Huge technological advancements in the tech space over the decade created lakhs of jobs in India. As per the recent reports from Accenture, it is estimated that the growth of AI can contribute US $957 billion to India’s economy in the next 15 years. 

AI has the potential to bring massive incremental value to various businesses, for example, manufacturing, Agriculture, energy, healthcare, and education. It does play an imperative role in generating jobs in all these businesses. 

However, Indian IT companies have been following the maneuver that is an essential factor to be successful in technology adoption and advancement. The right skilling by Indian tech start-ups has a massive contribution to creating 2 lakh job opportunities in the tech space in 2022. 

Indian tech start-ups experienced a 108% growth in 2018. However, the studies also disclosed that Artificial intelligence witnessed the fastest adoption compared to other distinct technologies. 

It is anticipated that with the growing youth population in India, most businesses will be switching to this groundbreaking technology AI. Many hi-tech cities like Bangalore, Hyderabad, Delhi, and Mumbai are banking on Artificial intelligence because of its potential to reach a larger audience base. 

A career in AI in 2023 & beyond

India has been lagging in terms of healthcare facilities, and less number of doctors are available. It’s an arduous task for the government to assure quality healthcare to its citizens.

However, AI is forecasted to aid India to a greater extent in facilitating better healthcare shortly.

Reputed tech giants like Microsoft are collaborating with startups to facilitate AI-based tools such as the 3Nethra that can detect eye issues. This is just an example, there have been so many projects under the pipeline for the healthcare sector. Hence, AI has the potential to transform the healthcare sector. 

Artificial Intelligence to transform the Education sector

India is becoming the hub of Science, Math, Technology, and Engineering graduates. Tech giants such as Google, Microsoft, and Intel have been providing short-term education plans like internships, classroom training, and other initiatives to assist learners in AI programming. 

Artificial Intelligence intends to enhance the Financial system

The world of finance and banking has transformed a lot with the significant adoption of Artificial Intelligence. 

The future of finance completely relies on AI. This technology enables individuals to not only manage the flow of money, but they can also ameliorate the cash flow, and profitability, optimize compliance, and more. 

Artificial Infrastructure to bring advancement in Infrastructure and Mobility

The ever-increasing population of hi-tech cities leading to a great challenge for India. AI is the ray of hope that could offer Smart infrastructure and mobility with civic tool systems. Further, the tools could help in assisting customer grievances, and appropriate repair, therefore enhancing the quality of living of migrators.

Artificial Intelligence to counter cyber threats

As technological advancements are increasing day by day, cyber threats have become more usual. 

AI is already competing with ever-emerging malware. The significant advancement in the IT field makes AI inevitable to handle cyber threats. India is already working on Artificial Intelligence based cybersecurity tools at a rapid pace. Hence, this enables massive opportunities for aspirants who want to build their careers in AI. 

Collectively, skilled AI engineers are in high demand, specifically, in India. The groundbreaking technology seems to be imperative when it comes to providing jobs for the future generation of India. 

NearLearn is one of the popular Artificial Intelligence and Machine Learning training institute in Bangalore with 4.8 Google ratings. The institute has the best faculty, so if you’re the one who is looking out to pursue a career in AI, then NearLearn is the top choice. 

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AI in investment management has resulted in the reduction of jobs, passive investments, decreasing confidence, and investment fees. On the other hand, it can all be a boon as it has started enabling people to make better decisions quickly and consistently. Since there is a great influence of artificial intelligence to overcome the challenges of asset management has resulted in great efficiency, risk management, and enhance decision making.

Let’s dive into some crucial areas where artificial intelligence in asset management can be easily leveraged and understand What is artificial intelligence currently used for?:

Data science use cases in asset management

AI in asset management in operational functions includes monitoring, quality maintenance, exception handling of the various amounts of information that is managed by managers alone.

The end customers can rely bank upon the data quality which makes fewer blunders and lessens operational risk.

