Conversational AI Chatbots vs Virtual Assistants: Siblings or Distant Cousins?

Being a foundational technology, conversational AI has many faces, with virtual assistants and conversational AI chatbots being the most common kins. These two — and the dilemma of conversational AI chatbot vs assistants in particular — spark up the most debates. Some experts say conversational AI chatbots and virtual assistants in the same breath, while others pigeonhole AI chatbots as a rule-driven chat based interface that dates back to the pre-generative and pre-conversational AI era.

So which one is it? Instinctools’ AI experts believe that virtual assistants and AI chatbots are different after all, serving distinct purposes and functions.

Exhibit #1: What is a conversational AI chatbot?

Let’s start from the basics and gradually build up the term. A chatbot is a blanket definition used to describe any software that can simulate human conversation, from traditional rule-based systems to cutting-edge conversational AI. 

Traditional rule-based chatbots were our first move toward the world of advanced conversational technology we know today. These systems, represented by simple FAQ bots and basic customer support bots, draw on a decision tree structure, where each user input triggers a specific conversation flow, based on predefined rules. The rules may be determined by the keywords, phrases, or specific patterns — whichever is scripted by developers. 

As you might guess, rule-based chatbots are limited to simple tasks, such as notifying the users of the order status or redirecting the user to a menu list.

But once a chatbot is powered by conversational AI, it evolves into a problem solver, capable of cracking more complex tasks and getting to the bottom of user intent. Conversational AI chatbots are an advancement from rule-based systems as they leverage such AI technologies as machine learning and natural language processing to interact with users in a human-like manner. 

Unlike pre-programmed chatbots designed for scripted responses, a conversational chatbot’s user interface can respond to a wider range of out-of-scope user inputs, including complex questions and open-ended sentences, and infer subtle nuances in language.

In the last few years, the democratization of large language models spurred a new generation of conversational interfaces. Built on the back of LLMs, generative AI chatbots can not only understand and respond organically to the input but are also capable of generating new content as the output. The output is not limited to high-quality text only as generative AI chatbots can also push out images, videos, and sounds.

Exhibit #2: What is a virtual assistant?

Both virtual assistants and cutting-edge conversational chatbots know who they’re talking to, predict where the conversation is headed, and self-improve over time. What differentiates intelligent virtual assistants from a text-oriented conversational user interface is their ability to act autonomously upon the user’s intent. To become active, virtual assistants use a combination of AI technologies and robotic process automation.

Let’s imagine you’re craving Italian food and looking for a nice Italian restaurant to enjoy your evening. With a traditional chatbot, a user needs to input a specific phrase “List Italian restaurants in the X area” to get the recommendations. With a conversational AI chatbot, the user can type in, “I’m in the mood for Italian food. Where’s a good restaurant nearby?” and the chatbot churns out a list of Italian restaurants in the user’s area. Virtual agents can check out online reviews, suggest five-star restaurants, and even make a reservation for the user.

The devil is in the differences: key functions of conversational AI chatbots vs assistants 

If we contrast the two, we’ll find out that both conversational AI chatbots and virtual assistants are adept at processing complex queries due to cutting-edge NLP — hence the overlap in functions. However, the action-oriented nature of virtual assistants makes them well-positioned to address a wider range of functions, unattainable for AI chatbots.

Here’s what conversational AI chatbots are capable of

Conversational interfaces, powered by generative responses from LLMs, are perfect for information-oriented and data-driven tasks. Conversational AI chatbots excel at pulling targeted self-service solutions and tailored guidance to address a specific user query. Such systems comprehend natural language commands, retain context, interpret dynamic user inputs, and enhance their output based on previous user interactions.

As for their data-driven function, AI chatbots can also capture essential user data or feedback, analyze it in real time, and identify trends or patterns that companies can use to improve their services or products.

The core capabilities of virtual assistants

Just like conversational AI, virtual assistants can also take over tasks that require deep analysis capabilities and dynamic, context-based interaction. However, AI personal assistants can go the extra mile and adjust to transactional scenarios. 

