Conversational AI (or conversational artificial intelligence), refers to technologies that enable machines to understand, process, and respond to human language naturally.
These include chatbots and virtual assistants hich can perform tasks or provide information based on voice or text inputs. For example, a conversational AI system could help users book appointments, answer FAQs, or help a company HR onboarding new employees through simple automated conversations.
Why do you need conversational AI in 2024?
Conversational AI has become increasingly important in recent years due to its ability to improve any business, impacting:
- Instant Support: Offers 24/7 customer service, meeting expectations for immediate assistance.
- Cost Reduction: Automates routine inquiries, lowering operational expenses by reducing reliance on human agents.
- Scalability: Manages numerous customer interactions simultaneously without additional resources.
- Personalized Interactions: Delivers tailored responses, enhancing customer satisfaction.
- Competitive Edge: Keeps businesses ahead by leveraging AI for improved customer engagement and operational efficiency.
Want to know more about it? Check our brand new 2024 conversational AI trends guide!
Top 3 conversational AI business values
- Enhanced Customer Experience: By providing immediate, 24/7 responses to customer inquiries, conversational AI improves satisfaction and engagement.
- Operational Efficiency: Automating routine interactions reduces the workload on human staff, allowing them to focus on more complex tasks.
- Scalability: Conversational AI can handle a vast number of interactions simultaneously, enabling businesses to scale their customer service operations without a proportional increase in resources.
How does conversational AI work?
If a simple chatbot operates based on pre-defined rules and scripts, handling inquiries with limited flexibility, often requiring specific prompts from the user to provide correct responses, a conversational AI software uses more advanced algorithms and machine learning, to respond to user inputs more naturally and contextually.
The first step involves Natural Language Processing (NLP). It’s the job of NLP to correct spelling, identify synonyms, interpret grammar, recognize sentiment and break down a request into words and sentences that make it easier for the virtual agent to understand.
Once the request has been prepared using NLP, a number of Deep Learning and Machine Learning models take over. Collectively known as Natural Language Understanding (NLU), these allow conversational AI to identify the correct intent (or topic) of a request and extract other important information that can be used to trigger additional actions i.e. context, account preferences, entity extraction, etc.
Proprietary algorithms also play an important role in enhancing the overall NLU capabilities of a chatbot. In the case of boost.ai’s conversational AI platform, we have developed Automatic Semantic Understanding (ASU), an algorithm that works alongside Deep Learning models as a safety net to further reduce conversational AI’s chance of misunderstanding user intent.
AI Trainers: the secret behind customer service automation
Technology is a crucial part of what makes customer service automation work, but it’s only one piece of the puzzle. Helping customers and solving problems has long been the domain of customer service teams and it’s their expertise and experience that can be leveraged into ensuring that conversational AI achieves its potential.
By transforming their trusted customer service teams into AI Trainers, rather than depending on external consultants or data scientists, companies can ensure their conversational AI stays true to their brand. AI Trainers represent a new wave of non-technical, self-sufficient professionals. Their role involves building, training, and collaborating with virtual agents to automate large-scale customer interactions, increasing self-service rates, reducing the workload for frontline staff, and enhancing the overall customer experience.
This symbiosis of machine efficiency and human expertise is the secret sauce behind what makes conversational AI such a powerful tool for automating customer interactions.
Learn more about the job of an AI trainer!
Your questions about conversational AI:
What is the future of conversational AI?
If voice chatbots are becoming a hot topic, the future of conversational AI is turned to enabling more seamless, human-like interactions across various platforms. We can expect conversational AI to play a larger role in customer service, healthcare, education, and personalized experiences, automating complex tasks while improving accessibility. Integration with emerging technologies will certainly enhance real-time, multimodal interactions, empowering businesses to provide faster, more efficient services while offering highly personalized and intuitive user experiences.
What is the best conversational AI solution?
There is no single "best" conversational AI solution, as the ideal solution will depend on a variety of factors, including the specific use case, budget, technical requirements, and preferred development platform. If some companies turn to Google, IBM or Amazon's tools to build up their own conversational AI chatbots, most companies are now looking for unlimited scalable, easy-to-train and no-code solutions, such as boost.ai!
How much conversational ai cost?
The price of conversational artificial intelligence can differ greatly based on various elements including the complexity of the system, the chosen platform, customization requirements, and the deployment scale. Simple chatbot services may cost as little as a few hundred dollars monthly, whereas advanced, enterprise-grade conversational AI systems can demand a substantial financial outlay, potentially ranging from thousands to tens of thousands of dollars each month. Expenses might also cover development, integration, maintenance, and continuous updates to enhance the AI's functionality and performance. It's essential for companies to assess their particular requirements and financial constraints when deciding on conversational AI.