Tag: AI & Chatbot Basics

The Crowdy.AI chatbot not only automates customer interaction, but also provides companies with detailed analytics of all customer chats. Detailed statistics are available in your personal cabinet, helping businesses to track key indicators, identify weaknesses and optimise the bot’s performance to increase efficiency.

The analytics system provides information on the number of dialogues, average chat duration, bot response rate and frequency of transferring conversations to operators. This data helps to understand how successfully the chatbot solves users’ tasks and identify situations in which it needs further improvement. Analysing frequently asked questions allows businesses to adapt interaction scenarios, improving the response system and anticipating possible customer requests.

The Crowdy.AI chatbot not only analyses text queries, but also takes into account customer behavioural factors. For example, the system records which sections of the site the user visited before starting a dialogue, what questions he asked earlier and what problems he faced. This allows you to adapt communication scenarios, offer personalised solutions and increase the probability of successful interaction. If a customer has already made a purchase or previously left a request, the bot can take this information into account and offer relevant products or services, which makes the communication process more holistic and efficient.

Additionally, analytics can help identify bottlenecks in business processes. For example, if the system records that customers often request clarification of delivery or return conditions, it may indicate that the site is not informative enough. In this case, the company can adjust the content on the web resource or introduce new scenarios in the work of chatbots, which will help to reduce the load on operators and increase customer satisfaction.

Thanks to integration with CRM and other systems, a chatbot can automatically transmit data on leads and closed transactions, generate reports on the effectiveness of interactions and provide recommendations for improving customer service. This approach makes the use of a chatbot not only a tool for communication, but also an important part of the customer service management strategy, helping the company to increase conversion rates and reduce the cost of handling requests.

Access to statistics helps companies not only improve customer interactions, but also increase sales conversion rates. A chatbot can capture potential customers’ conversations, analyze which dialogue scenarios work best, and provide recommendations on how to improve them. If users often interrupt the conversation at a certain stage, this is a signal that it is necessary to adjust scenarios, simplify wording or add new response options.

In addition to analysing current dialogues, the system allows for the study of historical data, which helps predict customer behaviour and adapt service strategies. Companies can track seasonal changes in demand, identify peaks in demand and prepare for them in advance. This is especially useful for businesses operating in areas where responsiveness and fast processing of requests are important.

Real-time reports allow you to react instantly to changes in user behaviour and adjust the bot’s work without delays. If a chatbot records an increase in requests on a particular topic, the business can quickly update the knowledge base or make adjustments to communication scripts. This makes the chatbot’s work more accurate and adaptive, reduces the workload on employees and increases customer satisfaction.

The use of analytics can significantly improve chatbot performance and enhance customer service quality. Companies get tools for flexible system customisation, which helps not only to automate interaction, but also to make it as efficient and user-friendly as possible. The Crowdy.AI chatbot becomes not just an assistant, but a full-fledged tool for analysing and optimising business processes.

At Crowdy.ai, our team starts AI chatbot development by defining the client’s goals and technical requirements. For example, the goal may be to reduce call centre workload or increase the level of automation in FAQ processing, while the technical requirement may be to integrate the bot with the customer’s CRM or to place the bot in a specific communication channel or on the website. Our dialogue design specialists develop the structure of a chatbot and create a diagram of the sequence of actions: define possible user goals and bot reactions. When we create a chatbot with natural language recognition based on machine learning, we train it to understand a variety of user requests. Our chatbots continue to learn even after they are launched – this is a useful practice because it is impossible to predict all user reactions, so it is important to communicate with real customers.

After creating the dialogue tree and integrating all the necessary components, the Crowdy chatbot is tested internally, refined and submitted to the customer for review. Once all the tests are completed, the bot is placed in the working channel, where real customers start communicating with it. The whole process is monitored and we analyse the bot’s performance.

What makes the Crowdy chatbot different from other solutions on the market is the ability to customize the chatbot according to the customer’s needs. The following are the customization parameters to make the chatbot work as efficiently as possible:

1) The customer’s business, its peculiarities and specificities.

2) Whether your customers are companies (B2B) or individuals (B2C)

3) The purpose of using a chatbot – selling a product or service, collecting customer contacts, answering frequently asked questions, etc.

4) The identity of the chatbot – is the chatbot a duplicate of an employee of your company, or does it appear to be a chatbot?

5) Tone and manner of chatbot communication

6) Speed of chatbot responses

7) Length of chatbot responses

8) Primary message of the chatbot

9) Maximum duration of the chatbot dialogue with the customer

10) Limitations on the information provided by the chatbot

11) Time interval and type of display of the chatbot on the website

12) Colour and visual solution of the chatbot

13) Sources of information used by the chatbot in its responses

14) Supported languages – the chatbot can start a dialogue with a customer in more than 30 languages.

15) Integration with CRM and ERP systems – the ability to connect the chatbot to popular business tools such as Salesforce, HubSpot, Pipedrive and others.

16) Integration with messengers and social networks – the ability to communicate via WhatsApp, Telegram, Facebook Messenger, Instagram and other channels.

17) Integration with email and SMS – the chatbot can notify customers and send personalized messages.

18) Automatic translation of messages – the ability to instantly translate messages into the required language when communicating with a customer.

19) Voice message handling – support for voice input and integration with voice assistants.

20) Transfer to live operator – flexible settings for transferring the dialogue to the manager and the ability of the manager to intervene in the dialogue with the customer in real time.

21) Level of analytics granularity – collect data on dialogues, customer behaviour scenarios, and bot effectiveness

22) Capture of previous interactions – the chatbot can take into account the history of communication with the customer and personalize responses

23) Intent recognition – understanding the customer’s request and tailoring responses accordingly

24) Content filtering and moderation – identifying unwanted words and preventing abuse

25) Different dialogue scenarios – the ability to create complex chains of interactions based on user actions

26) Personalized offers – adapt content and offers based on customer data (IP, device used by the customer)

27) Notifications and Reminders – Automatically notify customers about special offers, discounts, presentations and other events.

28) Flexible restriction settings – ability to filter which topics and queries the bot can and cannot handle

29) Detection and analysis of the customer’s emotional state – the ability to adjust the tone of responses depending on the user’s mood.