In certain cases, data can be old, missing, or can contain errors, hence the AI in asset management can be utilized to identify anomalies that are based upon statistical assessments.

Digital advice

AI and ML tools can be easily utilized by investors to take better access to the financial markets and gain digital advice. A financial investment requires the proper asset allocation mix to meet its proper objectives understand “How can artificial intelligence be used in businesses? To meet these objectives, various attributes like a client’s age, risk tolerance, and desired income in retirement and model-based AI digital tools can help you select the proper asset allocation.

Digital advisors can utilize the AI asset management tools and give an approach to people to offer personalized advice at a lower cost.

Operational Efficiency

In the current digital landscape, asset management firms are imposing various cost-sensitive concerning the applications of artificial intelligence in designing new guidelines, fee pressures, and the step towards the lower-cost passive products.

Various organizations are exercising various programs with an emphasis on outsourcing and process automation. AI asset management is putting an impetus for firms to incorporate innovative operational excellence into services.

Early AI asset management always proves advantageous as they have the upside of changing “as a service” abilities into profit centers and making an upper hand. The speed enhancement offered by artificial intelligence asset management services helps to improve and move at a particularly greater speed. The services become both a defensible advantage and a sustained revenue source for firms.

User experiences and interfaces

Gone are the days when an individual investor used to historically contact a stockbroker to gain information about stock transactions. As an additional thing, they need to consult with a tax specialist or accountant to consider tax implications and understand the value of these investments. With the utilization of AI and ML and the implication of machine language in asset management, the customers can easily select the right asset allocation based on a user’s age, income, risks, and desired income retirement.

Digital advisors also incorporate personalized investment at a lower cost to gain personalized investment. They also offer tax-loss harvesting, portfolio allocation, and digital documentation delivery.

The Conclusion

In the coming future, technology continues to play an integral role in various asset management. These innovative tools are more affordable and the more availability of data continues to increase its utilization of machine language in asset management. It can eventually result in mitigating risks, reducing costs, gaining better returns, and delivering products and services for clients.

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What is the definition of Artificial Intelligence(AI)?

While there have been numerous definitions of artificial intelligence (AI) throughout the previous few decades, John McCarthy provides the following description in this 2004 study (PDF, 106 KB),

“It is the science and engineering behind the development of intelligent machines, most notably intelligent computer programmers.

It is comparable to the analogous goal of utilizing computers to comprehend human intellect, but AI is not limited to physiologically observable methods.”

However, decades before this description, Alan Turing’s key work, “Computing Machinery and Intelligence” (PDF, 89.8 KB, was published in 1950.

Turing, frequently referred to be the “father of computer science,” poses the following issue in this paper: “Can machines think?”

From there, he proposes a test, now dubbed the “Turing Test,” in which a human interrogator attempts to discern between a computer-generated and a human-generated written response.

While this test has been subjected to considerable examination since its publication, it remains an integral element of the history of artificial intelligence as well as an ongoing philosophical notion due to its use of linguistic concepts.

Stuart Russell and Peter Norvig then published Artificial Intelligence: A Modern Approach, which quickly became one of the main textbooks on the subject.

They go into four distinct AI goals or definitions, distinguishing computer systems based on their logic and ability to think vs. their ability to act:

Humane method:

1.Human-like computer systems

2.Systems that behave similarly to humans

The optimal strategy is as follows:

1.Systems capable of rational thought

2.Systems that make rational decisions

Alan Turing’s notion would have been classified as “systems that behave like people.”

Artificial intelligence, in its simplest form, is a field that combines computer science with large datasets to facilitate problem-solving.

Additionally, it comprises the subfields of machine learning and deep learning, which are typically associated with artificial intelligence.

These fields are comprised of artificial intelligence algorithms aimed at developing expert systems capable of making predictions or classifications based on input data.

Today, there is still a lot of hype surrounding AI development, which is to be anticipated of any new emergent technology.

