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Why The HR Industry Should Implement AI Technology?

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Why The HR Industry Should Implement AI Technology?

Human resources is a significant part of business as it is in charge of managing the most valuable asset a company can have the employees. AI technology is being adopted in HR practices as businesses have started to identify the crucial areas in HR which could perform better by incorporating AI solutions.

Whether it is recruiting new talent, employee retention, or, performing administrative tasks, the HR department can surely benefit from the application of latest AI tools to make their tasks error-free, be more objective in their approach, automate tasks and not to mention gain deeper insight into employee data to get their strategies right. Implementation of AI requires having skilled personnel who have pursued artificial intelligence course in delhi.

Although the implementation is still happening on a small scale and somewhat sporadically,  the sector is waking up to the power of AI and slowly savoring the benefits.

How the HR sector is benefitting from AI

Hassle-free hiring

Recruiting new talent is one of the crucial jobs handled by any HR team in any organization. However, the usual hiring process can be a tedious task, and more often than not is not free from error or, worse bias.

Not just that but, screening of applications takes a painfully long time and scheduling interviews can be hectic.

AI can address all these issues, by automating the entire process. The task of screening application and replying to candidates can be automated, which means the employers can act faster and since it would not require much involvement from the HR team, they can focus their attention on some other productive work.

Smart screening of applications makes it easier and faster to shortlist candidates. Maintaining a database of past applicants can immediately alert the HR team to find a suitable candidate who might be considered for a new position instead of looking for new ones.

Furthermore, the task of background verification would become an efficient process, since, detecting any oddity in the datasets will get easier. 

Keep bias out of hiring

As more and more companies are now focusing on having a diverse workforce that promises to provide equal opportunity to employees regardless of gender, race, keeping the element of bias out of their recruiting process is essential otherwise it might harm their image.

The job postings that one comes across often have been detected to contain biased language which despite being apparently harmless in nature might turn off a potential candidate. During the screening of applications and interviews, the element of human bias can creep in as well.

With pattern detection of previous applications and subsequent response, this issue can get resolved. It would be possible for a recruiter to access a database that contains far more diversity and with the help of NLP, it would enable them to write a job posting more objectively. Having knowledge in Machine Learning Using Python, can enable the team to detect the negative patterns faster.

Improve the onboarding process for recruits

Once the hiring process gets over and done with onboarding begins, which could be a taxing experience for both new recruits and the HR team if not handled with care. As the new faces get integrated with the system, the HR team faces several queries, and investing a significant amount of time for doing this task is not possible for them.

As far as employees are concerned not having someone around to clear away confusing can be a daunting experience.

AI-powered chatbots can take care of this issue, by helping the recruits access all the necessary information whenever they need from anywhere, they can get the contact information, guidelines without constantly nagging the HR personnel. The onboarding process becomes faster, efficient, and less daunting for all.

Employee retention becomes easier

HR is not just about recruiting new talent, they are entrusted with the responsibility of nurturing the human assets in the firm. However, for any firm retaining their employees can become an issue and if not managed well they might also have to deal with employee attrition.

So, assessing the employee needs is essential and with smart AI systems in place, the HR team can measure the satisfaction rate, performance of the employees through personalized surveys.

Patterns could be detected in the response to indicate which employees might consider leaving.

The team could act on the findings and make the necessary effort where it’s needed to retain an employee. Conversational AI can also help them in keeping track of training progress.

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Repetitive tasks could be automated

The HR department has to deal with scores of administrative tasks that require dealing with a huge amount of data and most of these tasks can be dull. AI program can entirely automate the tasks such as interview scheduling, and help the HR team focus on something productive. They can focus the time on strategizing for the future. Automation not only saves time but also adds efficiency to the whole process.

In the field of recruiting there have been some interesting development due to AI and HR collaboration. Let’s check out a couple of these platforms

TextRecruit: It helps with both recruiting and onboarding process

ARYA: Employes machine learning to find the best candidates for employers

Restless Bandit: It is a bot recruiter that is hired by industry leaders like Adidas

Mya Systems: This AI assistant guides the candidates to better their communication with recruiters

The HR industry is opening up, but, due to issues like finance, lack of skill it has not been able to fully utilize the power of AI. Addressing the skill-gap is essential here and that could be done by encouraging workers to take up courses like artificial intelligence certification in delhi ncr.