30) Multi-level authorization – the ability to configure the chatbot to communicate with different categories of users, based on the page of the website from which the customer’s initial request came.

31) Support for integration with payment systems – the chatbot can be configured to make payments and invoices directly in the dialogue.

32) Work with promo codes and discounts – the chatbot can automatically issue personalized offers and promo codes based on user activity.

How a Crowdy chatbot can help your business, given the ability to customize on 32 parameters

A Crowdy chatbot is a powerful tool for automating customer communications that can be customized to your business needs. Unlike off-the-shelf solutions, our chatbot is customizable on 32 unique parameters, allowing you to create the most personalized tool to communicate with your audience.

The bot is tailored to your business, whether you’re working with B2B or B2C segments, and is customized for specific purposes: sales, customer support, lead generation, or answering frequently asked questions. Its communication style, tone, and dialogue can be customized to suit your company’s needs, and it can work as a digital assistant or simulate communication with a real person. Preferences allow you to set the speed and length of responses, the maximum length of dialog, and restrictions on the information provided, so that the bot meets your needs.

An important feature is the ability to customize the visual design. The chatbot is integrated into the website and can be styled according to the company’s corporate colours. Flexible settings for display time and appearance triggers allow you to interact with your visitors as efficiently as possible.

With support for more than 30 languages, the chatbot can serve customers from all over the world, automatically recognizing the user’s language and providing convenient communication. Integration with CRM and ERP systems, messengers, payment services, and other business tools makes it an integral part of the company’s operational processes. It can work with dynamic data to check order status, product availability or schedule appointments, as well as automatically filter and moderate content.

By using a Crowdy chatbot, you can significantly reduce the workload of your employees. It takes care of routine tasks such as handling initial inquiries, providing information, clarifying details, assisting with orders, and much more. This not only reduces customer service costs, but also speeds up the processing of inquiries and improves service quality.

The built-in analytics system allows you to track interaction statistics, analyse customer behaviour, and optimize communication scenarios. The chatbot learns from dialogues, remembers previous interactions, and adapts its responses to improve efficiency. If necessary, it can escalate complex requests to the operator and automatically prioritize them.

Data security and privacy meet international standards, including GDPR requirements. This is particularly important for companies operating in financial services, e-commerce and other sectors where protecting customers’ personal data is critical.

The Crowdy chatbot can also be used for marketing purposes. It can be used to segment audiences, conduct interactive surveys, deliver personalized offers and promo codes, and send notifications and reminders. As a result, businesses get not just a tool for handling inquiries, but a powerful digital assistant that can help increase sales, improve customer retention, and boost website conversion rates.

Investing in such a tool yields tangible results: reduced staff costs, increased speed of inquiry resolution, increased customer satisfaction and, as a result, increased profits. Crowdy’s chatbot is not just software, but a strategic tool that helps companies achieve their business goals and adapt to the modern demands of the digital world.

Increase annual income with up to 30%

In today’s business, competition requires a constant search for new tools to grow sales and increase conversions. One of the most effective solutions that has proven its efficiency is the use of a customised chatbot. Such an assistant can increase a company’s annual revenue by up to 30% by automating interaction with customers and providing personalised service.

The chatbot works around the clock, instantly responding to user queries, which significantly increases the level of engagement of potential customers. It not only answers questions, but also analyses the behaviour of website visitors, offering personalised recommendations and encouraging them to buy. Thanks to artificial intelligence algorithms, the bot adapts to the needs of customers, providing a personalised approach and thus increasing the chances of a successful transaction.

One of the key advantages of a chatbot is its ability to quickly qualify leads. It identifies interested users, collects their contact details and transfers them to the CRM system, allowing managers to focus on more complex tasks. This significantly reduces the time it takes to process requests and increases the efficiency of the sales department.

Additionally, using a chatbot reduces staff costs. Automating customer support eliminates the need for a 24/7 staff of operators, optimising business costs. At the same time, the quality of service not only does not decrease, but also improves due to instant responses and absence of human factor.

The use of chatbots is particularly effective in e-commerce, financial services, real estate, travel business and many others. Regardless of the industry, it helps companies interact with customers at a higher level, increasing their loyalty and encouraging repeat purchases.

Using a customised chatbot is an investment in the growth of your business. Thanks to flexible customisation, adaptation to specific business tasks and round-the-clock operation, such a tool can not only increase sales, but also bring customer service to a new level. Ready to increase your revenue by 30%? Contact us today!

Multilingual AI chatbot with support for 30+ languages

In a global marketplace, businesses need effective tools to engage with customers in their own language. Crowdy’s customised chatbot provides support in over 30 languages, helping businesses to build international communications, attract customers from different countries and increase their loyalty.

The Crowdy chatbot not only responds instantly to user queries, but also adapts to their needs thanks to artificial intelligence technologies. It analyses the behaviour of website visitors, offers personalised recommendations and encourages them to buy, which increases engagement and conversion rates.

Chatbot development is tailored to the requirements and specifics of the business. Personalised settings allow it to be integrated with the website, CRM systems and other digital tools. The interface design is fully adapted to the company’s corporate identity, making interaction with the bot an organic part of the customer experience.

One of the key benefits is comprehensive support at all stages of chatbot implementation. The Crowdy team helps in its training, customising responses, adding custom attributes and adapting it to business processes. In addition, clients are provided with a personal success manager who monitors the chatbot’s performance and helps improve it.

Automating communication with customers not only reduces the burden on staff, but also significantly reduces support costs. Thanks to round-the-clock chatbot operation, users receive instant answers at any time of the day, which increases their satisfaction and trust in the company.

Using a multilingual AI chatbot is a strategic solution that helps businesses reach new markets, improve customer service and increase revenue. Want to make your business more accessible to customers around the world? Contact us and we will create a personalised chatbot for you, capable of working in over 30 languages!

These days, in the business arena, companies are increasingly seeking different technological solutions to optimize company performance. Probably, the most widespread examples of such technologies are chatbots-the solution able to replace a whole list of tasks usually performed by employees. The Crowdy team would like to have a closer look at the key benefits that using chatbots provides in comparison with hired workers.