According to Gartner’s hype cycle, product innovations such as self-driving cars and personal assistants follow “a typical progression of innovation, from initial enthusiasm to disillusionment and finally to an understanding of the innovation’s relevance and role in a market or domain.”

As Lex Fridman observes here in his 2019 MIT speech, we are approaching the zenith of inflated expectations and the trough of disillusionment.

As discussions about the ethics of AI begin to emerge, we can witness the first signs of the trough of disillusionment.

Artificial intelligence classifications—weak AI vs. strong AI

Weak AI, also known as Narrow AI or Artificial Narrow Intelligence (ANI), is artificial intelligence that has been trained and focused on performing specific tasks.

Weak AI is responsible for the majority of the AI that surrounds us today.

‘Narrow’ may be a more true definition for this sort of AI, as it supports some quite strong applications, such as Apple’s Siri, Amazon’s Alexa, IBM Watson, and self-driving cars.

Artificial General Intelligence (AGI) and Artificial Super Intelligence are the two components of strong AI (ASI).

Artificial general intelligence (AGI), or general AI, is a speculative kind of artificial intelligence in which a machine possesses an intelligence equivalent to that of humans; it possesses a self-aware awareness capable of problem solving, learning, and planning for the future.

Artificial Super Intelligence (ASI) — often known as super intelligence — would outperform the human brain’s intelligence and capability.

While strong AI is yet purely theoretical with no practical applications, this does not mean that AI researchers are not investigating its development.

Meanwhile, the best instances of ASI may come from science fiction, such as HAL, 2001: A Space Odyssey’s superhuman, rogue computer aide. 

Machine learning vs. deep learning

Because deep learning and machine learning are frequently used interchangeably, it’s important to understand the distinctions between the two.

As previously stated, both deep learning and machine learning are subfields of artificial intelligence; in fact, deep learning is a subfield of machine learning.

A visual representation of the relationship between AI, machine learning, and deep learning

Deep learning is composed of neural networks.

The term “deep” in deep learning refers to a neural network with more than three layers—which includes the inputs and outputs.

This is often depicted by the diagram below:

The distinction between deep learning and machine learning lies in the manner in which each algorithm learns.

Deep learning automates a major portion of the feature extraction process, removing the need for manual human involvement and enabling the usage of bigger data sets.

Consider deep learning to be “scalable machine learning,” as Lex Fridman highlighted in the same MIT presentation mentioned above.

Machine learning that is more conventional, or “non-deep,” is more reliant on human involvement to learn.

Human specialists establish a hierarchy of features to comprehend the distinctions between data inputs, which typically requires more organised data to learn.

While “deep” machine learning can benefit from labelled datasets, commonly known as supervised learning, it does not require a labelled dataset.

It is capable of ingesting unstructured data in its raw form (e.g., text, photos) and automatically determining the hierarchy of features that differentiate distinct types of data.

Unlike machine learning, it does not require human assistance to interpret data, allowing for more innovative approaches to scale machine learning. 

Applications of artificial intelligence

Today, AI systems have a plethora of real-world applications.

The following are some of the more frequent examples:

Speech Recognition: is often referred to as automatic voice recognition (ASR), computer speech recognition, or speech-to-text. It is a capability that utilises natural language processing (NLP) to convert human speech to text.

Numerous mobile devices incorporate speech recognition into their systems to enable voice search—for example, Siri—or to increase messaging accessibility.

Customer service: Throughout the customer journey, online chatbots are displacing human agents.

They respond to commonly asked questions (FAQs) regarding issues such as shipping or offer personalised advise, such as cross-selling products or recommending appropriate sizes for users, fundamentally altering how we think about client involvement across websites and social media platforms.

Message bots on e-commerce sites with virtual agents, messaging programmes such as Slack and Facebook Messenger, and duties typically performed by virtual assistants and voice assistants are all examples.

