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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.

 

The New AI Machines on the Block: Is the Unicorn Turning Into a Behemoth?

Artificially intelligent or AI machines hit our imagination way before reality. Consider the Golem of Prague: a magically animated, mud-bodied robot with little to no imagination but insurmountable damage prowess. His character was written in the 1600’s – roughly 200 years before Frankenstein’s infamous monster. Of course, modern pages of robot-lore are filled with a laundry list of malevolent AI-powered machines ready to break out of 3D screens at movie theaters. Our imagination has even shaped AIs like Ultron and Skynet ready to threaten human extinction.

However, after all that hype and fear-mongering, what we really bring home is a mild-mannered AI-powered device playing songs and booking cabs with voice commands. You could term that split the difference between bestselling pop-culture content and ruthless consumerism.

We use the intelligence of AI machines like Google Assistant and Alexa to our heart’s content. After all, there’s the fear of missing out if not genuine use for such technology. But then, there’s the concern over privacy. It’s a just shadowy feeling of invasion though – like when you acknowledge that you might have left the front door open but are too lazy to get off the bed, so you trade off your sleep with what could be a potential disaster.

The industry is another dimension of AI in itself. Businesses continue to leverage AI and improve upon just about everything from chat support to workflow to brand intelligence. Aberdeen research indicates that one in every two organizations is leveraging artificial intelligence in some form or the other.

The Complexity of AI in Business

As we speak, AI is playing songs at our homes, gobbling up a hundred Excel sheets in seconds at the workplace and scaring us to the hilt in the movies. Effectively, AI participates in our lives all through the week – and on weekends, too. That’s smart to the point it’s scary.

Nevertheless, if you run an enterprise, you cannot possibly do without AI. But as is the case with every new technology, adopting AI into business can be trickier than it seems. You cannot just turn up at your technology provider’s and ask for an all-business-pervasive AI package. Businesses are feeling sufficiently roughed up even when it comes to implementing AI solutions to select verticals of operation.

Perhaps the most iconic hurdle to implement AI in business is the level of expectations of users. The best models of AI technology (Alexa and Google Assistant) are both affordable and accessible. This means regular folks all around the world now have hands-on experience of Artificial Intelligence making their lives easier and more entertaining. To wit, if your product is based on or makes use of any form or manifestation of AI, the experience has to be every bit as neat as Amazon, Google, or Facebook.

Concerns Over Privacy & Additional Regulations

Notwithstanding the gamut of applications and overwhelming contemporary relevance of AI, the technology is not free from concerns. So far, the outstanding concern over AI has been over privacy. Lately, there has been news of Alexa storing recorded conversations and Facebook displaying ads based on regular conversations between individuals. Such vaulting ambitions on the part of tech companies have aggravated the ‘AI-trumps-privacy’ stance of many consumers.

Plus, you have to consider something called the Turing Test. Wikipedia defines the Turing Test as

…the test of a machine’s ability to exhibit intelligent behavior equivalent to or indistinguishable from, that of a human.

The test itself was developed by a gentleman named Alan Turing in 1950. 68 years later, in 2018, we got to see something extraordinary at the Google IO – Google Duplex. Check out this clip:

Case in Point: Google Duplex

What we see in the clip above is either amazing or bone-chilling, depending on the way you look at it. While most tech enthusiasts rallied solidly behind Google and their technology (and how dumb they made Siri look), there were some that spotted an element of cheating in not immediately letting the respondent know that they were speaking to an AI assistant and not an actual human being.

To be honest, we already have AI chat assistants pretending to be humans and we can’t tell which is what. It’s just that the level of colloquial and sentential nuance with the ‘mm-hmm’s and ‘uh’s that the Duplex demonstrates at once injects a bunch of frightening realizations. If I recall this correctly, the only relevant examples of such trained AIs are Jarvis and Friday from the Marvel movies. That’s perhaps only one of the realizations that have been sending a chill down spines. And this value of shock will inadvertently play out every time we realize and materialize technologies from Sci-Fi movies.

The big question is about the necessity of something like Google Duplex. We understand the bit about the top brass of Silicon Valley decision-makers, who, in all probability, have shaped the theory of the Duplex. But is the average Google customer really that busy? Something tells me that technologies like Duplex are less about the ‘busi-ness’ of people and tell more of a story about their stark laziness.

Monetizing AI Machines: Cute or Cunning?

Cashing out on public laziness is not really close to being a danger – that’s just good enterprise. The real game where the plot gets murkier (and dangerous) is this: (how) does Google plan on monetizing a technology like Duplex?

