Human-machine collaboration is a performance lever
Artificial intelligence (AI) is a game-changer when it comes to streamlining time-consuming tasks in everyday business. Whether it is chatbots answering questions on a website or an interactive voice response system directing phone inquiries, automation done right can free up humans to do their best work. When artificial intelligence provides rapid processing capacity and humans provide their intelligence and creativity, the landscape of collaboration becomes a powerful tool.
Enter Conversational Intelligence
Conversational intelligence capabilities enable real-time intelligence in business conversations by recognizing action items, insights, questions, contextual topics, summaries, etc in a predictable and unbiased manner. Conversation intelligence refers to the ability to communicate in ways that create a shared concept of reality. It begins with trust and transparency to remove biases in decisions while also enabling knowledge workers to be more effective at their core function by eradicating mundane and repetitive activity.
How does conversational intelligence differ from virtual assistant technology?
Conversational intelligence combines sophisticated machine learning and natural language processing. This augments human interactions to amplify human capability by analyzing conversations and surfacing the knowledge and actions that matter. Chatbots and virtual assistants (Conversational AI) are usually command-driven and task-focused. They add value to direct human-machine interaction via audio or text while attempting to simulate how a human might respond.
Developers build chatbots by using existing intent-based systems like RASA, DialogFlow, Watson, Lex, etc. These systems identify intent based on the training data you provide. Intent-based systems enable developers to create rule-based conversation workflows between humans and machines.
Developer-first APIs for Conversational Intelligence
Companies should look for a platform that can contextually analyze natural conversations between two or more humans based on the meaning rather than keywords or wake words. Also of importance are models that require no training data so companies can analyze conversations on both audio and text channels without needing to train a custom engine for every new intent. Imagine embedding a passive intelligence in existing products or workflows, natively. Every bit of conversational data flowing through is parsed and used to surface real-time actions and outcomes.
If this space is new to you, feel free to check out our blog post on 13 questions to ask before investing in conversational intelligence.
We hope this was useful as you explore the benefits of conversational intelligence in your own products. If you have not already taken advantage, we have free trial credits so you can try Symbl’s Platform today.
To learn more about Symbl’s conversational intelligence solutions, visit our developer documentation.