The Forrester Wave™: Conversational AI Platforms For Employee Services, Q3 2024

Conversational AI Platforms For Employee Services Vendor Product Comparisons 2024

Key features and pros and cons of top Conversational AI Platforms For Employee Services products:

Aisera - Leaders

Pros:

Variety of functionality with strong support across agent augmentation, automation facilities, prebuilt assets, automated workflow development, and LLM testing suites
Strong language processing adaptability, allowing dynamic clarification from end users
Low total cost of ownership (TCO)
Commitment to customers as a partner

Cons:

Unrelenting expansion sometimes moves too quickly to its detriment
Users need to access multiple tabs and interfaces to take advantage of all different tools
Lack of standardized processes to ensure customer success with extensive tooling

Avaamo - Leaders

Pros:

Robust automation and integration capabilities
Language-training interfaces that support both pre-live and at-runtime enhancement suggestions
Language and integration flexibility give adopters more options
Dedicated customer support manager (CSM) for every customer and adoption programs to ensure customer value

Cons:

Requires significant configuration in language response scripting
Requires dedicated builder resources (developers or business analysts) to leverage platform
Vision and roadmap just now embracing newer capabilities like dynamic decisioning models
GenAI capabilities haven't fully replaced traditional conversational AI design in platform
Only one reference customer responded to Forrester's outreach

Espressive - Leaders

Pros:

Investments in pre-configuration and managed language models for customers
LLM chain capabilities significantly reduce configuration required for customers to get up and running
Dynamic disambiguation makes interacting with service requests easy to parse and process
Best-in-class customer success approach, focusing on ensuring customer value
REaCT capabilities promise to accelerate integration processes

Cons:

Can take a while to integrate with additional systems required outside of IT service management (ITSM)
Prebuilt connectors help but customers noted this was a velocity limiter in rollouts
REaCT capabilities still coming online for customers

Moveworks - Leaders

Pros:

GenAI investments have made language processing and answer-giving capabilities extremely flexible in understanding and responding to complex user language
Abstracted development makes configuration easy for extending platform beyond OOTB capabilities
Speed at which customers were able to adopt the platform
Ease of use for end users
Dedication to customers' success

Cons:

When expanding beyond OOTB employee services, net-new use cases are still slow to create
Customer knowledge bases and service requests remain important assets for onboarding
Customers must accept deferring their LLM approach to the Moveworks-managed LLM chain
Does not yet target external customer-facing use cases

Amelia - Strong Performers

Pros:

Configurability with extensive development interface, automation extensions, strong multilingual support, and robust technical analytics
Demand gap-to-action suggestion analytics providing option for human agents to directly demonstrate how to resolve issues
Description to bot capabilities like 'Amelia builds Amelia'
Capable of meeting organizational complexities in a way few other platforms could

Cons:

Configuration-heavy and complex platform requiring dedicated developer resources or managed services
Requires configuration time and effort from experts to identify where training data inadvertently trained model to over-index on specific terms
Lack of clarity in what's coming next for the Amelia platform due to genAI rearchitecting

Google - Strong Performers

Pros:

Versatility in building conversational experiences from traditional to fully dynamic
Experimental 'topics and actions orchestration' capability allows building experiences suitable to needs
Versatility in action-taking capabilities through broader Power Automate Platform
Agent App Instructions enable natural language-driven development
GenAI-powered reasoning engine creates development interface actively changing how conversational AI experiences are built

Cons:

Customer adoption strategy is exclusively tooling-oriented with no dedicated support
Pricing is unpredictable, dependent on consumption of overlapping products
Lack of in-platform LLM control and choice
Comparatively lightweight metrics and reporting
General lack of domain focus on employee services
Automation capabilities not fully absorbed into design process
Fewer templates available for employee services workflows
Declined to participate in full Forrester Wave evaluation process

Kore.ai - Strong Performers

Pros:

Extensibility of solution allowing customers to use Kore.ai as LLM development, orchestration, and observability solution
Model choice supporting BYO-LLM as well as providing Kore.ai's first-party XO GPT models
Granular LLM guardrails and extensive analytics
Monitoring suite provides high level of model visibility, drilling down into turn-by-turn mixed-model decisions
Flexibility as a platform

Cons:

Requires configuration by customers to tailor solutions to their needs
Need for developer skills in employing platform to its fullest potential
Foremost a solution for organizations to build on top of, intended for multiple domains
Lacks significant in-house adoption services compared to other vendors
Configuration with some development still required despite templates and domain language models

Microsoft - Strong Performers

Pros:

Versatility in building conversational experiences from traditional to fully dynamic
Experimental 'topics and actions orchestration' capability
Versatility in action-taking capabilities through broader Power Automate Platform
Strong vision and demonstrates innovation with generative actions and agentic capabilities

Cons:

Approach to customer success with Copilot Studio is hands-off, leaving assemblage up to customers
Lack of in-platform LLM control and choice
Comparatively lightweight metrics and reporting
General lack of domain focus on employee services
Platforms still undergoing integration (Power Virtual Agents with Copilot Studio)
Declined to participate in full Forrester Wave evaluation process

Atlassian - Contenders

Pros:

