Spotlight

Report:

The Forrester Wave™: Conversational AI Platforms For Customer Service, Q2 2026

How does Forrester define the Conversational AI Platforms For Customer Service market in 2026?

More than 650 conversational AI vendors compete for relevance in a market shaped by the needs of roughly 15 million contact center agents. The 14 vendors included in this evaluation demonstrate a clear understanding that success in this market depends on far more than impressive demos or novel AI techniques. Customer service leaders require platforms that integrate with entrenched, long-lived IT environments; support the creation of highly tailored, brand-aligned applications; and enable collaboration between software developers and business users. Equally critical are enterprise-grade observability and enforceable guardrails — prerequisites for earning the confidence of risk-averse legal and compliance teams. All vendors in this evaluation embed some form of agentic framework within their platforms, reflecting a broader shift toward more autonomous, task-oriented customer self-service.

Key Facts for The Forrester Wave™: Conversational AI Platforms For Customer Service, Q2 2026 in 2026

How did the Conversational AI Platforms For Customer Service market evolve in 2026?

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

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

No common features specified.

Scope Exclusions

Inclusion Criteria

Vendors must, among other requirements:

Offering Strengths — Relative Weighting

Strategy Strength — Relative Weighting

FAQs

Q: What does this research cover?

A: This research evaluates 14 conversational AI platform providers for customer service based on their current offering capabilities, strategy, and customer feedback. The evaluation focuses on how well platforms integrate into IT environments, align with internal development teams, and support table-stakes agentic frameworks for customer self-service.

Q: Who should use this research?

A: Customer service leaders should use this research to assess conversational AI platform vendors and determine how well their products fit safely into IT environments, align with internal teams in terms of development approaches and tools, and support agentic frameworks for autonomous customer self-service.

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

A: All vendors included in this evaluation must provide: (1) Broad, enterprise-level support with all core functions natively provided and demonstrated track record supporting large enterprises; (2) A conversational AI solution specifically targeted at and packaged for the customer service market; (3) A standalone product available for purchase with its own SKU and pricing, actively used by customers on a standalone basis; (4) At least $35 million in annual revenue from the conversational AI for customer service product in the last four quarters; (5) Significant mindshare among Forrester's enterprise clients, frequently mentioned in client inquiries, advisories, and consulting engagements, and recognized as a competitor by other vendors.

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

A:

  • Insufficient revenue (less than $35 million annually from conversational AI for customer service)
  • Product not available as standalone offering with own SKU and pricing
  • Lack of enterprise-level support or demonstrated track record with large enterprises
  • Product not specifically designed or packaged for customer service market
  • Insufficient mindshare among Forrester's enterprise clients
  • Product only available as free feature within larger portfolio rather than standalone purchase
  • Product not generally considered by customers looking for standalone conversational AI solution (e.g., Salesforce Agentforce)

Q: What should buyers consider when evaluating products in this market?

A:

  • How well the platform fits safely into existing IT environment with proper connectivity protocols, observability, and guardrails for data protection
  • Alignment with internal teams in terms of development approaches and tools - whether traditional developer toolchains, blended pro-code/no-code environments, or prescriptive frameworks are needed
  • Support for table-stakes agentic frameworks with controls that limit risk and align with legal and compliance requirements
  • Ability to integrate with entrenched, long-lived IT environments including CCaaS platforms and legacy systems of record
  • Enterprise-grade observability capabilities to ensure proper agent behavior and identify issues
  • Enforceable guardrails to earn confidence of risk-averse legal and compliance teams
  • Platform's ability to evolve alongside successive waves of AI innovation without forcing disruptive resets
  • Support for creating highly tailored, brand-aligned applications
  • Collaboration capabilities between software developers and business users
  • Scalability and reliability for handling contact center volumes
  • Multimodal and omnichannel support capabilities
  • Voice and telephony capabilities if phone-based service is critical

Q: How has the Conversational AI Platforms For Customer Service market evolved in 2026?

A:

  • Shift toward agentic frameworks for more autonomous, task-oriented customer self-service
  • Integration with multiple connectivity protocols including Model Context Protocol (MCP) for AI, RESTful APIs, and SDKs
  • Emergence of AI-assisted capabilities including use case discovery, draft application generation, and synthetic test data and persona creation
  • Increasing importance of enterprise-grade observability and enforceable guardrails for risk management
  • Growing need for platforms that can evolve alongside successive waves of AI innovation without forcing disruptive platform resets
  • Emphasis on collaboration between software developers and business users through blended pro-code and no-code environments
  • Focus on personalized, brand-aligned customer experiences across multiple channels

Q: What differentiates Strength of Offering vs. Strength of Strategy?

A: Strength of Offering evaluates the current capabilities and functionality of the conversational AI platform, including technical features like AI model management, development tools, scalability, security, and integration capabilities. It focuses on what the platform can do today. Strength of Strategy evaluates the vendor's future direction and market approach, including their vision for AI in customer service, innovation capabilities, product roadmap, partner ecosystem strength, pricing models, and service offerings. It focuses on where the vendor is heading and how well-positioned they are for future success.

Reference

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