Report:

Magic Quadrant for Revenue Action Orchestration

Top Vendor Products for Revenue Action Orchestration in 2025

• Clari
• Gong
• Outreach

Revenue Action Orchestration Vendor Product Comparisons 2025

• Aviso - Challenger
  

  Pros:

  • Pipeline analytics and forecasting: Aviso's time-series data architecture tracks deal changes over time using a four-rolling-quarter approach, and its hybrid deal health and pipeline risk models combine structured data (e.g., close dates, pipeline stages) and unstructured signals (e.g., call transcripts, emails). It offers a multidimensional Pipeline Health Pentagon scoring model, across coverage, quality, maturity, spread and qualification, with adaptive benchmarks from historical conversion trends.
  • Vertical/industry-specific offering: Aviso supports customization for verticals such as financial services and pharmaceuticals with tailored workflows, compliance features and domain-specific AI agents. Users can also configure sales methodologies, coaching frameworks and account planning modules. Aviso also partners with implementation firms and GTM consultants to support vertical deployment strategies.
  • Knowledge base flexibility: Aviso's revenue context repository supports AI-guided actions and seller workflows. Continuously updated with structured and unstructured data from internal and external sources — including product documentation, and market intelligence — it powers real-time guidance, premeeting briefs and automated collateral generation, with role-based access controls and audit trails.

  Cons:

  • Multichannel buyer engagement: Aviso offers baseline prospecting channels, but partnership ecosystem limitations inhibit the use of advanced engagement channels used for enterprise selling (e.g., video messaging, direct mail/gifting). This is especially relevant for SDR/BDR motions requiring high volumes of strategic multichannel outreach.
  • Scheduling automation: Aviso customers will need a Calendly subscription to offer automation for one-on-one and group appointment coordination, time zone coordination and reminder workflows. Aviso's native meeting coordination may limit flexibility for teams requiring embedded scheduling workflows or advanced calendar logic. This may introduce friction for sellers who need to coordinate meetings directly from within deal or account views, especially in high-volume or multistakeholder selling environments.
  • Customer success model: Aviso identifies customer success and implementation services as its primary retention drivers. It offers limited specialized programs for verticals or segments. This may reduce its appeal to buyers with complex onboarding, change management or renewal workflows.


• Clari - Leader
  

  Pros:

  • Deal scoring and pipeline analytics: Clari's multisignal deal scoring combines structured CRM data with unstructured activity intelligence, including sentiment, stakeholder engagement and conversation metadata. Its time-series pipeline analytics provide details on deal progression, conversion velocity and win/loss trends, enabling sellers and managers to act on real-time risk signals and historical patterns.
  • Sales forecasting: Clari supports flexible forecasting across subscription, usage-based and consumption models with configurable roll-ups for product lines, territories and split ownership. Its AI-driven forecast recommendations are grounded in historical deals and activity trends, contributing to forecast accuracy and adaptability across complex enterprise hierarchies.
  • Scheduling automation: Clari's scheduling capabilities streamline meeting coordination across time zones and teams. It integrates with calendar systems, enabling sellers to embed scheduling links directly into outbound customer engagement flows. It also supports automated reminders and collaborative scheduling, reducing friction and improving buyer engagement.

  Cons:

  • User experience: Navigating the various modules of Clari's capabilities suite can be complex and unintuitive. The user experience relies heavily on Clari Guide to surface relevant workflows, and effective usage often requires structured onboarding and training to avoid confusion across modules.
  • External buyer intelligence integration: Clari's AI-led buyer research connects to GTM data applications but lacks integration with external web content such as news, earnings calls and media sentiment. This limits the breadth of buyer context available during strategic planning and outreach.
  • AI sales assistant: The Ask Clari assistant is not yet grounded in a configurable knowledge base (e.g., connecting customer questions to specific product configurations, industry positioning, strategic sales methodology plays), limiting an enablement team's ability to embed enablement content into AI Sales Assistant workflows.


• Gong - Leader
  

  Pros:

  • Ease of use: Gong offers a seamless user experience across pipeline generation, deal management, forecasting and coaching. Its AI agents are context-aware and natively integrated into workflows, enabling sellers to complete tasks without switching tools. The platform has an intuitive design and consistent UX across web, mobile and extensions.
  • Data interoperability: Gong's proprietary architecture, Gong Revenue Graph, consolidates structured and unstructured data into a real-time, queryable layer. It supports consistent metric definitions (enabling RevOps to customize business metrics with exact calculations, using formulas and filters), cross-entity analytics, and AI-readiness across all modules. Gong's open ecosystem includes integrations, public APIs and native support for data lakehouses, enabling customers to unify revenue data and extend insights across their tech stack.
  • Customer experience: Gong's customer success program centers on business outcomes. Customers receive strategic planning, value realization workshops, and tailored enablement aligned to their operating rhythm. Gong's ability to correlate product usage with performance metrics supports retention and expansion.

