Conversational AI & Enterprise Implementations
Analysis Of The Gartner Peer Insights for Enterprise Conversational AI Platforms
In a previous post I performed analysis on the Gartner Peer Reviews and Ratings for Enterprise Conversational AI (CAI) Platforms.
In the Gartner rating matrix for CAI, Customers rate CAI frameworks according to a few criteria, the end result is an average score out of 5 for each framework.
Included in this ratings matrix is the:
- Implementation Industry
- Implementation Region, and
- Customer Size
This is a very telling metric and can serve as an indication of the size of the implementation. This of course is not an exact science as the ratings might not be complete, or might not be representative of all implementations.
However, it does serve is an indication as to the size of implementations a vendor has been involved in.
Reviewer Company Size 10 Billion USD +
The graph below shows Conversational AI Framework reviews from companies larger than 10 Billion US Dollars.
1️⃣ Boost AI has two ratings which are 5 out 5.
2️⃣ Kore AI has 29 ratings with an average rating of 4.9 out of 5.
3️⃣ Cognigy AI is at 23 ratings also with an average of 4.8.
4️⃣ The graph below shows the correlation between the average rating and the number of ratings per Conversational AI platform.
Reviewer Company Size 1 Billion To 10 Billion USD
The graph below again shows the number of ratings with the average score per Conversational AI platform for company sizes 1 Billion to 10 Billion USD.
1️⃣ Kore AI has 27 ratings with an average of 4.8 from 5.0.
2️⃣ Cognigy AI sits at 23 ratings with an average of 4.7.
3️⃣ OneReach AI has a sizeable volume of 10 ratings.
Reviewer Company Size 50 Million To 1 Billion USD
For the category of 50 million to 1 billion USD in company size…
1️⃣ The number of reviews per Conversational AI platform are more evenly spread and representative.
It needs to be emphasised that these ratings are not an exact science and there are various external factors which might influence reviews and ratings.
For instance, the ratings might not be representative of actual implementations, as customers might choose not to complete a peer review.
Please follow me on LinkedIn for the latest updates on Conversational AI. 🙂
I’m currently the Chief Evangelist @ HumanFirst. I explore and write about all things at the intersection of AI and language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces and more.
No-code tooling for NLU
The complete productivity suite to transform natural language into business insights and AI training data
Get an email whenever Cobus Greyling publishes.
Get an email whenever Cobus Greyling publishes. By signing up, you will create a Medium account if you don’t already…
Eliza Language Technology Community — Language Technology: Conversational AI, NLP/NLP, CCAI…
ELIZA — Where language technology enthusiasts unite.
Valuable Insights From The Gartner Peer Reviews Of Conversational AI Platforms
The Gartner Enterprise Conversational AI Platform Reviews & Ratings provide valuable implementation insights in terms…
Gartner Predicts In Five Years Chatbots Will Be The Primary Customer Service Channel
This will only be the case if chatbots are designed correctly...
The Low Hanging Fruit In CCAI
In this article I analyse the Gartner AI use-case Prism for AI in Customer Service. The question is often asked, what…
Analysis Of The Gartner Chatbot Deployment Guide
This deployment guide places significant emphasis on Intent Driven Design and Intent Driven Development of a chatbot…
The Gartner® Critical Capabilities for Enterprise Conversational AI Platforms Assessment
And How Does It Compare To the Gartner® Magic Quadrant?