With their value expected to reach $242 billion by 2030, AI-powered virtual assistants are going through an intense adoption surge. After the emergence of GenAI, businesses showed great interest in utilizing it to improve customer experience and engagement across the enterprise. The result of that effort is that the concept of virtual assistants and chatbots became firmly associated with AI, which means that enterprises see the technology as an integral part of their workflow.
At the same time, businesses continue to encounter adoption struggles. From failing employee buy-in to lackluster results, certain barriers between decision-makers and the full value of AI virtual assistant software keep popping up. To bypass these barriers, executives would dissect the very concept of an AI virtual assistant—from exploring how it's made to outlining successful adoption prerequisites.
What is a virtual AI assistant?
AI virtual assistant software is more complicated than a chatbot as it is capable of not just answering questions and injecting more humanity into interactions but also generating content, interacting with services, and performing basic tasks.
An AI-powered assistant doesn't just provide answers, maintain conversations, and generate content—it can handle other activities, such as making orders, booking time slots for meetings, and updating users on important events. However, an important distinction must be made: AI can't do all of the latter independently.
When we're talking about assistants like Amazon Alexa or Echo, we're talking about complicated AI systems composed of several modules. One module is an AI platform, while other modules are custom-written by humans. So, technically, an AI assistant can do almost anything—but it needs your help.
How does an AI assistant work?
It’s important to establish that we’re going to explore and dissect the latest iterations of intelligent virtual assistants—such as models powered by ChatGPT. The reason for that is their growing demand due to the number of advantages they offer in terms of development and information updates.
The way advanced intelligent virtual assistants perform can be broken into several segments:
Input
The user interacts with a virtual AI assistant by sending a text query or asking a question.
Interpretation
The assistant transcribes the input to process it.
Intent
The assistant recognizes the objective behind the request by analyzing keywords, sentiment, topic, and user data.
Execution
The assistant formulates an appropriate response (output).
At its core, the process looks rather simple. However, what makes intelligent virtual assistants so complex is how they process and transcribe inputs to deliver outcomes. The accuracy and helpfulness of their responses depend largely on two things: the quality of data AI models were trained on and the work done during prompt engineering.
Prompt engineering is the process of guiding AI models towards desired output by writing instructions and examples (use cases) that AI will use when interacting with user prompts. Essentially, prompt engineers try out numerous different inputs to identify scripts and templates that generate the most accurate outputs — and then they hone in on them, refining them even further. The ultimate goal is to teach AI context.
The latter is particularly important for intelligent virtual assistants that are supposed to interact with other modules to complete a user's request.
If I tell my Alexa assistant to buy some water on Amazon, it will translate my request to text. Then, it will define and form my intent — Amazon order — and send it to another system module. The code contains all necessary information: what account to log in to, what card to use, and what kind of item to buy.
Types of digital assistants: conversational AI vs chatbots
Given the complexity of intelligent virtual assistants, the logical question is: are they always necessary?
To provide an illustrative response, conversational platforms can divided into two groups: enterprise chatbots and non-enterprise chatbots.
Non-enterprise chatbots
These chatbots are tailored to a specific task.
For example, an e-commerce platform can have a chatbot to assist customers with processing their purchases. Such a bot is equipped with a list of prepared instructions and pre-defined answers for users, and since its workflow is static and repetitive, it doesn't need to be powered by AI.
It's very important to ask yourself whether you need a virtual AI assistant. No matter how trendy intelligent virtual assistants are, their popularity doesn't equal necessity. AI solutions are always costly, so if a simple rule-based chatbot works for your goals and you don't plan to update or expand its range of tasks, it makes more sense to invest in something else.
Enterprise chatbots
These bots are designed for working with a dynamic and constantly updated range of tasks. They must process a lot of organizational information, from enterprise policies and rules to department-specific content and data.
While such bots can be made without leveraging AI (transformation-based models like ChatGPT, in particular), doing so turns adding new information into a taxing and time-consuming process.
Developers must create intents (objectives behind user commands), train data, and manually configure all bot actions. Given the necessity of scaling the bot according to enterprise growth, such changes to the code will always be necessary.
Meanwhile, going the transformation-based AI chatbot route means doing much less developer work. Configuring all bot actions by hand is replaced by engineering prompts (instructions) to work and process data.
Another benefit of transformation-based AI models is that they are equipped with a system for quickly indexing new data and registering the changes in a database. With such a system, adding new knowledge to a virtual assistant and keeping it up-to-date with user queries takes around 30 minutes.
With iterations before ChatGPT, updating databases used to take several days because you had to read the documents, identify and create intents, create queries matching these intents — and only then deploy the model. With transformation-based models, you only need to upload new files, policies, and instructions, and the system will take care of the rest.
These capabilities make intelligent virtual assistants powered by transformation-based AI models powerful and versatile tools for enriching enterprise operations and enabling dynamic and seamless data management.
