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The Gartner® Critical Capabilities for Enterprise Conversational AI Platforms Assessment

And How Does It Compare To the Gartner® Magic Quadrant?

Cobus Greyling
6 min readJul 14, 2022



The Gartner report highlights 14 critical capabilities on which 21 Conversational AI frameworks were measured on.

These critical capabilities remind me of the Deloitte vectors of innovation and the Seven Vertical Vectors I recently wrote about.

Something to keep in mind, is that these vectors & critical capabilities do not need to be fully encapsulated in a single conversational AI development framework.

And the likelihood of this being the case in future is highly unlikely. Technology providers will increasingly focus on one or more of these critical capabilities to deliver it as a product, fully integrated into existing Conversational AI ecosystems. Hence allowing for customers to put together a Conversational AI platform constituted by the best technologies available.

In this article I want to take a detailed look at the Gartner assessment: Critical Capabilities for Enterprise Conversational AI Platforms Assessment

I also want to compare this assessment to the Gartner® report: Gartner® Magic Quadrant™ for Conversational AI Platforms

A further differentiator for Amelia is the ability to use virtual assistants to design virtual assistants — a unique feature in this group.

~ Gartner

A View Into The Conversational AI Landscape

The Platform Assessment is an interesting view into the Conversational AI landscape due to…

  • The fact that different platforms have originated and evolved from different starting points in the market. Some are strong in the IVR systems environment, others in messaging or integration. Others act as an aggregator, while others are highly technical, etc.
  • Secondly, Gartner is trying to look beyond Dialog State Management and NLU. And focus on areas like integration, specific use-cases, etc.
  • In the market there is emphasis on bootstrapping via various means; LLM, Search, Knowledge Bases, etc. This report focuses on alternatives to bootstrapping; like predefined intents, entities and dialogue flows which are offered by the platform, suitable for specific use-cases and industries.
  • 14 Critical capabilities were identified, and 21 platforms were assessed against these 14 capabilities.
  • Considering implementations, the required effort, competence and time were taken into consideration.
  • Voice enablement was considered, together with Conversational AI frameworks fitting into existing technology ecosystems.

Above are the 14 critical capabilities considered by Gartner in this report, which I divided into 4 categories. With level 3 being the more traditionally considered capabilities.

The no-code tooling of is very polished, giving a good user experience to end users.

~ Gartner

Consolidating Gartner’s Ratings

Below left, you see the 21 platforms listed, and ranked according to the average score from the report. On the right you see the Magic Quadrant.

There are now real surprises with regard to the ratings, except for two considerations…

Firstly, OneReach AI scored top in the Report, whilst just sneaking into the top quadrant. This makes sense considering that OneReach AI is very strong in orchestration and aggregation of services. And these elements are the areas of focus for the purposes of this report.

The biggest anomaly I would like to point out is IBM. In the Gartner Magic Quadrant IBM is the leader in completeness of vision (visionary)…and fifth in ability to execute. With its strengths listed as:

  1. Innovation
  2. Product capability
  3. Global sales and support

IBM Watson Assistant does have a few predefined use-case skills, and the new interface has a very simplified onboarding process. Hence IBM’s mediocre rating in the capabilities report as opposed to the Magic Quadrant is puzzling.

IBM’s strength in voice services and its broad overall strengths.

~ Gartner

Vendor Matrix & The 14 Critical Capabilities

This matrix below, takes into account all the vendors (21) considered, and the respective scores according to each of the 14 critical capabilities. If a score was 4.0 or higher, I marked that intersection dark-green.

A few insights emerge from this view:

  1. OneReach AI dominates in most of the categories.
  2. OneReach AI is successful even-though NLU is not a key focus of theirs. I would say their focus is on dialog management and flow orchestration, and leveraging existing customer NLU implementations.
  3. The usuals are represented by Cognigy, Kore AI, Omilia and to some degree Amelia.
  4. IBM is the aberration, leading in the very niche vector of voice capabilities.

[Boost AI] It has an intent architecture that enables it to scale to 10,000 intents while maintaining strong resolution rates.

~ Gartner

A few considerations:

  1. It would have been interesting to see how Nuance Mix fares against the other platforms. Especially from a voice capability perspective, customer service and channel integration.
  2. Technology companies like NVIDIA (Riva), Microsoft and others have products and solutions which can address these 14 critical capabilities quite well in isolation.
  3. This leads me to my next point, should one vendor or platform cover all of these 14 critical capabilities? Thinking of the Deloitte report and considering the seven vertical vectors
  • Why should one platform/vendor cover all 14 capabilities?
  • Should these 14 capabilities not be seen as vertical vectors within Conversational AI, which can be serviced by independent and specific vector-focussed companies?
  • There are specialised companies focussing on intent detection & clustering, STT, TTS, Orchestration, Lifecycle Management, etc.
  • AS the landscape grows in complexity decoupling is inevitable, and the best of breed will be selected for each area of Language Technology.

Rasa Open Source has all the machine learning components and is a developer-focused toolkit, while Rasa Enterprise adds enterprise features and no-code tooling.

~ Gartner

In Conclusion

Most organisations will have multiple use cases to consider with different requirements. A “single implementation, single vendor” approach become harder to maintain going forward. Three elements will emerge in Conversational AI:

  1. Management and orchestration of multiple and disparate Conversational AI components will become paramount.
  2. Diminishing single-vendor implementations for complex and growing environments will become the norm.
  3. Solution providers focussing on one or more of the critical capabilities (vertical vectors) with astute integration into existing Conversational AI ecosystems as a key focus will emerge.



Cobus Greyling

I explore and write about all things at the intersection of AI & language; LLMs/NLP/NLU, Chat/Voicebots, CCAI.