How AI will shape the future of Internet of Things (IoT)

The Internet of Things (IoT) has been the topic of discussion for the past few years. It seems as though everyday the IT universe is finding new applications for IoT and its mainstream plausibility is becoming more. While considered a brand new vertical with endless possibilities IoT is just an extension of Artificial Intelligence. The very idea of IoT spawned from the prospects that AI has shown in the past. The idea of devices being connected with each other and communicating is something that is truly a remarkable point in human civilization. When we take a closer look, it sheds light on the extent to which AI has grown and the development it has brought about in other verticals.

While the application of AI in other verticals such as robotics, automobile, marketing etc. create reason for argument due to the various threats they pose, IoT is a vertical that at the moment poses no such threat, unless they start transforming into little killer robots and tearing your house apart.

Device Development and AI

Today we have more machines around us than we have human beings. If we introspect it might seem that we are spending more time with our smartphones than we are with other human beings. While a frightening realization, it is the future that we have been building since we first sprouted on this earth. At the brink of achieving that reality, we are now at the stage where we are exploring choices and trying to make the right steps towards them. The devices that we are coming up with reflect these steps and that is where the concept of AI raises some interesting questions for IoT.

While AI is primarily used as the cornerstone of devices, in IoT it plays several roles. There is much there that could influence how the IoT would react with our world. Further along the way it would boil down to the popular paradox of the chicken and the egg. Which technology would shape our future- AI or IoT?

It is easy to argue that advances in both these technologies would be of equal consequence. However, that is not the case. The very correlation between these two verticals is just as defined as how contrastingly they could influence each other. Machine learning is a key aspect of the progress that IoT is making. An IoT network that would consist of devices with sensors, video surveillance tools etc. will be capable of monitoring the functioning of the other devices. For software related issues certain devices will be equipped with troubleshooting tools both for themselves as well as other devices. Data is the instigating factor that could influence all these technologies and it is data that will continue to govern them in the future. The expectations would again fall upon AI to make the best out of the data.

IoT in Data Analytics

The idea of developing actionable insights is something that in recent years has provided a huge update for the use of AI and IoT services. As these technologies function using data, the uses become well defined and the margin of error depends only on the validity of the data. This creates avenue for wearable ‘smart’ devices to actually function in a sentient manner. Devices such as the heart rate monitor watches, various goggles allow provide vital data that could be relayed to your doctor, your banker, even your barber, who could avail the analyzed output that they could use to customise the service they provide.

Deep Learning

Deep learning is a breakthrough in IoT. This technology facilitates devices to go beyond the prosaic machine learning algorithm. Deep learning draws from a plethora of sources to arrive at a solution on any given subject. This comprehensive approach to producing solutions could become a key driving force for IoT and how the various devices around us function under it.

Conclusion

The many exabytes of data that is being produced now allow for further proliferation on the IoT front. Going ahead, it is AI’s data analytics capabilities that could facilitate this growth. Both machine learning and deep learning both function on the data that is procured through AI data analytics.

With the AI data analytics process being non-stop, big data and other verticals are proving to be vital resources for IoT. Many industry experts believe, actionable insights will be the key to the future. The possibilities with actionable insights are endless and investments in AI have been made to speed up and increase the productivity.   

Machine Learning enhances User Experience for Music

When creativity meets technology, you get incredible outcomes. And that is what the music streaming industry is investing in these days to improve user experience amidst brutal competition. They push new boundaries with technology and diversify the music genre so that everyone can appreciate it.

The era of personalized music with machine learning

In the latest news, the music discovery process is getting personalized results with revolutionary machine learning. Nowadays, almost every big name of the industry is leveraging AI to create better and more personalized music lists.

So, you should not get surprised if the suggested music from Spotify, Pandora and Apple Music seems exactly what you want to hear. All these music-streaming providers implement complex algorithms to pick subtle cues and create personalized music list for you.

• Pandora combines the same technology with data analytics to make suggested playlists for listeners. The algorithms used by Pandora evaluate the songs or artists selected by a user. With that, it creates a playlist that has similar attributes, matching the personal preferences of that user.

• Spotify is probably the most enthusiastic player when it comes to using algorithm technology in music streaming. The company uses a collaborative filtering approach. The algorithms collect music streaming data from multiple users and compares it together. This comparison is conducted with Echo Nest, which is considered best in this technology for music search. Apart from collaborative filtering, Spotify also includes NLP and audio models in its method of providing personalized music.

