What are Conversational AI Agents?
Conversational AI agents, also known as virtual assistants or chatbots, depending on their level of sophistication, are technologies with text and or speech recognition capabilities that provide human-like responses to human inquiries.
For a conversational AI agent to function efficiently, it needs to understand the meaning behind the request from the human user and then match this meaning (or intent) to its database of prepared responses. Advanced conversational AI agents are capable of learning from these requests over time to respond in more improvised ways. They can do this through a subdivision of AI called Machine Learning.
The aspect of Artificial Intelligence most directly related to the functionality of Conversational AI agents is Natural Language Processing – a hybrid of linguistics and AI that aims to teach computers the capacity to understand text and provide spoken words just as a human can. When spoken words, rather than text, is the input that the computer receives, the technology that enables the computer to understand the input is more specifically called Automated Speech Recognition.
Conversational AI vs Chatbots vs Virtual Assistants
These terms are often used interchangeably. However, they mean different things and are defined in terms of their complexity.
Chatbots are the most superficial level of Conversational AI agents as they mostly function as “question-and-answer” systems designed to provide exact responses to question matches.
Virtual AI agents are in the middle bracket. They not only provide answers to questions but they also have a wider functionality range such as small talk. Common examples are the technologies we interact with daily e.g. Siri, Alexa, Cortana.
Virtual Assistants occupy the uppermost level and are designed to go beyond isolated conversations to ensure a smooth flow of interactions. They are also built for specialized purposes and typically have a dedicated database that handles specific conversations with a higher level of advancement.
The Rise of Conversational AI
Conversational AI agents are becoming more popular today. We live in a fast-paced world where everyone wants quick solutions. A survey by PSFK discovered that no fewer than 74% of consumers turn to chatbots in the search for quick answers.
Existing economic forecasts favor the continued ascension of this technology as Markets and Markets projects that the global Conversational AI market will reach $15.7 billion in 2024 from $4.2 billion in 2019. Much of this spending will be directed towards the development of chatbots which is estimated will be worth $7.5 billion by 2024 from $1.2 billion in 2018.
The Agriculture Sector and Chatbots
Chatbots and Virtual Assistants can house robust datasets of FAQs from users. Conversational AI agents trained on these FAQs can reduce call waiting times and queues by providing accurate and fast responses to inquiries and applications. Every interaction that a client has with a Conversational AI agent can be logged for easy automated tracking.
Furthermore, Conversational AI agents can be trained to personalize their interactions with users. Using machine learning techniques, they can learn more about their users (with consent) and deliver customized services and responses to the users.
In addition, the agents can be used to improve the supply chain of food produce. They can be connected to data showing the real-time location of crops, providing scheduled updates to interested parties.
The fascinating aspect about this is that the systems do not need to exist in isolation. They can be integrated into the Agriculture Ministry’s website, Facebook, Whatsapp, and other social media.
Conversational AI agents can bridge the gap between the policymakers and the users by providing real-time information and allowing the easy exchange of information between both parties. In line with this, they can also be used to facilitate accessibility through text-to-speech technology.
Depending on how well a conversational AI agent has been trained, it may be unable to recognise some user inquiries. This can be frustrating for the user. The solution is to have a culture of reviewing user complaints about the agents and updating the database of the AI. With time, the agent will become smart enough to provide improvised responses that are beneficial to the user.