AI has reached a tipping point in recent years, and generative AI capabilities are now within reach of most enterprises. Across sectors, businesses are seeing AI as a strategic part of their tech stack—something to help improve their workflows, workforce, and ultimately their bottom line. And this AI tip into the mainstream has largely been led by AI virtual assistants.

What is an AI virtual assistant?

Users talk to a virtual AI assistant by inputting a request, and the AI assistant then uses “large volumes of data, machine learning, and natural language processing (NLP) to help imitate human interactions, recognizing speech and text inputs and translating their meanings across various languages,” according to IBM. The NLP processes “flow into a constant feedback loop with machine learning processes to continuously improve the AI algorithms”.

You’ll find these assistants a lot in customer service, holding ongoing conversations to help customers troubleshoot or get information, but they can be deployed across sectors, including in regulated industries such as commercial insurance. They can take on the role of a hotel concierge, IT support, lead generator, product recommendation engine, medication reminder, expense tracker, and more—the applications are numerous, and incredibly useful when it comes to tricky everyday tasks.

Showing their popularity, the global healthcare chatbot market worth is predicted to reach $431 million by 2028, with the global conversational AI market as a whole expected to grow at a CAGR of 22% during 2020-2025, reaching almost US$14 billion by 2025.

AI virtual assistant examples: What other names might you know them by?

Just as there’s many different ways to refer to a real-world assistant, so too are there multiple names for the variations of AI virtual assistant. Among others, you may also know them as a:

Virtual assistant
Knowledge assistant
Conversational AI
AI-powered virtual assistant
Digital assistant
Predictive chatbot
Generative AI assistant
AI chat agent
Customer support assistant
Virtual agent

AI virtual assistant apps for consumers

You’ll also likely have interacted with an AI assistant before in your day-to-day life— smart assistants like Apple’s Siri, Amazon’s Alexa, Google’s Bard, and Microsoft’s Cortana have all embraced AI to answer consumer questions.

AI virtual assistants for enterprises

The best AI virtual assistants can help businesses improve productivity by automating simple and repetitive tasks. For example, AI can streamline the information-seeking process that can take up to half an employee’s work day every week.

When choosing an AI virtual assistant, consider the tasks and features that would be most helpful to your business. You should also review your current tech stack to understand where an AI assistant would best fit.

AI virtual assistants vs. chatbots: How AI assistants have evolved

Early chatbot models were simple computer programs that simulated and processed written or spoken human conversation. They allowed users to interact with digital devices to obtain answers—but the quality of answers depended on keyword input, which could result in less-than-useful results.

These simple programs grew in capabilities as machine learning and AI became more intelligent. From a basic Q&A evolved the more sophisticated digital assistant, an advanced type of AI-powered chatbot that could handle more complex interactions. AI virtual assistants could be empowered with natural language processing to make the conversation feel much more “human", while semantic search helped the model gain essential context to guide its responses.

Today’s AI assistants can now come pre-trained on large volumes of data in multiple languages, and can write, summarize, synthesize, and follow instructions out of the box. Advanced AI virtual assistants can also be built with retrieval-augmented generation (RAG), helping to mitigate the risk of hallucinations (which is when the model makes up information).

With RAG, AI assistants can be more relevant, more timely, and more accurate than ever before. They can also be fine-tuned so that the assistant meets specific requirements—such as in the format, tone and style of response—and can enhance user engagement and satisfaction by making interactions more natural and contextually appropriate. As for the next generation of AI assistants? The sky’s the limit.

How enterprises are using conversational AI virtual assistants

Today’s enterprise tech leaders have options when it comes to developing and deploying AI assistants in-house. While some are opting for off-the-shelf solutions, others are building bespoke solutions that fit the specific needs of their enterprise. These bring flexibility—the room to integrate into their own datastores, and to deploy privately on-premises for the ultimate in secure AI.

The productivity gains enabled by AI virtual assistants is especially noticeable in the knowledge sector—for example, those roles in consulting services, marketing, customer support, community management, sales, or software development that rely on quick access to company information to do quality work.