How Machine learning is evolving music streaming personalization

As mentioned earlier, music-streaming companies are using a variety of AI technologies to make song discovery advanced and personalized.

Here are three major technologies revolutionizing the music-streaming industry.

1. NLP or Natural Language Processing

NLP enables algorithms to understand human language. APIs are used for sentiment analysis, which harnesses the meaning behind spoken and written words. The model of NLP allows music streaming providers to collect data from a variety of resources all over the internet. Algorithms collect data from articles, news, blogs and other resources available on the internet. Using the written text regarding a music, the machines understand the characteristics and provide them with the right playlists.

2. Collaborative filtering

Collaborative filtering is a comparative study of the users’ music listening behavior. The technology helps in understanding the popularity and characteristics of songs. Algorithms collect data from a wide range of users. These datasets include information regarding stream counts, saved tracks, page visits and many others.

By incorporating all kinds of streaming data together, algorithms create a personalized list of tracks for the listeners.

3. AI audio models

Companies like Spotify understand that NLP and collaborative filtering cannot offer justice for new songs. That is why they use another form of AI-Integrated audio model. This technology works just like the face recognition technology. However, the algorithms inspect the audio models instead of pixels. With raw audio evaluation, companies provide new songs to the users in their playlist.

Thus, it would not be wrong to say that machine learning has found a strong place in the large ecosystem of music discovery. With proven phenomenal outcomes, the justification of marrying AI with music does make total business sense!

How AI is changing the Advertising and Media industry

Over the past decade or so advertising has changed drastically. From the humble copywriter/editor complement, advertising today has turned into a multidimensional effort with professionals from multiple verticals pitching in to achieve the end result. This, of course, is no surprise considering the deep impact that IT has had on almost anything and everything. If a copy editor were to tell you 15 years ago that his computer will be taking care of your advertisement and its standing, you would have thrashed him with his keyboard and taken him to a mental asylum. Yet here we are at the pinnacle of IT (as far as we know) and computers are planning ad placement, bidding for keywords and updating you the status of their efforts. So the question we need to ask ourselves is how far can this be leveraged.

The use of AI and Machine learning for such processes is nothing new. As a matter of fact, the current usage of AI in advertising is still relatively primitive. But the inroads we are making through the use of this technology is substantial. However, for AI and machine learning to make any sort of assessment the most important thing is data and it needs lots of it.

What is Data Driven Advertising? 

Anything you do on the internet required the use of data and while you do it generates data as well. From a business perspective, one of the biggest reasons why organizations use the internet is for advertising. Advertising is a multi-billion dollar industry with many dimensions within. Among them internet today is the most prominent and offers the most comprehensive results. So what kind of data is it that floats around the internet to help out with advertising. Well, the answer is pretty much everything, from search histories to personal information, social media updates to data pertaining to behavioral attributes, the internet is a repository for all these. Big data as we all have come to know it is what drives this process. While in the past data-driven advertising was largely based on manual analytics, the vertical today relies on automated technology.

The AI and Machine Learning Influence on Advertising     

While still in their inception stage, both machine learning and AI are being used more than we might have anticipated.

• Search History: Most of the data that is available today on the internet comes from search history. For advertisers, tools like Google AdWords offer keyword suggestions that tend to draw in more viewers based on their activity. This largely automated service provides an edge over competitors to place your ads with the right keywords. Be it product, service or information, anything you search for on the internet gets registered irrespective of the search engine. This information is then transferred to the highest bidder like in the case of Google as part of the google analytics tool. So advertisers who are registered with the tool gain access to your location, the products you were searching for, your brand preference if you have purchased anything and so on.

• Voice Recognition: Online shopping’s next frontier-voice recognition devices like Amazon’s Alexa and Google Home are currently taking the entire e-commerce sector by storm. The ability of these devices to relay your requests as well as make suggestions based on your activity is truly something that will be influencing e-commerce in the years to come.

• Social Media Bots: While being the cause of much controversy recently, the use of bots in social media has made the process of gathering information lightning fast. Social media is the source for a plethora of sensitive information and since inception has been exploited by advertisers and marketing companies to plug their products.

• AI Content Creation: The use of AI for content creation particularly for social media and BuzzFeed, have truly revolutionized advertising and marketing. Several types of content on multiple platforms are being written by AI today. Still, in a primitive stage, this is a technology that will surely pick up in the years to come and who knows maybe be even replace human writers. It is predicted that by 2043 we might have the first number one best-seller authored by AI.