Cost-effectiveness and Cost Reduction

They don’t need salaries, social benefits, and other components of compensation associated with employee workforce. Normally, the one-time cost of development and subsequent support for a chatbot far outweighs the cost of maintaining a live employee.
Since working in an electronic environment requires no physical workstation and hence no office equipment besides all the other stuff, a chatbot further minimizes the company’s operating expenses.

Boosting productivity of the company

A chatbot enables you to work and interact with your customers 24/7; unlike human beings, it can work around the clock without breaks and weekends, hence providing service to your customer without any interruptions, along with routine tasks.
Chatbots can process several inquiries at a time and give real-time responses. It greatly speeds up customer service and reduces waiting times.

Reducing human factor

It means that chatbots, being implicitly error-free, are programmed to perform particular tasks; thus, they do not make human mistakes. Therefore, this increases the degree of accuracy and decreases the risks of unwillingness or non-observance of work instructions or performance of job duties with a mistake. As a rule, a chatbot offers standardized services, which means every customer gets the same attention and quality-a thing that can never happen with a workforce of big size.

Flexibility to change and scalability of processes

Since the integration of chatbots with existing systems is smooth, this also can be updated quickly for newer tasks in order to improve functionality based on changes in business processes. This makes them ideal for serving a handful of queries as efficiently as thousands of queries at one time and in multiple languages.

The use of chatbots offers a number of advantages over hiring employees, including cost savings, increased productivity, reduced human error, and flexibility and scalability. Implementing such technologies allows companies to optimise many processes, improving overall efficiency and customer satisfaction. These factors make chatbots an important tool in the arsenal of modern businesses.

Main claims of employers to employees

Nowadays there is a great number of factors that influence productivity and performance of employees in the contemporary working environment. Still, there are several behavioural factors that can cause the greatest irritation and dissatisfaction on the part of employers. Below we would like to look at the main claims which employers make about their subordinates.
Unethical behaviour at work

An employee’s behavior is considered unethical if it runs counter to not only legal but also generally accepted moral behavioral norms and has an adverse effect on other people, coworkers and clients. Needless to say, such behavior has a bad effect on the psychological climate in the team, on the efficiency of the rest of the staff, on relations with partners, customers, buyers, and on the business reputation of the firm.

In our opinion, unethical behavior may include the following features:

• Rude and obscene comments, remarks, or gestures directed at colleagues and customers;
• Use of foul or taboo (for example, on religious or moral grounds) words and expressions obscene address to a certain person;
• Obnoxious comparisons;
• Touching a person without his consent;
• Aggressive form of communication and other manifestations of aggression;
• An action causing lack of respect to the chain of command.

Lack of Willingness to Learn and Self-Confidence

Also, one of the key problems in employer-employee relations is the unwillingness of the latter to learn and develop. Employees who believe that they already know the best way to do their job often ignore the approaches used in the company and new technologies, which may lead to the obsolescence of their skills and knowledge.

Lack of willingness to learn and unshakable self-confidence significantly hamper the effectiveness of corporate interaction. In practice, employees who are convinced that their working methods are the best, often refuse to adopt innovations and progressive methods. All this prevents not only personal growth, but also professional one, because the world is not standing still, and technologies and work processes are constantly improving.

As a result, a situation may occur in which labour forces no longer correspond to contemporary market requirements, which, in turn, reduces the competitive advantages of the company and make its opportunities on the market more restricted. In the highly competitive and rapidly changing market environments, failure to update one’s knowledge and skills may become a critical threat to the professional future of employees and the strategic development of the organisation itself.

Laziness and irresponsibility

Laziness and irresponsibility have a big impact on barriers to keeping corporate performance at a high level. The results of this kind of behavior not only diminish the quality of certain activities but also demoralize the whole team. The result can be an overall drop in productivity as effort and resources that could be used to develop and achieve corporate goals are spent on compensating for the performance shortcomings of unscrupulous employees.

Because of fraudulent workers, when there is a redistribution of duties among employees, this puts the rest of the team under greater pressure and can lead to professional burnout, reduced motivation, and job satisfaction among responsible employees. It also gives rise to the risk of establishing an improper corporate climate in which fraudulent behavior becomes typical and the principles of justice and equality are thus violated.

Defending personal boundaries at the expense of work duties

Employees who actively assert their personal boundaries, but do not show the same zeal in their immediate work responsibilities, create problems in teamwork and may be perceived as not being fully engaged in the work process. Protecting employees’ personal boundaries is an important aspect of modern corporate culture that supports psychological well-being and professional satisfaction. However, when employees focus on personal boundaries at the expense of professional responsibilities, this can cause difficulties in teamwork and can give the perception of not being fully engaged in the organisation. This situation contributes to conflict, reduces overall productivity and may have negative consequences for team morale.

Separation of “own” and “common”

Clearly dividing employees’ interests into “personal” and “corporate” interests can create a number of problems in the workplace, including diminished loyalty and a reduced willingness to compromise in the interests of the common cause. When employees perceive their tasks as “not their own,” they may be less actively involved in the company, negatively impacting their contribution to common goals. The problem of the separation between ‘own’ and ‘shared’ is often accentuated in settings that lack a culture of mutual respect and co-operation. It may result in employees’ alienation, not seeing a direct link between their efforts and the success of the company. Overall, motivation and performance decrease in such settings.

Inactivity

Employee inactivity can be viewed as a considerable obstacle to the innovation and dynamism of an organization. When employees are not proactive, eager, and ready to input into the common cause, this could be perceived as lack of interest in work and unwillingness to contribute to the development of the organisation. Such behaviour reduces overall productivity and lowers team morale because active and motivated employees may be perceived as being underappreciated and overburdened.

When is it better to use a chatbot instead of a human?

With all those facts in mind, there are 4 cases when it will be reasonable for companies to substitute employees with chatbots.

1. As a Virtual Assistant
   Chatbots never sleep, and that means you can be sure there’s always someone there to answer a customer’s question, even if they arrive at an unreasonably late hour.
2. As a Lead Generation Channel
   In the chatbot, one can request contacts of customers, distribute checklists, guides, and other useful materials, warm up the cold audience, announce webinars, marathons, and other events that one holds in social networks.
3. As a sales channel
   It also accelerates the selling cycle and improves customer satisfaction because people get answers faster compared to talking to a human on the phone or email, and it accepts payments automatically 24/7.
4. learning new tasks
   By training the chatbot to do new tasks, you will be able to scale faster than by training a new employee.