  Cons:

  • Scheduling automation: Gong does not offer native scheduling automation. Meeting coordination is supported only through integrations with scheduling vendors, which may limit flexibility for teams requiring embedded scheduling workflows or advanced calendar logic. This may introduce friction for sellers who need to coordinate meetings directly from within deal or account views.
  • Advanced sales forecasting: Customers seeking advanced forecast hierarchies may find limitations in Gong's forecasting capabilities. Customers needing custom hierarchies, roll-ups for cross-functional or overlay sales teams, multiowner deals, or alternate business models may face configuration complexity. Gong lacks some configurability and scenario modeling features found in more mature RAO forecasting modules.
  • Pricing: Gong's premium list pricing is expensive relative to peers. Customers facing budget constraints may find Gong's pricing creates friction in the RAO buying process, especially when IT or procurement prioritize cost over feature depth. Modular pricing includes separate fees for core seats, platform access and application add-ons.


• HubSpot - Niche Player
  

  Pros:

  • Product innovation: HubSpot restructured its platform architecture to support agentic AI through Breeze, which integrates structured CRM data, unstructured communications, and external sources into a unified context layer. This enables AI agents to execute autonomous actions such as prospecting, meeting prep and deal follow-up. The platform's embedded AI capabilities operate with minimal configuration, supporting real-time guidance and contextual recommendations.
  • Vendor growth: HubSpot has demonstrated consistent and strong year-over-year growth in both customer count and revenue across its Sales Hub tiers. It continues to expand its global footprint and partner ecosystem while maintaining high levels of product adoption and usage across its customer base.
  • Customer experience: Customers report rapid time-to-value and high satisfaction with Sales Hub's usability and embedded AI features. The platform's intuitive design and unified architecture contribute to strong adoption rates, particularly among small and midsize businesses. HubSpot's onboarding options, partner ecosystem and HubSpot Academy resources support ongoing customer success.

  Cons:

  • Sales skills coaching: Sales Hub provides limited functionality for structured coaching workflows; there is no dedicated interface for coaching based on performance metrics or engagement effectiveness. Buyers seeking robust coaching capabilities may need to supplement with third-party tools.
  • Buyer intelligence: AI-powered buyer-level health scoring is not available in Sales Hub. AI health scores are calculated only at the account level. Buyer-level insights depend on signals captured through Breeze agents and integration with Marketing Hub, which may require additional configuration or licensing. Organizations seeking to monitor individual stakeholder engagement and risk within complex deals may need to implement additional tools or workflows to achieve contact-level visibility.
  • Industry strategy: HubSpot's industry-specific implementation partnerships focus primarily on its CRM and broader platform. Sales Hub does not offer preconfigured vertical solutions. Buyers in regulated or specialized industries should evaluate whether HubSpot's extensibility and partner ecosystem meet their needs.


• Oracle - Visionary
  

  Pros:

  • AI-guided actions: Oracle uses a unified revenue data model that integrates ERP, CPQ and back-office signals with front-office engagement data. This interoperability enables highly contextual next best actions. The ability to combine operational and commercial data for prescriptive guidance provides differentiating precision in product-level orchestration.
  • AI sales assistant: Oracle embeds its AI Agent Studio directly into the seller workflow across web, mobile and Microsoft Outlook. This allows sellers to interact with conversational AI for research, content generation and task execution without leaving their primary workspace, reducing friction and accelerating adoption compared to bolt-on assistants.
  • Integrated product strategy: Oracle's RAO capabilities are natively integrated with collaboration tools such as Microsoft Teams and Slack, enabling deal teams to coordinate actions and share context without switching systems. Aligning marketing, sales and operations on a single platform supports matrixed go-to-market motions, thus reducing silos and improving execution speed.