Conclusion

While the current state of AI does leave much to be desired for advertisers, we are not that far from perfecting it for that purpose. There are plenty of prospects to be had on the advertisements themselves as AI and machine learning develops. Machine learning, in particular, could be leveraged to design ads without any human involvement. Currently, technologies such as deep learning are being used in the imaging process in games and movies, which point to good prospects on that front.

There are many more technologies that are still in the prototype stage being tested under various scenarios to eventually integrate into the mainstream processes within advertising. So let us wait and watch as this bit of technology evolves and its story unfolds.  

Myths about Cognitive Technology Debunked

Cognitive technology is one of the widely discussed concepts in the world of business. These discussions help businesses understand the importance and opportunities of the technology. However, there are several myths associated with cognitive technology that limit the knowledge of enterprises.

According to a survey on cognitive technology, businesses and enterprises feel confident about the future of AI and cognitive technologies. However, it would be important to clear the myths in order to successfully adapt cognitive technologies.

In this article, we are going to clear the air around the common misconceptions around congnitive technology.

1. Cognitive technology is all about automated functions

There is a myth among enterprises that cognitive technologies are only used to bring automation in the workforce. The technology is used to reduce the required human labor. However, this is not the whole truth.

AI and cognitive technologies are much more than automation solutions. The applications of these technologies can be used in multiple processes including insights. For instance, cognitive technologies can be used to create better customer service for the end users. The technology helps in understanding the customer data through insights and provide relevant and satisfactory services to the customers. So, the application is more about the intelligence, rather than just automation.

2. The financial outcomes are very basic with cognitive technologies

There are business owners who feel that AI technologies require a lot of investment and result in very basic financial outcomes. Also, they argue that the time lag between the investment and benefits is too long for general organizations.

However, the above-mentioned survey suggests that about 83% of companies that invested in cognitive technologies have obtained impressive or moderate benefits in terms of finances. So, the improved functions of the business get much better economic results with the application of cognitive technologies.

3. Cognitive technologies increase unemployment

One of the most argued topics in the application of cognitive technologies is the automation that brings unemployment. However, this is all wrong. In fact, cognitive technologies present great opportunities for the human employees to work side-by-side with the artificial intelligence.

The technology definitely enhances the productivity of employees, but it doesn’t reduce their importance in any manner. Plus, the arrival of cognitive technologies has created multiple new jobs for professionals, which is also a positive outcome in terms of the future of employment.

4. Cognitive technology is just a trend that will fade away

Many people suggest that AI and cognitive technologies are just a trend that is getting hyped by the media. But what they don’t know is that AI presents clear signs of acceptance and growth on a global scale. In fact, the AI market is expecting $59.8 billion revenue worldwide by the end of 2025. And that says a lot about cognitive technology’s future.

5. The application of cognitive technologies requires a complete transformation

This is another myth that stops companies and organizations from implementing AI in their business. A few company leaders think that cognitive technology application changes the functionality of the business drastically. However, it is not about the transformation, but the integration of cognitive technologies in business.

Final words:

The studies are presenting clear signals towards the success of the cognitive technology. It is the time that you understand it too. Debunking these myths will help businesses embrace this technology wholeheartedly and gain from it.

How Machine Learning can help with Business Development

Accelerated business growth has always been about innovation in functionalities such as customer experience, employee management, and others. And with advanced machine learning technologies, businesses are now able to make useful changes to bring better results for their companies.

The biggest advantage of machine learning in the corporate sector is the ability to make automated decisions without taking risks in any manner. This is why the corporate sector is expecting $59.8 billion revenue with machine learning and AI by 2025. More and more companies are integrating machine learning into their businesses to innovate various aspects.

Here are the top 4 ways that businesses can leverage machine learning.

1. Bringing personalization to customer service

Businesses keep on looking for effective ways to improve the quality of customer service and reduce the investment requirements. And machine learning offers those exact solutions to obtain those goals. With ML technologies, businesses get the ability to combine their years of data related to customer services and merge it with natural language processing technology.

The NLP algorithms make customer interactions more personal by leveraging the data to provide satisfactory services. Each and every customer gets the most accurate answers to their questions, which makes them happy. Plus, the same technology reduces the need for too much investment, resulting in lower customer servicing costs.

2. Making recruitment process convenient and successful

For a long time, hiring and recruitment processes faced multiple struggles. The difficulty in shortlisting the right candidates, removing the human biases, asking the right questions and keeping it cost-effective have presented troubles for recruiters.