Based on this comparison, there is a need for implementing an AI virtual assistant when:
- There are several different tasks. The more activities a bot is expected to perform, the more capacity it requires to be efficient. Even though GenAI excels at handling mostly basic tasks, its flexibility allows it to maximize outcomes and provide consistent quality.
- The data is constantly updated. A GenAI solution has everything necessary for scaling in synergy with the company (its policy changes and constant influx of new enterprise data).
- Personalization is a priority. Each department has its particular requirements and pain points on the enterprise level. In such cases, using a rule-based chatbot will not suffice—employees and managers need a platform that adapts to their queries and can be fine-tuned to their workflow.
AI assistant: benefits and challenges
The advantages of modern AI assistants were acknowledged by enterprises such as Walmart and PwC, prompting them to invest in intelligent virtual assistants for employee use. What is even more impressive is that 50% of employees admitted being more likely to learn how to use virtual AI assistants out of all other AI tools.
What is the reason for such a warm reception?
- Data management and organization
GenAI is generally estimated to automate up to 70% of enterprise employees' work, including finding necessary information across multiple data systems. Whether generating a cybersecurity report or getting a summary from the last hands-on meeting, intelligent AI assistants help every employee access the necessary information and organize it comprehensively. - Improved employee satisfaction
Employee engagement and happiness matter as much as customer happiness. Accordingly, employees who don't have to deal with taxing tasks such as admin-based work are happy employees. With intelligent assistants taking care of the monotony, achieving those levels of happiness becomes possible. In addition, implementing an AI virtual assistant also helps accelerate HR request processing—which, according to 44% of employees, should be automated to decrease the frustration of long waiting times and take the pressure off the HR department.
Whenever someone mentions that intelligent virtual assistants can automate a large fraction of people's work, the idea is to automate bits and pieces that don't rely on human intervention that much. It's the most basic activities that accompany and precedes valuable tasks — and can take too much time due to unnecessary steps or general repetitiveness. The strongest aspect of an AI virtual assistant is its ability to create a healthier workplace for employees.
- Customer experience personalization
Nearly 70% of customers expect conversational service when they interact with a brand. Moreover, brands that deliver personalized experiences to their customers are 71% more likely to increase customer loyalty. An AI virtual assistant often becomes the first step in the right direction, as by identifying the client's tone and intent, it provides detailed replies tailored to their needs. - Shorter wait times
Not only do clients want their replies personalized, they also want them fast. A 2023 study shows fewer waiting times lead to a 1.6 customer happiness increase. Meanwhile, 18% of clients who wait longer than expected are likelier to be unhappy with their experience. So, whenever a customer contacts a call center or customer support with an issue, they expect a personalized response within minutes. Going through volumes of data and finding information related to a certain customer, their history, and their specific issue can take hours for a human employee. However, an AI virtual assistant app can do it within seconds.
These capabilities of AI virtual assistant software explain the growing number of AI vendors rushing to introduce new versions of intelligent helpers and businesses willing to invest in AI-powered virtual assistants.
And yet, despite the positive reception and outcomes, the adoption journey can often turn into a bumpy ride due to the following factors:
- Lack of adoption strategy
It's not enough to implement a technology—leaders and executives need to map the entire adoption process and see it through. They need to be confident that they provide the right tool with the right features for the right purpose. They should also develop a system for introducing employees to new technology and encouraging them to explore it. Additionally, they need to understand the value they expect to receive from the change and identify KPIs they want to monitor. - Data privacy issues
The demand for transparent dialogue with AI vendors and the development of proper security measures has been growing in parallel with the adoption of AI virtual assistant software. As the MITRE ATLAS™ framework documented over 60 methods to attack AI models, security concerns and issues became one of the most common reasons for enterprises either backing away from adopting AI virtual assistants or ending up dissatisfied with the results. Even without hackers, there is a constant risk of enterprise AI sending sensitive data (source codes, financial information, or NDA information) to training data sets used by all AI models outside the enterprise.
- Unclear ROI measurement
The shift towards identifying AI ROI directly results from decision-makers getting overwhelmed with enthusiasm. Despite the promises of greater cost savings, business efficiency, and productivity, executives and leaders struggle to determine whether their enterprise AI tools meet these expectations. However, such vagueness is normal when a new tool introduces a new workflow. Instead of trying to fit it in with other operations, executives need to explore it and identify specific metrics for tracking value.
Another common cause of AI virtual assistant adoption issues is the veil of mystery surrounding the technology. Although it's convenient from a marketing point of view, GenAI should not be perceived as a magic solution to many business pain points.
It’s a powerful tool for reaching enterprise goals — when those who use it know how it works and what they should do during development.
AI virtual assistant technology: building a platform that works
So, what does it take to build an AI virtual assistant? To dissect the most important stages of a development journey with the help of an example.
