Conversational AI (or conversational artificial intelligence) is a technology that enables machines to understand, process, and respond to human language naturally and contextually.
These include chatbots, virtual assistants, AI agents and driven automation tools that assist users via text or voice inputs. From booking appointments and handling customer inquiries to help onboarding new employees, conversational AI is revolutionizing business interactions.
Why do you need conversational AI in 2025?
The role of conversational AI in businesses continues to expand, making it an indispensable tool for enhancing efficiency and customer engagement.
Key benefits include:
- Instant support: Provides 24/7 automated customer service, meeting consumer expectations for real-time responses.
- Cost reduction: Automates routine inquiries, reducing the need for human intervention and lowering operational costs.
- Scalability: Handles thousands of interactions simultaneously, ensuring consistent service without increasing resources.
- Personalized interactions: Leverages AI to analyze data and deliver responses tailored to each user.
- Competitive advantage: Keeps businesses ahead by integrating AI for improved customer engagement, operational efficiency, and data-driven insights.
Want to stay ahead of the competition? Check out our brand new 2025 conversational AI trends guide!
Top 3 conversational AI business values
- Enhanced customer experience: By providing immediate, always-available assistance, conversational AI boosts customer satisfaction and engagement.
- Operational efficiency: Reduces workload for human staff, enabling them to focus on more complex or high-value tasks.
- Scalability: Supports an ever-growing customer base without the need to proportionally expand human support teams.
How does conversational AI work?
A traditional 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.
By training customer service teams to become AI Trainers, businesses ensure their AI aligns with brand values and customer expectations:
- Design and refine AI interactions
- Train models to improve accuracy
- Enhance self-service rates
- Reduce frontline workload while improving customer experience
This human-AI synergy is what makes conversational AI a game-changer for customer service automation.
Learn more about AI trainer careers!
Your questions about conversational AI:
What is the future of conversational AI?
Conversational AI is evolving beyond chatbots, with a focus on seamless, human-like interactions across multiple platforms. One of the most common questions we receive from our customers is about the difference between agentic AI and generative AI. As businesses explore these technologies, understanding their distinctions is crucial. Agentic AI focuses on autonomous, goal-driven actions, while generative AI specializes in creating content and responses.
For a deeper dive into this topic, we recommend reading our latest article: Agentic AI vs. Generative AI.
What is the best conversational AI solution?
There is no one-size-fits-all conversational AI solution—the best choice depends on factors like the specific use case, budget, technical requirements, and preferred development platform. While some businesses opt to build their own chatbots using tools from Google, IBM, or Amazon, many are now seeking scalable, easy-to-train, no-code solutions like boost.ai!
How much conversational ai cost?
The cost of conversational AI varies significantly based on multiple factors, including system complexity, chosen platform, customization needs, and deployment scale.
- Basic chatbots: Start at around $100–$500 per month, typically used for handling simple FAQs and basic customer interactions.
- Mid-tier AI solutions: Range from $1,000–$10,000 per month, offering greater flexibility, integrations, and AI training capabilities.
- Enterprise-grade conversational AI: Can cost anywhere from $10,000 to $50,000+ per month, often incorporating advanced AI features like agentic AI and deep learning models for high-volume automation and complex workflows.
In addition to base subscription costs, companies should consider expenses related to development, integration, ongoing maintenance, and continuous AI improvements to ensure optimal performance.
Need help choosing the right AI solution for your business? Let’s talk!