  Cons:

  • Product packaging: Oracle's RAO functionality is embedded within the broader Fusion Cloud Applications Suite. Clients evaluating Oracle for RAO should consider the implications of adopting a multiapplication platform, which may require enterprise alignment beyond the sales organization.
  • Pricing complexity: Implementing a full RAO solution with Oracle requires purchasing additional components such as Oracle Unity CDP, Oracle CPQ, and Oracle Account and Contact Enrichment. Pricing is based on multiple metrics (e.g., hosted named users, profiles, sessions), which can increase the total cost of ownership as adoption expands across marketing and sales. Buyers should model scenarios carefully to avoid underestimating costs.
  • Platform complexity: Administration can be complex due to the breadth of Oracle's Fusion platform. Organizations without strong internal governance or implementation partners may face longer deployment timelines and hidden costs related to customization and integration.


• Outreach - Leader
  

  Pros:

  • Product strategy: Outreach's integration and extensibility model supports enterprise interoperability (e.g., CRM, collaboration tools). Additionally, a self‑service developer portal and marketplace allow customers and partners to build, test, and publish applications and custom objects. Product direction highlights agent interoperability and the connection of first- and third-party data to ground agentic workflows, and the roadmap centers on AI agents for engagement.
  • Account intelligence: Sellers can assemble and configure AI‑generated research tiles that synthesize signals from web sources, past conversations, CRM, enrichment providers and enterprise data. Relationship mapping supports drag‑and‑drop buying group visualizations and role designations, with account‑level engagement and custom-made intent indicators. This helps surface where to engage, with whom, and why.
  • Customer value realization: The customer experience is supported by ongoing success services. Customers report tangible productivity gains and measurable value following implementation. They also report achieving core RAO outcomes such as improved pipeline generation discipline and the reallocation of seller time from administration to selling.

  Cons:

  • Mobile app limitations: The mobile experience is limited to executing queued tasks (e.g., calls, emails) and reviewing deals. Outreach does not yet provide an AI sales assistant experience on mobile, which limits in‑the‑moment research and AI‑grounded Q&A when users are away from their desktops.
  • AI‑guided actions usability: Next best actions are often lower-level tasks (e.g., make a call, send an email, update account or deal records), and are operationalized as project‑style task lists derived from sequencing and deal activity. Customers will need to educate their sellers on how to primarily work out of sequence tasks, requiring digital dexterity. Prioritizing next best actions requires sellers to navigate multiple task views and filters to determine what matters most in a given operating rhythm.
  • Programmable action orchestration: The available seller action library is best used for sequence‑oriented steps and is less suited to more sophisticated or complex sales motions. Additionally, workflow creation relies on menu‑driven configuration rather than a visual builder, and views cannot be natively reframed to a management operating rhythm, making it essential for users to choose the right view.


• People.ai - Visionary
  

  Pros:

  • Multisource buyer intelligence: People.ai synthesizes buyer engagement across email, meetings, and call transcripts to generate AI-led insights on deal health, risk identification, stakeholder influence and next best actions. Its ability to enrich contact data and dynamically map buying groups enables sellers to identify economic buyers and champions without manual input, supporting more targeted and personalized outreach.
  • Product innovation: People.ai has rearchitected its platform to operate independently of CRM data quality, ingesting and enriching first-party signals from across the enterprise. Its platform is built on a graph-based data architecture, which enables dynamic relationship mapping and contextual linking of buyer, seller and activity data. This structure supports flexible modeling of stakeholder maps. The roadmap includes an MCP framework, which will enable agentic workflows with external AI systems such as Microsoft Copilot and Claude that combine People.ai data, productivity suites and third-party signals.
  • Workflow automation: Responses to People.ai's new customer survey indicate high satisfaction with embedded workflows and sales task automation. Customers, especially those managing complex accounts, also expressed appreciation for the platform's ability to surface custom-made coaching insights (e.g., the number of deals adhering to a qualitative selling methodology), automate scorecards, and generate meeting prep from real-time engagement data.

  Cons:

  • Execution model: Prospective customers should consider People.ai's limited channel presence and pricing structure. The vendor's direct sales approach and per-user pricing may not be suitable for buyers seeking broader ecosystem support or flexible consumption models.
  • Multichannel buyer engagement: People.ai does not support direct buyer interaction across multiple channels from a single interface. Organizations prioritizing omnichannel engagement capabilities may need to supplement the platform with other tools.
  • Scheduling automation: The platform does not offer native scheduling automation features. Buyers requiring integrated meeting coordination or calendar management should assess whether third-party integrations are sufficient to meet their workflow needs.