 


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Learn How To Do Image Recognition Using LSTM

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Learn How To Do Image Recognition Using LSTM

This is a tutorial where we teach you to do image recognition using LSTM. To get to the core you have to understand that how a convolutional neural network perceives the data. In this tutorial the data we have is four-dimensional data, so, you need to convert the dataset accordingly. You can find the tutorial video attached to this blog.

Now suppose there is an image 28 by 28 pixel, if the image is black and white then there would be only one channel. So how will you put the data in CNN, it will be like the number of samples, then followed by the number of rows of the data, then the number of columns, then channels. These are the four values that need to be provided in the input layer, at the very beginning. Now, these values must be converted according to the LSTM. Now the LSTM wants the STF, like the number of samples, time steps like how many time steps back you want to go for making further prediction because LSTM is a sequence generator and the number of features. So, we will be converting the image that is the number of sample 28 by 28 one pixel into one sample of 28 by 28, that’s the only job you have to do and all you need to accomplish this is to prepare the data accordingly.

There will be no mysteries here, in fact, it is a normal neural network LSTM, that anybody can run in a most simple form, and in this tutorial, it is also run in the most simple form there is no complexity involved and only a few epochs will be run.

You can find the code sheet you need for this at

 

Also follow this video that explains the process step by step, so that you can easily grasp how LSTM can be used for the purpose of image recognition. To access more informative tutorial sessions like this follow the DexLab Analytics blog. 


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DexLab Analytics Presents Mega Artificial Intelligence Course In Python: An Online Demo

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DexLab Analytics Presents Mega Artificial Intelligence Course In Python: An Online Demo

Dexlab Analytics is undoubtedly a leading name in the field of the Big Data Analytics industry. The latest offering from this institute is a course that is remarkable in so many ways. The course is Mega Artificial Intelligence Course In Python, which aims to cover everything you ever need to learn regarding artificial intelligence. To help you get a better grasp of the course we have also prepared an online demo and the demo video is attached at the end of the blog do check that out to clear away any confusion you might have.

Before getting into the course details, there are certain features of the course that we think you should know about. To begin with, you do not need any special educational background, you can hail from any stream and can still pursue the course because here we will teach you from scratch. Just having some mathematical knowledge is fine. We have kept things flexible here, so you can repeat the course if and when necessary. The notes that you will be needing for the course including the code sheets, will be provided to you in the beginning so that you do not have to waste precious time in class taking notes.

However, the nature of the course will be online, because due to COVID 19 situation offline classes are temporarily not possible. You will be given all the classroom videos, furthermore, there will be guidelines regarding Kaggle.com where we will teach you how to participate in this pioneering data science website, how to compete over there and offer you tips to increase your ranking. All in all the course aims to transform you into a super data scientist.

You can find the detailed course information, the online demo and brochure in the PPT format at

 

The course will be divide into three sections starting with PYTHON  PROGRAMMING for Data Science. Throughout the sessions, you will get familiar with the language, its libraries. You will be taught to use Plotly and handle projects before moving onto the second section which is AI( Artificial Intelligence) comprising three components namely Statistics, Machine Learning, and Deep Learning. Along with picking up the nuances, you would handle mega projects including one on self driving cars. Moving on to the next segment of Big Data get introduced to PySpark. Handling a growing amount of data could be tough, so, an introduction to Quantum Computing seems necessary before wrapping things up.

Do check out the course details in the video attached below that gives you a thorough tour of the entire course and also check out the course brochure. Our contact number is provided there along with our website address, feel free to contact us regarding any query.


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How AI is Reshaping The Finance Industry?

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How AI Is Reshaping The Finance Industry?