Enterprises across sectors have found uses for AI assistants, even in the most regulated and strictly-governed industries. For example, a leading North American asset management company built an AI knowledge assistant connected to its cloud repository, enabling analysts to ask questions in plain English and receive conversational responses they could use to answer customer questions, along with citations to the source documents.

A digital health company, meanwhile, built a fully conversational health and wellness AI coach that considered demographics, health goals, and content history. AI assistants have also been built to answer common HR and operational questions, to support executive decision-making in the finance sector, and to act as a proof of concept RAG-enabled knowledge assistant for professional services clients.

Benefits of AI virtual assistants

Conversational AI delivers richer, more intuitive customer experiences, driving acquisition and retention while lowering customer service costs. This is one reason why they have grown so popular for customer service: Staffing a customer support department around the clock and the world is resource-intensive; AI assistants can respond to requests instantly, providing 24-hour availability.

AI virtual assistants can also be transformative for productivity in customer service. ServiceNow reports a 15% improvement in productivity related to time spent per case or incident, with agents saving between 80 to 110 minutes of reading and writing each day. Accenture looked at 22 occupation groups and found 40% of today’s tasks have high potential for automation or augmentation with AI, a figure that will get higher as the LLM learns more about a business and is trained on more enterprise data.

In a similar vein, conversational AI can also be used to improve accessibility for customers with disabilities, or those who have limited technical knowledge, different language backgrounds, or non-traditional use cases.

An AI virtual assistant can be easy to deploy without the need for a large specialized team, making it more affordable and more scalable, and bringing efficiency, standardization, and better experience to the fore. For example, McKinsey has their own AI assistant, named Lilli, which provides streamlined, impartial search and synthesis of its vast stores of knowledge to bring insights quickly and efficiently to clients.

How AI virtual assistant software works

Conversational AI uses a combination of LLMs and machine learning to take a user question and generate an answer in natural language. The systems are trained on large amounts of data, usually an enterprise’s own documentation and/or the wider internet, which helps the model to understand and process human language and converse naturally with users.

The principal components of these AI assistants—machine learning and LLMs—are designed to help it process, understand, and generate responses in a natural way.

Machine learning algorithms mean that, as the input grows, the AI assistant gets better at recognizing patterns, using these to predict responses.
LLMs work in tandem with these algorithms to analyze language in four steps:
1. The user provides text input.
2. The AI assistant uses LLMs to decipher the meaning of the input and its intention.
3. It then formulates a response, known as dialog management.
4. Finally, the machine learning algorithms refine responses over time to maintain accuracy and relevancy.

Limitations of artificial intelligence assistants

Given chatbots have been around for a while, the limitations and challenges involved in their usage are well documented. For example, many companies have banned employees from using consumer chatbots while on company systems because of the risk of sensitive data leaks.

Users may also be reluctant to share personal or sensitive information with an AI assistant, while chatbots may not be programmed to answer every query they may get. It’s important when using an enterprise AI assistant to ensure there is ample opportunity for the user to connect with a human representative to escalate their challenge.

Then there is the risk of hallucinations. These are an inherent part of using generative AI and are difficult to identify. However, the advent of RAG and its ability to cite its sources—giving the user the option to follow the information trail—is a huge step towards mitigating this.

Recap: Modern AI virtual assistants can help supercharge productivity

AI virtual assistants are one of the most common use cases for AI and LLMs, often acting as customer service agents.
Today’s AI assistants can work with retrieval-ugmented generation (RAG) to build a powerful product experience, producing grounded and verifiable generated answers using up-to-date information with no knowledge cut-off date.
Companies across sectors and industries are using AI assistants to empower both employees and customers to find the information they need to move forward.
AI assistants can unlock greater workforce productivity by freeing employees from tedious or administrative tasks so they can focus on more strategic work.
They do, however, face challenges of security, meaning, and context interpretation, potential customer frustration, and potential hallucinations.

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