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Automation in Recruitment Sector

Automation in HR/Recruitment Sector

The COVID pandemic has greatly affected the business world, crashing down economies with people getting laid off left and right by companies. We are slowly recovering from its devastating impacts. Getting back to our routines with companies opening back up and employees returning to their workspace. There has been a growing trend of automation-assisted recruitment by HR departments to simplify the process and bring back employee strength and performance to pre-COVID levels. Here are a few insights on how automation can influence recruitment

Introduction 

Automation has been an essential part of our work process since its inception and has diverse applications in various steps of our work. It has helped simplify work by handling repetitive, mundane work allowing employees to focus on more critical tasks that require dedicated focus and time, thus improving the overall performance and results of the company. HR is no exception to the growing applications of automation and has discovered great effectiveness in the incorporation of automation in HR. 45% of company representatives and HR leaders feel that automation makes their job processes better helping them save time. Polls suggest that HR employees spend nearly 14 hours per week on routine work that can be automated to save their time signifying how automation can simplify the recruitment of new employees. 

 

Applications of Automation in Recruitment

Robotic process automation involves the use of chatbots and digital software to communicate with new applicants and analyze various parameters required for recruitment of suitable candidates such as communication skills, personality assessment, etc providing HR and management with a holistic picture of their abilities. Polls suggest that only 26% of companies think HR utilizes data analytics efficiently and 61% of companies are modifying their jobs and recruitment tactics to better employ AI and automation in their work processes. Here are a few applications of automation in recruitment:

 

  • Sourcing of Candidates

    There are millions of candidates applying for job opportunities for different positions at companies and enterprises making it a humongous task for HR teams to go through their applications and assess them efficiently. Automated software helps weed out applications by intelligently weeding out unsuitable candidates and retaining only appropriate candidates for the interview process. Intelligent coding and properly planned out criteria establishment helps companies identify ideal candidates using techniques such as keyword tracking, applicant experience evaluation, etc. 

 

  • Communication

    Proper communication with applicants is essential to maintain company credentials and reliability to new applicants. Polls suggest 95% of the applicants would apply for a company again if they had a positive experience the first time and 80% of the applicants would not apply at a company again if they didn’t receive any form of communication from them. Applicants also report their bad experiences with such companies at social media platforms like Glassdoor affecting the overall reputation of the company. It is impossible for HR teams to directly engage with every applicant and that is where automation comes in to save the day, with efficient chatbots conversing with the clients and responding to them using bulk email systems. Proper analysis of their applicants is done using the software systems and appropriate feedback is generated by the systems to provide them suitable feedback about their application. Potential employees suggest that 69% of potential applicants prefer a reduced time of response from the companies.  

 

  • Enhanced Marketing Strategy

    Automation can help you plan out the email and social media campaigns meticulously with the software handling the major bulk of implementation of the strategy. Drip emails to active and passive candidates create a higher level of engagement with potential employees and improve correspondence with them. They help improve the reach of information and campaigns to people contributing to building robust talent pipelines and improving a company’s reputation. 

 

  • Interview Scheduling

    Automation also can assist HR teams in scheduling interviews with clients online easily after selecting suitable candidates from the initial pool of applicants. They interact with applicants and provide real-time access to the calendar of HR teams allowing them to select appropriate slots for their interviews making it easy for both parties. Chatbots can also answer frequently asked questions from applicants while leveraging computational linguistics and sentiment analysis to interpret communication and provide suitable responses. Companies with strong on-boarding processes after interviews increase employee retention by 82%.

 

How to Efficiently Tap in Automation Resources

Automation has a wide range of features to offer and as with all good things available; they come at a high price. HR teams should analyze and understand their requirements properly to employ automation efficiently in a cost-efficient manner. They need to train their teams to get them familiarized with the end-to-end automation of their recruitment processes. It helps them identify any inconsistencies in their software at the earliest to correct them as soon as possible. 

 

Compliance risks should be analyzed before initiating recruit processes to protect the company from potential risks and recruitment strategy should be discussed with stakeholders and partners thoroughly to ensure transparency in work processes.

 

Conclusion

Hiring suitable employees in companies is a tedious process with great responsibility ingrained in it. Companies need to recruit the right applicants for their job openings and retain them in their companies as it would cost them nearly 33% of their employee’s salaries in hiring a new one to replace their positions. Automated recruitment processes help build a diverse and robust network of talented employees improving the office culture and work environment. Understand automation processes and customize them to best suit your requirements to better enable your HR teams to empower your entire company.

 

10 Real World Examples of Deep Learning Models & AI

For the vast majority of us, concepts like deep learning and Artificial Intelligence are still alien. Most people who come across these terms for the first time react with mixed feelings of skepticism and intimidation. How can we make machines learn and execute jobs meant for humans?  What really explains an entire industry bent upon making machines behave like humans?