Technology is bringing about rapid changes in almost every field it touches. Traditional finance tools no longer suit the current tech-friendly generation of investors who are now used to getting information, service at their fingertips. Unless the gap is bridged, it would be hard for firms to retain any clients. Some of the financial firms have already started investing in AI technology to develop a business model that satisfies the changing requirements of the customers and leverages their business.

The adoption of AI has finally enabled the firms to have access to customer-centric information to develop a plan that suits their individual financial goals and offer customer-centric solutions to offer a personalized experience.

AI is impacting the financial industry in more ways than one. Let’s take a look

Mitigating risks

The application of AI has enabled institutes to assess risk factors and mitigate risk. Implementation of AI tools allows the processing of a huge amount of financial records that comprise structured as well as unstructured data to recognize patterns and predict the risk factors. So, while approving a loan, for example, an institute could be better prepared as it would be able to identify those customers who are likely to default and having personnel with a background in credit risk management courses can certainly be of immense help here.

Detecting fraud

One of the most niggling issues faced by the banking institutes is a fraud, and with AI application being available fraud identification gets easier. When any such case happens it becomes almost impossible for institutes to recover the money. Along with that the banks especially also have to deal with false positives cases that can harm their business. Credit card fraud cases also have become rampant and give customers and banks sleepless nights. AI technology could be a great weapon in fighting and preventing such cases. By analyzing data regarding the transaction of a customer, his behavior, spending habits, past cases if any, an oddity could be easily spotted and an alarm could be sent to monitor the situation and take measures accordingly.

Trading gets easier

Investment always comes with a set of risks, the changing market scenario could certainly put your money in a volatile situation. However, with AI in place, the large datasets could be easily handled, and detecting market situations can help to make investors aware of the trends and they can change their investment decision accordingly. Faster data processing leads to quick decision making and coupled with an accurate prediction of the market situation, trading gets smarter as an investor can buy or, sell stock as per stock trends and stay risk-free.

Personalized banking experience

The integration of AI can offer customers a personalized financial experience. The chatbots are there to help the customers manage their affairs without needing any intervention. Be it checking balance or, scheduling payments everything is streamlined. In addition to this, the customers now have access to apps that help keep their financial transactions in check, track their investments, and plan finances without any hassle. There have been a dynamic progress in the field of NLP and the chatbots being developed now are getting smarter than ever and pursuing a natural language processing course in gurgaon, could lead to lucrative job opportunities.

 Process Automation

Every financial institution needs to run operations with maximum efficiency while adopting cost-cutting measures. The adoption of RPA has significantly changed the way these institutes function. Manual tasks which require time and labor could easily be automated and there would be fewer chances of error. Be it data verification or, report generation every single task could be well taken care of.

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Examples of AI implementation in finance

  • Zest Automated Machine Learning (ZAML) is a platform that offers underwriting solutions. Borrowers with little or, no past credit history could be assessed.
  • Kensho combines the power of NLP and cloud computing to offer analytical solutions
  • Ayasdi provides anti-money laundering (AML) detection solutions to financial institutes
  • Abe AI is a virtual assistant that helps users with budgeting and saving while allowing them to track spending.
  • Darktrace offers cyber security solutions to financial firms

The powerful ways AI is helping the financial institutes excel in their field indicate a promising future ahead. However, the integration is slowly taking place, and still, there is some uncertainty regarding the technology. With proper training from an analytics lab could help bridge the knowledge gap and thus ensure full integration of this dynamic technology.


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Data Science: What Are The Challenges?

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Data Science: What Are The Challenges?

Big data is certainly is getting a lot of hype and for good reasons. Different sectors ranging from business to healthcare are intent on harnessing the power of data to find solutions to their most imminent problems. Huge investments are being made to build models, but, there are some niggling issues that are not being resolved.

So what are the big challenges the data science industry is facing?

Managing big data

Thanks to the explosion of information now the amount of data being created every year is adding to the already overstocked pile, and, most of the data we are talking about here is unstructured data.  So, handling such a massive amount of raw data that is not even in a particular database is a big challenge that could only be overcome by implementing advanced tools.

