For as long as computers have existed, one of the biggest barriers to their widespread application has been the difficulty in communicating with these systems in ways that they can understand—and be understood in return.
Recent advances, however, are providing a solution: Conversational AI. Built on various support technologies such as machine learning (ML), natural language processing (NLP), speech recognition and integrations with backend programs for carrying out requested actions, conversational AI works towards eliminating the language barrier between human and machine. This makes it possible for users to obtain information from, converse with, and give directions to digital systems in real time, just as easily as if they were speaking to another human being.
As previously addressed, conversational AI can take various forms. Each category serves different purposes and enhances user experiences in unique ways:
- Generative AI agents
- These agents use generative models to create original content (such as text or images) based on input data.
- AI chatbots
- Automated programs that simulate human conversation are commonly used in customer service to answer frequently asked questions and provide basic support.
- Virtual assistants
- Advanced virtual assistants are types of conversational AI that can perform a wide range of tasks—from setting reminders to controlling smart home devices.
- Text-to-speech software
- Text-to-speech (TTS) converts written text into spoken words, making information accessible to visually impaired users and improving the interactivity of conversational systems.
- Speech recognition software
- Conversational AI that employs speech recognition enables machines to understand and process human voice commands. This technology is used for voice assistants and other applications where speech input is the primary form of interaction.
Conversational AI has a wide range of applications across various industries. The following are only some examples of conversational AI in action:
- Online customer support
- AI-powered chatbots and virtual assistants can provide 24/7 support, answering questions and resolving issues quickly and efficiently. This reduces wait times and improves customer satisfaction.
- Accessibility
- Conversational AI makes technology more accessible by providing voice-activated commands and converting text to speech for visually impaired users. This enhances the usability of digital services for a broader audience.
- HR processes
- Conversational AI can assist with employee onboarding, answering common HR questions and providing information about company policies and benefits. This streamlines HR processes and improves the employee experience.
- Healthcare support
- Conversational AI can be used for virtual consultations, patient triage and providing medical information. It improves access to healthcare services and helps healthcare providers manage patient inquiries more efficiently.
- Internet of things (IoT) devices
- Voice assistants integrated with IoT devices can control smart home appliances, enhancing convenience and automation in daily life. This empowers users to interact with their devices in a more intuitive and natural way than constantly having to use control applications.
- Brand ambassadorship
- Conversational AI can be used to create interactive brand experiences, engage customers and enhance brand loyalty. AI-driven brand ambassadors can provide personalised interactions across social media and other channels to foster a stronger connection between the brand and those who support it.
While conversational AI offers a simplified approach to communicating with machines, the technologies that support this approach are anything but rudimentary. To allow digital systems to understand and respond to natural human communication, conversational AI builds upon the following:
Machine learning allows systems to learn from data and improve over time. ML algorithms process large amounts of data from previous interactions to identify patterns and predict user needs. This continuous learning process helps conversational AI systems become more accurate and efficient in understanding and responding to user inputs.
Speech recognition technology allows conversational AI systems to convert spoken language into text. This is crucial for voice-based interactions, such as those with virtual assistants like Siri or Alexa. Speech recognition systems interpret spoken words, recognise different accents and dialects and convert them into a format that the AI system can process.
The dialogue manager is responsible for ensuring the natural flow of the conversation, tracking what has already been said and making sure that the ongoing conversation makes sense. To do this, it incorporates the current intent of the user along with any additional personal or historical context. Thanks to dialogue management, AIs can follow discussions and respond logically—asking for clarification, restating details for confirmation, naturally transitioning back and forth between topics or adjusting responses based on evolving user input.
Natural language processing is the foundation of conversational AI. NLP helps conversational AI systems manage various linguistic features such as sentence structure, grammar exceptions, idiomatic expressions—even sarcasm. Machine learning algorithms within NLP continuously learn from vast amounts of textual data, recognising diverse linguistic patterns and nuances.
Natural language understanding (NLU) is a subset of NLP focused specifically on comprehension. It enables the AI system to understand the intent behind the user's input. NLU differentiates between various meanings of similar phrases based on context and user intent. This understanding is crucial for determining the appropriate response and ensuring the system can handle complex and ambiguous queries effectively.
Natural language generation (NLG) is the process of constructing coherent and contextually appropriate responses in human language. Once the system understands the user's intent through NLU, natural-sounding replies are generated using NLG. These responses are designed to be relevant, clear and human-like, enhancing the interaction's overall quality and making the AI appear more conversational and engaging.
NLP, NLU and NLG work in tandem to enable conversational AI to process and respond to human language. NLP breaks down and analyses the input text, NLU interprets the intent and context, and NLG generates an appropriate response. This integrated approach ensures that conversational AI systems can handle a wide range of interactions—from simple queries to complex conversations—providing users with a seamless and natural experience.
Conversational AI systems can continuously learn and improve from each interaction. This iterative learning process involves analysing previous interactions to identify patterns and enhance the system's ability to understand and respond to user inputs. Machine learning algorithms play a primary role in this process, allowing the system to refine its responses and become more flexible as it gains experience. As the AI interacts with more users, it accumulates more data, which helps it improve its performance and provide increasingly accurate and personalised responses in future interactions.