Other advantages of AI over humans

In today’s world, where digital technology permeates all spheres of business, the introduction of chatbots is a very important integral part of the customer service strategy. Among the advantages of using chatbots over live operators are: handling a big number of requests simultaneously, continuous availability 24/7 in several languages. Below are listed key aspects that accentuate the advantages of the use of chatbots in online communications.

1. Scalability and accessibility
   The main peculiarity of such robots consists in the possibility of holding a parallel dialogue with several users, which seriously saves the resources of customer support. This is especially important for companies that have a huge customer base and receive thousands of enquiries every day. Bots do not get tired, do not require breaks, and work in a round-the-clock mode, allowing one to get answers at any time of the day without delays.
2. Reduction of Operation Costs
   Replacing or supplementing live operators with chatbots can reduce payroll, training, and infrastructure costs drastically. Chatbots require one-time setup and periodic support, making them a cost-effective solution for many businesses.
3. Standardisation of responses
   Chatbots provide a high degree of standardisation in customer service. They are programmed to provide the right and consistent answers to standardized questions without human error and this helps to enhance the service quality.
4. Integration into various Platforms
   Modern chatbots are easily integrated with various communication platforms such as websites, social media and messengers. This enhances customer interaction and user experience by providing information wherever it is convenient for the user.
5. Data analysis and training
   Modern chatbots are able to collect and analyze some data about user behavior, which helps improve the quality of service and optimize marketing campaigns. Using machine learning, bots get smarter every day, answering queries with more precision and anticipating customer needs.
6. Addressing customers in their native language
   As of the year 2024, ethnic structure in Estonia looked like the following: permanent residents consider themselves the following: 70% Estonians, approximately 23% Russians, 4% Ukrainians, 1% Belarusians, and 0.6% Finns. A chatbot has an immediate and colossal advantage compared to a human: a chatbot can speak with a customer in his native language.
   Chatbots are indeed powerful for customer service automation, providing high response rates, lowering operational costs, and improving customer experiences. Their integration into a digital engagement strategy allows companies to maintain a competitive advantage in an ever-changing marketplace.

The strengths of chatbots in customer service

Chatbots enhance customer service over a live employee.

1. Reduce costs at least 30%
   One chatbot can serve many more customers than one manager and at much cheaper costs.
2. Response immediately after contact
   The key is to respond immediately before the attention of the customer is lost; this increases the chance that the customer will stay with you. It’s a well-known fact in online marketing that responding within the first 5 minutes of making contact increases customer conversion by at least 20%. This accelerates the sales cycle and raises customer satisfaction, as people receive a response faster than if they were communicating with a person by phone or email.
3. Availability 24/7
   A single chatbot can serve an almost unlimited number of customers at the same time.

How can Crowdy reduce the cost portion of employee compensation for your company?

Today, the use of innovative technological solutions plays a key role in optimising costs and increasing the efficiency of companies. One promising tool in this direction is the use of chatbots, such as Crowdy.ai, which can significantly reduce employee labour costs.
Overview of Crowdy.ai chatbot functionality
Crowdy.ai relies on sophisticated natural language processing technologies while providing immediate responses to customer queries. Such interaction not only accelerates the customer service process but also considerably lightens the workload for support staff. This is because the chatbot response system automatically handles regular questions and requests, saving staff’s time for more complex and creative tasks.

Reducing the workload of staff

The integration of Crowdy.ai into the website of a company reduces hours that employees spend on direct communication with customers. This leads to staff optimization and, correspondingly, a reduction of salary costs. Automation of routine processes allows for reducing operational costs while sustaining a high level of service quality.

User behavior analytics

Crowdy.ai answers not only questions but also gathers valuable data on user behavior on the website. The data can be used to further optimize marketing and sales strategies and bring analytics staff costs down. Using deep analytical tools, you will be able to fine-tune your marketing campaigns, reduce execution costs, and further increase your overall ROI.

Increasing customer loyalty and retention

Continuous interaction with clients through chatbots allows one to socialize their loyalty and trust. That decreases the cost of attracting new customers since usually, it was more expensive than retaining existing ones. Crowdy.ai works effectively at all levels of the sales funnel, improving conversion and customer retention without additional investment in human resources.

Using a Crowdy.ai chatbot can significantly reduce labour costs, increase the efficiency of marketing campaigns and sales, and improve the quality of customer service. Implementing such technologies becomes not only a cost-effective solution, but also a strategic step towards digital business transformation.

Gaining popularity in customer service, e-commerce, marketing, and practice within the legal arena are chatbot programs based on artificial intelligence and machine learning that simulate conversations with real advisors. They depend on the technology of natural language processing for understanding, interpreting, and answering human speech. Using the machine learning technique, chatbot systems adapt and improve response quality through learning from large pieces of textual data. With the integration of databases and APIs, their functionality can be extended by enabling them to perform certain types of operations, such as booking or providing personalized information.
This, in turn, requires careful design of secure processing, storage, and transmission of information. Needless to say, legal liability also must be demarcated regarding chatbot acts, such as when information is provided with errors. The developers and owners of chatbots must be clearly identified in terms of liability to avoid any potential legal risks.
In the end, chatbots offer a huge chance to increase customer service in the digital world. However, using them requires not only technical skills but also consideration of legal aspects. Therefore, if businesses and societies want to successfully integrate chatbots, they must develop and implement clear rules and policies.

A chatbot is a computer program that directly simulates human dialogue. Its applications range from handling customer queries to automating repetitive tasks. Chatbots are based on different technologies; not all use Artificial Intelligence. In recent developments, though, some AI techniques, such as NLP, are being used to understand user queries and send automated responses, reducing human involvement to a minimum.

The more advanced chatbots use generative AI that extends their capabilities to answer more complex questions, adopt the user style of conversations, and be empathetic. This would enable them to self-create answers based on one vast knowledge base and, therefore, be really helpful for enterprise applications. With the power of generative AI, it’s expected to actively engage customers within two years, claim enterprise executives.