Lack of skilled personnel

 One of the biggest challenges the data science industry has to deal with is the shortage of skilled professionals that are well equipped with Data Science training. The companies need somebody with specific training to manage and process the datasets and present them with the insight which they can channelize to develop business strategies. Sending employees to a Data analyst training institute can help companies address the issue and they could also consider making additional efforts for retaining employees by offering them a higher remuneration.

Communication gap

One of the challenges that stand in the way, is the lack of understanding on the part of the data scientists involved in a project. They are in charge of sorting, cleaning, and processing data, but before they take up the responsibility they need to understand what is the goal that they are working towards. When they are working for a business organization they need to know what the set business objective is, before they start looking for patterns and build models.

Data integration

When we are talking about big data, we mean data pouring from various sources. The myriad sources could range from emails, documents, social media, and whatnot. In order to process, all of this data need to be combined, which can be a mammoth task in itself. Despite there being data integration tools available, the problem still persists.  Investment in developing smarter tools is the biggest requirement now.

Data security

Just the way integrating data coming from different sources is a big problem, likewise maintaining data security is another big challenge especially when interconnectivity among data sources exists. This poses a big risk and renders the data vulnerable to hacking. In the light of this problem, procuring permission for utilizing data from a source becomes a big issue. The solution lies in developing advanced machine learning algorithms to keep the hackers at bay.

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Data validity

Gaining insight from data processing could only be possible when that data is free from any sort of error. However, sometimes data hailing from different sources could show disparity regardless of being about the same subject. Especially in healthcare, for example, patient data when coming from two different sources could often show dissimilarity. This poses a serious challenge and it could be considered an extension of the data integration issue.  Advanced technology coupled with the right policy changes need to be in place to address this issue, otherwise, it would continue to be a roadblock.

The challenges are there, but, recognizing those is as essential as continuing research work to finding solutions. Institutes are investing money in developing data science tools that could smoothen the process by eliminating the hurdles.  Accessing big data courses in delhi, is a good way to build a promising career in the field of data science, because despite there being challenges the field is full big opportunities.

 


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Gradient Boosting In scikit-learn 0.22 For Handling Missing Values

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Gradient Boosting In scikit-learn 0.22 For Handling Missing Values

A new tutorial session regarding the scikit-learn 0.22 is here and our sole focus is going to be updating your knowledge regarding the new features that have been added to this library. For this particular session we have decided to introduce you to the concept of gradient boosting that can handle the missing values. This concept is being introduced to clear out a previous misconception regarding the functioning of gradient boosting for this particular purpose.

The earlier notion surrounding GBM or, the gradient boosting algorithm in scikit-learn, was that it was unable to handle the missing values. In this tutorial we want to clarify that misconception, because, contrary to the notion XGBoost library or, XGB library is perfectly capable of handling the missing value analysis.  It has been found that XGB library performs better than the normal method taken to find the missing values.

Now getting back to the scikit-learn 0.22 way of solving the issue of missing values. There has been an enhancement in the algorithm gradient boosting due to which you no longer have to handle the missing values because it will handle it of itself.

So take a look at how the concept of native support for missing values for gradient boosting works.

The ensemble algorithm, ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor, both classification regression now have the power of native support for missing values or, (NaNs). This is indicative of the fact that there is no need now for imputing data during training or predicting.

To gain an insight into how you perform this you need to follow the complete code sheet that you can find here

 

Now, as you go through the code you will find the word enable, which might surprise you and make you question why it says enable here? Well, this is because it is still being developed.

So, basically all of the algorithms in the scikit-learn 0.22 that are under development process have to run an extra line of code that goes like enable_hist_gradient_boosting. After further development there won’t be any need of that.

The video attached below will further explain how the algorithm works.

There will be more informative tutorial sessions like this, so to stay updated keep following the DexLab Analytics blog.

Watch the video here.


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An Introduction To The 5 V’s of Big Data

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An Introduction To The 5 V’s of Big Data

The term big data refers to the massive amount of data being generated from various sources that need to be sorted, processed, and analyzed using advanced data science tools to derive valuable insight for different industries. Now, big data comprises structured, semi-structured, and mostly unstructured data. Processing this huge data takes skill and expertise and which only someone with Data Science training would be able to do.

