Which capabilities should a company consider while searching for a conversational automation vendor?

Adopting a virtual assistant is a significant decision that should be part of the overall digital transformation strategy of an institution. For the last 6-7 years, the vendor landscape for virtual assistants has become very complex, therefore the companies who aim to invest in this field need to know what they are looking for and navigate through this complex environment with the correct set of capabilities. Thousands of vendors are able to deliver basic chatbots worldwide, however, when it comes to complex virtual assistants, the number of vendors that are capable of delivering “conversational automation” gets lower. In this blog, we’ll discuss what capabilities are important when evaluating a virtual assistant vendor for an enterprise to implement conversational automation, based on a recent report published by Gartner – Critical Capabilities for Enterprise Conversational AI Platforms.


NLP as a commodity in the conversational automation market


In the early phases of conversational AI-based automation, NLP (natural language processing) was the most important feature that companies were looking for when searching for a vendor. The issue was considered as just understanding the user question and responding with the right answer. But over the years, the dynamics of this landscape changed, NLP became a commodity as it was offered by large  technology companies and accessing to NLP became easy. As the expectations of the end-user and the companies that serve them were raised, the basic question-answer type conversations became unsatisfactory, the scope of the projects was enlarged and the requirements for integrations as well as other concerns, such as deployment, security, channels, multi- modality (text+voice), emerged.


Currently enterprises are looking for “enterprise conversational AI platforms” that are capable of multiple use cases and no-coding tools that scales the implementation throughout the organisation into different business units. To help enterprises when evaluating potential vendors, Gartner identified 5 use cases and 14 capabilities that an enterprise conversational AI platform should satisfy. The point here is that enterprises should consider these capabilities and use cases but define their own set of criteria when evaluating vendors, based on the unique needs of their organisation.


Who needs scalable conversational automation? 


According to Gartner, although different use cases do not need different dialogues, they require different deployment environments, privacy compliance, security measures and integration abilities with the existing systems. It is important to remember that the capabilities defined by Gartner below are not for simple question-answer chatbots but for the companies that are looking for scalable, easy to operationalize platforms with a considerable level of conversational automation. These companies prefer to cooperate with enterprise level conversational AI platforms that are capable of deployment into complex architectures, including cloud and on premise systems, integration with multiple channels including both text and voice modalities as well as internal and external systems such as CRM, ERP, login, email, agent, analytics tools. Enterprise level platforms are not limited to only one use case but can be deployed for multiple use cases including customer service automation, sales, marketing, employee help desks, etc.


As displayed below CBOT is an enterprise conversational AI platform that satisfies all the capabilities and use cases that Gartner defines as a comprehensive tool for conversational automation.


What are the capabilities for enterprise level conversational automation?

Is CBOT complaint with them?



What are the use cases that enterprise level conversational automation covers?

Can CBOT be implemented for them?


To check out how CBOT realised these capabilities in different use cases, please read our client stories.