ops roles. Tools built specifically for sales engineers are rare, interactive demo platforms being the exception.

While the industry seems to settle on the term "RFP Automation," it’s not just about boosting sales engineer productivity; it’s also about driving win rates.

RFP Automation is also about helping all revenue teams respond to detailed, technical questions—something both sales and customer service increasingly face as buyers engage later and more pointedly.

It eventually addresses a deeper need: capturing and vetting knowledge that lives in experts’ heads but never makes it into formal documentation.

The company is said to have crossed the $30M ARR. Daily posts on LinkedIn detail go-to-market plays built using the tool. Companies have followed suit with several startups self-describing themselves as cheaper or simpler versions of Clay.

So, is Clay trailblazing a new category?

CRM’s history is paved with adjacent categories, each arising to work around the rigidity of its lead–contact–account–opportunity model and meet the needs of then-emerging sales/engagement motions.

• Marketing Automation Platforms (MAPs) unified leads and contacts into a person-centric model, enabling scalable email campaigns.

• Account-Based Platforms (ABX) restructured the data model to support account-driven, coordinated sales and marketing plays.

• Customer Success Management (CSM) added subscription and usage data to drive adoption, renewal, and expansion.

• Sales Engagement Platforms (SEP) focused on the individual—prospect or buyer, to enable multi-touch, sales-driven, outreach sequences across digital, social, and voice channels.

• The short-lived PLG CRM was built around a user-level data model, federating product usage signals to identify key moments in the journey that warranted engagement.

• Customer Engagement Platforms (CEP) like Braze are built to ingest and process real-time signals, orchestrating cross-channel outreach across email, messaging, and mobile.

All these categories share core traits: a motion-centric data model, synced back via data replication to the CRM system of record. While the terminology varies, they all feature list builders and campaign tools designed for specific GTM motions and are far easier to use than their CRM counterparts.

Data enrichment and housekeeping typically rely on a single sales intelligence provider, either enriching CRM records directly or supplying lists for use in the above tools, along with utilities for cleansing, normalization, and deduplication within the CRM.

Clay can be seen as the latest iteration of this pattern, designed to support highly-targeted, signal-driven playbooks. It begins with building razor-precise, data-driven Ideal Customer Profiles (ICP) from the analysis of a company’s best customers, enriched with firmographic and technographic data. From there, it assesses the addressable market, identifies lookalikes, and layers in buying signals to surface prospects likely to be in-market. These inputs drive scoring models and trigger targeted sales and marketing plays based on each prospect’s stage in the journey. All of this is powered by a mix of commercial data providers and Clay’s own data, scraped or monitored from the open web.

A few characteristics set Clay apart:

• It leverages data from multiple providers instead of relying on a single source

• Enrichment and signal usage are credit-based, tied to consumption rather than flat subscriptions

• Its spreadsheet-like UI enables building lists and triggering plays by federating all necessary data into tables.

From a data standpoint, Clay provides a staging workspace to upload, enrich, and process data, then pushes lists to downstream systems via no-code workflows.

I’m also looking at this development against the backdrop of how businesses have addressed data fragmentation caused by the proliferation of systems discussed earlier.

Most large and mid-size enterprises have turned to data warehouses to consolidate customer data, primarily for 360-degree views and analytics. While reverse ETL enables campaign activation by pushing data into systems of engagement, its adoption remains limited. Moreover, data warehouses lack the usability and interfaces that non-technical GTM teams need to build and execute campaigns without engineering support.

Customer Data Platforms (CDPs) ingest and unify data from diverse sources, including real-time events and signals, offering GTM teams easier-to-use environments. However, as packaged software, their adoption remains largely focused on e-commerce and a handful of industries. Most also rely on a person-centric data model, a key limitation for businesses with account-based sales motions.

Within the GTM ecosystem, revenue intelligence vendors like Clari, Gong, Outreach, and Salesloft are evolving into revenue orchestration platforms. They’re upgrading their time-series data stores into revenue data marts with two-way sync to enterprise systems (CRM, ERP, email) and integration with sales intelligence providers.

Meanwhile, emerging players like Cargo and Unify create unified repositories across sales and marketing by aggregating data and signals from internal (1st party) and external (3rd party) sources. Their flexible data models and toolsets for GTM teams take the complexity out of managing data and executing coordinated revenue motions.

In this ever-evolving environment, I’ve identified three main architecture styles, each shaping a distinct mini-category:

1. Multi-system engagement stacks hinge on data orchestration to enrich and sync the various repositories. It represents today’s dominant approach.

2. Unified customer and prospect data platforms built on modern data technologies let you create a data model tailored to your business that supports all sales and marketing motions while providing toolsets to eliminate the complexity of data warehouses. They effectively act as revenue/GTM data marts.

3. Clay-like staging workbenches where users upload lists, enrich them with third-party sales intelligence data and signals, and trigger workflows and sales plays through an intuitive, spreadsheet-like interface.

The current landscape remains fluid. It is poised for disruption again by AI conversational interfaces and agents, two breakthrough technologies set to simplify the data layer and streamline workflow execution.

These are my current working assumptions as I update the salestech industry landscape. I welcome your feedback, including alternate or contrarian perspectives.

Are agents creating entirely new categories or simply reshaping existing ones?

I'm leaning toward the following taxonomy:

Three types of agents:

1. Embedded/Integrated Agents: Built into SaaS applications or platforms, these agents introduce a new—conversational—UX layer. They simplify user interaction by executing tedious workflows or navigating interfaces on the user's behalf. However, they don't change the core functionality of the underlying system.

2. JTBD/Standalone Agents: Purpose-built to perform a specific job to be done (JTBD), these agents replace existing applications. They use an "agentic" execution model to deliver functionality. For instance, an autonomous SDR handles qualification and meeting scheduling for inbound inquiries. A voice AI agent replaces an IVR. These agents might expand existing category boundaries, but they essentially provide functional substitutes for legacy apps.

3. Agent Frameworks/Platforms: These platforms provide tools to build, deploy, and manage agents. Agents can be invoked by humans via a Copilot or automate cross-application workflows. While they often include prebuilt agents, their core value lies in offering a development environment for custom agents. They come with trust and governance frameworks, testing tools, and management layers. These support lifecycle operations and orchestrate execution at scale. They are often compared to digital workers.

Tentative verdict:

• Embedded Agents enhanced established categories

• JTBD Agents reinvent existing categories

• Agent Platforms are a new category enabling cross-application workflows