Like chatbots, conversational artificial intelligence (AI) can handle large call volumes, offer quicker resolutions, improve the consumer and agent experience, and increase profits. For instance, a global hotel chain used [24]7.ai’s conversational AI solution to create a self-serve option for tens of millions of customers annually. Rapid adoption enabled over 112,000 monthly interactions, increased agent satisfaction by 97%, and led to a 3x increase in sales conversions.
The recent buzz around generative AI models like ChatGPT and Bard has also kindled confidence in chatty tech and created innovation opportunities.
Seven Considerations for Designing an Effective Conversational AI
Effective conversational AI design rests on deep user, intent, and interaction understanding and includes:
- Personalization: Understand context, past interactions/history, or personal data to tailor responses for a particular user.
- Multimodal Representation: Incorporate multiple modes of communication (combined voice and digital experiences or augmented reality [AR]/virtual reality [VR]) to increase understanding and compliance.
- Efficient Handover to Human Agents: Seamlessly escalate the issue to a live agent, passing information and creating an efficient handover.
- Mixed-initiative and Flexibility: Understand multiple pieces of information in an utterance or follow up to ask for missing data.
- Maintain Context: Recall past interactions and understand the user’s situation to provide relevant responses.
- Effective Repair Mechanisms: Guide users back to the main interaction in case of errors, distractions, lack of agreement, and diversions.
- Sentiment Understanding: Detect and tailor for user sentiment, understand intent even if handling is not defined, and adjust responses based on context, personalization, and emotion to avoid staleness.
Optimizing Conversational AI for Success
Launching conversational AI is like nurturing a baby—you must train it, hand-feed it, and monitor it frequently. As the baby grows, it becomes easier to teach it, and finally, when it’s an adult, it becomes self-sufficient and keeps learning in response to new environmental input.
Even with new technology, such as generative AI and self-learning AI, there is a period where it is incredibly important to evaluate and refine the data you feed it over time. Yet, the approach most companies take with conversational AI is to launch it and walk away.
Without human guidance in the initial phases, the algorithm may learn incorrect information and give out wrong (or worse, biased) answers. The key to conversational AI success is post-launch optimization with a technology layer that learns and evolves, bringing ever smarter and more accurate automation into the equation.
Tempering AI with a Human Touch
While a mature conversational AI can drive sweeping changes in the customer experience, more involved requests still need that human touch. The delicate balance to understand when a bot should answer versus when it should bring in a human is not understood and implemented by most companies.
Contributing author: Celene Osiecka, Senior Director, Conversation Design at [24]7.ai
