Ten business use cases for generative AI virtual assistants

Contents

By integrating large language models with proprietary data sources and systems, AI virtual assistants can unlock powerful new capabilities across industries. These advances range from streamlining customer service and employee support to personalizing product recommendations and facilitating seamless e-commerce experiences. In this article, we will explore ten potential use cases for AI-powered virtual assistants, demonstrating their transformative impact on business operations and customer interactions.

 

What is a generative AI virtual assistant?

Businesses across industries are now looking to adopt generative artificial intelligence to increase efficiency, enhance customer experience, and unlock new capabilities. The specific application of artificial intelligence (AI) tools that we would like to look at in this text are AI-powered virtual assistants based on large language models (LLMs) that can engage in human-like dialogue. By integrating these conversational AI assistants with proprietary data sources and business systems, companies can provide employees and customers with secure, on-demand access to institutional knowledge while ensuring data privacy and compliance.

A generative AI virtual assistant utilizes a natural language processing model (like OpenAI’s famous GPT family) which is trained on vast datasets to understand and generate human-like text. These AI models can respond to natural language queries using information from multiple sources, and generate fluent, contextual responses.

 

Problems with generative AI

The main obstacles usually raised in employing AI natural language processing for building a personal virtual assistant as are data privacy and the propensity of AI chatbots to hallucinate. These issues need to be overcome if AI features are to be implemented in building a virtual assistant.

Data privacy is perhaps easier to address, as it is primarily a matter of keeping your proprietary data away from general training data and not allowing your information to be used in training AI for other assistants (otherwise, confidential information may come up in answers to other users’ queries). This, however, is merely a matter of using pretrained models, the right infrastructure, and safe rules for data handling. The way we get around this problem is by using Azure’s infrastructure, which already provides very high data security standards.

The hallucination problem seems more inherent to the way LLMs are trained. An AI chatbot can sometimes behave like a semi-competent student in an exam—in the absence of a perfect answer, it will give an approximation of an answer in the hope of hitting the mark. Obviously, an enterprise-level AI-powered virtual assistant has to do better. To mitigate this, we apply a technique called retrieval augmented generation (RAG), which combines the language model with a separate information retrieval component that provides factual grounding from verified data sources. The AI’s outputs then accurately reflect the provided knowledge while maintaining an engaging, conversational flow.

Finally, truly unlocking a generative AI assistant’s potential requires integrating it with a company’s existing systems and business logic, from customer databases and knowledge bases to workflows and approval processes. This integration enables future AI virtual assistants to securely access and apply an organization’s proprietary data when communicating with users.

Ten powerful generative AI use cases

 

Knowledge management

AI virtual assistants present a compelling solution for democratizing access to an enterprise’s invaluable institutional knowledge. By connecting the language model to a centralized knowledge base containing data from documentation, manuals, policies, and more, these assistants can provide accurate, contextual answers to user queries on a wide range of topics.

This approach would transform workplace knowledge sharing from a tedious process of searching through documents and submitting help desk tickets to a conversational, self-service experience. Employees can simply ask the AI assistant natural language questions to get instant answers, drastically reducing wait times and support costs. Unlike traditional search solutions, AI virtual assistants can perform complex tasks drawing on a number of sources. This would also flatten the learning curve for new employees who would otherwise have to spend time searching the company’s knowledge base for information.

Applying techniques like RAG to an AI virtual assistant would ensure response accuracy by avoiding hallucinations, while functionalities already known from search-oriented AI assistants such as MS Copilot or Perplexity can tell the user where a given piece of information can be found.

 

Retail and e-commerce

In the e-commerce realm, generative AI assistants can revolutionize the entire shopping experience through engaging, personalized conversations at every stage of the buyer’s journey. Imagine this scenario. A customer visits an electronics retailer’s website and is immediately greeted by the AI virtual assistant. “Hi, I’m looking for a new laptop for video editing,” the customer says. The AI assistant draws from the retailer’s product database to understand the customer’s needs, provide education on available options that fit their use case, and make tailored recommendations.

As the conversation progresses, the customer can seamlessly complete their purchase through the AI interface, which integrates with their customer account, payment systems, and order fulfillment processes. The assistant can also offer customized upsell opportunities like extended warranties. This AI-powered conversational commerce model would keep customers engaged, facilitates sales, and elevates the entire shopping experience. Most importantly, the system could improve customer satisfaction by giving the customer exactly what they want faster and more accurately than a traditional search would.

Two potential issues must be dealt with here. One is accuracy, which can be solved using the aforementioned RAG technique. Another potential problem is linguistic: Machine translated product descriptions from foreign suppliers can often be awkward or confusing. In a traditional model, it is on the buyer to deal with that, but when an AI assistant responds to user queries, it is the seller that will be blamed for any confusion. This can potentially be dealt with by using original-language descriptions and manuals in the AI assistant’s knowledge base. However, efforts must be made to ensure consistency, so that the virtual assistant can correctly identify analogous features and present them without itself sounding like a bad translation.

