What is Conversational AI?

Conversational AI is an intuitive tool that enables us to interact with technology using natural, everyday language. Be it virtual assistants on our smartphones or chatbots on websites, it is transforming the way we access information and complete tasks more efficiently.

How businesses and their customers interact with AI has evolved from traditional chatbots to sophisticated AI agents that can reason, learn, and take actions on their own with some human guidance and oversight. Using technologies like natural language processing, machine learning, and speech recognition, AI can understand questions and instructions, which helps it provide better responses.

Here's what it looks like in action.

In sales: After analysing your team’s video calls with customers, conversational AI interprets customer sentiment and suggests next steps for your reps.

In marketing: You ask conversational AI to create personalised landing pages for your customers, drawing from their preferences and history.

In service: Conversational AI responds to a customer service inquiry after hours, offering a solution based on past interactions, with a friendly tone.

It has been widely adopted by service teams to provide 24/7 first-line support that sounds human and goes beyond scripted answers. And for good reason — research shows it frees up 30% more time for employees to focus on increasing revenues and spending time with customers.

Here's a look at how conversational AI works and what it could do for your organisation.

Table of Contents:

• What is Conversational AI?
• How does conversational AI work?
• Core components of conversational AI
• Types of conversational AI
• Conversational AI Vs. Generative AI
• How does conversational AI help transform business
• Key benefits of conversational AI for businesses
• Overcoming challenges of conversational AI
• What’s next for conversational AI?

In simple terms, Conversational AI is an artificial intelligence AI technology that can talk to you — not just respond based on pre-programmed knowledge, but actually process what you say and reply in a way that sounds natural. Unlike older chatbots, which just respond based on pre-programmed knowledge, autonomous agents that use conversational AI actually process and reply in a way that is easy to understand and is trustworthy.

This technology has become essential across industries, from helping sales teams generate close plans to retail brands using it to provide personalised and relevant recommended products to healthcare professionals relying on it to manage patients’ complete medical history.

97% of executives say they feel an urgency to integrate AI tools, with conversational AI becoming essential across industries. By providing it with data and customer relationship management (CRM) systems, modern conversational AI can understand context, remember previous interactions, and take action autonomously.

Conversational AI has evolved significantly over the years, transforming from simple scripted responses to complex, context-aware systems that can engage in meaningful interactions.

Early conversational AI systems often struggled with understanding complex user queries and maintaining context during an interaction. They were primarily rule-based and could only respond to specific commands or phrases they were programmed to recognise. This led to frequent misunderstandings and a frustrating user experience when the conversation deviated from expected patterns. This also meant it lacked the ability to personalise interactions.

As machine learning technology advanced, conversational AI began to incorporate natural language processing techniques, allowing systems to learn from data rather than follow strictly programmed rules. This shift enabled conversational agents to improve their responses over time-based on interactions with users and adapt as required, leading to more natural conversations.

Conversational AI uses Machine Learning and Natural Language Processing (NLP). It is trained on large amounts of data — ideally, trusted internal data, making it perfectly tailored for your business. When AI has learned to recognise words and phrases, it moves on to natural language generation — in other words, talking back.

Say you want to find out what happened during a meeting without reading a 40-page transcript. Drop the transcript into the AI tool and simply ask: ‘What were the outcomes of this meeting?’ Your query will be converted into machine-readable text, processed, and analysed by NLP, along with the transcript.

Thanks to Machine Learning and its training data, AI will scan the transcript and create a summary of the actions for you. Then, if you have any feedback on the AI response, you can share it, and it will provide improved answers to you.

Early conversational AI systems were limited to rule-based responses and struggled with complex queries, but that is changing. Powered by both large language models(LLMs) and specialised small language models, these systems can now understand context, maintain conversational flow, and use autonomous action.

NLP (natural language processing) is the foundation of conversational AI, using tokenisation to break down text, sentiment analysis to understand emotion, and natural language generation to create human-like responses. NLP uses machine learning to help AI systems to understand and respond to human language. The system then analyses these pieces to understand sentiment (emotions and attitudes), extract key information, and determine user intent.

