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

Conversational AI Platforms For Customer Service Vendor Product Comparisons 2026

Key features and pros and cons of top Conversational AI Platforms For Customer Service products:

Kore.ai - Leaders

Pros:

Above par in capabilities related to AI expertise, particularly in building its own LLMs and small language models (SLMs)
Provides significant flexibility in management of AI models, agentic frameworks and resources, and tools for testing applications
Includes sturdy guardrails to keep bots out of trouble and security measures to keep customer data safe
Strong connectivity to multiple communications channels and ability to integrate with back-end systems

Cons:

Needs to continue to prioritize customer service solutions to avoid competing head-to-head with general-purpose providers for broad corporate AI functionality
Customers note opportunities for improvement around the speed and responsiveness of support teams

NiCE Cognigy - Leaders

Pros:

Demonstrates strong capabilities in managing AI models and providing an agentic framework that can perform complex operations for customers at scale
Development and testing tools help organizations build and manage complex AI-driven solutions for their customers
Customers appreciate its ability to help deliver genAI applications at scale
Solid roadmap positioned to benefit from access to NiCE's larger partner ecosystem

Cons:

Reporting capabilities fall short of the more comprehensive customer journey insights provided by some of its peers
Customers mention a lack of end-to-end reports, noting that they often export data to solutions like Tableau to provide reporting that extends beyond the confines of the contact center
Roadmap will require balancing new capability development with product integration work

Omilia - Leaders

Pros:

Provides phone-based self-service applications at a very high scale
Strong multimodal capabilities and omnichannel connectivity, demonstrated by deployment in drive-through kiosks
Approach prioritizes safety and security, particularly around AI models, with strong AI guardrails and support for numerous security standards including FedRAMP
Highly scalable with strong uptime numbers
Customers praise Omilia as a supportive and helpful partner

Cons:

Less open approach includes limited bring-your-own-model (BYOM) options
Customers say they would like more proactive communications on product updates

Automation Anywhere - Strong Performers

Pros:

The Aisera platform provides strong capabilities for application observability
Effective reporting and administrative tools
Strong guardrails to ensure proper bot behavior and standards adherence to protect customer data
Customers say the Aisera platform enables them to deliver services to customers that would be impossible without it

Cons:

Delivering on vision of combining conversational AI with process automation hinges on how effectively it can execute its acquisition of Aisera
Needs to rationalize areas such as pricing, which is currently complex and confusing
Aisera typically delivers applications at a relatively small scale, limiting its scalability score
Customers raise concerns about Automation Anywhere's acquisition impacting the Aisera roadmap
Only one reference customer responded to Forrester's outreach

Intercom - Strong Performers

Pros:

Platform shines with many prebuilt components that speed deployment
Prebuilt integrations shorten the time required to integrate with legacy systems
Customers praise Intercom as a strong partner and communicator; they appreciate its honesty on the platform's abilities and limitations

Cons:

Tightly governed approach can be constraining for those with complex application requirements
Not partner focused in the conversational AI space; relies primarily on direct sales and deployment models
Below par on some developer-centric elements including application lifecycle management, application testing tools, and its agentic framework
Customers note challenges in getting the data they need to personalize their applications

Rasa - Strong Performers

Pros:

Platform strengths emphasize resource orchestration and application lifecycle management
Architecture and on-premises deployment model support differentiated data sovereignty and customer data protection capabilities
Provides solid APIs for legacy system integration
Customers praise how Rasa's platform allows them to control their customer experiences while protecting customer data

Cons:

Development environment is built for coders rather than no-code users
Partner ecosystem is limited and focuses on technology partnerships
Customers note that it takes time for users to get comfortable with the platform's user interface

Sierra - Strong Performers

Pros:

Agentic framework is central to its ability to deliver customer self-service capabilities
Platform has been used in large, complex deployments despite being only two years old
Customers praise the applications they have been able to deliver to their customers
Uses outcome-based pricing and forward-deployed engineer model to minimize deployment and adoption obstacles

Cons:

To continue growing, Sierra must face the challenges of scaling, filling roadmap gaps, building out a partner program, and moving to a more traditional service and deployment model
Needs to further strengthen its capabilities around reporting and administration and development tools
Below par in some capabilities important to traditional contact center teams, such as connecting to legacy systems and escalation to live agents
Some customers express nostalgia for the level of support they received during the vendor's earliest days

SoundHound AI - Strong Performers

Pros:

Provides proprietary voice capabilities that support high-quality customer conversations
Above par in multimodal interactions: A high percentage of its customers report using three or more interaction channels
Provides the speech interface for multiple automotive manufacturers
Customers praise the vendor's partnership and teamwork

Cons:

Roadmap is challenging, as it must keep up with rapid AI developments while integrating products that originated from three different organizations
Development and observability tools are below par on usability compared with other vendors in this evaluation
Customers note shortcomings in the product's outbound capabilities

Uniphore - Strong Performers

Pros:

Built a new set of testing and development tools, including AI tools that identify use cases, help build applications, test them, provide scoring, and identify fixes to improve performance
Voice-centric with a high percentage of customer interactions happening on the phone
Customers praise the modularity and flexibility of the Uniphore platform

Cons:

Service and support offerings lack some of the clarity and specificity provided by other vendors in this evaluation
Currently supports customer interactions only by voice and webchat
Personalization capabilities are below par relative to others in this evaluation
Customers note subtle architectural limitations that need to be worked around
Only one reference customer responded to Forrester's outreach

Yellow.ai - Strong Performers

Pros:

Provides above-par support for a range of developers with a mix of low-code/no-code and pro-code tools
Nexus AI assistant supports application creation and testing and can identify applications to build based on usage and generate synthetic data for testing purposes
Approach to legacy system integrations includes an on-premises application that acts as a translator to connect its platform with older legacy systems
Customers laud Yellow's deployment services and how quickly they generate value

Cons:

Vision of providing a 'universal agentic interface' is a broad ambition that likely is not Yellow's to own
Partner ecosystem focuses more heavily on resellers and developers than on technology or integration partners
Customers report needing to leverage third-party reporting tools to get an overall summary of their systems and customer experiences
Only one reference customer responded to Forrester's outreach

Ada - Contenders

Pros:

Platform includes on-par capabilities to help manage an agentic system, including strengths related to guardrails, observability, and resource orchestration
Helps organizations deliver personalized customer service across multiple communications channels
Customers appreciate Ada's platform as their 'front door' and say that their customers are satisfied with their use of the application

Cons:

Focus on operations over development shows up in below-par tooling for building, testing, and managing applications
Platform offers fewer capabilities than others for integration into continuous-integration/continuous-delivery (CI/CD) pipelines
Largest customer deployments are smaller than those of most vendors in this evaluation
Customers comment that Ada's roadmap delivery is less predictable than desired

LivePerson - Contenders

Pros:

Platform works well at scale and manages multiple AI models through an LLM gateway to control model access
Strong support for customers that bring their own AI model
Multimodal and omnichannel capabilities are well supported
Customers praise LivePerson's partnership and mentoring and value the data and insights available from the platform

Cons:

Goes to market with conversational AI as a standalone offering, but the product works best within the company's overall ecosystem
Roadmap is less focused than others, as critical capabilities for its conversational AI platform are supported through other applications in the product suite
Voice capabilities are limited
Lags in modern AI capabilities, with a less sophisticated agentic framework and less robust resource orchestration
Customers point out that reporting and dashboards do not always meet their needs
Only one reference customer responded to Forrester's outreach

Netomi - Contenders

Pros:

Platform provides superior capabilities for managing customer data to enable personalized customer interactions
Tools for observability enable customers to use AI safely and help identify issues when they arise
Customers praise its speed of execution and AI expertise

Cons:

Vision for policy-bound autonomy is sound but does not help Netomi's lack of visibility in the market
Below par on partnerships, with minimal programs in this area
Service offerings are below par, reflecting unclear definitions of available services and mixed feedback from customers
Strongly digital-first, with a limited number of customers using voice
Application lifecycle management tools are less automated than those of other vendors
Tools for customizing interactions for specific channels are not intuitive to use
Customers note that the team appears spread thin at times
Only one reference customer responded to Forrester's outreach

PolyAI - Contenders

Pros:

Excels in voice, supporting a large percentage of its interactions on this channel
Platform supports large-scale deployments with multiple implementations running well in production
Provides superior tools for knowledge management for FAQ and other informational applications
Customers praise service and support and express pride in the applications they have been able to build

Cons:

Roadmap includes catch-up items around lifecycle management and application governance
Technology partnerships are limited
Lower profile in the market and less mature tools for developers and administrators weighed on innovation score
Application development and lifecycle management tools are more developer-centric and less streamlined than those provided by others in this evaluation

FAQs

Q: Which vendors are Leaders in 2026?

A: Kore.ai, NiCE Cognigy, Omilia

Q: What does "Leader" mean in Forrester Wave terms?

A: Leaders in the Forrester Wave demonstrate strong capabilities in managing AI models and providing agentic frameworks that can perform complex operations for customers at scale. They have solid roadmaps, well-established partner programs, and provide development and testing tools that help organizations build and manage complex AI-driven solutions. Leaders show a clear understanding that success in the conversational AI for customer service market depends on platforms that integrate with entrenched IT environments, support the creation of highly tailored applications, enable collaboration between developers and business users, and provide enterprise-grade observability and enforceable guardrails.

Q: How do I interpret a Forrester Wave?

A: https://www.forrester.com/policies/forrester-wave-methodology/

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Top Vendor Products for Conversational AI Platforms For Customer Service in 2026

Conversational AI Platforms For Customer Service Vendor Product Comparisons 2026

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FAQs

Q: Which vendors are Leaders in 2026?

Q: What does "Leader" mean in Forrester Wave terms?

Q: How do I interpret a Forrester Wave?

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