Beyond static responses, virtual assistants can handle tasks that involve any transaction, whether it’s placing an order or scheduling appointments.

Moreover, a virtual AI agent thrives in a setting that requires proactive intelligence whereby the system sets in motion particular mechanisms based on specific triggers or predictive analytics. For example, virtual assistants may automatically schedule maintenance appointments or tasks based on the maintenance history in a CMMS system.

Under the hood of conversational AI chatbots vs assistants  

Custom conversational AI chatbots and virtual assistants are like fingerprints — they are unique in their complexity, training data, and industry focus. But what remains consistent is their multi-layered foundation that enables both to fly through the assigned task. 

The plumbing behind conversational AI chatbots

AI chatbots have two sides to them: the one visible to the user and the one hidden in the background. A user interface makes the client side of conversational systems, acting as a bridge and enabling users to communicate and interact with a chatbot. 

Core to the chatbot’s offstage architecture is the NLP engine that comprises advanced Natural Language Understanding (NLU) and Natural Language Generation (NLG) components to establish a free-flowing, two-way communication with the end user. The NLU part is focused on tokenization, part-of-speech tagging, semantic analysis, and other behind-the-scenes mechanisms that allow a chatbot to understand human language in every manifestation. 

The NLG layer builds on pre-trained language models to generate authentic text responses based on the input and the chatbot’s understanding of the context. It’s also where chatbots’ text summarization capabilities come from that allows for accurate and concise summaries from input documents.

Once the user’s intent is deciphered by the system, an AI chatbot initiates a dialog manager to monitor and update the conversation context. A dialog manager is a building block in conversational interfaces that stores the current intent along with the identified entities throughout the conversation, asking for additional context from the user when needed.

To respond to user queries, intelligent chatbots connect to a dynamic knowledge or backend systems, sourcing relevant data and personalizing the response based on, say, integrated CRM data. Additionally, the conversational system is augmented with machine learning capabilities that allow for continuous learning based on textual data.

The underlying technology behind virtual assistants

Virtual agents inherit the architecture of conversational AI chatbots, but extend it to the actionable realm with robotic process automation. Unlike talk-only AI chatbots, given a goal, virtual assistants walk the talk, breaking down the task into a sequence of subtasks and acting on them until the mission is completed. 

Besides RPA and machine learning techs, many virtual assistants are also kitted out with reinforcement learning from human feedback (RHFL) and neuro-symbolic AI capabilities to level up their performance and supercharge their decision-making engine.

Unlike chatbots, virtual assistants can also interact with the real world to gather the necessary data and perform actions. For example, enterprise-grade virtual assistants are usually integrated with mission-critical systems, such as CRMs and ERPs, to orchestrate workflows inside and outside of these platforms. So once a new user signs up for a service,  an AI agent can collect their information and create a new contact in the CRM without further human intervention.

Intelligent assistance in action: the difference between chatbots and virtual assistants reflected in five use cases

While both conversational AI chatbots and virtual assistants prove to be effective in the wild, the suitability of these co-pilot technologies for your project depends on the application.

Timely, always-on assistance for customer service 

According to Gartner, in 2025, 80% of customer service and support organizations will be employing generative AI technology in some form to reduce the workload on agents and improve customer experience (CX). The surge in demand is predictable: both AI chatbots and virtual assistants allow companies to do more with less, delivering an estimated 94% in cost savings.

Purpose-built for specific use cases, grounded in company data and integrated with backend systems, both technologies can make sense of complex customer queries, enable customer self-service, and support intelligent routing and information capture. By scaling versatile conversational interfaces across all channels and touchpoints, companies can also make their heartfelt presence seen through and through.

But when it comes to specific use cases, these technologies hit different.

Conversational AI chatbots have a flair for customer service tasks that include:

• Providing information — answering questions about different features, attributes, or plans, offering product recommendations based on customer preferences, sharing company/product/order updates, and redirecting to the company’s resources.
• Handling routine inquiries — addressing customer concerns and issues, prioritizing queries and escalating them to human agents, and offering self-service solutions and specialized guidance.
• Gathering feedback — collecting customer feedback and insights.
• Integration with other systems — obtaining necessary data from the connected business systems.