Computer Vision: This artificial intelligence technology enables computers and systems to extract meaningful information from digital photos, videos, and other visual inputs and to take appropriate action based on that information.

This suggestion capability distinguishes it from image recognition tasks.

Computer vision, which is based on convolutional neural networks, has applications in social media photo tagging, radiological imaging in healthcare, and self-driving automobiles in the automotive industry.

Recommendation Engines: By analysing historical data on consumer behaviour, AI algorithms can assist identify data trends that can be leveraged to design more effective cross-selling techniques.

This is utilised by online businesses to give relevant add-on recommendations to customers throughout the checkout process.

Automated stock trading: Designed to optimise stock portfolios, AI-powered high-frequency trading platforms execute hundreds, if not millions, of trades daily without human interaction. 

The History of Artificial Intelligence: Significant Dates and Persons

The concept of a ‘thinking machine’ extends back to ancient Greece.

However, significant events and milestones in the evolution of artificial intelligence since the introduction of electronic computing (and concerning several of the subjects mentioned in this article) include the following:

1950: Computing Machinery and Intelligence is published by Alan Turing in Turing—famous for cracking the Nazis’ ENIGMA code during WWII—proposes in the article to address the topic ‘can machines think?’ and introduces the Turing Test to assess whether a computer can display the same intelligence (or the equivalent intelligence) as a person.

Since then, the Turing test’s utility has been contested.

1956: John McCarthy coined the phrase ‘artificial intelligence’ at Dartmouth College’s inaugural AI conference.

(McCarthy would later design the Lisp programming language.)

Later that year, Allen Newell, J.C. Shaw, and Herbert Simon develop the Logic Theorist, the world’s first functioning artificial intelligence computer programme.

Frank Rosenblatt creates the Mark 1 Perceptron, the world’s first computer built on a neural network that ‘learned’ via trial and error.

Only a year later, Marvin Minsky and Seymour Papert publish Perceptrons, which becomes both a seminal work on neural networks and, for a while, an argument against further neural network research.

1980s: Backpropagation neural networks, which train themselves using a backpropagation algorithm, become widely employed in artificial intelligence applications.

1997: IBM’s Deep Blue defeats Garry Kasparov, the global chess champion at the time, in a chess match (and rematch).

2011: IBM Watson defeats Jeopardy! champions Ken Jennings and Brad Rutter

2015: Baidu’s Minwa supercomputer utilises a type of deep neural network called a convolutional neural network to detect and classify images more accurately than the average person.

2016: DeepMind’s AlphaGo programme defeats Lee Sodol, the world champion Go player, in a five-game match powered by a deep neural network.

The victory is important in light of the game’s enormous number of possible plays (nearly 14.5 trillion after only four moves!).

Google later acquired DeepMind for an estimated $400 million. 

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According to an Oxford Economics and NTT DATA survey of 1,000 business leaders conducted in early 2020, 96 per cent of businesses were exploring AI solutions and more than 70 per cent had completely deployed or at least piloted the technology. Nearly half of survey respondents said that failing to introduce AI would result in customer loss, while 44% stated that their company’s bottom line would suffer. 

Simply put, AI allows business analyzers to quickly make informed and important business decisions in large quantities of business information.

In particular, the transport management sector uses this intelligence and its technology, Computer Lesson (ML), to increase process efficiency and visibility to execute, which lead to impacting improvements that support the bottom line. 

Cost reduction, sales growth tools 

Research by McKinsey shows that 61% of managers report cost reduction, and 53% report sales increases as a result of the introduction of AI in their supply chains.

The supply chains are some of the major areas for the savings received by high volume shippers, with lower inventory prices, reduced inventory costs and lower transport and labour costs.

In addition, AI improve revenues, forecasts, analytics and network optimization of the supply chain management.

AI is used effectively by the shipping industry and other freight carriers, so as to minimise the amount of unprofitable empty miles or “dead head” trips a carrier takes home after loading with an empty trailer.