With each passing interaction, AI chatbots use machine learning to upgrade the responses and fine-tune conversational flows continuously. Further, they can answer questions, provide personalized content, translate texts, or even foresee what a user may need because interacting with them would be as quick and easy as possible.

This can ease the user’s way of gathering information, as it instantly answers any question through text or audio input, or even both, without needing a human or manual search. This class of chatbots also integrates mission-critical systems for workflow automation and organization across and outside CRM systems. They can handle multistep and real-time processes such as password resets or service requests that cover several applications.

This can also be utilized in a conversational analytics capacity to extract data from naturally occurring conversations between customers and the company via chatbots or virtual assistants. This enhances service quality and provides valuable insights for further development and optimization of the respective products and services.

With time, AI has emerged as a potent tool in marketing, especially in developing conversational marketing strategies. AI-powered chatbots provide 24/7 customer service and analyze data about customer engagement and buying preferences. This enables much better personalization in conversations, thus creating deeper, more consistent digital experiences on websites and messaging apps.

The early generations of chatbots worked more like an interactive FAQ, strictly staying within basic scenarios with pre-prepared answers. They required the user to make a choice between predefined keywords and phrases. Systems like these could not interpret natural language-which significantly limited their functionality.

Over time, chatbot technology has evolved much in concatenation with programming rules and natural language processing. Modern AI Chatbots understand queries expressed in conversational form and take into perspective the meaning of the communication; hence, they are much more functional. They are integrated with machine learning algorithms that help them improve their ability to understand and predict customer queries by analyzing behavioral data and previous interactions.

Thus, chatbot development has enabled organizations not only to improve customer service but also to make interactions with clients a valuable source of analytical data for further development of products and services and the general approach to engagement.

AI-powered modern chatbots have become sophisticated, especially because of the integration of natural language understanding technologies that allow them to recognize and correct typos and translation errors while semantically understanding the user’s input. Understanding here means being capable of defining a user’s “intent,” which further drives the actions of a chatbot toward forming an appropriate and accurate response.

Based on real-time interactions, chatbots use machine learning and deep learning to develop and refine their question and answer databases. This allows chatbots to improve their answers over time and make them more personalized. The recent development of LLMs, such as those applied in OpenAI’s GPT, has further enhanced customer service and expanded chatbots’ work areas.

Creating a chatbot may require more or less time, depending on several factors: the technology stack, the complexity of the tasks the bot needs to fulfill, data availability, and further integrations with other systems or platforms. With recent developments in creating chatbot platforms with little to no coding, though, development can be significantly expedited.

Also, the meaning of such terms as “chatbot”, “chatbot AI” and “virtual agent” should be underlined. Though very often these terms are used as synonyms, still they can mean different levels of sophistication and capability depending on the context of their use. For instance, a simple chatbot can follow a certain script, whereas an AI chatbot and virtual agents already have more advanced features of adaptation and self-learning, making them much more powerful in terms of user interaction and service.

Chatbots: the broad term that includes any software that may simulate a conversation with a human. They can range from simple systems that follow a number of predefined scenarios with rigidly defined navigation to others that make use of elements of artificial intelligence.

Where AI-powered chatbots are concerned, they are way advanced: they make use of technologies like machine learning and NLP to understand the natural language queries of users and learn from the interactions in order to optimize the responses. These bots will not only be able to recognize the users’ languages but also be able to understand their intentions for better matching of responses with queries.

Virtual agents represent another evolution in the class of AI-based chatbots. They embed conversational AI capabilities with robotic process automation in their ability to converse but also carry out particular actions, which range from transaction processing and request management to business process automation. These systems can perform many tasks independently, without human interference.

These technologies are crucial in enhancing customer and business process interaction; therefore, these are powerful tools for companies in the improvement of quality service and operational optimization.

With interactive chatbots based on Artificial Intelligence, information about interactions with users gets stored and integrated into further communications. Coupled with automation capabilities, such as robotic process automation, this allows users to resolve even complex tasks in a self-service manner via one single communication interface. Where live operator intervention becomes necessary, seamless call handover is possible to the operator, who will have access to the history of interactions with the bot.

Chatbots already find their applications in various environments, from social media to specialized messaging platforms, corporate websites, and applications, including even telephone systems, where they can work as a part of integrated voice response systems. Some key applications for such systems include:

• Real-time customer and employee support.
• Personalized e-commerce recommendations.
• Marketing and the promotion of products using chatbots.
• Automatic filling and processing of forms and financial applications.
• Scheduling appointments with healthcare facilities.
• Reminds you of the activity related to a particular time or place.

Therefore, in this way, chatbots will help make customer experiences smooth and business operations more effective.

Benefits of Using Chatbot

AI-based chatbots can understand human natural language with great precision. As a result, there are some huge advantages for both businesses and customers alike in automating and personalizing the service. They help increase customer interaction along with brand loyalty.

Before the era of extensive use of chatbots, every customer contact had small human involvement. The mere possibility of urgent customer problems arising during non-working time, a weekend, or a public holiday made the service even more complicated; it was expensive and organizationally cumbersome to keep the helpdesk going to meet unpredictable demand.

Chatbots can provide sequenced, high-quality customer interaction 24/7 while reducing operational costs by enhancing efficiency. They automate regular activities and free up employee resources to deal with higher complexity issues. This immediate availability reduces queues compared to contacting support via phone lines, emails, or web interfaces, hence improving customer experience, building brand loyalty, and encouraging customer retention.

Operating customer support services involves many financial costs. Replying to frequent queries and training personnel to standardize those responses is also costly. Many multinational enterprises address these issues through outsourcing, which involves additional costs and also impairs the control over the quality of customer interaction.

The integration of chatbots can be transformative in that respect, as it provides support on a 24/7 basis. Besides serving as first-line support, chatbots can do much to supplement support during peak periods and take the heat off staff facing the barrage of more routine queries, enabling them to give more time to the more complex queries. That saves human intervention substantially and hence provides greater efficiency in workforce scaling for increasing demands or requests out of hours.

Besides, chatting robots not only reduce support costs but also increase general operational efficiency and hence enhance service quality and customers’ satisfaction.