 

Personalized product recommendations

Along similar lines, generative AI can power virtual assistants that proactively recommend highly relevant products and services through natural language conversations. Rather than relying solely on conventional recommendation systems that suggest items based on past purchases, the AI assistant can pick up on more nuanced interests and preferences expressed by the customer.

For example, if an AI assistant recognizes a person is passionate about outdoor adventures based on their social media or product reviews, it may start a conversation: “Since you love hiking, have you considered getting a GPS watch? They are great for tracking your routes and distance travelled.” A dialogue could then ensue where the assistant provides advice and recommendations tailored to that individual. A machine learning algorithm may be able to use more data points to identify the products the customer is most likely to buy and offer insights into the particular features that make a given product desirable.

 

Travel assistant

Few industries are as ripe for conversational AI virtual assistants as the travel and hospitality sector. A virtual travel assistant integrated with airline, hotel, and other travel systems can seamlessly handle customer requests through natural language interactions. Searching for package tours online can be really tedious using commonly used current tools, but a personal AI assistant could sift through dozens of offers taking into account a host of user needs, all through a natural-sounding conversational AI.

But search is not all. Need to change a flight, book ground transportation, or add amenities to an upcoming hotel stay? Rather than navigating various websites and struggling through clunky interfaces, a customer could simply say, “I want to change my flight from Atlanta to Denver and upgrade to first class.” The assistant can verify details, retrieve the customer’s itinerary, check prices and availability across providers, and facilitate the changes. It could even offer helpful advice and alternate solutions: “Flying to this airport will require you to take a two-hour train journey at a cost of $30, whereas this slightly longer and more expensive flight will take you much closer to your destination.”

The assistant could also answer passengers’ questions about visa requirements or travel advisories by securely querying verified data sources from government agencies or travel authorities.

 

AI service desk

Every company has processes for managing internal service requests, whether for provisioning new hardware, resetting passwords, troubleshooting technical issues, or navigating HR policies. Traditionally, these requests funnel through ticketing systems and support desks.

AI assistants integrated with service management solutions like ServiceNow or internal knowledge bases present a far more efficient model. Employees can simply engage in a conversational dialogue with the AI assistant, describing the issue or request in plain language.

For instance, an employee could say, “My laptop is running really slow and crashing frequently. What should I do?” The assistant would understand the context, ask clarifying questions as needed, provide troubleshooting steps from internal IT knowledge bases, and, if the issue persists, automatically create a support ticket and route it to the appropriate IT personnel. Most importantly, by the time the ticket reaches the IT department, it should be way past the “have you tried switching it on and off” stage.

 

Healthcare assistant

Healthcare providers are also exploring the potential of generative AI assistants to streamline the patient experience while improving workforce productivity. An AI assistant integrated with electronic health records and clinical knowledge bases could assist patients through initial triage for nonemergency conditions.

A patient could simply describe their symptoms to the AI assistant: “I have a high fever, muscle aches, cough, and fatigue.” Through a dialogue assessing additional context like medical history, the assistant could determine whether the situation requires an urgent consultation with a doctor, going to the ER, or if home care would suffice. The AI could then either facilitate scheduling an appointment or virtual consultation with a provider or give home care instructions pulled from verified medical sources. This last aspect is crucially important, as it would offer a counterbalance to googling for information and being directed to a bunch of pseudoscience, snake-oil salesmen, and conspiracy theorists. Obviously, accuracy is paramount here, which can be attained using RAG techniques.

The human aspect must also be considered here. On the one hand, human contact is often absolutely crucial in medical services. On the other hand, sometimes people may be embarrassed to talk about their health and may actually prefer to talk to a conversational AI assistant. It might even be possible, with the use of AI-powered voice assistants to meet at least some of the need for human contact that is sometimes associated with doctors’ visits. The time where conversational AI can truly replace human contact may be in the distant future, but given the current limited access to medical staff, it may already be very helpful.

The assistant could also handle prescription renewal requests while adhering to proper medical oversight by routing new prescriptions or controlled substances to a doctor or nurse for approval. Capabilities like these improve patient access while alleviating the administrative burden on medical staff. However, issues of responsibility must be dealt with carefully in this case.

 

Customer service

One of the most natural fits for conversational AI assistants is elevating customer service experience across industries. Whether integrated into contact center platforms like Zendesk, CRM systems, or e-commerce channels, AI assistants can engage customers with personal, human-like conversational support.

Customers could get answers to order status inquiries and billing questions, receive product guidance, and easily resolve common issues through back-and-forth AI dialogue—without waiting for a human representative or sifting through documentation. Multilingual support further improves accessibility—this issue is discussed in more detail at the end of the article.

For complex situations needing human expertise, the AI could collect pertinent details before handing off the conversation to a sales or service rep, giving them the full context so they can quickly resolve the customer’s needs.

 

HR assistant

AI assistants show powerful potential for enhancing HR functions and improving the workplace experience. From the earliest stages of employee onboarding, an AI assistant could serve as a “virtual buddy” providing new hires with guidance, answering questions about company policies and benefits, and assisting with initial administrative tasks. As mentioned before, this can flatten the learning curve for new employees.