AI would also identify other secret trends in historical data for freight selection, the most effective labour resource planning and loading and stop-sequences, the rationalisation of rates and other improvements in the process by using historic uses to better prepare and achieve results.

The Machine Learning section of this new technology allows companies to refine routing and also prepare for disturbances caused by the weather.

ML allows transport management experts, for instance, to understand how weather conditions affected time it took to transport loads in the past and then takes current data sets into account for predictive advice. 

The pandemic speeds up AI and ML adoption 

The Coronavirus disease (COVID-19) placed enormous strain on a variety of industries, including transportation, but it also offered a silver lining — the potential for improvement. Due to the growing pressure on companies to function smarter in order to meet consumer demands and desires, there is an increased willingness to retire outdated legacy resources and invest in more effective processes and technology tools.

Applying AI and machine learning to pandemic-related challenges may mean the difference between accelerating or decelerating transportation management professionals’ development. When used properly, these tools enhance logistics visibility, provide data-driven planning insights, and aid in the efficient automation of processes.

As with many other promising new technologies, AI and machine learning have often been misrepresented or, worse, overhyped as panaceas for vexing market challenges. Transportation logistics organisations should exercise caution and due diligence when determining when and how to implement AI and machine learning in their operations. Panicked recruiting of data scientists to incorporate expensive, complicated technologies and overengineered processes can be a costly boondoggle that sullies the perception of these truly effective and useful tech tools’ viability.

Rather than that, companies should spend time learning about the technology and how it is already delivering value to early adopters in the transportation logistics industry. Which measures should a logistics operation take before embarking on an AI/ML initiative? 

The accuracy of the data is paramount 

Keep in mind that your data’s quality dictates how quickly your AI journey progresses The primary virtue of successful artificial intelligence (or any big data project) is constant data management and hygiene. Compiling, arranging, discovering, and getting access to this information is a big difficulty for many. The survey revealed that 70% of respondents say that error-filled data and uninformative data are a big problem Other popular data quality complaints included third-party data (~42%), messy stores of disorganised metadata (50%) and unstructured data (44%).

Historically, the transportation industry has been slow to recognise the need to adopt new technologies, and it is making up ground with 54% of respondents expecting it to be ubiquitous in the next five years 75% of enterprises will implement a streaming AI infrastructure that will drive a fivefold rise in data and analytics application delivery

For a number of transportation management firms, it will be the first step to getting access to the right data. At the moment, the best artificial intelligence is just as good as the amount of data you offer it and the variety of the data you provide. 

Review options and contemplate enlisting the assistance of an editor

Businesses need to look into the integrity of their data and current technologies to better understand the knowledge they already possess before jumping on the AI bandwagon.

AI-based approaches do not demand that you be a data scientist, when it comes to investing in digital transformation should be used. In the event, you aren’t certain how to begin, go about it, consider partnering with a transportation management (TMS) that has demonstrated proven success and expertise with AI. 

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The advantages of AI in higher education are amazing. For example, AI makes access to education easier because students can use their smartphones and tablets instead of taking lessons. In addition, AI offers countless possibilities for automation and personalization. With automation, educational institutions can save a lot of time and spend more time teaching students, rather than performing repetitive tasks.

According to research, the use of AI in the university world should increase by 47.5% by 2021. AI will affect all areas of education, from elementary school to high school and college. With AI, educators can offer personal approaches that take into account the learning style of each individual student. AI offers adaptive learning and many other advantages. Here are some of the main advantages of AI in education.

1. Automation

First, AI is able to automate many administrative tasks that are very time consuming when done manually. Educators have to spend too much time evaluating homework, marking exams, and doing other tasks that can often be done by machine. According to Adam Simon from LegitWritingServices, computers have no problem evaluating multiple-choice tests even if they lack creative skills for evaluating student essays.