But now, with machine learning, it is possible to bring automation in the hiring process. Corporates are now able to shortlist candidates among thousands of applications without skipping a valuable candidate. The machine learning tools are able to analyze credentials and match them with relevant job profiles.

Also, the same technology can detect biases and remove those factors while conducting the assessment. It all makes machine learning a cost-effective and successful way of hiring people.

3. Improving finance management and handling methods

Machine learning also offers the capacity to manage financial processes of a company. In fact, the processes such as payment, invoice analysis, and others can become automatic with machine learning.

A huge number of invoices can be analyzed in no time. The companies can reduce their efforts and time on managing their finances and save a lot of cash too. Plus, the security of machine learning technologies provides protection to the processes at the same time.

4. Marketing and Management

Marketing and management can also get innovative results with machine learning. The AI tools are already being used in gathering customer data, supply chain management, and other processes.

Companies are leveraging machine learning tools to find data related from social media about products, logos, and other factors. All this data is used to create a better brand exposure and to get successful outcomes.

All in all, AI and machine learning offers innovation to almost every part of a business. So, it would be wise to integrate right tools and algorithms to improve ROI and make your business a success.

Artificial Intelligence in Social Media Marketing

It is increasingly being proven that AI is helping marketers find leads and engage like never before through social media. Targeting social media users over the online space is a highly specialized and complex field. Artificial Intelligence (AI) can be a potential catalyst that can drive the fortunes of digital businesses across the globe.

Why the need to introduce AI into marketing?

73% of B2B users need to be nurtured for quite some time before they are converted to active leads for the business. In a traditional ecosystem, marketers simply cannot keep pace with the lead management scenario that evolves daily for each and every enquiry. AI can help devise and deliver personalized content to nurture leads to a better degree.

How does this happen?

Marketers can leverage the basic principle of AI to understand human psychology and its implications in the real world scenario. When marketing automation uses AI, it helps understand and monitor various aspects of customer behaviour like –

1 – How they spend their time online

2 – What posts or products get the most visibility from them on social media

3 – What do they use the social media for

They combine this with past marketing campaign data and then build appropriate marketing messages that have good potential for success. Here are some ways in which this can be done.

1. Better insights on CRM

Bits and pieces of useful information may lay hidden in plain view in various mediums like emails, phone calls, or social media posts/comments. With Artificial Intelligence, you get the right hints about what steps can be taken to subtly induce the prospect to move up the sales funnel towards successful conversion.

With the presence of sufficient amount of data, you can also configure a sentiment analysis activity to grasp the purchase goals/motivators behind the words used by the prospects on social media.

2. Align social media content with buyer persona

With a structured buyer persona, a marketers’ targeting efforts yield better fruits. AI can help smartly segregate customers into personas for a better level of personalized marketing. AI can also learn about which type of content (blog posts, whitepapers, videos, case studies, or other marketing collaterals) will help which type of personas at a particular level in the sales funnel. This way, customers will find only the content which is relevant and timely to their particular need.

3. Social sentiment analysis

With continuous social media analysis a viable brand analysis picture can be formed. However, keeping track of all the posts and ads and analyzing these on different platforms can be complex.

It eats up a lot of time for the marketer which could have been otherwise used to push fresh and unique content for the readers. This is where AI comes in. Take NY Times’ case – it has been using chatbots in innovative ways to create a deep one-on-one experience for its readers.

Thus, with astounding levels of competition to capture eyeballs on the social media, these advantages prove that AI adopters will have an upper hand in this regard.

UX Design for Chatbots

If you look at the market and business trends, Chatbots are available on almost every list. All the big businesses and brands are leveraging chatbots. On the other hand, small brands are planning to have one for their business.

Experts say that chatbots are going to cover about 85% of customer service related interactions in the coming years. However, the popularity has also increased the demand for a quality experience. Hence, businesses can’t compromise the UX design of their bots in any manner.

Sure the functionality of the bots matters a lot, but it is the user experience of the design that brings customers again and again.

Here, in this article, you will find the most valuable UX guidelines to create an impressive chatbot for your business.

1. Make it easy to understand

The initial impression matters the most in your chatbot design. The users should be able to understand the functions and the processes of the chatbot very easily. Only then, you can expect them to come back for further interactions.

So, make sure you include exciting and helpful elements in the onboarding process of the chatbot. This will make the design more impressive for the users.