The concept of big data is relatively new and it started emerging post the arrival of internet closely followed by the proliferation of advanced mobile devices, social media platforms, IoT devices, and all other myriad platforms that are the breeding grounds of user-generated data. Managing and storing this data which could be in text, audio, image formats is essential for not just businesses but, for other sectors as well. The information data holds can help in the decision-making process and enable people to understand the vital aspects of an issue better.

The characteristics of big data

Now, any data cannot be classified as big data, there are certain characteristics that define big data and getting in-depth knowledge regarding these characteristics can help you grasp the concept of big data better. The main characteristics of big data could be broken down into 5Vs.

What are the 5Vs of data?

The 5Vs of data basically refers to the core elements of big data, the presence of which acts as a differentiating factor. Although many argue in favor of the essential 3 VS, other pundits prefer dissecting data as per 5Vs. These 5Vs denote Volume, Velocity, Variety, Veracity, Value the five core factors but, not necessarily in that order. However, Volume would always be the element that lays the foundation of big data. Pursuing a Data Science course would further clarify your idea of big data.

Volume

This concept is easier to grasp as it refers to the enormous amount of data being generated and collected every day. This amount is referred to as volume, the size of data definitely plays a crucial role as storing this data is posing a serious challenge for the companies. Now the size of the data would vary from one industry to the other, the amount of data an e-commerce site generates would vary from the amount generated on a popular social media platform like Facebook. Now, only advanced technology could handle and process and not to mention deal with the cost and space management issue for storing such large volumes of data.

Velocity

Another crucial feature of big data is velocity which basically refers to the speed at which data is generated and processed, analyzed, and moved across platforms to deliver insight in real-time if possible. Especially, in a field like healthcare the speed matters, crucial trading decisions that could result in loss or profit, must also be taken in an instant. Only the application of advanced data science technology can collect data points in an instant and process those at a lightning speed to deliver results. Another point to be noted here is the fact that just like volume the velocity of data is also increasing.

Variety

The 3rd V refers to the variety, a significant aspect of big data that sheds light on the diversity of data and its sources. As we already know that the data now hails from multiple sources, including social media platforms, IoT devices, and whatnot. The problem does not stop there, the data is also diverse in terms of format such as videos, texts, images, audios and it is a combination of structured and unstructured data. In fact, almost 80%-90% of data is unstructured in nature. This poses a big problem for the data scientists as sorting this data into distinct categories for processing is a complicated task. However, with advanced data science technologies in place determining the relationship among data is a lot hassle-free process now.

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Veracity

It is perhaps the most significant aspect of all other elements, no matter how large datasets you have and in what variety, if the data is messy and inaccurate then it is hardly going to be of any use. Data quality matters and dirty data could be a big problem especially because of the fact that data comes from multiple sources. So, you have apparently no control, the problems range from incomplete data to inconsistency of information. In such situations filtering the data to extract quality data for analysis purposes is essential. Pursuing Data science using python training can help gain more skill required for such specific tasks.

Value

The 5th V of big data refers to the value of the data we are talking about. You are investing money in collecting, storing, and processing the big data but if it does not generate any value at the end of the day then it is completely useless. Managing this massive amount of data requires a big investment in advanced infrastructure and additional resources, so, there needs to be ROI. The data teams involved in the process of collecting, sorting, and analyzing the data need to be sure of the quality of data they are handling before making any move.

The significance of big data in generating valuable insight is undeniable and soon it would be empowering every industry. Further research in this field would lead to the development of data science tools for handling big data issues in a more efficient manner. The career prospects in this field are also bright, training from a Data analyst training institute can help push one towards a rewarding career.

 


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A Quick Guide To Natural Language Processing (NLP) And Its Applications

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A Quick Guide To Natural Language Processing (NLP) And Its Applications

When you interact with Alexa, or, conduct a voice search on Google, do you wonder about the technology behind it? What is it that makes it possible to communicate with machines as you would with a human being?

