Chatbots have been clunkily automating customer engagements in single-serve verticals for years.
But the conversational support tools never really had that “wow” factor. For as long as they’ve been helping firms cut support center costs, it was always apparent that on the other side of the conversation was a nascent, inflexible algorithm best suited to A-to-B requests and incapable of any sort of sophisticated or nuanced conversation.
That was then. This is now — and rapid developments in generative artificial intelligence (AI) are giving chat robots a next-generation reboot.
This, as Uber is reportedly developing an AI chatbot to integrate into its app, per Bloomberg, joining the long list of firms — including delivery peers DoorDash and Instacart — that have already done so; while Meta announced a new era of personalized chatbots for its social platforms, as noted by the Financial Times.
The company formerly known as Facebook hasn’t had much luck with previous iterations of chatbots. But if there’s one thing true about AI, it is that it lives and it learns.
Google is also racing to prioritize the integration of advanced generative AI chat capabilities into many of its products, reportedly making at least a dozen organizational changes across its voice technology units, including layoffs, as it looks to revamp its seven-year-old Assistant product — which has been looking somewhat stale compared to recent marketplace innovations.
Many other top companies are hoping that the future-fit capabilities of these new, human-like chatbots will be able to provide highly scalable efficiencies.
But where precisely is the best use case of the new AI chatbot technology?
Read also: Are Smarter Chatbots the Answer to AI’s Right-Now Utility?
The latest wave of AI chatbots are meant to streamline the customer interaction process, provide quick responses to queries, and optimize customer experience by responding to text and voice-based queries with intelligent and creative answers.
They represent a revolutionary step in conversational AI, as well as one of the best use-cases to date of generative AI’s foundational large language models (LLMs).
LLMs are trained on hundreds of billions more words than a typical human encounters by age 10 and display a sophisticated capability for language processing and conversational response generation.
By improving customer service, marketing, and automating other tasks, conversational chatbots can offer recommendations, provide users with new search functions, and more generally leverage conversational experiences that are fun for consumers to interact with.
AI chatbots can provide personalized product discovery, travel planning, banking concierge assistance, as well as serve as a personal individual assistant by automating tasks like food ordering, appointment booking, online shopping and more; as well as provide exciting new avenues for device control such as conversational manuals, in-car assistants, and other innovative use cases.
Generative AI’s capability to produce natural-sounding, human-like chat- and voice-bots that can answer questions accurately will revolutionize nearly every touchpoint businesses across all industries have with both their own staff and their customers.
Organizations can leverage LLM-powered chatbot capabilities to surface answers grounded in their own company data and public information alike, as well as provide for live agent handoff when a query is bottlenecked — allowing for more flexibility and a better experience than a canned “I can’t answer that” response.
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Actually intelligent chatbots will help organizations realize highly scalable cost savings around formerly labor intensive interaction areas and customer support verticals.
They will also transform employee training and upskilling by offering expert assistance and everywhere-staffing for on-demand education around any imaginable topic or task.
However, responsible AI practices are essential to protect users. While they have huge potential for boosting user engagement, chatbots can also be used to collect vast amounts of data on users’ interests which can then be abused in order to nudge or manipulate their behavior, as well as spread misinformation or hate speech.
With the increasing role of AI in generating chatbot responses, there is a risk of introducing biases in the interactions. As AI technology evolves, it is crucial to address the challenges of privacy, bias and misinformation.
Ensuring robust data protection measures and obtaining explicit user consent become critical aspects for responsible chatbot implementation.
As more and more companies integrate humanlike AI-agent capabilities into their workflows, the way businesses engage with customers and users will be transformed — but responsible implementation, thorough testing, and ongoing refinement are vital to harness the full potential of AI-powered chatbots while ensuring they remain trustworthy, fair and respectful of users’ privacy and data.
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