Thanks to great advances in automation, teachers can spend more time with their students. Developers are also constantly developing new AI-centered solutions that can be used to write down students’ written responses. Another area that can benefit from AI is the registered office. AI can automate many processes that involve a lot of bureaucracy and thus improve overall efficiency.

2. Teachers and AI Collaboration

AI is already used in various teaching aids to develop skills as well as in many test systems. As AI solutions evolve, teachers expect AI to help teach, so teachers and schools can do much more. AI can not only rationalize various administrative tasks and increase efficiency, but also create many opportunities for personalization.

Developments in AI mean that teachers can provide a better learning experience and focus on tasks where machines can’t help. The collaboration between AI and teachers allows teachers to focus on tasks that require adaptability and deep understanding. It is also important to remember that AI is an integral part of our daily life. Therefore, it becomes necessary to familiarize students with this technology and to teach them how to use it.

3. Personalization

Whenever you see Netflix, you’re dealing with an advanced algorithm that offers personalized recommendations. The same approach is used in many other areas and its educational potential is difficult to overestimate. Traditional pedagogical approaches are uniform and aim to offer all students the same learning experience. In practice, however, traditional methods only target around 80% of students. The top 10% fail to reach their full potential, while the bottom 10% of students struggle to meet academic needs.

With AI, teachers can offer a personalized learning experience. For example, AI can customize classroom exams and assignments to help students get the best possible academic support. The success of the tutoring largely depends on effective feedback. AI-based tutoring systems allow students to get quick feedback, while teachers can use AI-driven software to provide personalized answers.

4.  Smart Content

Another hot topic related to the use of AI is intelligent content. Robots are already able to create different types of digital content. However, one of the main advantages of AI for content creation is that personalized digital learning interfaces can be created.

AI solutions like Cram101 can also summarize educational content in brief instructions. These programs allow students to read summaries of textbook chapters, run quick tests, and receive flashcards to store important information on a particular topic. There are also AI-based platforms like Netex that allow teachers to create digital programs and digital content that are supported by all types of devices, including smartphones and online assistants. Thanks to AI, conferences, and virtual conferences are becoming a reality.

5. Accessibility

One of the biggest challenges in the education sector is accessibility. AI enables educators to make learning more accessible to everyone, including students who speak other languages ​​and students who are hearing impaired or visually impaired. For example, Microsoft created an AI-based plug-in presentation translator. It is a PowerPoint plugin that creates subtitles for presentations in real-time. Everything a teacher says can be immediately translated into other languages ​​so that he can work with a wider audience. These solutions are also useful for students who are unable to attend classes due to illness. AI can also help students who need to learn at a different level. In addition, many students want to study subjects that are not available in their school. With this software, you can listen to lectures from practically anywhere in the world

Read More: How to Grow your Small Business with Artificial Intelligence

Massive Changes in Academia

The academic world is constantly evolving, and new technologies offer teachers and students countless new opportunities. AI has already demonstrated its disruptive potential in many industries, and education is just another example of what AI can do. First, AI-based solutions can help teachers save a lot of time because they can easily handle huge amounts of information. Teachers have the ability to automate many repetitive tasks so teachers can spend more time with their students.

However, automation is not the only area of ​​application for AI. This technology can also be used to provide a personalized learning experience. Artificial intelligence can facilitate access to education and enable educational institutions to create a richer learning experience through the use of different types of digital content tailored to the needs of a particular student. Of course, AI can also help reduce administrative costs. Although education is introducing AI more slowly than other sectors, AI is beginning to change the academic world and this process will not stop.

Conclusion

I hope you have understood how AI is helping in the education industry. NearLearn is the best Artificial intelligence institute in Bangalore. It provides various courses like machine learning, data science, blockchain, and full-stack development, etc.

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If customers are already waiting for an AI-compatible experience, it’s up to you to meet expectations. Artificial intelligence already exists and is designed to help you and your team do better and more effectively what you are already doing. In other words, it’s not about replacing people or switching to the next business mode, but about making the people you have more effective and efficient by making better decisions and doing what they do best.