While these questions are important and call for discussion, we can easily do away with much of the skepticism. That is, if we are willing to look at some real world applications of deep learning and artificial intelligence. In this article, we show you ten ways in which artificial intelligence and deep learning are turning wheels across industries.   

Where does deep learning come from?

Machine learning and deep learning are both subsets of artificial intelligence. Deep learning is the evolved and advanced phase of machine learning. In machine learning, human programmers create algorithms that learn from the data and derive analyses.

Deep learning is different from machine learning in that it works on an artificial neural network which closely represents a human brain. The same network allows machines to analyze data just the way humans do. Such machines with deep learning capacities do not require to act upon the instructions of human programmers.   

Deep learning is made possible through the ginormous amounts of data that we create and consume daily. Every deep learning model makes extensive use of data to facilitate data processing.

10 Real World Applications of Deep Leaning

Here are ten ways deep learning is already being used in diverse industries.

1. Computer vision

High-end gamers interact with deep learning modules on a very frequent basis. Deep neural networks power bleeding-edge object detection, image classification, image restoration, and image segmentation. So much so, they even power the recognition of hand-written digits on a computer system. To wit, deep learning is riding on an extraordinary neural network to empower machines to replicate the mechanism of the human visual agency.

2. Sentiment based news aggregation

Carolyn Gregorie writes in her Huffington Post piece: “the world isn’t falling apart, but it can sure feel like it.” And we couldn’t agree more. I am not naming names here, but you cannot scroll down any of your social media feed without stumbling across a couple of global disasters – with the exception of Instagram perhaps.

News aggregators are now using deep learning modules to filter out negative news and show you only the positive stuff happening around. This is especially helpful given how blatantly sensationalist a section of our media has been of late.

3. Bots based on deep learning

Take a moment to digest this – Nvidia researchers have developed an AI system that helps robots learn from human demonstrative actions. Housekeeping robots that perform actions based on artificial intelligence inputs from several sources are rather common. Like human brains process actions based on past experiences and sensory inputs, deep-learning infrastructures help robots execute tasks depending on varying AI opinions.

4. Automated translations

Automated translations did exist before the addition of deep learning. But deep learning is helping machines make enhanced translations with the guaranteed accuracy that was missing in the past. Plus, deep learning also helps in translation derived from images – something totally new that could not have been possible using traditional text-based interpretation.

5. Customer experience

Many businesses already make use of machine learning to work on customer experience. Viable examples include online self-service platforms. Plus, many organizations now depend on deep learning to create reliable workflows. Most of us are already familiar with the use of chatbots by organizations. As this application of deep leering matures, we can expect to see further enhancements in this field.

6. Autonomous vehicles

The next time you are lucky enough to witness an autonomous vehicle driving down, understand that there are several AI models working simultaneously. While some models pin-point pedestrians, others are adept at identifying street signs. A single car can be informed by millions of AI models while driving down the road. Many have considered AI-powered car drives safer than human riding.

7. Coloring illustrations

At one point, adding colors to black and white videos used to be one of the most time-consuming jobs in media production. But thanks to deep learning models and artificial intelligence, adding color to b/w photos and videos is now easier than ever. As you read, hundreds of black and white illustrations are being recreated in colored form.

8. Image analysis and caption generation

One of the greatest feats of deep learning is the ability to identify images and generate intelligent captions for them. In fact, image caption generation powered by AI is so accurate that many online publications are already making use of such techniques to save time and cost.

9. Text generation

Machines now have the power to generate new text from the scratch. They can learn the punctuation, grammar, and style of a piece of text and pen down effective news pieces. Robo-journalists riding on deep learning models have been producing accurate match reports for at least three years now. And the skill isn’t limited to match report writing exclusively.

AI-based text generation is fully equipped to handle the complexity of opinion pieces on issues concerning you and myself. As of now, text generation has helped create entries on just about everything from children’s rhymes to scholarly topics.

10. Language identification

At this point, we are looking at a preliminary stage where deep learning machines can differentiate between different dialects. For example, a machine will make the decision that someone is speaking in English. It will then make a distinction based on the dialect. Once the dialect has been established, further processing will be handled by another AI that specializes in the particular language. Not to mention, there is no human intervention in any of these steps.

These were just a few applications of deep learning that exist already. The further growth of deep learning models will bring to us many more uses of artificial intelligence around us. At Futran Solutions, we work with top-of-the-line AI resources that make the above industry applications of AI come to life. Contact us today to find out more about our RPA, AI, and deep learning solutions.

Jyoti Vazirani is the co-founder and CEO of Futran Solutions. She is a certified SAFe Agile coach and an out and out deep learning enthusiast.

AI, NI And The Future!