Any discussion about conversational AI must also acknowledge its connection to another powerful AI technology: generative AI (GenAI).
Generative AI is a specific type of AI that can create new content based on input data. This can include generating text, images, music, video etc. In the context of conversational AI, GenAI can enhance the system's ability to produce more varied and contextually appropriate responses. For example, a generative AI-powered virtual agent can generate human-like responses that are more nuanced and personalised compared to traditional rule-based agents.
While conversational AI focuses on enabling interactive communication with users, generative AI extends this capability by allowing the system to create new and unique responses.
As with many intelligent technologies, GenAI and conversational AI can be used in tandem to deliver improved results and enhance the overall user experience.
It is also worth considering the impact of multimodal AI on both of these technologies. The term multimodal refers to the ability to process, interpret and generate content across various data formats (such as text, images, audio, video etc.).
In conversational AI, multimodality has the capacity to significantly enhance AI contextual awareness. Depending on the data available, multimodal AI can expand beyond simply understanding what is being said, going as far as to analyse the user's tone of voice, facial expressions, gestures and more, quickly inferring meaning in the most natural way possible. For businesses, this means the possibility of creating seamless, intuitive user/AI experiences.
Multimodal AI also expands GenAI capabilities, linking various content modalities to create cohesive and contextually rich outputs. Generating audio clips based on visual prompts, generating video to match a piece of music, writing detailed textual descriptions from images—this interconnected approach makes it possible for GenAI to support increasingly complex creative workflows, enhancing the overall quality and relevance of generating content.
At its core, conversational AI is about improving communication between users and digital systems. Although this may sound like a simple goal, it has the capacity to significantly benefit organisations that use this technology correctly. Among the most noteworthy advantages are:
Conversational AI can automate routine tasks, address common customer-service requests and handle a large volume of interactions without human intervention, reducing operational costs in the process. Businesses can save on training and labour costs as they allocate their human resources to more complex, strategic tasks.
Conversational AI can analyse customer interactions to gauge sentiment, providing valuable insights into how customers feel about products or services. This can help businesses address issues more proactively, without having to wait for their customers to fill out surveys or reach out to provide feedback.
Different customers have different abilities and needs. Disabilities, language barriers, limited technical experience—each can be overcome through effective conversational AI. AI can provide additional directions, provide translation services, explain complex topics in simple terms and more. These systems are also more accessible during what are traditionally 'off' hours, providing 24/7 support and faster responses even when human service teams may not be available.
By leveraging user data and preferences, conversational AI can deliver tailored responses that meet individual customer needs. AI agents and chatbots can access customer history to better understand the context of the customer's queries, make personalised recommendations and provide more immediate support.
Conversational AI systems can handle a virtually unlimited number of interactions simultaneously, making them highly scalable. This is particularly beneficial for businesses experiencing rapid growth or seasonal spikes in customer inquiries.
Thanks to personalised interactions and recommendations, 24/7 availability, faster response times and a clearer understanding of customer meaning, conversational AI offers a better experience. This results in a happier customer base; it also drives sales, as customers are more comfortable working with conversational AI, and the AI itself is more effective at analysing customer sentiment to suggest upsells and cross-sells.
While there are many advantages to using conversational AI in business, it can also present certain challenges that may impact its effectiveness and adoption. Addressing these challenges is a prerequisite for getting the most out of this exciting new technology:
- Handling various language inputs
- Conversational AI systems must be capable of understanding and processing multiple languages and dialects (including slang). This presents a significant challenge due to the complexity of different grammatical structures, cultural nuances and regional variations in language use. Ensuring accurate language processing and understanding across diverse linguistic inputs requires extensive training data.
- Privacy and security breaches
- As conversational AI systems often handle sensitive user data, ensuring the privacy and security of this information is extremely important. These systems must comply with data protection regulations and implement strong security measures to prevent data breaches and protect user privacy. This involves encrypting data transmissions, securing databases, deploying monitoring systems and regularly updating security protocols to address potential vulnerabilities.
- User hesitancy
- There may be some apprehension among users regarding the use of AI technologies. Concerns about data privacy, job displacement and the reliability of AI systems can hinder user acceptance and trust. Overcoming this challenge requires transparency in how data is collected and used. Additionally, companies should provide options for users to interact with human agents when needed, ensuring a balance between automation and human touch.
Conversational AI democratises how we interact with digital systems—delivering significant value by bridging the gap between humans and computers. ServiceNow is leading this charge.
ServiceNow Conversational AI provides a comprehensive and centralised platform for automating customer interactions, optimising workflows and improving user accessibility. It offers pre-built templates, seamless integrations and machine learning to ensure accurate and personalised responses—applying the advantages of conversational AI to your growing business. And now, with the added benefit of GenAI, Conversational AI can take your organisation even farther.
See what powerful conversational AI can do for you; demo ServiceNow today, and enjoy the benefits of intelligent, intuitive solutions.