• Revenue Grid - Niche Player
  

  Pros:

  • Knowledge base persistence: Revenue Grid uses a semantic knowledge architecture to transform emails, meeting transcripts, and collaboration messages into structured insights that inform deal strategy and AI-guided actions. Its approach to grounding AI recommendations in verifiable facts supports explainability and trust in AI-driven workflows. Knowledge persistence across accounts and opportunities enables consistent reasoning for account planning, meeting preparation, pipeline reviews and coaching.
  • Integrated seller execution: The platform integrates into sellers' daily tools, reducing application switching and improving adoption. Structured onboarding, proactive success management and tailored enablement programs contribute to positive customer outcomes and sustained engagement.
  • Product investment: Revenue Grid demonstrates consistent growth in its customer base, supported by continued investment in AI-native capabilities. Its focus on evolving the platform toward proactive and autonomous execution signals long-term commitment to the RAO market.

  Cons:

  • Sales forecasting: Revenue Grid does not support custom forecast hierarchies or multiple business models. Organizations with complex forecasting requirements, including consumption-based forecasting, should plan to utilize native SFA forecasting or integrate third-party solutions.
  • Programmable action orchestration: Revenue Grid offers limited low-code orchestration for AI agents and workflow automation. Buyers seeking extensive customization, granular policy controls and governance for autonomous workflows should assess the need for additional tools or integration support.
  • Buyer intelligence: Revenue Grid's solution lacks robust contact-level intent scoring and analysis. Organizations that prioritize granular contact-level intent signals should plan to identify external data sources and validate how those signals will be normalized, surfaced in action queues and measured for impact.


• Revenue.io - Niche Player
  

  Pros:

  • AI sales assistant: Revenue.io enables sellers and managers to query deal/account/conversation intelligence that is grounded in a configurable knowledge base (e.g., playbooks, messaging, competitive notes). This function is accessible in Salesforce or the Revenue.io web and mobile app, and its guided-prompt UX helps different sales roles brainstorm deal strategies and compose contextual messaging.
  • Real-time guided selling: Revenue.io's platform provides prompts during virtual calls and web meetings to cue behaviors and next steps. Its guidance can align with sales methodologies, objection handling and compliance requirements. Admins can configure checklists by meeting type, customer and role, and connect context-aware guidance to call scorecards for postcall coaching.
  • Context-aware recommendations: Revenue.io's knowledge architecture ingests documents (e.g., PDFs, presentation decks) and links the content to interaction data so the assistant can return specific, context-aware recommendations for GTM team members. This feature improves the precision of coaching feedback, follow‑up summaries and message composition.

  Cons:

  • Sales forecasting: Forecasting is delivered primarily through managed dashboards inside Salesforce. Customers using Revenue.io that also want advanced forecasting logic, support for complex roll-ups and RAO-specific insights would need to configure these in Salesforce. This creates a fragmented experience to view forecasts, adding friction and reducing usability for customers desiring a single RAO system.
  • Pipeline analytics: Pipeline analytics are likewise delivered mainly through managed Salesforce dashboards, requiring customers to rely on Salesforce dashboards for multiteam or period‑over‑period conversion analysis. Revenue.io's at‑a‑glance roll‑ups of team pipelines with associated risks and trend snapshots are less prescriptive than other vendors.
  • Geographic strategy/presence: Its product supports multiple languages and complies with country‑specific call recording regulations, but Revenue.io has limited region‑specific partnerships and delivery ecosystems. Organizations with distributed sales teams outside North America may need additional internal resources or third‑party services to support regional rollout and change management.


• Salesforce - Visionary
  

  Pros:

  • AI sales assistant: Salesforce offers configurable flows, prompts, and agent actions that can be embedded across multiple surfaces, including Slack, CRM and browser extensions. It supports both point-and-click and conversational UX, enabling sellers to query records, generate collateral and execute tasks in context. Administrators can create reusable templates grounded in CRM data, supporting personalized and brand-compliant communications, and configure Agentforce agents to proactively interact with sellers.
  • Sales skills coaching: Salesforce's seller coaching supports role-play simulations and AI-generated feedback, and proactive coaching workflows triggered by sales events. Sellers receive scorecards based on conversation analysis, with explainable insights tied to deal context and activity signals. Managers can review coaching alerts, inspect transcripts and leave feedback directly in the CRM or Slack, enabling structured and scalable coaching.
  • Product strategy: Agentforce Sales is built on Salesforce's unified platform with deep integration across its other products and third-party ecosystems. Data 360 supports zero-copy data access from lakehouses like Snowflake and Databricks, and includes semantic modeling for AI readiness. Strategic partnerships with AWS, Google, and OpenAI extend agentic capabilities, while AppExchange and AgentExchange provide access to prebuilt agents, connectors, and vertical solutions.

