Magic Quadrant for Conversational AI Platforms

How does Gartner define the Conversational AI Platforms market in 2025?

Gartner defines conversational AI platforms (CAIPs) as SaaS products that primarily enable the development of applications simulating human conversation across multiple channels and media. CAIPs leverage composite AI, including generative AI (GenAI) and natural language technologies. Conversations can use a mix of modalities such as text, voice and visual content. To support the building of conversational applications, platforms provide extensive coding options, from pro-code to no-code. Application areas include chatbots, virtual assistants (VAs) and conversational AI (CAI) agents. CAIPs are used to create, deploy and manage AI-driven conversational interfaces that facilitate both customer-facing and internal interactions through pro-code/low-code/no-code tools. CAIPs empower businesses to centralize and democratize the development and management of multiple, diverse CAI initiatives, leading to more cohesive and efficient operations.

Key Facts for Magic Quadrant for Conversational AI Platforms in 2025

Publication Date: 13-Aug-2025

Document ID: G00826020

Coverage: Global

Authors: Gabriele Rigon, Justin Tung

Core Purpose: This report guides application leaders in selecting conversational AI platforms for complex automation and multimodal interactions as the conversational AI platform market evolves, with use cases expanding to include conversational AI agents and tools increasingly leveraging generative AI.

How was the Conversational AI Platforms market evolved in 2025?

The conversational AI platform market is evolving with use cases expanding to include conversational AI agents and tools increasingly leveraging generative AI

CAIPs are SaaS products that enable development of applications simulating human conversation across multiple channels and media

CAIPs leverage composite AI, including generative AI (GenAI) and natural language technologies

Platforms provide extensive coding options, from pro-code to no-code

Application areas include chatbots, virtual assistants (VAs) and conversational AI (CAI) agents

The market has undergone significant transformation over the past two years, driven by rapid advancement and adoption of generative AI and emergence of AI agents

Contemporary CAIPs are increasingly hybrid in nature, integrating GenAI, agentic AI, natural language technologies and multimodal capabilities

Buyer maturity remains relatively low and solution differentiation is unclear and more nuanced

Many vendors are repositioning as AI agent platform providers and rearchitecting their product portfolios to incorporate generative and agentic features

CAIPs maintain distinct value in scenarios requiring high levels of accuracy, scalability, regulatory compliance and governance

What product features are required to be included in this year's evaluation?

Coding options: CAIPs provide multiple coding options, which may include low-code, no-code, GenAI-assisted and pro-code. Low-code, no-code and GenAI-assisted tools support rapid application development, deployment, execution, testing and management using declarative, high-level programming abstractions via dedicated graphical UIs (GUIs) or GenAI-driven assistants. Pro-code tools enable the use of scripting and programming languages, such as Java or Python, to develop highly configured functionalities and integrations, giving technical users access to deep configuration and customization of the CAI applications.

Composite multilingual NLP: Predefined and customizable pipelines of techniques that allow the processing of the user's natural language leveraging the multilayer composition of, for example, rule-based and machine learning techniques, including large language models (LLMs).

Workflow building: GUIs for visual conversational or nonconversational workflow building, including tools to easily manage any relevant fulfillment logic during the dialogues. The resulting scripted workflows should be able to work in combination with, and back when appropriate, agentic AI planning and execution features, if those are available.

Integration with back-end systems and data sources: Tooling meant to set up or personalize communication with critical back-end systems, such as services (e.g., cloud services, AI frameworks), data sources (e.g., CRM, customer data platform) or applications (e.g., contact center as a service, martech, and analytics and business intelligence). It includes natural language query (NLQ) functionalities to interface with underlying BI systems in natural language.

Support for LLM prompt engineering: Support for LLM prompt engineering via dedicated low-code/no-code modules or embedded functionalities. Prompt engineering is the discipline of providing inputs (e.g., in the form of text or images), to GenAI models to specify and confine the set of responses the model can produce. Retrieval-augmented generation (RAG) is a common composite prompt engineering architecture. It includes GenAI content-anomaly guardrails.