For existing employees, the HR assistant becomes a conversational interface for tasks like requesting vacation time, asking HR policy questions, or even discussing career development and mentoring opportunities. Similarly, common HR requests like reporting department transfers or submitting vacation time could be handled through these conversational AI flows, with the assistant ensuring requests adhere to corporate policies before approval and routing to payroll systems. This approach transforms rote request processes into seamless self-service experiences for employees while substantially reducing support volumes.

Many companies have faced the dire consequences of ignoring employee concerns for too long. With AI assistants, concerns could be addressed without prejudice and with strict adherence to company policy, ensuring full anonymity if needed. Recurring complaints could be automatically compiled and sent up the chain of command, avoiding much of the human bias that is often the bugbear of internal complaints procedures. Last, but not least, employees might be more comfortable talking about their concerns to an HR virtual assistant.

 

Workspace assistant

For businesses embracing hybrid or remote work models, AI assistants embedded directly into workforce collaboration tools like Microsoft Teams or Slack can facilitate improved productivity from any location. A chatbot that can answer general knowledge questions is already an integral part of Skype, but employees could also use natural language to book conference rooms, order new equipment or supplies, or resolve various workplace requests by simply conversing with the assistant.

Leveraging the assistant app’s integration with IT services, facilities management systems, procurement platforms, and company policies, employees gain streamlined access to resolve issues through a centralized conversational interface without leaving their collaboration app. This eliminates the need to constantly switch between disparate tools, apps, and websites, minimizing distractions and friction.

Multilingual support and AI voice assistants

A key advantage of generative AI technology is its ability to comprehend and generate fluent responses in dozens of languages. This allows businesses to provide exceptional service experiences tailored to the language preferences of their users.

A particular point should be made here about AI voice assistants. While passable machine translation has been available for a while, allowing customer or employee support to communicate through written messages with people using other languages, on-the-fly translation of oral communication is still more of a gimmick than a viable technology—there are just too many steps in this process to make it work with actual human speech. If you combine the inadequacy of speech recognition with the inadequacy of machine translation, you should not expect too much. However, conversational AI is already better at interpreting imperfect text (and speech recognition, as we all have experienced, often produces imperfect text messages) than traditional machine translation models (even ones based on machine learning). An AI virtual assistant can also be trained on data in the target language, meaning that it does not need to translate at all but generates answers in the training language.

Furthermore, current voice assistants (like the “hey Google” assistant, Apple Siri, or Amazon Alexa-enabled devices like smart speakers) need to send a recording of your voice commands to a server, which requires Internet connection and often makes the voice assistant stumble when the Internet connection is poor, which is particularly a problem on mobile devices. However, speech recognition software based on machine learning can potentially perform tasks like these locally. Researchers have developed highly optimized speech recognition models that require fewer computational resources, while transfer learning makes it possible to adapt and fine-tune pretrained models for specific applications on-device, and federated learning allows for improving central speech models by aggregating model updates from many users’ locally processed speech data in a privacy-preserving manner. All this may make AI voice assistants much more viable in the future. In fact, Microsoft has just announced the release of a new line of AI-powered laptops with dedicated chips, and one of the features is supposed to be a voice assistant

 

An AI assistant in your company

To experience the transformative potential of generative AI assistants across your organization, we encourage you to explore a customized demo tailored to your specific business needs. Our knowledge management solution seamlessly combines the power of LLMs with RAG to ensure your AI assistant provides accurate, data-driven responses.

The process begins with our team analyzing your potential use case, whether it’s technical documentation, customer support channels, product databases, or internal knowledge bases. We will work closely with you to gather the relevant source materials to train the AI assistant. Next, we will set up the required infrastructure, calibrating the language model with your proprietary data using proven RAG techniques. Rigorous testing and optimization follow to fine-tune the solution’s performance.

From gathering your training data to delivering a fully customized generative AI demo, the entire process takes approximately 2-3 weeks. Do not let your organization fall behind—unlock the future of conversational AI today. Schedule a consultation and let’s revolutionize how your business operates and engages its audience.

Please email us at sales@fabrity.pl, and we will reach out to discuss all the specifics with you.

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Book a free 15-minute discovery call

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Bartosz Michałowski

Head of Sales at Fabrity

The controller of the personal data is FABRITY sp. z o. o. with its registered office in Warsaw; the data is processed for the purpose of responding to a submitted inquiry; the legal basis for processing is the controller's legitimate interest in responding to a submitted inquiry and not leaving messages unanswered. Individuals whose data is processed have the following rights: access to data, rectification, erasure or restriction, right to object and the right to lodge a complaint with PUODO. Personal data in this form will be processed according to our privacy policy.

You can also send us an email.

In this case the controller of the personal data will be FABRITY sp. z o. o. and the data will be processed for the purpose of responding to a submitted inquiry; the legal basis for processing is the controller’s legitimate interest in responding to a submitted inquiry and not leaving messages unanswered. Personal data will be processed according to our privacy policy.

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