Modern NLP systems can also understand context and nuance. For example, they can differentiate between "I need to return this order" and "When will my order return to stock?" NLP helps AI recognise that while both sentences contain "return" and "order," they require completely different responses. This context combined with language generation training helps the AI craft responses that sound natural and appropriate to the situation.

Machine learning (ML) is what makes conversational AI truly intelligent and adaptable. Rather than following static rules, AI algorithms analyse patterns in vast amounts of data to understand how humans communicate and what responses are most effective. This enables the system to improve continually with each interaction, learning from successful exchanges and adjusting its approach based on user feedback.

For example, when integrated with data platforms like Data Cloud, algorithms can analyse previous customer interactions to understand which responses led to positive outcomes. AI can then use these insights to personalise future conversations, predict user needs, and proactively offer relevant solutions. The algorithms also help the system recognise when a conversation should be escalated to a human service rep, ensuring seamless handoffs when needed.

While the application of conversational AI bots is deeply intertwined with most of our interactions online, they can be broadly classified under the following categories.

Simple Bots: Chatbots, Transactional Bots, Social Media Bots.
Advanced Assistants: Virtual Assistants, Voice Assistants, AI-Powered Customer Support Bots.
Complex Systems: Intelligent Virtual Agents (IVAs), Multimodal Conversational AI, Emotionally Intelligent Bots.
Specialised: AI-Powered Content Generators.

Each type serves a unique purpose, ranging from simple, task-oriented bots to complex systems that can engage in nuanced, multi-turn conversations and handle business-critical functions.

Conversational AI is transforming the way businesses function across departments, leading to more efficient workflows and improved customer experiences. Here are just some of the ways organisations are combining this technology with virtual agents to improve their operations.

AI for sales is fundamentally changing how teams prospect and close deals. Conversational AI, particularly through tools like Agentforce— the agentic layer of the Salesforce platform —autonomously engages with prospects, handles initial qualification, and books meetings for sales representatives. The technology also helps sales teams prioritise leads, prepare for meetings, and maintain consistent follow-up communication with:

• Autonomous outreach to warm leads across email, SMS, and social channels
• Real-time meeting scheduling and qualification
• Deal guidance and next-best-action recommendations
• Automated creation of sales collateral and proposals

 In AI service operations, conversational AI provides omnichannel support that adapts to customer preferences. Advanced AI agents can handle complex queries across multiple channels while maintaining context and personalising responses based on customer history. This allows employees to focus on cases that require more empathy and complex problem-solving while agents provide:

• 24/7 multilingual support across web, mobile, and messaging platforms
• Proactive issue resolution based on customer behavior patterns
• Automated case routing and prioritisation
• Personalised self-service experiences

With marketing AI, teams can use conversational AI to create more engaging, personalised campaigns. The technology helps analyse customer data, optimise content, and automate campaign management while maintaining a consistent brand voice across all channels. The benefits to marketing teams include:

• Real-time campaign optimisation
• Personalised content recommendations
• Automated email response management
• Social media engagement and monitoring

Conversational AI for commerce can help agents act as personal shopping assistants, helping customers find products and making tailored recommendations. This technology can boost conversion rates by providing immediate, tailored guidance for customers in their shopping journey, including:

• Personalised product recommendations
• Inventory and availability updates
• Automated order status tracking
• Interactive buying guides

For technical teams, combining conversational AI with your operations platform streamlines development processes and improves code quality. Developers can work more efficiently while maintaining best practices and security standards with skills like:

• Code review and optimisation
• Documentation assistance
• Bug detection and troubleshooting
• Development environment setup

Conversational AI is transforming financial services by automating complex processes while maintaining security and compliance. The technology can handle everything from routine transactions to sophisticated financial analyses that include:

• Fraud detection and prevention
• Automated invoice processing
• Personalised banking services
• Regulatory compliance monitoring

AI doesn't just handle support tickets - it builds relationships. Here's what that looks like in different industries:

• Healthcare: Smart scheduling and friendly reminders
• Automotive: Timely maintenance heads-ups
• Transportation: Real-time updates when plans change
• Government services: Clear, simple information delivery
• Hospitality: Personalised guest experiences

These are just a few examples. Conversational AI can help everyone ‌ turn data into insights faster, automate tasks, write copy and summarise outcomes. But you can also use it to address industry-specific pain points. When it comes to AI, you are only limited by your imagination.