2. Add elements to maintain the conversational flow

Many times, the users don’t realize that they are interacting with a chatbot. So, if the bot does not maintain a conversational flow, the users might leave and never come back.

To avoid this, it is important to add elements that can help you maintain a conversational flow. A chatbot can ask pre-defined questions or present suggestions to the user. These elements in the design help out the user throughout the conversation. Some of the advanced chat platforms such as Facebook Messenger and Kik leverage such elements in their chat methods. These platforms offer regular response suggestions during an ongoing conversation, which helps the users.

3. Give it a consistent personality

The personality of the chatbot is probably the most important UX design component. The goals should be to attain consistency. Plus, the bot should sound friendly during the conversation.

To achieve that, you need to focus on providing clear diction capacity and simple language to the chatbot. Use a vocabulary that is generally used in the common language. This will make the conversations more smooth and friendly.

4. Prepare chatbot for anticipated issues

A conversation between a human and a chatbot presents some difficulties. Sometimes, a user might ask an invalid question or a query, which won’t allow the Chabot to answer. However, that should not stop the conversation. Your design should get the user on the right track for the conversations to flow. For that, you can include polite reminders of the purpose of the bot. The bot can provide suggestions and tips to help the user ask the right questions and queries. This way, the conversation won’t end in the middle.

So, in this way a good UX can help you create an impressive chatbot and also create immersive user experiences for your customers.

Natural Language Processing tools for Chatbots

Language understanding tools have the capacity to make chatbots smarter. These tools are designed to enhance the communication capabilities of the chatbots. The ability to understand the sentiment, create an automatic summary and find a relationship between the topics. All these abilities can make your chatbot much more effective for the users.

Here are 5 NLP tool choices that can help your chatbot deliver high impact performance in customer servicing.

1. LUIS

Microsoft offers cognitive services to provide language intelligence and other capabilities. The LUIS or Language Understanding Intelligence Service offers high-quality models that help chatbots understand various entities and intents. The availability of the models related to times, places and others enhance the performance of chatbot. The users can get much better experience, as an active understanding of the language is used.

The tool is compatible with various platforms such as KiK, Slack, Facebook Messenger, Skype, SMS, and others. You can get both free as well as paid versions of the tool.

2. RASA NLU

When you are looking for an open source method of classifying the intent, RASA NLU is your answer. The tool offers a complete set of APIs that make entity extraction highly convenient for the chatbot.The libraries come along with the tool that enhances the capacity of any bot. The corporate sectors like health, insurance, travels, telecoms and banks get the maximum advantage of this tool.

The tool is available for free and works on platforms such as Facebook Messenger, Telegram, SMS, Skype, Line, and others. This is why the tool has gained an incredible level of popularity in multiple sectors.

3. Amazon Lex

The tool can provide high-quality language capabilities to your mobile applications. The specific properties include natural language recognition and speech recognition too. Hence, the chatbot can provide both text and voice experience to the users.

This will improve the ability of the bot to assist and help the users conducting various tasks such as placing orders, opening an account, making bookings, and others. The tool offers a simple console, which makes the processes much more convenient for the chatbot.

4. API AI

Known as one of the most trustworthy language understanding tools, API AI makes brand specific interactions easier. The businesses can leverage this tool to create bots that can provide multiple services to the users.

The conversational interface with this tool takes not much effort. Plus, the powerful features of the tool allow the bots to answer highly complex questions asked by the users. The bots get to leverage the stored knowledge along with the machine learning. Hence, the bot learns and gets better.

Some other valuable features include multilingual interactions, cross-platform support, easy integrations and others.

5. ChatScript

ChatScript allows you to create a script for the dialog conducted by the chatbot. The tool uses the scripting rules to create extensive texts. Plus, it scans documents, memorizes old interactions and manages a large volume of users.

All the mentioned tools have their own set of properties. You can add these tools and enhance the capabilities of your chatbot.

Artificial Neural Networks – The basics

Artificial Neural Networks are simulations that are derived from the biological functions of ‘neurons’ which are present in the brain. Thus, Artificial Neural Networks are essentially artificial neurons configured to carry out a specific task. ANN has gained a lot of popularity as it is used to model non-linear processes.

Artificial Neural Networking allows solving problems like clustering, classification, pattern recognition, prediction, and determining outliers. This has made ANN a very useful tool.

How does ANN work?

Artificial Neural Networks acquires knowledge through learning continuously. Like in humans, the knowledge acquired is stored in the artificial neurons designed within the ANN and used to perform the required task. ANN has a wide range of syntax, semantics, and speech-tasks which help ANN solve a wide range of problems.