When you come to think of it, the same technology that books an appointment for a customer at a hair salon can also respond to the customer’s call on behalf of the salon. Add one value to the other and the receptionist could summarily be forced into hunting for an alternative career – in a market with fewer jobs for humans anyway.

Be that as it may, there’s no doubting that this is a remarkable point (somewhere close to the crest?) in the historical revolution called Artificial Intelligence. Businesses will adopt AI because of its manifold benefits, which, at least on face value, outweigh the concerns. Artificial Intelligence will help companies reduce costs, make decisions with greater certitude, cut down on ‘human errors’ and hustle through process speeds to deliver a real winning strategy.

Trend Picking Intelligence

AI Machines and systems play a big collective role in predicting customer trends. As these trends are fragmented into categories and the categories subsequently analyzed, innovations will rise out of nowhere.

We will witness rather sudden products and platforms, many from unsuspecting market players. Moreover, there’s always the blessing of artificially intelligent customer service interfaces and bots that eliminate the inefficiency and time lag of the ‘the-team’s-getting-used-to-it’ phase. Aberdeen research indicates that businesses that use AI machines save significant IT costs and serve customers faster.

The irony with Artificial Intelligence in business is this: the line was drawn way before we discovered any scope for debate on the technology. Whether we like it or not, this is what the future looks like. Businesses that avoid Artificial Intelligence today will suffer the same fate as businesses that avoided computers back in the day. Put plainly, only fools would miss out.

AI, Machines & the Future of Business

No one’s asking anyone to overlook a definitive gamechanger like artificial intelligence. But organizations must also weigh in, what I like to call from here on, the Cost-to-Human (or CTH) factor of it. By all means, go for it if the consequences are a) irrevocable but small or b) substantial but revocable. However, if the underlying cost of leveraging AI machines in business is both grave and irrevocable, step back and find an alternative (not necessary an alternative technology though). Now that we live in Industry 4.0, it must be the collective responsibility of us all to ensure human well-being before anything else. Jyoti Vazirani, CEO of Futran Solutions has previously written elaborately about the conflict between artificial and natural intelligence.

With that, let’s go back to the question I started this piece with. In all likelihood, the golden unicorn called AI is turning into an all-pervading, oversized behemoth. But not all giants torture and maim and kill. That’s what our collective psychology would surmise since the tale David and Goliath was written. However, recent literary evidence shows us that the opposite could be just as true (think Hodor and Wun Wun from GoT).

My point is AI could go either the Goliath way or the Hodor way. What’s certain is that it will go one day (just like Goliath and Hodor). The thing that really matters is how we treat it while it stays. At the end of it all, history will remember us for the way we treated humans when we had a set of options to choose from.

Our stance

At Futran Solutions, we offer solutions in RPA and Artificial Intelligence. While respecting automation, Futran Solutions also participates in re-training of the employees who may lose their jobs to AI machines and systems.

Futran Solutions supports robotic process automation and artificial intelligence. We are a pro-technology company. And we believe that if a technology deserves to go viral, we must do our part in making it viral. We provide a range of RPA and AI solutions to industries across the board. Adjacently, we run a series of training programs to aid the displaced workforce.

Drop us a line to know how we can help you with RPA consulting and project implementation.  

Krishna Vemuri is the co-founder of Futran Solutions and the CEO of the up and coming tech startup Onata. He writes on technology industry dynamics and the rather eclectic tantrums of his husky, Loki.

RPA and GDPR: Security Governance in the Automation Era

RPA and GDPR: Security Governance in the Automation Era

The data on security breaches is overwhelming on many fronts. Over a billion records of consumers have been compromised since 2005. The total number of breaches in the period is threatening at around 8000. As late as 2017, big companies like Target, Equifax, and Neiman Marcus could not shield themselves from data breach attempts. Mind you, one of these is a top national credit reporting agency.

Noted analyst Avivah Litan predicts the following instances of misuse for the stolen data:

  • The data can get tossed around in an endless sell-and-resell loop of underground data piracy
  • Sensitive data can be used to steal bank accounts from customers
  • Identity thieves can use the data to update their existing records of targeted individuals
  • Adversarial nation states can use the data to disrupt peace or launder money out of the US

None of these constitutes stray casualty. The cumulative implications of the breaches are beyond grave. In fact, it is very difficult to quantify the damage dealt by these breaches to the society at large. That is where the General Data Protection Regulation (GDPR) swings into action. It gives consumers greater control over their own data while making corporates bite the bullet on their data processing practices.