More businesses are turning to AI to enhance customer satisfaction and improve operations. The advantages of AI across a diverse range of industries and roles continue to grow. Here are some of the top benefits businesses are getting with conversational AI solutions:

• 24/7 availability and instant response times:
  Conversational AI systems powered by AI assistants can engage with customers around the clock, eliminating wait times and ensuring consistent service quality regardless of time zone or volume.
• Improved efficiency:
  With AI automation that handles routine inquiries and tasks, conversational AI significantly reduces the workload on human teams. In fact, 93% of service professionals report significant time savings on routine tasks, allowing staff to focus on more complex, high-value activities.
• Personalised and accessible experiences at scale:
  By integrating with data and CRM systems, conversational AI can use customer history and preferences to deliver tailored interactions that feel personal even while serving thousands of users simultaneously. Conversational AI also makes access to services easier for people with disabilities.
• Improved data insights and decision-making:
  Every interaction with conversational AI generates valuable data about customer needs, preferences, and pain points. This information helps businesses identify trends, improve processes, and make more informed strategic decisions.
• Cost reduction while maintaining quality:
  Implementing conversational AI can significantly lower operational costs by automating routine tasks while also maintaining or even improving service quality through consistent, accurate responses.
• Multilingual support and global reach:
  Modern conversational AI can communicate fluently in multiple languages, enabling businesses to serve global markets without the need for extensive language-specific support teams.

While conversational AI offers tremendous benefits, organisations must address some key challenges to ensure the successful implementation of AI agents. Here's how businesses can effectively manage these concerns, particularly through advanced technology like Agentforce.

Data security and privacy: Handling sensitive customer information in AI conversations requires robust security measures. AI agents address this through enterprise-grade security protocols, encryption, and strict data governance policies. For example, Agentforce's Atlas Reasoning Engine ensures every interaction is grounded in your trusted business data while maintaining security compliance.

Application of a secure AI architecture like the Einstein Trust Layer to implement enterprise security standards allows companies to benefit from conversational AI without compromising customer data.

Bias and fairness: AI systems can inadvertently perpetuate biases present in training data. To mitigate this, modern conversational AI platforms should employ regular monitoring and auditing of AI responses and diverse training datasets that represent all user groups. Clear guidelines for inclusive language are also essential, as are built-in safeguards against discriminatory outcomes.

Workforce impact and adoption: Concerns about AI replacing jobs are common, but conversational AI serves to augment human capabilities rather than replace them. To successfully implement these changes — and promote adoption of the technology — put together a communication plan about AI's role in supporting, not replacing, employees.

Training programs will help staff work effectively alongside AI and show them the ways AI will free them up to focus on high-value tasks that require emotional intelligence and complex decision-making. Also, be sure to set up an ongoing motion to measure and share productivity gains from AI adoption.

System reliability and performance: Ensuring consistent, accurate responses is crucial for maintaining user trust. Agentic systems like Agentforce address this through continuous monitoring of AI performance, built-in escalation paths to human agents, regular updates that improve response accuracy, and testing capabilities to ensure reliable ongoing operation.

If you are excited about Conversational AI, get ready for copilots. AI copilots are integrated with platforms such as CRM and use Large Language Models (LLMs) to help users with various tasks related to their roles. They supercharge productivity and efficiency by proactively supporting users with the right help at the right time. They automate dull tasks, analyse large amounts of data quickly, streamline communication between multiple stakeholders, and connect multiple platforms and tools.

An AI copilot for CRM is a powerful assistant that can do the work of hundreds or thousands of individual applications, making every aspect of your business more efficient. You can learn more about how to design your own here, and Agentforce Assistant, our own Conversational AI assistant, here.