(Futran solutions is a company that offers RPA solutions. While respecting automation, Futran Solutions also participates in re-training of the employees who lose jobs owing to RPA)

Is AI thrusting upon us and our future generations a new technological, psychological and socio-economic change? Slowly but surely, are we humans becoming redundant for office, society and the universe at large? More terrifyingly, are machines really going to take control of humans? I believe we have looped this routine too many times on Hollywood to make it true? But we would all be too stupid to ignore the fact that the seeds have been planted already!

As companies prepare to make their businesses AI-powered right from smart virtual assistants to robotic process automation, we are looking at a tech-future with inevitably dwindling demand for human resource and Natural Intelligence.

At this point, we are a bit hard-pressed to figure how our LIMITED(!) natural intelligence will compete with the GOD-LIKE-POWER(?) of artificial intelligence. For once, let’s be honest and not pretend that there is no competition. There is tremendous competition already and it’s swelling up as we speak.

AI & NI: The Battle

When we speak about the competition between Artificial Intelligence (AI) and Natural Intelligence (NI), we are not expressly looking at a Terminator styled battle between human-made intelligent robots from the future and gym-vest wearing men and women of our times. In all reality, it will be the battle of employment between AI-powered robots (that are fast and accurate) and Natural Intelligence (NI) powered Humans of present times (that are slow and error-prone). A single AI-powered robot can potentially leave an entire team of 50, 500, or 5000(!) unemployed. The war has been waged; the winner is known! Only time confirms the facts!

Commercial manufacturing is already automated to a very large extent. Now imagine a situation where the smallest of factories are automated. The army is automated and no country has human armies. Governments are automated. The judiciary is automated where deep learning interprets cases and generates the judgments in seconds. And entertainment, too-imagine that you can virtually produce your own Marvel(like) movie for private viewing and that displaces thousands of people working for months together. Transport, heavy engineering, and several other industries-all automated to a point of no return.   

If such a future were to really transpire, Tesla would become a company with one employee. Elon, alone and his giant team of robots with greater caliber than human intelligence fill everything from boardrooms to shop-floors. Similar fates will embrace Amazon, and Google, and Microsoft. In that hypothetical situation, since all the workers are laid off and replaced with robots, humans shall stay back in their homes for the lack of work. They shall no longer be able to afford the luxuries, necessities, wants, and pleasures that they did when they were employed.

They’ll reach a stage where they have no money to BUY!

No jobs will soon mean no difference in affordability and no need to travel from Los Angeles to San Francisco in 30 minutes. No need to travel to Singapore in an hour. On the flip side, the sole shareholders of the big companies will lose business faster than we can imagine because no one will be able to buy their service/product. It’s pretty much the story of Bee Movie that was released in 2007. From that point, humans while battling machines, start living like our ancestors; growing their own food in their backyards, flocking to one another like never before to form the small habitats of self-reliant communities!

The situation has been discussed in detail about several eminent personalities of the Silicon Valley. While some people see no net change occurring, others have warned of grave consequences.  

How Does it Unfold?

Where do we stand speaking from a totally neutral plane of judgment? For the start, it is always wise to read from the pages of history when throwing light on the future. Remember the time when the coal mines were shut down and all the workers in the coal mines summarily lost their jobs! America became a global hero for taking the first big step in the fight against global warming at the cost of the joblessness of thousands of Americans.

The mine workers were never re-trained and re-employed. Not by the holier-than-thou corporates. Not by the holiest-among-the-holy governments of the world! So was the case when the factories were shut down!!

Aren’t mining and technology two totally different coins. Yes, on the face of it. But when you dig deeper, it is controlled by the same industry dynamics and the overall economics is same for every industry.

Companies will make a mad rush toward profits for shareholders, as the main job of the CEO is to increase the wealth of the investors. (A suggestion from us is; let the company’s performance also be measured by give-back-to-customer-index every year like the dividends to the shareholders, under the control of SEC!) To show investors an unimaginable P/E (price to earnings) ratio, corporations will lay off workforce by the hundreds and the thousands.  

The Takeaway 

For effective integration of Natural Intelligence and Artificial Intelligence, Governments may have to intervene with a legislation in favor of the “rehabilitation of the displaced workforce.” Without such planning mandated as a law, it will be easy for corporates to replace the people with AI-powered bots.

Futran Solutions specializes in RPA and is a proud partner of AA (Automation Anywhere). We provide solutions to our clients tailored to the RPA niche. In addition, as a part of our integrated solutions, we also offer re-training to the part of the workforce that will be affected by RPA. This allows employers to use the employees’ skill elsewhere and helps the latter explore other realms of technology without a day of unemployment.

Both the bridge and the boatman help us cross the river. The bridge promises to make the journey smoother and shorter. So we choose the bridge. But while the bridge is being built, the system must reward the years of service by the boatman and find him another career option before he wakes up jobless one day. How about training and rehabilitating him as a toll collector?

We, at Futran Solutions, support AI while respecting Natural Intelligence.

Jyoti Vazirani is the CEO of Futran Solutions. She can be reached at vjyoti@futransolutions.