Some of the interesting applications of ANN are discussed below.

1. Text Classification

Applications like web searches, language identification are some of the applications that use text classification. Neural Networks are widely employed for this type of classification. Experts agree that deep learning can be applied to enhance the value delivered by text classification. Artificial Neural Networks can be applied from character-level inputs as well as abstract text content.

CovNets or Convolutional Networks can deliver good outcomes in text classification without prior knowledge of words or phrases by applying them along with deep learning and Neural Networks.

2. Semantic Parsing

Artificial Neural Networks can be actively helpful in answering questions. A Q&A system will automatically answer any question asked in natural languages like definition questions, biographical questions and so on. Using Neural Networks in these systems makes it possible to maintain a high performing question answering system.

Developers have released semantic parsing framework for answering questions using a specific knowledge base. ANN uses this framework to quickly identify the type of questions and then answers it using semantic matching. There are other frameworks available which can further improve neural networks’ performance in this field.

3. Speech Recognition

Voice technology has advanced and now it is used for automated telephone conversations, speech-only computing, and much more. Neural Networks are being used extensively in this area. neural networks can specifically be programmed to handle multiple types of queries over a wide range and with continuous learning, neural networks help you achieve a great speech recognition software.

4. Character Recognition

Character recognition has become vital in today’s world across different industry verticals. There are many practical applications in this realm. Some instances include character recognition on receipts, invoices, checks, or legal billing. The Character Recognition framework for Artificial Neural Networks has been effectively used in this field and tests have shown the accuracy to be above 85%.

5. Spell Check

Text editors help you find out misspelled words to help you rectify them. Neural networks have been incorporated in many of these text editors nowadays to provide easier spell checks. It uses the personalized spell check framework and it outperforms many other text editors that don’t use Artificial Neural Networks today.

To conclude, we can say that Artificial Neural Networks are very versatile and make a lot of jobs easier in different functions within an enterprise.

Chatbots in Recruitment

Did you know that 74% of the candidates for a job recruitment drop out after starting the job application process? The lengthy process and reams of paperwork are some factors for this stat. How good would it be if technology could present a solution that takes out the tediousness from the entire recruitment process?

Well, technology already has a solution ready that fits in perfectly in this context – chatbots

What is a chatbot?

A chatbot is an AI programme that converses with humans in a meaningful and contextual way. Their ability to be accessible to customers round the clock adds multiple business advantages. They not only elevate user experience but also reduces costs of maintaining full-time customer support personnel.

Because of the immense business value that a chatbot offers, it finds applications in multiple industries. The recruitment industry is the latest one to have enjoyed the benefits of having a chatbot. Mya and Job Pal are two examples of chatbots that are revolutionizing the recruitment industry to a great deal.

How can a great chatbot make recruitment effective

1. Save time and money

Chatbots have evolved to be smart and useful. Right from sifting resumes to answering initial queries from a job applicant, it can do it all without the need for an actual executive to sit in front of a computer to carry out these tasks. It can also determine if a particular job opening is aligning well with a particular candidate during a conversation.

2. Application process made more effective

A chatbot is a better option to engage in the initial phase of candidate application. It can save time and get the needed information by taking the course of a natural conversation. This way the high chances of midway drop-outs through the application process can be brought down significantly.

Even if the candidate leaves midway, the chatbot can nudge him/her later on in subtle ways to try and get the entire application process carried out. From scheduling appointments for interviews to sharing information on new job openings and letting the candidate know about the application status, a chatbot can help make the job application process less cumbersome.

3. Pre-screening process made transparent

The traditional interaction between a recruiter and a candidate is filled with uneasy periods of silence post the interview. A chatbot can help fill this gap by a pre-screening process and making the entire activity an interactive and transparent one.

By instantly providing information on approval or rejection, the candidate can take the next appropriate step. This transparency of application process helps candidates get a quick update on the status of their job applications and reduces a lot of back an forth procedures for the HR management and candidate.

4. Automate routine tasks

Mechanical tasks such as sifting through resumes, scheduling interviews, and internal coordination are routine yet necessary tasks within the recruitment activity. With a chatbot, all these routine tasks can be easily automated and HR professionals can focus on more complex activities such as employee branding, improving the outreach and other management related aspects.

To wrap up, while many recruiters feel that chatbots are likely to make jobs obsolete, the fact remains that they actually make the recruiters’ job more powerful and effective rather than making them redundant.