Chatbots are a very powerful tool for generating leads and increasing sales conversion. While visiting the website, one customer may be looking for information on products or services, and having a chatbot means they get immediate answers to their questions about features, prices, or terms of cooperation. This not only helps make the purchase decision but also increases the chances that the customer will opt for your company. Besides, chatbots can qualify leads of prospective customers in the context of complicated purchases with a multi-stage funnel by performing an initial assessment and preparation and, further on, redirecting customers to contact the manager for further discussion of details.

Artificial Intelligence, AI is a scientific discipline that was officially presented to the world community in 1956 on a seminar in Hanover, USA. The event was an initiative of four American scientists: John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon. From its very beginning, the term “artificial intelligence”, probably invented to attract public attention, has become incredibly popular.

The field has gained importance rather steadily in the last sixty years, with much of the intelligent technologies impactful to change the world order. Despite that, the term “artificial intelligence” is a misinterpretation because it is understood as an artificial being with intelligence capable of competing with the best of any human being.

For John McCarthy and Marvin Minsky, AI first meant an attempt to computer model intellectual abilities, human-animal-plant-social-phylogenetic ones. The assumption that all cognitive functions can be described precisely and programmatically reproduced served as the basic of this scientific area. Despite more than sixty years of history, the hypothesis of reproducibility of intellectual functions by computers has not yet been confirmed or disproved definitively, which stimulates scientists to new discoveries.

Modern AI finds its applications in literally every field of life and is very much in a phase of constant development, drawing from an enriched background that was laid down starting mid-twentieth century.

Artificial Intelligence

Artificial intelligence development started just after World War II, when scientists like Alan Turing explored the possibility of machines being able to “think.” In 1950, Turing published “Computing Machines and Intelligence,” where he proposed the Turing Test as a method for determining whether a machine was capable of imitating human intelligence. Artificial intelligence attracted a great deal of attention in the 1960s, spawning the first chess-playing programmes and algebraic problem-solving ones. However, the first “winter period” of AI came in the 1970s, where real-world advances did not quite reach the lofty expectations set by many, and the funding of research was reduced.

Interest in AI took over in the 1980s as a result of a combination of the development of algorithms for machine learning and increased computing power. This era is marked by improvements in the realization of expert systems-which can simulate the decisions of human experts within a particular domain. Starting with the new millennium, a new era of AI had begun, accelerated by developments in the internet, big data, and greater computing power. Breakthroughs in deep learning and neural networks have thus far led to a number of systems now capable of speech and image recognition, underpinning recent work on autonomous cars, personalized medicine, and other applications.

Artificial intelligence is breaking new frames and challenges, finding its place in daily life, and changing many spheres radically: business, medicine, education included. AI history is the way from utopian ideas to real technologies, which inspire scientists and developers to create new things.
Artificial Intelligence has undergone many changes in such a short time since its existence. It is possible to single out six stages in the history of its development.

In the early years of development, encouraged by early successes, a number of researchers including Herbert Simon made optimistic predictions. Simon predicted that “within ten years a digital computer would be the world’s chess champion”. However, when in the mid-1960s a ten-year-old boy defeated a computer at chess and a US Senate report highlighted the limitation of machine translation, progress in AI had slowed significantly. These were considered to be the dark times for AI.

The next one was semantic AI, in which the researcher became interested in the psychology of the memory and comprehension mechanisms. By the mid-1970s, methods of semantic knowledge representation started to appear along with expert systems that made use of skilled knowledge so as to reproduce thought processes. These systems promised very much, especially in medical diagnosis.

In the 1980s and 1990s, the development of machine learning algorithms and bettering technical capabilities resulted in the development of intelligent systems capable of carrying out various tasks such as fingerprint identification and speech recognition. The period was marked by integrating AI into other disciplines for the creation of hybrid systems.

Later in the 1990s, AI began to combine with robotics and a human-machine interface to form something similar to affective computing, which analyses and then reproduces human emotions; this helped in the development of dialogue systems like chatbots.

Since 2010, new opportunities in computing have enabled a marriage of big data with deep learning techniques inspired by artificial neural networks. Advances in speech and image recognition, natural language understanding, and unmanned vehicles are signalling a new AI renaissance.

Artificial intelligence applications

Artificial intelligence technologies have demonstrated great advantages compared to human capabilities in different activities. For example, in 1997, the Deep Blue computer from IBM defeated Garry Kasparov, at the time a world chess champion. In 2016, computer systems defeated the best go and poker players in the world to manifest their capabilities of processing and analyzing huge amounts of data measured in terabytes and petabytes, respectively.

The applications, ranging from recognising speeches to identifying faces and fingerprints from millions of others like those used by secretarial typists, use machine learning techniques. The same technologies permit cars to drive themselves and computers outperforming dermatologists to diagnose melanoma from pictures of moles taken with mobile phones. Military robots and automated assembly lines in factories also make use of the power supplied by artificial intelligence.

In the scientific world, AI has been used to break down the functions of biological macromolecules, including proteins and genomes, according to the order of their components. This separates in silico-from historical methods like experiments in vivo-on living organisms-and in vitro-in laboratory conditions.

The applications of self-learning intelligent systems range from industry and banking to insurance, healthcare, and defence. The automation of numerous routine processes transforms professional activity and makes some professions potentially extinct.

Distinction of AI from neural networks and machine learning

Artificial Intelligence, more commonly referred to as AI, is a general field in computer science that addresses the creation of intelligent machines able to continue activities that usually require human intelligence. It covers, but is not limited to, specialized programs and various technological approaches and solutions. AI makes use of many logical and mathematical algorithms which can be based on neural networks for the purpose of emulating human brain processes.

Neural networks represent a specific kind of computer algorithm, which can be viewed as a mathematical model composed of artificial neurons. Such systems do not require preliminary programming to carry out certain functions. On the contrary, they are capable of learning from previous experience, just like neurons in the human brain create and strengthen their connections during the learning process. Neural networks are tools within AI for the accomplishment of tasks involving recognition or processing of data.

While AI is the general term describing machines that can think and learn like humans, the key subset of AI concerning technologies and algorithms which make programmes learn and improve without human intervention is called machine learning. Such systems analyze input data, find some patterns in it, and use this knowledge to process new information and resolve more complicated problems. One of the methods for organizing machine learning is called neural networks.