What is the GDPR?

The GDPR is a regulation adopted by the European Union. It lays out the norms for data protection and privacy for the individuals that live in the European Union. It is one among the series of regulations that have helped formalize governance around security concerns of the average consumer.

In addition to strengthening consumer rights, GDPR aims at formalizing security standards that companies must establish to protect the data of their consumers.

Every organization functioning out of Europe and non-European organizations that collect the data of European citizens are expected to comply with the GDPR. The latest GDPR guidelines regulate how personal data is used, processed, stored, and deleted.

The GDPR also lays out that data subjects can request for both access and real-time usage information from organizations. If there’s any breach involving the personal data of users, it must be reported to the appropriate authority that oversees the regulation.

Security Governance: The Onus is on the Enterprise

At the crux of the GDPR is the impetus the regulation puts on enterprises to do all things necessary to protect consumer information. This has forced every enterprise software vendor to re-evaluate their policies regarding storage and management of sensitive user data.

This is where Robotic Process Automation (RPA) is impacting the industry in a big way. RPA platforms like Automation Anywhere are instrumental in offering comprehensive features in security and reliability. Starting with automation at once promises the following benefits for organizations:

  • Data encryption at all levels – when the data is in memory, in motion, or at rest.
  • A robust security framework (either built-in or third party) that guarantees security in the management and storage of user information. As a default practice, machine that store user credentials meant for critical purposes and the machines that run the software should always be exclusive.
  • Analysis of codes on both static and dynamic parameters, including manual pen testing for unbreakable application security.
  • Seamless enterprise based authentication system integration
  • Expansive logs of audits to support forensic analyses and audit processes
  • Secure operations that that make sure data is not exposed to business process threats during standard execution of processes

RPA platforms work with many ERP tools and in effect touch extensive sets of data within your organization. In case you are already using an RPA platform, make sure to check with them on GDPR compliance and the security measures they follow to ensure compliance.

How is RPA Easing up GDPR Implementation?

The first and absolutely unavoidable threat with manual processing of customer data is the guarantee of human errors. It does not really matter what level of security you follow. Even the slightest margin of error means that the organization is at the risk of non-compliance.

With RPA, you can automate the process defined by the legal and business teams to become GDPR compliant. Here is a collection of ways in which bots are helping enterprises with GDPR compliance:

Audit Logs

Enterprise RPA platforms are loaded with audit logs which monitor every operational process, creating logs for users and events at every stage of a given process. When there’s a data breach, audit logs swing into action with recurring spells of root cause analysis. What follows is routine forensic analysis to recognize and thereafter report the breach.

Content that relates to specific internal or external events can be gathered concurrently in real time. This comes in especially handy in case an organization is attempting to decode a fraudulent activity.

Documentation of data

There’s a lot of data pouring in from devices, sensors, and systems at the office. From the organizations perspective, it must be able to document all the data that is held in its directory, along with the source of its origin. The organization must be able to submit updated reports to the authorities in charge of data protection. GDPR mandates companies to purge personal data once it has crossed the holding period.

This is another area where RPA can help organizations by using bots that automate the process of masking PII data that is identifiable across applications. For the PII data that does not adhere to established policy, Natural Language Processing (NLP) lets bots recognize such data and generate alerts that help in intercepting the issue.

Data Breaches

GDPR makes it mandatory that subjects affected by data breaches be informed about it within 72 hours. For data breaches of a magnanimous nature, sending out information to everyone involved within 72 hours can become almost impossible. Imagine the case of Equifax, where 143 million users directly affected by the breach.

On the flip side, it is way easier to automate software bots to perform the job. In most instances, it does not even take 72 hours and makes sure the security governance timeframe is met.

Right to Access Information

European customers can request to access their information and know how an organization stores and uses the information. GDPR guarantees this right to all European consumers. If an organization wants to do this manually, it would need a dedicated team of individuals. Plus, every individual on the team must have access to such information.

It is way easier for bots to navigate through different systems and pull out data relevant to every user in question.

Right to Information Deletion

If a user requests an organization to dispossess their personal information, GDPR mandates the organization to delete such information promptly. Consider there is no automated process to do this. An employee or a team will have to access the information and then delete it from dozens of applications. Bots can not only pull out the relevant information on users but also email the report back to the concerned customers.

Some Data Cannot be Seen

There are legacy systems hiding data more than a decade old. Data can be accessed from these systems when needed. However, it’s never been as important to uncover sketchy data as it is now. RPA is the most convenient way to integrate the current technology platforms with legacy systems. Automation is also perhaps the only way to document and recognize available data that might be the cause of non-compliance.

