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Peer Insights Of Enterprise Conversational AI Platforms From Gartner®

And Why Market Comparisons Will Become Harder

Cobus Greyling
6 min readJul 19, 2022



Market analysis and comparing frameworks always make for interesting reading and there are usually quite a bit to glean from product and framework comparisons.

However, these comparisons will become increasingly difficult for three reasons

Reason One

The first reason being that the sheer number of offerings under Conversational AI is expanding. These Conversational AI platforms have different origins and differ in key focus areas. Hence comparing complex frameworks on a simplified one-to-one matrix of functionality is becoming less feasible.

Reason Two

And secondly, I wrote about this in a previous post…the fact that implementing a one-vendor Conversational AI platform for an enterprise solution will become less feasible. Implementations will start seeing different components being orchestrated into a single solution.

The Conversational AI landscape is becoming fragmented, which is a good thing!

One step back, though

One can argue under AI, there is a subset of technologies addressing Cognitive Computing, or Cognitive AI. A further subset of Cognitive AI is Conversational AI.

One needs to realise that Enterprise Conversational AI Platforms is really just again a subset of Conversational AI.

Under Conversational AI there are vertical vectors emerging from the horizontal categories. These verticals are solving for specific problems within the Conversational AI landscape. And it will contribute to a scenario where organisations will start creating more complex chatbot/voicebot environments making use of the best-of-breed verticals to constitute their conversational AI solution.

Reason Three

Thirdly, are we seeing the emergence of Conversational AI marketplaces? Or one could refer to it as orchestration engines. Will a Twilio of Language Technology emerge?

And is this the direction in which Cognigy is moving with the Cognigy AI Marketplace?

Is OneReach AI also focussing on becoming a single Conversational AI portal to act as an orchestration engine / aggregator for Conversational AI experiences?

Back to Gartner’s Peer Review Ratings…

What Is Gartner’s Peer Insights

First of all, what underpins Gartner’s Peer Insights?

Like most reports, the peer insights are not definitive, especially compared to an evaluation with set criteria like the Magic Quadrant report. But it does provide insights to some degree and obviously it is peer-driven.

Gartner does state that every review is verified before publishing to ensure reviews are authentic assessments from true users.

The Gartner peer-driven review is made available by Gartner for enterprise IT solutions and services covering over 300+ technology markets and 3,000 vendors.

Peer Insights vs Magic Quadrant

In November 2021 Gartner released their Magic Quadrant for Conversational AI frameworks. There are obviously set criteria for the Magic Quadrant report, which is detailed towards the end of the report.

Comparing the Magic quadrant with the Peer Insights…

  • The red arrows (added by me) indicate the platforms rated in the peer review. The vast majority of frameworks in the Magic Quadrant have received peer-reviews.
  • The omissions from the peer review which surprised me most were Rasa, AWS and Google Dialogflow CX, and to a lesser degree Avaamo and SenseForth AI.
  • Some elements of Google are reviewed, but it is related to Google’s custom voice.

The frameworks which has peer reviews, but which is not on the Gartner Magic Quadrant are:

Platforms Reviewed and Ratings By The Numbers

When looking at the number of reviews per platform, Boost AI leads with the most number of reviews at 50. Watson Assistant & Cognigy are in the thirties. Followed by an array of platforms with 20 or more reviews.

A few general observations on the numbers:

Below is an extract of the Garner rating page, listing the platforms…

And here is a graph with average scores per Conversational AI platform, with five platforms above a 4.5 average score:

  1. Cognigy AI ~ (32 Reviews)
  2. Oracle Digital Assistant ~ (28 Reviews)
  3. EVA (OpenStream) ~ (25 Reviews)
  4. Kore AI ~ (20 Reviews)
  5. Teneo ~ (2 Reviews)

In Conclusion

When posting analysis of the Deloitte reports and Gartner framework assessments on LinkedIn, the feedback from the community has been invaluable. And the feedback has been very much inline with the questions posed by Gartner in the platform review process.

Listed below is the list of questions asked by Gartner in the platform review process. Again, which is strikingly similar to the general LinkedIn feedback on what is important to enterprises.

From the feedback I have received, it seems like companies do take a few additional factors into consideration when selecting a platform:

  1. The degree of collaboration, involvement and pre-sales consultation from the technology provider are really important. Each Conversational AI technology provider sells a partnership, yet seemingly only a few companies truly deliver in this aspect and partner with their customers.
  2. Specific use-cases and implementations will favour certain frameworks, this has partly to do with the origins of each platform; which contributes to their strengths.
  3. Certain platforms are more conducive to orchestration, utilising external components and technologies. Here I want to mention the orchestration capabilities of OneReach AI and Cognigy Marketplace.
  4. Existing partnerships and alliances between consultants, technology providers, agencies, etc are important.
  5. Obviously cost and ease of access to the technology play a role.



Cobus Greyling

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