With that in mind, let’s look at four different ways artificial intelligence can now help your small business succeed.

1. Turn data into measures to stay ahead of the competition

CRM platforms (Customer Relationship Management) are indispensable for companies of all sizes for a good reason. They help you keep track of every interaction with your customers so you don’t have to start over every time someone calls to ask for help or to follow a conversation. Had chat with a seller, customer service or live chat.

Salesforce, the industry’s largest CRM, is busy integrating AI into everything it does. With the release of Einstein, your goal is to provide actionable forecasts and recommendations that can help take your business to the next level. With this information, you can coach sales reps, help service reps, guide marketers in the right direction, and find new ways to review existing data. If you already (and you should) collect data about every customer interaction, AI can help you turn that data into actionable steps that pay off by getting a head start on your competition

2. Support your sales teams with advanced tools

For most small businesses, the sales team is how you generate revenue. When you consider that sales are critical to the success of your SME, don’t you want to use the right tools to be successful?

AI tools such as gong, jog, and chorus are specially designed for you to record and transcribe every call from your salespeople. You can then compare your sales reps’ strategies to those of your team’s most successful members and offer an analysis based on the choice of words or the relationship between the time spent speaking and the time spent listening.

In other words, you can use AI tools to share successful techniques across the team without the need for a full review. Instead, you can start with what works and discover the secret sauce that will change your business.

3. Customer support 24/7 using chatbots

Chatbots are one of the most basic ways that AI can directly help your small business. Whether on Facebook Messenger or WhatsApp, you can now start a chatbot with minimal programming knowledge. Instead of focusing on technology, all you have to do is think about the kind of things a user is likely to ask and then develop a full script that can help. (As AI continues to improve to understand what your customer wants, it’s always better to think of it as a user interface that allows someone to interact directly with your business.)

Chatbots for customers can save you a lot of time on frequently asked questions. If you think about it, a large percentage of your calls are likely related to the same thing every day: What time did you open? Do you deliver to my address? Do you have this item in stock? Answering these types of questions is relatively easy, but it takes a lot of time that could be better used to grow your business.

The best way to get the most out of a customer-oriented chatbot is to check whether you can summarize your customer service calls on the five most frequently asked questions. What do you do regularly so that you can switch to a chatbot? The good news is that this not only makes your job easier, it also eliminates friction for your customers and makes it easier than ever for them to make a purchase decision.

4. Free up your HR and management using chatbots to answer frequently asked questions

Chatbots for customers have been around for a while, but what about the routine questions that arise for your employees in the daily management of your company? Questions about vacation, company policies, health care, standard work instructions, etc. are always asked, but it is not necessarily what your HR staff should spend the most time on. Fortunately, new HR chatbot solutions are constantly being developed to help you meet routine requirements and ensure that your employees focus on the essentials. If you spend a lot of time looking after your team, it may be time to get more help from an AI solution.

Read More: How to build career in blockchain technology

What Can be done Right Now

Artificial intelligence is popular for a reason. From film recommendations to voice control, we have integrated AI into our lives, and your customers are no different. If you haven’t already, expect your company to use this technology soon.

Fortunately, there are a number of solutions that can help you run your small business faster and smarter than ever.

• Use AI for CRM to get new information from the data already collected.
• Framework sales with information on artificial intelligence.
• Use chatbots to answer frequently asked questions about customer service.
• Answer the most frequently asked HR questions via chatbot.

Conclusion

I hope you have understood how artificial intelligence helps small business. Artificial intelligence helps in a very different area in your business. So you must use AI for your small business that can increase your business revenue 2x time.

NearLearn is the best institute that provides the best artificial intelligence classroom training in Bangalore. It provides another course also like machine learning, data science, blockchain, full-stack development, etc.

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