Most companies are still taking their own sweet time understanding and dissecting the General Data Policy Regulation. At this, there is the threat of flooding of requests by consumers. Adhering to these requests will be compulsory. Doing it manually will mount up heavy costs on the administration. But the fact is responding to such requests might only be subject to a few well-defined requests. That makes it a great process for RPA to flex muscles.

The crux of it is organizations will have a hard time maintaining GDPR compliance in the absence of RPA. RPA solves security governance through GDPR wholly with the promise of zero errors.

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.

Industry 4.0: The Era of Cyber-Physical Systems and Intelligent Analytics

Self-driving cars. Intelligent bots. Neuro-tech psycho-development. Genetic editing. Geoengineering. Artificial lifeforms. Mobile supercomputing of a surreal standard. Welcome to Industry 4.0.

Industry 4.0 or the Fourth Industrial Revolution is not a purely industry-specific phenomenon. Like the three industrial revolutions of the past (water and steam-powered motors, electricity-powered assembly lines, and computerized systems), the fourth industrial revolution will challenge the very way we work, live, and connect with one another – and with bots. The adoption and amalgamation of distinct values like the internet of systems, internet of things, and the cyber-physical grid will redefine and reform the very fabric of industry as we know it.

First off, we need to understand that Industry 4.0 or the Fourth Industrial Revolution is not a gimmicky marketing stunt pulled off to sell a new technology. It’s a living phenomenon described in very fine detail by Professor Klaus Schwab, founder and executive chairman of the World Economic Forum in his book, The Fourth Industrial Revolution. In his book, Professor Schwab argues:

The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential peril.

While Schwab highlights that the dawn of the fourth industrial revolution promises exponential growth, he also points out a set of risks that constitute the “potential peril” in the above statement. We’ll get to that in a minute.

What constitutes Industry 4.0?

By now, it is understood that both computing and automation have leaped to the next level with Industry 4.0. Core principles of robotics are connected to computer systems, which are in turn equipped with ML (machine learning) algorithms. The resultant loop can learn, control, and execute robotics and allied operations with minimum (almost negligible) human supervision.

Then, there’s the introduction of what is called the “smart factory.” Within the setup of a smart factory, cyber-physical systems have the capability to make decentralized decisions while monitoring physical processes. The wireless web connects the physical systems enabling interaction with one another and with humans.

By this structure, a setup can be considered within the Industry 4.0 structure when it has the following four attributes:

  • Independent (or decentralized) decision making: removing the “approval” cap off simple, obvious decisions for cyber-physical systems, allowing them to be as autonomous as possible.
  • Sound technical assistance: assisting humans with technical aid for tasks that are either too labor-intensive or too dangerous for humans along with the general ability to assist humans with decision making and problem-solving.
  • Transparency of information: contextualizing information and datasets through systems that create virtual copies of the physical world through sensor data.
  • Heterogeneous network interactions: devices, sensors, machines, and humans connecting and communicating with one another in a complex yet seemingly manageable environment

The concerns with the major shift in Industry 4.0

If your organizational infrastructure has woven through the above elements, you are already riding the Industry 4.0 wave. But like it has happened every time a new technology has set in, a set of concerns follow the benefits.

First off, the natural extension of allowing new systems entry into existing data banks brings the threat of security breaches. The more access you give to these systems, the more data security issues you will have to deal with. Plus, proprietary production knowledge presents an adjacent concern related to IT security.

The degree of stability and system reliance required for successful cyber-physical bonding will be very high. Such states can be difficult to obtain as well as maintain. Additionally, we have the challenge of maintaining the integrity of manufacturing and production processes with dwindling human control.

Perhaps the most crucial challenge in all of this will be the loss of high-paying human jobs. We simply cannot brush under the carpet the corporate accountability for humans in a world of bots. Finally, there’s always the risk of running into expensive production outages arising out of technical inconsistencies.

First-time blues matter less than the expansive potential

The singular most challenging part of implementing new technology across industries is the lack of experience. The systemic shortage of manpower in implementing these new changes would be augmented with the general reluctance of stakeholders in trusting new technologies upfront. Low trust naturally translates into lower investments.

Irrespective of the initial round of suspicion, there’s a lot of benefits offered by the 4.0 model. More importantly, the benefits outlive the concerns by a long shot. Safety of human workers in dangerous work environments can be dramatically improved. We will exercise far greater control over the supply chains when there is processed data at every level of the delivery process.