Analytics module: Dedicated tools, including dashboards, to collect, monitor and analyze performance data to get meaningful and actionable insight for reporting, oversight and improvement purposes, which is key for the overall app life cycle management.

Data security and privacy controls: Dedicated functionalities and tools to handle privacy, enterprise compliance and security aspects in the platform itself, as well as when building and deploying the CAI app and at runtime.

What are the common features of top products in the Conversational AI Platforms space?

Voice interaction support: Native or deeply embedded services leveraged to implement sophisticated voice experiences (e.g., listening and speaking) on either dedicated devices or using telephony. It may include voice biometrics.

Knowledge graph support: Native tooling to build and maintain knowledge graphs (KGs). KGs are machine-readable representations of the physical and digital worlds. They include entities (people, companies, digital assets) and their relationships, which adhere to a graph data model — a network of nodes (vertices) and links (edges/arcs).

CAI app orchestration: Dedicated tools that enable the orchestration of multiple CAI applications, either within a single use case or targeting multiple use cases. This functionality includes different collaboration patterns, ranging from scripted CAI app pass through to CAI apps escalating and routing to other CAI apps, or AI agents. It also includes the orchestration of language assets and dialogue assets across several CAI implementations.

Multichannel connectivity: Prebuilt and customizable integrations to a variety of different digital channels, use specific rich features of different channels and operate multiple channels centrally. Channels may include messaging platforms, website chats, webhooks, telephony, voice speakers and others.

Custom integration with non-native LLMs services: Tooling to enable custom integrations to platform-external LLMs, whether hosted by the deployer or provided by third parties.

Video interaction support: Native or deeply embedded functionalities aimed to process video content in batch or in real time. It may include support for gesture controls in input and the deployment of digital-humans interfaces.

Support for numerical data processing: Numerical-data-processing features that enable the CAI apps to interpret, analyze and respond to numerical inputs, facilitating data-driven interactions and insights.

Support for image upload: Native functionalities that allow CAI app users to upload images, enabling the CAI app to analyze, interpret and respond to visual content effectively.

GenAI engineering: GenAI engineering tools enable enterprises to operationalize models faster, balancing both governance and time to market. AI engineering tools can be subdivided into model-centric and data-centric tools. We consider DataOps, LLMOps, LangOps or FMOps, or more broader terms, such as ModelOps or MLOps, as a subset of AI engineering.

Vendor-specific LLMs: The ability vendors may have to provide a self-hosted domain-specific LLM service, such as LLMs being built by the vendors in-house, or a fine-tuned version of open-source or third-party proprietary LLMs.

Agentic AI planning and execution: Includes tooling for enabling autonomous process planning and execution in CAI applications. These features are emerging, but are not standardized in the broad market of CAIPs yet. The processes automated by agentic AI planning and execution should be mostly planned and designed by the relevant engine, typically LLM-based, and autonomously optimized leveraging heuristics based on past experiences or user behavior.

Continuous optimization tools: Functionalities meant to facilitate CAI apps' maintenance and continuous optimization, to enable an implementation to easily improve over time as more data is collected.

Deployment options: Functionalities that allow to install or grant access to the platform, as well as to deploy applications, on-premises, in the cloud or on the edge.

Scope Exclusions

Offerings that do not meet all mandatory features as defined by Gartner

Products that are not single, unified stand-alone offerings

Solutions that require clients to replace other aspects of their chat operations or contact center software

Vendors without sufficient customer base size (less than 70 total customers, 20 new in 2024, or 35 active spending $100k+)

Companies with limited geographic presence (more than 75% in single region or lacking significant presence in two regions)

Vendors with insufficient employee base (less than 150 in CAI practice or less than 25% in R&D)

Platforms where less than 33% of customers use the CAIP for customer service and support

Products where mandatory features are not GA in 1Q25 or have less than 50% customer adoption

Products where common features are not GA in 1Q25

Inclusion Criteria

Vendors must, among other requirements:

Single, stand-alone offering that meets the market definition with all mandatory features

Company established prior to 1 January 2021

Active CAIP operation prior to 1 January 2024

Minimum 70 customers, 20 new customers in 2024, 35 active licensed customers spending $100k+ annually