Natural Language Processing (NLP) AKA computational linguistics is a subset of Artificial Intelligence, makes all of it possible by combining artificial intelligence, machine learning, and language to facilitate interaction between computers and humans.

So how does NLP work?

When you put a voice command, it gets recorded and then the audio gets converted into text, and the NLP system starts working on the context and the intention of the command. Basically, the words that we speak are labeled and get converted into a set of numbers. However, Human language is complex and has many nuances and underlying subtexts. The same word under a different context can have different connotations. So, when a simple command is put is gets easier for the machine to follow through as it contains simpler words and clear context, but, the system needs to evolve more to fully process the complex language patterns that evolved through ages. There are courses available such as natural language processing course in gurgaon that can help one acquire specialized knowledge in this field.

NLP and its applications

NLP despite being in a nascent stage is getting recognized for its potential and being applied for executing various tasks.

Sentiment Analysis to assess consumer behavior

  This functionality of NLP is an important part of social media analytics that parses the comments and reactions of users regarding products, brand messages spread over social media platforms to detect the mood of the person. This helps businesses gauge customers’ behavior and to make necessary modifications accordingly. 

Email filtering and weeding out spam

If you are a user of Gmail then you must have noticed the way your emails get segmented once they arrive. Primary, Social, and Promotions are the three broad categories followed by others, there is a segment for spams as well. This smart segmentation is a stark example of NLP at work.

Basically text classification technique is used here to assign a mail to a certain category that is pre-defined. You must have also noticed how well your spam is sorted, this is another result of the application of NLP where certain words trigger the spam alert and the mail gets sent straight to the spam folder. However, this sorting is yet to be perfected via further research.

Automatic summarization to find relevant information

Now thanks to digitization we have to deal with huge amounts of data which has led to information overload. This massive amount of data needs to be processed to find actionable information. Automatic Summarization makes it possible by processing a big document and presenting an accurate and short summary of it.

Chatbots

No discussion on NLP can ever be complete without mentioning the chatbots. The customer service segment is gaining huge benefits from these smart chatbots that can offer virtual assistance to the customers 24×7.  Chatbots can not only enhance customer experience but, are also great for reducing costs for any business. However, modern-day chatbots can handle simple, mundane queries that do not require any special knowledge and skill, in the future we could hope to see the bots handling specialized queries in real-time.

Spell and grammar checker

If you have ever used Grammarly and felt impressed with the result then you must have wondered at some point how does it do it? When you put in a text, it not only looks for punctuations but also points out spelling errors and also shows grammatical errors in places where there is no subject-verb agreement. In fact, you also get alternative suggestions to improve your writing. All of this is possible thanks to transformers used by NLP.

Machine Translation

If you are familiar with Google and its myriad apps then you must be familiar with Google Translate. How quickly it translates your sentences in a preferred language format, machine translation is one application of NLP that is transforming the world. We always talk about big data but making it accessible to people scattered across the globe divided by language barriers could be a big problem. So, the NLP enabled us with machine translation that uses the power of smart algorithms to translate without the need of any human supervision or intervention. However, there is still huge room for improvement as languages are full of nuanced meanings that only a human is capable of understanding.

 What are some examples of NLP at work?

We are not including Siri, or, Alexa  here as you are already familiar with them

  • SignAll is an excellent NLP powered tool that is used for converting sign language into text.
  • Nina is a virtual assistant that deals with banking queries of customers.
  • Translation gets easier with another tool called Lilt that can integrate with other platforms as well.
  • HubSpot integrated the autocorrect feature into its site search function to make searching hassle-free for users.
  • MarketMuse helps writers create content that is high-quality and most importantly relevant.

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Just like AI and its various subsets, NLP is also a field that is still evolving and has a long journey ahead. Language processing is a function that needs more research because simulating human interaction is one thing and processing languages that are so nuanced is not a cakewalk. However, there are plenty of good career opportunities available and undergoing an artificial intelligence course in delhi would be a sound career move.

 


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How Can AI Help In Streamlining Small Business Operations?

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How Can AI Help In Streamlining Small Business Operations?