Therefore, if we seek to find an analogy of AI within the human body, the AI will act like the entire functioning of the brain, whereas machine learning will be the analogy to information processing and problem-solving techniques, and neural networks will be structural elements-like neurons-which will perform data processing at an atomic level.

Application of AI in Modern Life

AI has found its place in almost every sphere of life in the modern world, starting from commercial use to medical and up to manufacturing technologies. There exist two main types of artificial intelligence: weak and strong. The weak ones are specialized in narrower tasks, like diagnosis or data analysis, while strong AI is created to solve global complex problems deeper by imitating human intelligence.

Big Data analysis with the use of AI finds high applicability in commerce by enabling big commerce platforms to study consumer behaviour and optimise marketing strategies.

Artificial intelligence manufacturing has had its application in monitoring and coordinating workers’ activities, greatly increasing efficiency and safety in the work process. In the transport sector, AI serves in traffic control, monitoring of road conditions, and development and improvement of unmanned vehicles.

The luxury brands are incorporating AI that will perform deep analysis of customers’ needs and personalize products for them. In healthcare, AI is changing the face of diagnostics, development of drugs, health insurance, and even clinical trials, thus making healthcare services a far more accurate and efficient affair.

The reasons for this technological development are rapid growth in information flows, stepped-up investment in the AI sector, and demands for higher productivity and greater efficiency in all sectors. Artificial intelligence continues to expand its influence, penetrating new areas and transforming traditional approaches to business and everyday activities.

Areas of Application of AI

Artificial Intelligence has been covering every other aspect of human life, creating new opportunities for traditional industries to improve efficiency and accuracy.

Medicine and Healthcare: AI operates patient data, analyzes medical images such as ultrasounds, X-rays, and CT scans, and it diagnoses diseases based on symptoms. Intelligent systems give treatment options and help you lead a healthy lifestyle through mobile apps that can monitor your heart rate and body temperature.
Retail and e-commerce: Through AI, users’ online behavior is analyzed to provide recommendations or advertising tailored to them. This also includes the advertisement of products that users viewed in online shops and similar product suggestions based on analyses of user interests. Politics: During presidential campaigns, even that of Barack Obama, AI has been in use for data analysis in order to optimize campaign strategies-choosing where and when to speak-to increase his chances of winning.
Industry: AI helps in controlling manufacturing processes, equipment loads analysis, and demand predictions to ensure proper resource utilization and cost-cutting. Gaming and education: AI generates more realistic virtual opponents, personalized game scenarios in the field of gaming. In education, it is being put to use to plan curricula to suit the needs and capabilities of students, manage educational resources, etc.

Other fields where AI finds application include legal services, finance, and urban infrastructure management, to mention but a few of the areas that really underline its contribution to modern innovation and technological advancement.

Artificial Intelligence (AI) is a scientific discipline that was officially introduced to the world community in 1956 at a workshop in Hanover, USA. The event was initiated by four American scientists: John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon. Since its inception, the term “artificial intelligence”, probably created to attract public attention, has gained immense popularity.

The importance of AI has grown steadily over the past six decades, with intelligent technologies having a significant impact on changing the world order. Despite its widespread use, the term “artificial intelligence” is often misinterpreted, especially when it is understood to mean an artificial being with intelligence that can compete with humans.

For John McCarthy and Marvin Minsky, AI was first an attempt to computer model intellectual abilities – human, animal, plant, social or phylogenetic. The assumption that all cognitive functions can be accurately described and programmatically reproduced became the foundation of this scientific field. Despite more than sixty years of history, the hypothesis of reproducibility of intellectual functions by computers has not yet been confirmed or disproved definitively, which stimulates scientists to new discoveries.

Modern AI is widely applied in various spheres of life and continues to evolve, building on a rich legacy of research and development that began in the mid-twentieth century.

Development of Artificial Intelligence

The development of artificial intelligence began just after World War II, when scientists such as Alan Turing explored the potential for machines to “think.” In 1950, Turing published “Computing Machines and Intelligence,” proposing the Turing Test as a method of determining a machine’s ability to mimic human intelligence. In the 1960s, artificial intelligence attracted considerable attention, spawning the first programmes for playing chess and solving algebraic problems. However, the 1970s marked the first “winter period” of AI, when real-world advances failed to live up to high expectations, leading to a reduction in research funding.

Interest in AI revived in the 1980s due to the development of machine learning algorithms and increased computing power. This period is characterised by advances in the development of expert systems capable of mimicking the decisions of human experts in certain fields. With the start of the new millennium, AI entered a new era accelerated by the development of the internet, big data and increased computing power. Breakthroughs in deep learning and neural networks have led to the development of systems capable of speech and image recognition, underpinning the development of autonomous cars, personalised medicine and other applications.

Artificial intelligence continues to break new boundaries and challenges, integrating into everyday life and radically changing many spheres, including business, medicine, and education. The history of AI is a path from utopian ideas to real technologies, inspiring scientists and developers to make new discoveries.

Artificial Intelligence (AI) has undergone numerous changes in the short time of its existence. Six stages can be distinguished in the history of its development.

In the early stages of development, fuelled by early successes, researchers such as Herbert Simon made optimistic predictions. Simon envisaged that within ten years, machines could become world chess champions. However, progress slowed in the mid-1960s when a ten-year-old boy beat a computer at chess and a US Senate report pointed out the limitations of machine translation. This period became known as the dark times for AI.

The next stage was directed towards semantic AI, where scientists focused on the psychology of memory and comprehension mechanisms. The mid-1970s saw the emergence of semantic knowledge representation methods and expert systems that used skilled knowledge to reproduce thought processes. These systems showed great promise, especially in medical diagnosis.

In the 1980s and 1990s, the development of machine learning algorithms and technical improvements led to the development of intelligent systems capable of performing a variety of tasks such as fingerprint identification and speech recognition. This period was marked by the integration of AI with other disciplines to create hybrid systems.

By the late 1990s, AI began to be combined with robotics and the human-machine interface, leading to the creation of affective computing aimed at analysing and reproducing human emotions. This trend helped to improve dialogue systems such as chatbots.