Example: A customer reaches out to a company via chatbot to get comprehensive information about one of their products. The chatbot quickly provides the necessary product specs, recommends alternatives if necessary, and references the customer to the ordering page.

As for virtual assistants, they operate in the actionable realm, assisting customers with tasks associated with:

• Complex interactions — responding to in-depth textual-, audio- and video-based conversations with customers, recognizing the sentiment in customer input, predicting the conversation flow, and offering proactive guidance.
• Task automation — completing tasks on behalf of the customer, such as placing orders, making appointments, or troubleshooting technical issues.
• Integration with other systems — performing actions in the connected business systems and applications — either on the customer’s behalf or based on specific triggers. 

Example: A customer asks the company’s virtual assistant about one of their products. The assistant provides comprehensive product information, recommends alternatives if necessary, and proceeds with ordering the product on the customer’s behalf. 

Personalized recommendations in an ecommerce context

Over 71% of buyers want personalized experiences and companies are responding with personalized searches and product recommendations lined up at the bottom of the page. But what if a company could provide a dedicated shopping assistant that can tap into the customer’s mind? Customer satisfaction would go through the roof. That’s what both conversational AI chatbots and virtual assistants are made for.

By dispatching a conversational shopping assistant chatbot on their sales channels, companies can automate the following tasks: 

• Product recommendations —  seamless integrations to backend systems enable AI chatbots to inform their recommendations with data on user preferences, search history, previous interactions, cart items, customer location, and purchase history.
• Data collection — custom layers in conversational chatbots can analyze interactions with customers, gather insights on customer preferences, pain points, blockers, and feedback, and consolidate it in a dedicated system.
• Action recommendation — after suggesting relevant items, conversational chatbots can list tasks or actions that are relevant to the user’s goals, such as placing an order.
• Product comparisons — intelligent chatbots can make comparisons on the fly, resort to product databases, pricing information, and other systems to provide up-to-date product comparisons, and drone on specific characteristics.

An AI virtual assistant has no problem completing the same scope of tasks as AI chatbots do, but it also raises the bar, resembling a personal human assistant customers crave when shopping:

• Actionable product recommendations — providing a hands-off shopping experience where an approved product recommendation is followed by automated order placement.

Marketing and sales automation

If there’s one match made in heaven for automation, it’s marketing and sales. However,  merging artificial intelligence with marketing and sales is a balancing act as teams have to keep it personalized, yet at scale. 

Equipped with unrivaled language understanding superpowers, conversational interfaces can subtly promote company products and services by integrating them into natural conversations with customers. In particular, AI chatbots can:

• Clock in customer engagement stats and conversational history to inform granular marketing and sales activities.
• Nurture potential leads by following with prompts about specific company’s offerings.
• Qualify leads based on predetermined criteria and report this data to a connected system.
• Support strategic upselling and cross-selling by recommending complementary products or services.

Virtual assistants take up where AI chatbots left off by directly contributing to marketing and sales goals. For example, virtual assistants can automate simple and repetitive tasks such as streamlining follow-ups based on lead quality, distributing campaigns across channels, adding new customers to a CRM, and more.

Example: A customer looking for additional product information is greeted by a virtual assistant on a company’s website. The assistant provides detailed product information, answers the customer’s questions, and offers to compare three products to help the customer make the right choice. The assistant then collects the customer’s email to send them comparison details along with personalized recommendations while also subscribing the customer to a newsletter. After a few days, the virtual assistant follows up with the customer.

Streamlining HR processes

Any company’s journey is peppered with challenges, many of which are rooted in managing human resources. Bringing conversational AI on board allows companies to ease the strain on HR workers and offload routine tasks to smart company based solutions.