Running a business is a challenging job, especially when business operations take place on a small-scale platform. Small business owners need constant motivation and brainstorming to keep their business in a profitable position.

However, as the world is busy deriving benefits from AI technology, and professionals opting for artificial intelligence course in delhi for better career prospects, small business owners too should seize this opportunity to power up their businesses. 

Why the small business owners are shying away from AI?

With biggies like Google, Amazon,  Apple, Microsoft empowering themselves with AI tools, small businesses are somewhat showing reluctance towards the new technology. Only a small percentage of businesses ranging from small to medium have so far been influenced by it. A 2018 survey showed the number to be around 13.6%.

This indicates there is some inhibition in the small business community, but, it might not just be that, when questioned most small business owners often cite reasons like lack of expertise, financial concern to be the causes.

They are mostly under the misconception that such advanced technology is best suited for giant platforms and their small scale businesses are not going to rake in any profit, even if they invest. They don’t even have tons of data like most businesses, to begin with. So, AI being a data-driven technology, might not work for them.

Their perception is gradually changing because of the way AI has started seeping through the very fiber of civilization and impacting so many aspects of life. It is not possible for small businesses to indulge in AI research or, develop a platform specifically for their business needs for feasible reasons but, they can get ideas regarding how best to conduct business the AI way.

Let’s find out how AI can be incorporated into small business infrastructure to improve five core areas.

Smarter sales and marketing with AI-powered CRMs

A CRM is an indispensable tool for any business, let alone a small one. Basically, a CRM works to garner customer data from various platforms to enable the sales and marketing team to keep track of their valuable customers while pursuing new leads.

The fusion of AI and CRM could do wonders as it is evident from the way Einstein AI, introduced by SalesForce is working. 

You stand to gain insight into the customer mindset as this fusion will work to analyze customer mindset by analyzing the conversations that happened across different channels. This insight can help shape your sales and marketing efforts accordingly especially if you can upskill your team with customer market analysis courses.

Keep an eye on your rivals

Staying one step ahead of your biggest competition in the market is a crucial need no business owner big or small can afford to ignore. However, it is not easy to monitor every move they make, but, AI can be your biggest ally in helping you track your rival’s every digital move.

AI-powered Crayon, is a smart tool that monitors what your competition is doing on social media, across websites and applications, you can gauge their performance and activities and keep a tab on their marketing strategies, pricing, and other such issues to make suitable modifications to your own.

Automate customer service

 Handling your customers is an important but, tedious task and as your business starts to grow so does your customer base and their queries. Investing in a big support team might not always be a feasible option for you, so why don’t you take advantage of chatbots to automate the whole process and make it more efficient?

Not all queries are important, some of these are generic which the chatbots can handle while your sales team can focus on more personalized or, technical queries to keep your customers happy. Answering support tickets can be easy with a tool like Digital Genius. It is a great option even for those businesses which can’t afford a support team.

Smoother HR operations

No matter how small scale your business might be, you still have to manage your employees and hire new ones, which means having an HR team ready round the clock. Now you can manage this segment bypassing all hassles thanks to AI-powered platforms that automate your HR functions, be it screening applications, scheduling interviews or, onboarding the new employees, every segment will be well taken care of. Not just that but, the administrative jobs that the HR have to do repetitively could be automated too.

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Customize your customer journey

Customers like to be pampered, when they shop from big brand names they hardly get to experience that personalized approach. The trendy products aimed at mass-market leave them wishing for something that would suit their style. Retail Analytics Courses can help you develop a better understanding of the whole issue.

 Being a small business owner you already have the advantage to take care of this issue and you can be near perfect in your approach if you get support from smart AI algorithms that can browse through customer data and detect patterns to help you understand the personal preferences of customers and thereby allow you to modify your products accordingly to suit their needs.

Coupled with AI power you could also improve your logistics to ensure your supply chain does not experience any glitch.

The development of AI platforms programmed to perform specialized tasks need to be recognized by the small business community, only then they would find the motivation to channelize the power in the right direction. They can also consider upskilling themselves with deep learning for computer vision course, to be able to harness the power of AI.

 


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