In today’s digital world, you’ve probably interacted with a chatbot. Consider some familiar scenarios.
- Thank you for visiting our site. How can I help you today?
Perhaps you’ve been looking at a new pair of shoes and a chat window pops up with a friendly greeting, asking if you need help. Or you’ve asked Siri, Alexa or Google to schedule a meeting, remind you about an upcoming task or set a timer for the cookies in the oven. You may have even used ChatGPT to help craft a letter, brainstorm strategies and concepts, or write computer code for a class project.
Dating back to the 1960s, chatbots have a fascinating history. Today they can be powerful tools for businesses, customers and individuals alike. Technologies like generative artificial intelligence (AI) have made conversations with these "bots" surprisingly human-like, providing answers and solutions in real time.
A chatbot is a form of conversational artificial intelligence (AI) designed to simplify human interaction with computers. Using a computer program that simulates human conversation, chatbots can understand and respond to user questions and input through spoken and written language.
Rudimentary chatbots use rules to follow specific paths based on user input. They are commonly used to answer simple questions or route customers to log a ticket. These bots are widespread, so you may encounter them on commercial websites, phone trees, messaging apps – like Facebook Messenger – and other social media platforms.
More sophisticated chatbots use technologies like transformer-based large language models (LLMs) to process customer queries and provide human-like responses. LLMs help the bots understand question intent, despite typos or translation barriers.
As the conversation continues, sophisticated chatbots learn and gather information to adapt to user preferences and provide personalized responses and recommendations – serving as a digital AI assistant. They can engage in complex conversations on everything from technology to the best ingredients for a family dinner.
Some examples of voice assistants include Siri, Alexa and Google Assistant. Examples of chatbots based on generative AI technology include OpenAI ChatGPT, Google Bard, and Meta Llama2.
Learn about generative AI
Explore the core analytics technologies that power chatbots – including generative AI – and learn how to use the technology in a meaningful way.
The value of chatbots – and primary applications
Across industries, businesses use chatbots to respond to customer demands around the clock. Enhance the customer experience. Improve accessibility. And streamline customer service and e-commerce.
There are many ways chatbots can help in interactions with users and customers. Here are just a few:
- Customer service. Many businesses use chatbots as a first contact for customer support. Almost every industry employs them to help customers navigate their websites, answer simple queries and find relevant points of contact.
- E-commerce. Retail companies and telecommunication providers use chatbots as an additional interaction channel for their customers. These bots are designed to lead customer interactions through a linear process flow to complete requests or transactions. When needed, they can initiate human intervention by escalating requests to a customer service representative.
- Virtual assistants. Personal virtual assistants like Alexa have risen in popularity as they’ve become more broadly available and are easily embedded into consumers’ daily lives. People use them to quickly retrieve information, schedule appointments and interact with smart home features. With the rise of generative AI, customers and bots can interact in an increasingly human-like way.
Generative AI chatbots
With generative AI, chatbots go beyond simply answering or predicting answers – they generate new data as their primary output. For example, users can provide a few words to describe an idea, like some basic information they’d like to include in a speech, and the bot can create a full-length script in seconds. It can even go back and forth and provide changes until the speech is perfect.
How do chatbots work?
Chatbots communicate through speech or text. Both rely on artificial intelligence technologies like machine learning, natural language processing (NLP), natural language understanding and generative AI.
Natural language processing is a branch of artificial intelligence that teaches machines to read, analyze and interpret human language. This technology gives bots a baseline for understanding language structure and meaning. NLP, in essence, allows the computer to understand what you are asking. NLU then takes that information and allows the computer to act based on the request.
Advanced chatbots try to understand the intent behind your questions. These chatbots are programmed to simulate human conversation and exhibit intelligent, human-like behavior. The more they communicate with you, the more they understand and the more they learn to communicate like you (and others with similar questions). Your positive responses reinforce its answers, and then it uses those answers again.
Chatbots with a specific purpose, like routing customer complaints or inquiries, are designed with a limited scope of potential answers and replies. But more complex AI assistants are designed to respond to a wide range of scenarios and queries, from current weather and news updates to personal calendars, music selections and random questions.
Over time, people will continue to expand the way they use chatbots. For example, programmers will continue to experiment with using generative AI-based digital assistants to help them write code. And enterprising individuals will use them to develop more novel ideas and strategies for businesses that provide innovative services and products.
Chatbot applications in analytics
Introducing chatbot functionality into analytics solutions combines conversational capabilities with advanced analytics, opening an array of possibilities. For example:
- A chatbot can automatically query and describe large corporate or public data sets.
- You can request summarized or analyzed results verbally by saying, for instance, “Which marketing campaigns are generating the most leads this quarter?
- A chatbot can provide an answer and then offer additional information or suggest a related report to view based on patterns in the data and in previous related queries.
- You can ask a chatbot to share results with others in an automatic way. You can even combine chatbots with specialized analytics solutions to perform explicit tasks within an application.
Enhancing marketers’ creativity and efficiency: An example
AI-powered assistants in SAS® Customer Intelligence 360 provide tools to help modern digital marketers with their efforts across the entire customer engagement journey.
Using generative AI technologies, the assistant interfaces with a customer’s chosen LLM to understand customer behavior and marketing trends. Not only can it help brainstorm customer audiences and journeys, it also helps marketers craft compelling and channel-specific creatives as well as engaging content tailored to different platforms.
The results of generative AI, at their core, are a reflection of us, humans. ... Consumers must continue to apply critical thinking whenever interacting with conversational AI and avoid automation bias (the belief that a technical system is more likely to be accurate and true than a human). Reggie Townsend Vice President, Data Ethics Practice SAS
The ethics behind chatbots
New chatbot technology has made AI accessible to all of us. But as more organizations combine bots with AI and analytics solutions, it’s important to ensure the technology is used responsibly and ethically. While the potential for generative AI tools is promising, the technology can perpetuate misinformation, infringe on privacy, and more.
To use chatbot technology ethically, people developing it need to be aware of the risks. Remember that data is the fuel of chatbots and the AI that powers them. It’s essential to use high-quality data that’s well-suited for the task at hand – and models that account for privacy, compliance and bias. Always exercise critical thinking when developing and using chatbot technology.
- Risk data infrastructure: Staying afloat on the regulatory floodWhat are the challenges of a risk data infrastructure and how can they be addressed? Here's what you need to know to build an effective enterprise risk and finance reporting warehouse that will effectively address compliance requirements.
- Top prepaid card fraud scamsThe margin for prepaid cards is slim, so it's particularly important to root out the scams. Here are some tips for combating and mitigating prepaid card fraud.
- Risk data aggregation: Transparency, controls and governance are needed for data quality and reportingFinancial institutions’ data aggregation and reporting techniques and systems are receiving increased attention both internally and externally. Find out how to take a comprehensive approach to BCBS principles and risk data aggregation and management.