Conversational AI chatbots can pick up the slack in a raft of HR areas, including:

• Onboarding — providing information about company policies and functions, collecting necessary documents, and answering often-asked questions.
• Employee support — providing round-the-clock assistance for inquiries related to benefits, time off requests, vacations, bank information, accounting data, coverage, and more.
• Performance management — conducting surveys and helping employees track their milestones
• Talent acquisition — vetting candidates and collecting basic information.

Following in the footsteps of chatbots, virtual assistants can not only provide and track HR data but also log the changes in the integrated HR and business systems. For example, along with informing employees about their PTO balance, virtual assistants can punch in PTO dates in a PTO tracking software and track the status of the PTO approval.

Tackling the data and task overload in banking and finance

No single industry provides a better foundation to demonstrate conversational AI success than the embattled banking and finance domain that’s grappling with thousands of transactions per month. 

Financial organizations bank on AI banking chatbots for capability building across more than 50 support functions, including:

• Account management services — empowering customer self-service by handling processes such as verifying and authenticating customers, reviewing account balances, and updating account information.
• Customer support — handling routine help tasks, such as reporting lost or stolen cards, disputing transactions, checking credit scores, and more.
• Mortgage and lending — pre-qualifying applicants, checking loan application status, and collecting documents.
• Trading and investment — providing real-time market analysis, directing customers to educational resources, and offering personalized investment advice based on risk tolerance and other data points.

Working backward from the customer, virtual assistants can undertake a similar range of tasks, but besides coming back with a static response, they can also initiate actions on the customer’s behalf: 

• Account management services — resetting account passwords/PIN, transferring funds, making payments, paying bills, and blocking lost or stolen cards.
• Customer support — processing refunds and chargebacks, troubleshooting technical issues, and scheduling appointments with financial advisors.
• Mortgage and lending — making a payment, submitting loan applications, and coordinating the closing process.
• Trading and investment — placing buy or sell orders, rebalancing and adjusting asset allocation, and acting on investment strategies.

Intaking and appointment scheduling for healthcare offices

Understaffed and overstretched, healthcare organizations have to reimagine care delivery ways, with conversational AI being a central piece in redefining patient experiences and boosting operational efficiency.

Dispatched across key digital channels such as websites, online portals, SMS, and email, gen AI-enabled chatbots can provide 24/7 patient support and take on the following critical functions:

• Patient and symptom intake — jotting down initial patient information, such as symptoms, medical history, and contact details.
• Triage — sorting and prioritizing patients based on the urgency and severity of their condition.
• Appointment scheduling — suggesting appointment times based on patient availability and provider schedules
• Information provision — tailoring treatment options and personalized self-care advice based on specific patient needs and EHR data.
• Hand-off to medical professionals — referring a patient to a medical professional, when a patient’s condition requires medical evaluation, along with the data logged during the interaction.

Built to take action, rather than reflecting on it, autonomous virtual assistants ease even more burdens healthcare professionals have on their shoulders:

• Сollecting patient information and recording it in the EHR system.
• Allocating healthcare resources, such as staff and equipment to meet the needs of the urgent patients. 
• Sending appointment confirmations to the patient and updating the HCP’s schedule, rescheduling or canceling appointments, if needed.
• Referring a patient to a medical professional and booking appointments in the EHR appointment scheduling module.

So, who’s talking? It depends on your needs

Both conversational AI chatbots and virtual assistants allow companies to slash cost, improve customer satisfaction, simulate a high-touch experience, and be there for the customers at all times. But while conversational AI chatbots talk your customers through a problem, virtual assistants take direct action to tackle the problem head-on. 

Whichever type of automation you choose, it’s equally important for both solutions to build on precise prompt engineering to enable more human-like conversations. As for data security, we recommend deploying the solution and the LLM behind it on a local server to prevent data sharing with third parties. With data security best practices such as data minimization, encryption, data privacy compliance and others at the core of your software, you can also make sure your AI solution is both effective and secure.

As an ISO 27001:2022 certified company, *instinctools specializes in developing secure multimodal AI chatbots and virtual agents rooted in your company’s data and designed to meet your specific needs.