One thing is certain. The cyber-physical combination kind of guarantees increased productivity and faster deliveries. With that, the revenue, market share, and profits – all shoot up.

Industry 4.0 demonstration city

Mayor John Carnely of Cincinnati, Ohio has declared that Cincinnati, Ohio will be an Industry 4.0 demonstration city. The primary objective of the proclamation is to create a hub for investment and manufacturing within the Industry 4.0 environment. The resolution also thanked the contributions of Prof. Jay Lee and the Center for Intelligence Maintenance Systems for their positive contributions to the global revolutions surrounding Industry 4.0. The Cincinnati move is not just strategic but also historical given the potential leverage it provides to the manufacturing hub in the city.

Besides the Cincinnati move, reports suggest that emerging economies like India will reap great benefits of Industry 4.0 practices, largely because of readily available resources and the willingness to participate in new technologies.

Are we ready for the transformation?

To be honest, most industries will follow conventional practices unless there’s a standout example of blazing success in the Industry 4.0 model. But I am afraid much time will be lost with respect to realizing the largest gains promised by the revolution. Companies that show the courage to dive in fast will well and truly emerge as the frontrunners in maximizing profits.

There are additional issues that play beyond the willingness to adapt to change. Legacy systems cannot be overhauled in a night. The efficacy of tenured systems with proprietary applications should also continue to be just as important as they are today.

The analysis of big data and dark data will be one of the first challenges that the 4.0 architecture will need to address. We presently have immense volumes of data generated by digital systems, existing sensors, and other existing equipment. Much of these data banks that are presently unaccounted for will influence decision making in the industry.

Robotic Process Automation and Industry 4.0

That we are generating more volumes of global data than ever is one side of the story. ERP systems, bots, sensors, and even cookies are collecting pools of data that is currently unused. Most manufacturers are concerned about making this connection between machines from the production unit and back office systems. Finding a way to channel the unstructured data requires more than conventional computing. It requires software-based intelligent automation.

The best bet about RPA is that it seamlessly gels in with the existing IT setup inclusive of hardware and legacy applications. Large volumes of enterprise data can be indexed and structured using RPA. When clubbed with AI, RPA presents learning capabilities with advanced data models to organize and classify all the information, thereby making it valuable. On the whole, the platform visualizes these insights and eventually helps in predicting the future.

Notwithstanding the magnanimity of the promise presented by the Industry 4.0 model, the realization of success can only come with the right RPA solutions and resources. Futran Solutions specializes in delivering composite RPA solutions and resources. To expand upon a wholesome list of RPA offerings, Futran recently partnered with Automation Anywhere – a leading platform for implementation of automation solutions across the industry.

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

What Does Corporate Accountability Stand for When Bots Do All the Work?

What Does Corporate Accountability Stand for When Bots Do All the Work?

Only one event commands the same absolute degree of certainty as death – disruption of the status quo. Unless you concede that transformation is the new status quo. In every industry.

We now live in an era where the promise of unprecedented growth is almost unchallenged. It takes no second guessing to formulate that such growth cannot be realized without an increasingly digitized workforce headed by business leaders that are not afraid to weave AI, RPA, and cognitive machine learning technologies into existing processes.

But like Jyoti Vazirani had stated in a previous article, the more we lean toward automation and machine learning and integrate them to our core business processes, the faster we realize that there’s an expansive human workforce calling for imminent, preferably immediate consideration. What happens to the corporate accountability of our human resources? More importantly, what happens to our human resources?

For the start, let’s not bluff ourselves with arguments that make rats and bats crack up. Intelligent, emotionless bots are far more ruthless when it comes to beating known standards of efficiency and productivity. It’s time the high and mighty leaders in business work to create the right synergy between humans and bots.

The audacity of bots

What has for long been an expectation and a speculation is now looking at us in the face as a guarantee of sorts. Bots will outperform humans, they’ll champion cognitive functions, and in the very near future even develop the capacity for thought, albeit with a debatable degree of independence. That explains why even as business leaders are running hay and hill behind robotic process automation, the workforce at large is unwelcoming of the change.

The madness of reaping early-bird profits from automated processes is so extremely insane that almost no business leader is ready to acknowledge the unavoidable employment crisis, let alone starting a meaningful dialog on it.

Crisis #1: Saving the value-based ecosystem

In the last two decades, organizations have successfully embedded a sense of value in their core missions and brand philosophies. Fortunately, the largest part of this change was brought about by the technology companies. If your mind is already reading out names of tech giants, you see my point.