Function as service management platform with workflow development engine and inherent integration support
Supports variety of different language integrations thanks to Confluence
Easy to get started with and use
Enterprise graph and common data model innovations setting up to be nexus of insight to fuel genAI outcomes

Cons:

Newer conversational AI platform supporting only English
Missing some features like conversational transcript PII redaction
Interaction development driven through dialog scripting and reliance on Jira Service Management's automation engine
Took time to refine and build full end-to-end automations interactions
Developers recommended to assist with the tool
Agentic and self-developing capabilities just starting to be addressed strategically
Opinionated approach to LLMs by partnering with OpenAI

BMC Software - Contenders

Pros:

Underlying Helix platform strengths with workflow creation through enabling existing service requests
Integration and automation capabilities
Focused on accelerating service requests to help end users simplify employee support process

Cons:

Still working on bringing latest conversational AI capabilities online
Sometimes causes less-fluid conversations
Requires enabling and testing time to ensure service requests and knowledge translate well to end user
HelixGPT-enabled virtual agent limited in dynamic clarification capabilities, causing generative answers to be fragile
Platform easy to launch but requires ongoing tuning and optimization
HelixGPT capabilities currently act as integration layer for customer-managed LLMs rather than full agentic/domain expert models

IBM - Contenders

Pros:

Accelerated workflow creation capabilities with automation libraries, 'description to action' scripting
Analytics that tie insights to explicit action improvement suggestions
Ecosystem-friendly design approach with wide integration support
Extremely granular API coverage
Supports wide variety of LLMs creating extremely extensible solution

Cons:

Integrations with genAI and LLMs are configuration-driven in utilization
Flows remain partially scripted
Functions like error handling must be specifically configured to trigger generative fallback for each interaction
Historically more domain agnostic with limited employee services focus
Watson family has additional reconciliation and integration of features needed to make adoption simpler
Declined to participate in full Forrester Wave evaluation process

Inbenta - Contenders

Pros:

Strong linguistic-tuning interface and integrated knowledge base
Lends itself well for building Q&A and knowledge summarization bots
Base models facilitate fast knowledge uptake

Cons:

'Build it yourself' approach without many workflow creation acceleration tools
Interaction-scripting and API/webhook-reliant approach requires technical resources for development
Demonstrated genAI capabilities limited to knowledge summarization
Slower adoption of newer capabilities has caused vision and platform to lack important emergent capabilities
Lacks generative workflow and language accelerators, agentic functionality, or customer extensible LLM management layers
Only one reference customer provided for evaluation

ServiceNow - Contenders

Pros:

Now Platform is significantly capable automation platform with multiple OOTB extensions into other enterprise platforms
'Description as workflow' promises to be extremely versatile in platform
ServiceNow's managed Now Platform development models

Cons:

Configuration-heavy nature of platform increases developer resource requirements
Integration of genAI has not been seamless with work remaining to complete genAI embedding
Strategy remains 'use genAI and virtual agents to improve Now Platform' with less prioritization of conversational AI extensibility
Less value for customers where ServiceNow is less embedded or cross domain
Declined to participate in full Forrester Wave evaluation process

Amazon Web Services - Challengers

Pros:

Language training with generative testing and language test workbench
Language and intent generation population reducing time to develop given intents
Extremely granular API coverage
Supports wide variety of LLMs
Extremely extensible solution

Cons:

Requires significant amount of manual configuration to integrate
Limited domain templates
Lacks dynamic language/workflow configuration capabilities to reduce configuration time
GenAI offerings only partially embedded into product and require configuration
Vision doesn't match needs for employee services market where developer resources are often limited
Declined to participate in full Forrester Wave evaluation process

Serviceaide - Challengers

Pros:

Orientation to value with reporting that immediately connects conversational AI performance to savings
Identifies additional opportunities for enhancement
Integration with ChangeGear ties closely to agent workflows with capabilities like knowledge suggestions

Cons:

Platform being previous-generation conversational AI builder with less language support
Limited generative capabilities outside knowledge summarization
Dialog-scripting-heavy way of building experiences results in less intuitive experience for end users
Vision and roadmap-targeting features only just now catching up to competitors
Configuration-heavy experience with fewer LLM features
Forthcoming major release remains to be evaluated
Declined to participate in full Forrester Wave evaluation process

FAQs

Q: Which vendors are Leaders in 2024?

A: Aisera, Avaamo, Espressive, Moveworks

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

A: Leaders in the Forrester Wave are vendors that excel in both current offering and strategy. In the context of Conversational AI Platforms For Employee Services, Leaders demonstrate strong capabilities in mixed-model orchestration, agentic AI, generative governance, customer success assurance, ease of use, language processing, automation, pre-configuration, and managed services. They have rearchitected their platforms to leverage LLMs and genAI effectively, providing natural conversations, dynamic action development, and significantly reduced configuration requirements while maintaining strong customer adoption support.

Q: How do I interpret a Forrester Wave?

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

Report:

Top Vendor Products for Conversational AI Platforms For Employee Services in 2024

Conversational AI Platforms For Employee Services Vendor Product Comparisons 2024

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FAQs

Q: Which vendors are Leaders in 2024?

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

Q: How do I interpret a Forrester Wave?

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