  Cons:

  • Premium pricing: Salesforce's RAO capabilities are offered in their Unlimited and Agentforce 1 Sales editions. While all editions include capabilities such as Data 360, Agentforce and Slack, some customers may need to supplement their RAO solution with Flex Credits.
  • Ease of use: Users have shared that Salesforce's RAO capabilities can be complex to navigate and administer. Setting up agents, orchestrations, and semantic models requires familiarity with Salesforce's metadata framework and automation tools. Buyers should plan for enablement and governance to ensure adoption and consistency.
  • Mobile app limitations: Salesforce's AI sales assistant is not fully available in the mobile interface. Sellers working in field environments may experience limitations in accessing conversational AI or executing agentic workflows.


• Salesloft - Visionary
  

  Pros:

  • Signal orchestration: Salesloft's Rhythm capability enables sellers to act on prioritized signals across accounts and buyers. It integrates multiple data sources to generate next best actions and allows administrators to tailor workflows to align with business operating rhythms. Its ability to suggest time-sensitive actions and coordinate across seller workflows distinguishes it from more static, queue-based systems.
  • Ease of use: Salesloft's interface supports consistent workflows across web, mobile, and browser extension interfaces. Sellers can execute prospecting, engagement and follow-up tasks without switching tools, and the platform's guided workflows reduce cognitive load. The user experience is optimized for quick action and minimal friction.
  • AI-guided manager coaching: Salesloft gives frontline managers curated coaching queues with call and email reviews, sentiment analysis and deal-scoring insights. Managers can access relevant moments and generate coaching recommendations with explainability, which supports scalable, data-driven coaching.

  Cons:

  • Sales forecasting: Salesloft's AI-driven forecast predictions are based on a weighted pipeline and do not factor in deal win probability, creating the potential for forecasts that over- or under-represent pipeline risk. This separation between win rates and forecasting logic may limit visibility into true deal health for customers with multiple forecasting dimensions (e.g., product, overlay).
  • Vertical/industry strategy: Salesloft does not offer industry-specific configurations or content. Prospective customers in regulated or specialized industries may need to customize workflows or rely on partners for vertical alignment. Additionally, the partner ecosystem includes limited industry-specific providers, which may constrain deployment speed in niche sectors.
  • Enterprise-level data interoperability: Salesloft requires a separate iPaaS subscription to integrate with enterprise data warehouses. Clients with complex data architectures seeking to connect Salesloft with third-party data warehouses should plan for additional cost and implementation effort. This may increase complexity for organizations aiming to unify GTM workflows.


• ZoomInfo - Visionary
  

  Pros:

  • Data interoperability: ZoomInfo offers a unified, enriched commercial dataset that customers can blend with first‑party CRM, MAP and data‑warehouse records. It supports "bring-your-own-data" patterns, partner datasets and custom signals, enabling teams to stack and normalize multiple intent and engagement sources for signal hunting and downstream activation.
  • AI sales assistant: ZoomInfo Copilot converts company, contact, intent, and engagement signals into actionable seller guidance. It produces shareable artifacts (e.g., account plans, meeting prep summaries) and can be invoked where sellers work, reducing the effort of going from insight to outreach. ZoomInfo's data foundation improves targeting precision and reduces manual research time.
  • Account & buyer intelligence: ZoomInfo's GTM Studio curates account‑level "plays," buying group definitions, and custom signals that can be routed to ZoomInfo Copilot and frontline users. Users can assemble audiences, enrich records, and push context into seller workflows without building custom-made data pipelines.

  Cons:

  • Sales forecasting: ZoomInfo does not provide a dedicated forecasting module with hierarchical submissions and weighted roll-ups. Probability and momentum indicators are accessed via workbooks and AI‑generated summaries. Forecast governance is also limited. Organizations that need structured forecasting cadences, audit trails, and manager roll‑ups would need to retain native-SFA forecasting or add a specialized tool.
  • AI-guided actions: Guided actions are fragmented across ZoomInfo Copilot, GTM Studio, and Chorus; sellers and managers must switch contexts to move from call insights to action queues. Organizations seeking a single, continuous workflow from signal detection through guided execution should validate handoffs, notification paths, and action logging across these surfaces.
  • Scheduling automation: ZoomInfo's RAO portfolio does not include native scheduling automation. Meeting coordination relies on external email/calendar tools or adjacent products, which can add steps between outreach, qualification and booking. Teams that depend on automated scheduler workflows should plan to integrate a scheduling tool or confirm partner options during evaluation.

Reference

• Gartner, Magic Quadrant for Revenue Action Orchestration, 15 December 2025, ID G00821969

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