No more than 75% of customer base in single region; significant presence in at least two regions

Minimum 150 employees in CAI practice/business unit; 25% in product development/R&D

Minimum 33% of customers using CAIP for customer service and support use cases

All mandatory features GA in 1Q25 with 50%+ customer adoption; all common features GA in 1Q25

Ability to Execute — Relative Weighting

Product or Service - High

Overall Viability - Medium

Sales Execution/Pricing - Medium

Market Responsiveness/Record - Medium

Marketing Execution - Medium

Customer Experience - Medium

Operations - Medium

Completeness of Vision — Relative Weighting

Market Understanding - High

Marketing Strategy - Medium

Sales Strategy - Medium

Offering (Product) Strategy - High

Business Model - Medium

Vertical/Industry Strategy - High

Innovation - High

Geographic Strategy - Medium

FAQs

Q: What does this research cover?

A: This research covers the conversational AI platform (CAIP) market, evaluating vendors that provide SaaS platforms for developing conversational AI applications across multiple channels and modalities. It analyzes vendors' ability to execute and completeness of vision, examining product capabilities, market presence, innovation, customer experience, and strategic positioning. The report includes evaluation of mandatory features (coding options, composite multilingual NLP, workflow building, back-end system integration, LLM prompt engineering support, analytics, and security controls) and common features (voice interaction, knowledge graphs, CAI app orchestration, multichannel connectivity, and agentic AI capabilities). It covers typical use cases including customer interaction automation, employee assistance, sales and marketing automation, and enterprisewide conversational AI agents.

Q: Who should use this research?

A: This research should be used by application leaders, technology decision makers, and executives evaluating conversational AI platforms for complex automation and multimodal interactions. It is particularly valuable for organizations in regulated industries (financial services, healthcare, government) that require high levels of accuracy, scalability, compliance and governance. The research helps buyers understand vendor differentiation, assess platform capabilities against specific use cases, evaluate deployment options, and make informed decisions when selecting between CAIPs, GenAI-native apps, or targeted extensions in enterprise applications. It guides organizations in strategic planning for conversational AI adoption, understanding the shift toward agentic AI, and ensuring alignment with both current and future business needs.

Q: What are the mandatory features of vendors included in this market?

A: Vendors must provide: (1) Multiple coding options including at least low-code and no-code; (2) Composite multilingual NLP with customizable pipelines leveraging rule-based and machine learning techniques including LLMs; (3) Visual workflow building tools for conversational and nonconversational workflows; (4) Integration tooling for back-end systems and data sources including NLQ functionalities; (5) Support for LLM prompt engineering via low-code/no-code modules including RAG and GenAI guardrails; (6) Analytics module with dashboards for monitoring and reporting; (7) Data security and privacy controls for platform, deployment, and runtime. All mandatory features must be generally available in 1Q25 with over 50% of customers using them in production (except LLM prompt engineering which needs GA but no minimum adoption threshold).

Q: What are some reasons for not being included in this report?

Inbenta: Agentic AI planning and execution features were not generally available in 1Q25
Microsoft (Copilot Studio): Could not confirm 33% of licensed customers using CAIP for customer service/support, or that 50% of customers are using analytics module, composite multilingual NLP, and workflow building features
Parloa: Limited geographic presence with vast majority of customer base in Europe only
Uniphore: Agentic AI planning and execution and CAI app orchestration features not GA in 1Q25; fewer than 50% of customers using composite multilingual NLP in production

Q: What differentiates Ability to Execute vs. Completeness of Vision?

A: Ability to Execute emphasizes factors that directly impact a vendor's capacity to deliver robust, scalable, and high-quality conversational solutions. Product capabilities receive the highest weight, as organizations seek platforms with advanced features, flexibility, and enterprise-grade reliability. Operational excellence, customer experience, and market responsiveness are also key factors. Completeness of Vision focuses on whether vendors demonstrate deep understanding of customer needs, anticipate future trends, and articulate compelling long-term differentiation strategies. The highest weights are given to market understanding, offering strategy, vertical/industry strategy, and innovation—reflecting the vendor's ability to shape market direction through differentiated capabilities and forward-looking product strategies.

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