Since 2010, new opportunities in computing have made it possible to combine big data with deep learning techniques based on artificial neural networks. Advances in areas such as speech and image recognition, natural language understanding and unmanned vehicles are signalling a new AI renaissance.

Applications of artificial intelligence

Artificial intelligence technologies have demonstrated significant advantages over human abilities in many areas. For example, in 1997, IBM’s Deep Blue computer defeated Garry Kasparov, then world chess champion. In 2016, computer systems defeated the world’s top go and poker players, demonstrating their ability to process and analyse vast amounts of data measured in terabytes and petabytes.

Machine learning techniques are used extensively in applications ranging from speech recognition, similar to the secretarial typists of the past, to accurately identifying faces and fingerprints among millions of others. The same technologies allow cars to drive themselves and computers that outperform dermatologists to diagnose melanoma from pictures of moles taken with mobile phones. Military robots and automated assembly lines in factories are also the result of artificial intelligence.

In the scientific field, AI is used to analyse the function of biological macromolecules such as proteins and genomes based on the sequence of their components. This distinguishes in silico (computer-based experiments using big data and powerful processors) from traditional methods such as in vivo (on living organisms) and in vitro (in laboratory conditions) experiments.

Self-learning intelligent systems find application in almost every sector: from industry and banking to insurance, healthcare and defence. The automation of many routine processes is transforming professional activities and, potentially, making some professions extinct.

Distinguishing AI from neural networks and machine learning

Artificial Intelligence (AI) is a broad field of computer science concerned with the creation of intelligent machines capable of performing tasks that require human intelligence. This includes not only specialised programs, but also a variety of technological methods and solutions. AI uses many approaches, including logical and mathematical algorithms, and can rely on neural networks to mimic the workings of the human brain.

Neural networks are a special type of computer algorithms that represent a mathematical model consisting of artificial neurons. These systems do not require prior programming to perform specific tasks. Instead, they are able to learn based on previous experience and elementary calculations, similar to the way neurons in the human brain form and strengthen connections during the learning process. Neural networks are a tool used within AI to solve tasks related to recognising and processing data.

Machine learning, in turn, is a subset of AI that focuses on developing technologies and algorithms that allow programmes to learn and improve without direct human intervention. These systems analyse input data, find patterns in it and use this knowledge to process new information and solve more complex problems. Neural networks are often used as one of the methods for organising machine learning.

Thus, if we draw an analogy to the human body, AI can be compared to the full functionality of the brain, machine learning would be analogous to information processing and problem solving techniques, and neural networks are structural elements similar to neurons that provide data processing at a fundamental level.

Applications of AI in modern life

Artificial Intelligence (AI) has found widespread application in many different areas of modern life, ranging from commercial applications to medical and manufacturing technologies. There are two main types of AI: Weak AI and Strong AI. Weak AI is specialised to perform specific tasks such as medical diagnosis or data analysis, while Strong AI aims to solve global, complex problems by mimicking human intelligence at a deeper level.

In commerce, AI is being used extensively for Big Data (Big Data) analysis, enabling big commerce platforms to study consumer behaviour and optimise marketing strategies.

In manufacturing, AI is being used to monitor and coordinate the actions of workers, increasing the efficiency and safety of work processes. In the transport industry, AI is helping with traffic management, monitoring road conditions, and developing and improving unmanned vehicles.

Luxury brands are integrating AI to deeply analyse customer needs and personalise products. In healthcare, AI is revolutionising diagnostics, drug development, health insurance and clinical trials, improving the accuracy and efficiency of healthcare services.

This technological advancement is fuelled by the rapid growth of information flows, increased investment in the AI sector and demands for greater productivity and efficiency across all industries. Artificial intelligence continues to expand its influence, penetrating new areas and transforming traditional approaches to business and everyday activities.

Areas of use of AI

Artificial Intelligence (AI) is infiltrating many aspects of everyday life, transforming traditional industries and creating new opportunities to improve efficiency and accuracy:

1. Medicine and healthcare: AI is used to manage patient data, analyse medical images such as ultrasounds, X-rays and CT scans, and diagnose diseases based on symptoms. Intelligent systems offer treatment options and help you lead a healthy lifestyle through mobile apps that can monitor your heart rate and body temperature.
2. Retail and e-commerce: AI analyses users’ online behaviour to offer personalised recommendations and advertising. This includes advertising products that users have viewed in online shops and suggesting similar products based on analyses of user interests.
3. Politics: During presidential campaigns, such as Barack Obama’s, AI was used to analyse data and optimise campaign strategies, such as choosing where and when to speak, increasing his chances of winning.
4. Industry: AI helps manage production processes, analyse equipment loads and forecast demand, optimising resources and reducing costs.
5. Gaming and education: In the gaming industry, AI is creating more realistic virtual opponents and personalised game scenarios. In education, AI is being deployed to tailor curricula to the needs and abilities of students, and to manage educational resources.

The application of AI spans many other fields, including legal services, finance, urban infrastructure management and more, emphasising its role as a major driver of modern innovation and technological advancement.

Artificial Intelligence (AI) is a field of computer science dedicated to creating machines that can perform tasks that require human intelligence. These tasks include learning (obtaining information and rules to use the information), reasoning (using rules to reach approximate or certain conclusions), and self-correction. Especially in the area of machine learning, AI is able to learn without explicit programming and perform automatic data processing.

The main components and methods of AI include:

1. Machine learning – technologies that allow computers to learn from data and make predictions or decisions based on previous experience.
2. Deep learning is a subsection of machine learning that uses complex neural networks with multiple levels of abstraction to process data.
3. Neural networks are algorithms inspired by the structure of the human brain that are able to learn and recognise patterns from large amounts of data.

AI is being applied to a wide variety of industries:

• Healthcare for diagnosing diseases, creating personalised treatment plans and managing medical data.
• Finance for trading automation, risk management and fraud prevention.
• Automotive industry to develop autonomous vehicles and driver assistance systems.

The ethical and legal aspects of AI require special attention as issues of privacy, security and responsibility for decisions made by machines arise. There is a need to develop legislative and regulatory frameworks that will govern the use of AI to ensure its safe and effective use in the public interest.