In recent years, organizations have been actively incorporating AI assistants to automate internal processes. These AI assistants are already addressing customer inquiries, supporting sales teams, and even engaging in negotiations. However, the development of such systems involves more than merely integrating widely used chatbots into company operations. The effectiveness of these AI assistants requires a meticulously designed communication framework.
Currently, artificial intelligence assistants manage 60-80% of customer phone calls in multinational corporations. For example, Bank of Cyprus has deployed a digital assistant within its support service for corporate clients. This assistant handles call distribution to operators, evaluates their performance, assists with information retrieval, and autonomously resolves certain inquiries. Over the past year, the implementation of this system resulted in a 59% reduction in costs, while the average routing time decreased by a factor of 3.5, now standing at just 18 seconds.
Smarter Than Chatbots
An intelligent digital assistant is a program designed to perform a specific task. These AI-powered virtual assistants integrate the best elements of robotic process automation (RPA), chatbots, and voice assistants.
Unlike traditional RPA systems, which simulate human interaction with interfaces, AI digital assistants possess the ability to recognize and generate speech. In contrast to chatbots and voice assistants, they can learn autonomously and, when necessary, reach out to a human to complete tasks, such as composing text or creating a logo.
These advanced capabilities enable businesses to delegate certain processes to AI assistants. For example, a well-known yogurt brand has developed a neuro influencer that autonomously manages the company's social media platforms and communicates updates on new product launches to its audience.
From Client Service to Development
According to McKinsey, more than 46% of companies globally are incorporating AI solutions into their business processes. Furthermore, 68% of those already leveraging generative AI have reported a significant financial impact, with EBITDA increasing by up to 5% over the past year.
The sectors leading in AI adoption include banking, technology and IT, retail, and consumer goods.
Digital assistants can assist businesses in optimizing processes across various domains. AI technologies are predominantly utilized in corporate communications, marketing, and sales. However, an increasing number of companies are integrating AI into their development processes. For example, a multinational telecommunications company recently unveiled its own generative network, designed to reduce program code development time by 35%, which, according to the company's projections, will result in annual savings of up to $2 million.
Phases of AI Assistant Development
Implementing an AI assistant for a business requires a comprehensive approach that includes several key steps.
Defining Goals and Objectives
Before proceeding to the software development part, it is important to clearly define the list of business processes to be automated by the AI assistant, the list of tasks and competencies, and the expected KPIs (reduced workload on employees, increased sales, faster processing of requests, etc.).
Selection of Technologies and Architecture
There are several options for implementing an AI assistant. Using off-the-shelf solutions such as Dialogflow, ChatGPT API, and IBM Watson allows for fast launches but offers limited customization options. Custom development based on OpenAI, LLaMA, Rasa, LangChain, and NLP libraries provides a more flexible and business-adapted solution.
Integration with Business Systems
An AI assistant should work in conjunction with CRM, ERP, databases, reporting systems, etc. At this stage, it is important to plan all API integration and data processing workflows.
Model Development and Training
- Collecting and cleaning data for AI training.
- Using ML models (GPT, BERT, T5, etc.).
- Training with real-world business examples (customer conversations, typical scenarios).
MVP Launch and Testing
- Launching the first version for a limited audience.
- Testing the quality of responses, speed of operation, and usability.
- Iterative improvements based on feedback.
Scaling and Support
- Adding new features.
- Optimization based on interaction analysis.
- Monitoring security and data protection.
Examples of AI Assistant Applications
Retail. A U.S. retailer utilizes virtual assistants for recruitment purposes. With a network of over 6,000 stores requiring constant staffing, the company encountered a shortage of HR specialists, despite having doubled the number of hiring personnel in recent years. To address this challenge, the company opted to automate its recruitment process through the implementation of AI assistants. These assistants are responsible for analyzing resumes, assessing candidates based on a variety of criteria, conducting initial screenings, and scheduling face-to-face interviews with HR representatives. As a result, each AI assistant now facilitates the selection and coordination of more than 1,000 candidates for interviews each month.
Real Estate. In a real estate agency located in the Czech Republic, employees routinely contacted their client base whenever new property offerings became available. This process required substantial time and resources. By integrating a virtual assistant, the company was able to reduce labor costs by nearly five times. The AI assistant now independently engages with buyers, informing them of new listings. This solution not only automated the real estate sales process but also improved conversion rates, leading to a 13x increase in the number of repeat visits.
Software Development.One of the primary products of a software development company based in England is a corporate knowledge management platform. This platform is employed by the company’s clients to manage information related to their services, including regulations, contract templates, and other relevant data. Initially, the service operated similarly to an internet search engine: users would submit a query and receive links to documents, requiring them to independently review the results to identify the necessary information. The implementation of an AI assistant, which not only provides links but also generates comprehensive answers, has led to a fourfold reduction in search time.
Barriers to Mass Adoption of AI Assistants
The primary obstacles to AI adoption include the considerable hardware requirements necessary to implement such solutions in business environments, as well as the limited number of specialists and teams equipped to effectively integrate AI into business. This also applies to AI assistants, which represent a specific application of the technology. Additionally, the implementation of AI-based solutions requires substantial financial investment from organizations.
A significant human-related factor impeding the development of personal AI assistants is users' reluctance to engage with them. However, experts anticipate that AI-based assistants will gradually become more "humanized," facilitating easier communication and incorporating emotional elements into robot-human interactions. The global market for AI assistants is expected to grow by approximately 20-30% annually in the near future.