Ironically, the first and perhaps the biggest industry that will be faced with the workforce imbalance ushered by bots is the technology industry – because of obvious reasons. How, when, and if at all the industry stands up to the challenge is shrouded under expansive dubiety.

If the technology industry – the very one that created the bots in the first place – does not act swiftly to save the value-based corporate ecosystem, it will become doubly difficult for other industries to follow suit.

Crisis #2: Maximizing convenience and minimizing pain

That’s just what bots will do. For their corporate masters. Will the effect be the same for lower-rung employees? Hardly. Here are some more questions that must be answered:

  • Will we create new jobs for displaced employees – jobs that we didn’t know existed?
  • Are we sure that bots guarantee a better and more sustainable corporate future?
  • Is there a long-term reskilling program that gives employees the flexibility to try out newer careers?
  • Does the combined human-bot workforce turn out to be as effective as it looks on paper?
  • Do business leaders even care?

There’s no definitive answer to any of these questions. But we must remind ourselves that history will remember us not for creating bots, but for what we did to humans after bots were created.

Crisis #3: Weighing down by Peter Principle

The Peter Principle is a benchmark corporate ideal laid out by Canadian educator Dr. Laurence J. Peter. It states that in organizational hierarchies, employees rise up the ranks through promotion until they are promoted to a position for which they are incompetent. In effect, it highlights the logical assumption that save a few exceptions, one individual cannot have mastery over many diverse fields within a corporation.

The principle also states that every position in the hierarchy of an organization will at some point be filled by people who are incompetent to fulfill their job roles in those respective positions. Dr. Peter also stressed upon the fact that such outcomes might not be related to the general incompetence of employees. It’s largely because new positions might require additional skill-sets which cannot always be imparted through training.

The Peter Principle has been as true lately as it was in 1968 when the term was coined. But there were no automated bots in 1968. So the principle applies exclusively to human competence, and to wit, to human employees.

Here’s how the principle unfolds with the bot-human amalgamated workforce:

  1. It will still apply to humans only because bots are not likely to receive promotions
  2. A large part of the human workforce will be saved from the Principle because they will no longer exist as a part of the workforce
  3. Of the humans that will still be a part of the workforce, very few will be engaged in the lower rungs of the hierarchy; the long chain of systematic promotion to the top will break and the Peter Principle will lose much of its premise
  4. Since there will be very few human employees, most of them will comprise of the top management of the organization. We can safely assume they will come with enough training required for their positions

If you’re reading this right, the Peter Principle might cease to even exist if the human-bot work grid is laid out at large. A solid, 50-year old tried and tested corporate theory based solely on logical deduction, human psychology, and organizational observation might die a sudden death without people even noticing. If this doesn’t send the alarm bells ringing in wild abandon, no corporate activist screaming off rooftops ever will.

Back to the drawing board

Let’s resume thought with the truth.

The sun is setting on thousands of employees. Nothing in the corporate universe is as lucrative as the prospect of making big money in small time. Most corporations impart skill-training to employees not really to help them grow their skill set. But to extract greater competency from the same resource. If they can replace skill training with an additional program on an automated bot and save both time and money, make no mistake – they will.

You could argue that the Top 100 companies might actually retrain staff for new jobs. I say yes, they very well may. But the best of the organizations have laid off staff by the hundreds for much smaller reasons. Plus, retraining might not automatically amount to retaining. More like “we teach you fishing and there’s a lake at 6 o clock.”

Like Jyoti had written earlier, the human workforce cannot be saved without government intervention. Might sound grim, but it’s just as necessary. Tech and other industry giants will plant so many trees that no one will ever talk about the human workforce they uprooted. They’ll make so many of those feel-good inspirational videos that no reporter will cover the plight of the jobless that were once employed with them. But are the governments even listening? They won’t unless people who care about jobs speak up. For very soon, jobs will make way for tasks in the most cruelly literal way.

Futran Solutions supports robotic process automation. We are a pro-technology company. And we believe that if a technology deserves to go viral, we must do our part in making it viral. We provide a range of RPA and AI solutions to industries across the board. Adjacently, we run a series of training programs to aid the displaced workforce.

Drop us a line to know how we can help you with RPA consulting and project implementation.  
Krishna Vemuri is the co-founder of Futran Solutions and the CEO of the up and coming tech startup Onata. He writes on technology industry dynamics and the rather eclectic tantrums of his husky, Loki.

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.