Let's say, you need an AI virtual assistant that needs to do the following: make orders online, schedule meetings, and set up notification alarms per your request. It means that you will need an AI system with several custom-developed modules. So, you will need a business analyst, a prompt engineer, an AI architect (or architects), and programmers.
1) Identifying types of tasks
This stage requires active engagement from decision-makers and stakeholders, who need to outline a full list of tasks that the future virtual AI assistant will be responsible for. If necessary, additional enterprise research is conducted to gain a 360-degree understanding of enterprise workflows and how AI interactions should look.
The better you as a stakeholder know your enterprise processes and flows, the more of that knowledge you can provide and communicate to the team, the better the result will be. Intelligent virtual assistants need as many details as possible to understand context in a way you need. So, before investing in implementing an AI virtual assistant, you must ensure you can easily list all the tasks you want it to handle. If there are gaps or blindspots, talk to other stakeholders and gather employee feedback to find all the pieces of the puzzle.
2) Preparing use cases
Once the list of tasks is prepared, a prompt engineer takes over. Their goal is to explain the outlined tasks to the AI assistant, creating instructions on how it should be doing them. Every AI model has its specifics and nuances, so the prompt engineer’s skills and experience play a critical role in the AI’s ability to generate outcomes.
For instance, our example AI assistants has 3 intended tasks. Therefore, a prompt engineer needs to work on creating 3 use cases with thoroughly refined prompts to ensure the assistant’s efficiency and proper context navigation.
3) Creating a structure
Parallel to prompt engineers, architects work on a structure, integrating the AI assistant into the enterprise's framework. Since the AI model is supposed to interact with several other services, their tasks include the following:
- Creating the means for AI to interact with users: writing and integrating the web interface for an AI virtual assistant app or embedding the acoustic modeling and feature extraction if the assistant is supposed to be voice-based.
- Writing a code to allow the AI virtual assistant to execute all extractions as needed.
4) Custom action development
Programmers write models that enable the AI assistant to perform its use cases:
- Online buying: logging into the buyer's account, interacting with the e-commerce platform, selecting payment methods, and confirming the transaction.
- Meeting scheduling and alarm setting: logging in, interacting with Microsoft API to get access to the scheduling tool and book time slots, or interacting with the My Notifications tool.
If you want your AI assistant to only interact with internal enterprise data (to provide summaries of all-hands meetings, for example), you don’t need this stage. However, if you want your intelligent virtual assistant to automate more complex activities and interact with other services, this step is a must.
After all these stages are closed and the AI virtual assistant is deployed, the work is far from over. Although transformer-based models make it much easier to add new information, it is still important to monitor the AI assistant's performance and watch for signs of bias or incorrect outputs.
Implementing an AI virtual assistant: best practices for getting results from Day 1
Functional and efficient intelligent virtual assistants are shaped by the knowledge of the people who need them and the experts who know what makes them tick.
However, to maximize the results and secure a seamless user experience, executives need to lay down the groundwork by preparing for the AI-powered virtual assistant:
- Establishing a safe, fair, and transparent AI use framework
Before working with any GenAI tools, leaders must work with their data analysts and data governance teams to work on the principles and practices for safely embedding AI into the enterprise (AI TRiSM). Within this collaboration, they must outline the risks related to AI use and ways of mitigating them. - Taking care of data security
Data protection is always a sensitive issue. For that reason, vendors usually seek to address the concerns of business leaders by offering them custom options—such as separate cloud storage with the model they support and update. Microsoft's Azure OpenAI service practices this approach, keeping users confident about their data protection.
Enterprises can add an extra layer of security by data obfuscation: removing all sensitive information (numbers, names, emails, IP addresses) from the data before it is sent to the cloud and replacing it with placeholders.
This method has its downsides: it can impact output accuracy due to lacking key elements. Also, once you get an output, you need to add information back for the response to make sense.
- Refreshing enterprise process knowledge
Ultimately, any digital and AI project owes its success to robust insights and transparent communication. The best preparation for implementing an AI virtual assistant starts with making the exchange of experiences and thoughts between leaders, teams, and stakeholders a habit. With diverse perspectives and improved awareness of underlying department-specific issues, executives can discover ways to intelligent virtual assistants from an unexpected angle, gleaning even more value than anticipated.
At the end of the day, an AI virtual assistant is the amalgamation of your collective organizational experience and understanding of routines and processes. It needs to be taught, which means that you should learn everything it's possible to learn about your enterprise.
Are you inspired to implement the best AI virtual assistant for your employees’ needs and enterprise operations? Let’s chat!
As a trusted global technology partner with experience implementing innovative AI solutions for Fortune 100 companies, we can assist you with not just developing a product but also mapping out the perfect implementation strategy that meets employee needs and helps you address security concerns.
With our vetted prompt engineers, architects, and programmers, you can turn your concept into an impact-driving tool.
