Customer service is rapidly changing thanks to advances in artificial intelligence (AI). Two types of AI that are having a major impact are generative AI and conversational AI. Each offers distinct capabilities that can greatly improve customer support when applied properly.
The difference between generative AI and conversational AI can be seen in how they're used. Generative AI creates content. Conversational AI responds and communicates in a natural way.
In this post, we’ll look at what each entails and how they can work together to elevate the customer experience.
What is Generative AI?
Generative AI refers to AI systems that can generate new content, data, code, and more from scratch. Unlike most AI which is focused on analysis or classification, generative AI can autonomously create materials without simply reconfiguring existing content.
Prominent examples of generative AI include systems like DALL-E which can generate images from text descriptions, GitHub Copilot which suggests new code based on comments, and systems like Anthropic’s Claude which can generate lengthy texts on a wide variety of topics. These systems utilize machine learning techniques like deep learning to produce outputs that mimic human-quality work.
In customer service, generative AI has powerful implications. Potential use cases include:
- Automatically generating article suggestions and drafting full articles that address common customer questions and issues. This provides faster self-service support.
- Producing descriptions and specs for products based on structured data and product requirements. This cuts down on manual documentation needs.
- Generating code to add new features or modifications per customer feature requests. This makes development faster.
Overall, generative AI allows agents to scale their productivity by offloading repetitive, pattern-driven work. This lets them focus on truly complex interactions.
What is Conversational AI?
Conversational AI refers to AI systems designed to understand natural human language and carry on helpful dialogues. Key examples include chatbots and voice assistants that can interpret customer questions and provide relevant answers.
These tools utilize natural language processing (NLP) to analyze text conversations. Some key capabilities include:
- Sentiment analysis - Identifying positive and negative emotional sentiment.
- Intent recognition - Determining goals and intents from language.
- Named entity recognition - Pulling out key nouns like product names.
- Dialogue management - Guiding conversations with users.
In customer service, conversational AI takes over routine customer inquiries, reducing human agents’ workload. Common applications include:
- Chatbots on websites and messaging apps that answer frequently asked questions.
- Virtual agents that pull data from knowledge bases to address user questions.
- Smart compose features that suggest responses for agents to edit or approve.
- Human resources tools that allow agents to easily manage their schedules and workloads.
These tools save substantial time by resolving common issues instantly without needing human input. They also give agents flexibility to step in for inquiries that are too complex.
Examples of Generative AI in Customer Service
Here are some real-world examples of companies already using generative AI to improve customer service:
GitHub: Autoclosing Issues
For software issues tracked in GitHub, Copilot can suggest autoclosing stale issues by generating notes explaining why issues are obsolete. This saves engineer time.
Stadium Goods: Product Descriptions
The online sneaker marketplace uses generative AI to draft catchy product descriptions that are optimized for search based only on structured product data.
AWS: Automated Documentation
Amazon Web Services leverages generative models trained on past documentation to produce first draft cloud service descriptions for engineers to refine.
Stripe: Code Workflows
Stripe is piloting GitHub Copilot to generate repetitive code for workflows like issuing refunds. This boosts developer productivity.
Kunduz: Automated Ticket Classification
This customer service platform generates classifications for incoming tickets to instantly route issues to the right agents according to predicted topic and urgency.
As these examples illustrate, generative AI is gaining real traction in streamlining all areas of customer service. Companies like Mosaicx are working to expand generative AI to front-line customer service.
People are excited about generative AI, but there is still fear that it may give customers information that is inaccurate or unfavorable. Mosaicx is working to create solutions with "guardrails." Essentially, tools that answer FAQ using an approved database of information, not the entire internet. This allows virtual agents to give personalized information to customers while maintaining brand standards and reputation.
Examples of Conversational AI in Customer Service
Leading companies have also integrated smart conversational AI to allow efficient self-service issue resolution. Here are some examples:
Amazon: Customer Service Chatbots
Amazon customer service leverages chatbots to handle enormous volumes of order inquiries without hiring thousands more humans.
Alaska Airlines: Virtual Agent
Alaska Airlines uses AI to optimize air traffic flow, but they also employ a virtual agent that sounds a lot like a human agent. It asks questions, speaks naturally, and can complete common tasks like checking flight status.
Netflix: Recommender Experts
Subscriber recommendation chatbots initiate millions of 1-on-1 conversations monthly to suggest personalized, relevant titles.
Spotify: User Curators
In messaging channels, Spotify conversational assistants suggest playlists and music based on listeners' taste using advanced machine learning algorithms.
As these examples show, conversational AI has become pivotal to scaling great service. Natural dialogue keeps resolution seamless while offloading agents for higher value tasks.
See more examples of generative AI in customer service >
How Generative AI and Conversational AI Work Together
While generative and conversational AI each offer distinct strengths, they work best together in customer service. Popular tools like Google Bard and ChatGPT are known for generative AI, but they actually use both. Within a chat-based interface, these tools are accepting input and communicating in a natural way. That aspect of the experience is powered by conversational AI.
Here are more powerful ways they can integrate:
Conversational AI Agents Making API Calls to Generative AI
Frontline conversational bots can generate better answers on the fly by calling backend generative models to produce custom content for unique cases and topics.
Assistants Classifying Tickets Before Generative AI Assignment
Smart conversational assistants analyze inbound ticket data then assign issues to specialized generative models capable of producing solutions.
Generative AI Drafting Responses for Human Refinement
Agents can save vast amounts of time by having AI draft comprehensive responses to unusual or complex inquiries which teams can then finalize.
Generated FAQs Powering Conversational Experiences
Conversational agents can pull from databases of generated articles to address a wider range of customer questions with individualized answers.
As these examples illustrate, weaving conversational and generative together amplifies the strengths of both approaches. Bots handle high volume routine interactions while generative backbends create tailored solutions to unique cases.
Over time, more customer service platforms will seamlessly integrate these capabilities. This will reduce response times, automate more complex exchanges, and deliver support experiences that feel personalized through AI working together behind the scenes.
Conversational AI vs. Generative AI? Use both.
Customer service is being revolutionized by artificial intelligence. With innovations in conversational AI, companies can scale interactive support on websites, apps and popular messaging platforms. Meanwhile, generative AI allows creating helpful, tailored content for each client.
As these technologies mature, generative and conversational AI will blend together to deliver swift, satisfying issue resolution using whichever modes customers prefer. Adoption is just beginning – over the 2020s, AI will transform service to be highly individualized yet effortlessly scalable.
We expect to see some big progress on this front in 2024. Now's a good time to prepare by learning about new ways to use AI in customer service in the next year.