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11 Workplace Improvement Ideas That Every Employee Wants to Give Their Employers, But Doesn’t

Workplaces are complex environments with a constant flurry of activities that keep everyone on their toes running behind their tasks and objectives. Employees often imagine innovative ways to develop their workspaces but do not enjoy the liberty to share their ideas with their superiors. Advancements in science and technology have brought about a great revolution in workplace designs with an increasing focus on developing employee-friendly workspaces to improve the efficiency and performance of employees. It is essential to consider the suggestions and ideas of employees to build a high-performance workplace that optimizes their performance. Here are a few workplace improvement ideas that will significantly improve the efficiency of your employees to reach their potential.

Recreational Zones

Most companies and enterprises often have stressful work environments with employees working extended hours to meet their objectives and tasks. Incorporation of zones of recreation in the office spaces help employees bust stresses in between their tasks and work efficiently after their breaks. 

The recreation zones consist of various fun activities like board games, indoor sports, music areas etc. that meet the diverse interests of different employees. It helps build a sense of togetherness and bonding amidst your employees that can improve trust and communication between them.

Offer Career Development Prospects

Employees look out for mentorship and guidance as they set out to shape their career paths and grow in their careers and personal lives. Dedicated employees focus on upgrading their skills and show immense interest in learning new and interesting aspects of their careers that could help them grow. Companies should organize regular training modules to improve the knowledge of their employees and keep them updated about recent developments and advancements in their fields. Providing opportunities for education and improving their knowledge also helps in retaining the employees in the companies, and putting their skills to efficient use. 

Focus Teams and Collaboration

Companies and enterprises operate with multiple teams of employees handling the different steps in the work processes independently to produce the final product. Lack of well-established channels of communication between the teams often leads to decreased efficiency and defects in the quality of the final product. Focus groups and collaborative meetings between the different teams of employees will help analyze different ideas and methods proposed by the teams that will best suit the design of new projects. Enhanced communication and interpersonal relationships between different teams of employees improve the overall performance and quality of the products and services provided by the companies.

Technological Advancement

Employees regularly update themselves with modern advancements in their fields and sectors and find that the equipment and software available in their companies are outdated and inefficient when compared to them. They look forward to working with new software tools and platforms and wish to work with them to improve the quality of their services with the ease and comfort that accompany them. It allows your teams to finish projects at a faster rate and reduce the turnover time, improving customer service and satisfaction. Employees also show interest in attending educational courses and hands-on sessions on the modern tools to improve their knowledge and skillsets and better enable them to use them in future projects.

Feedback and Suggestions

Employees face a lot of obstacles and difficulties in their work processes and the impact it has on their personal lives. They come up with different suggestions and innovations that can help them find the ideal balance between their work and personal lives but are often unheard of as they do not have a proper platform to convey their ideas. Regular meetings and assemblies should be conducted to provide an opportunity for the employees to voice out their concerns and suggestions to improve the quality of their lives at work. This also helps gain a consensus regarding employee acceptance of different ideas and suggestions to help prioritize the changes.

Social Events 

Social events and gatherings help bring employees together and provide them with an entertaining escape from their busy work lives. These social events allow employees to meet the families of colleagues and office staff to improve the quality of relationships between them. Employees from different cultural backgrounds can celebrate festive occasions with their colleagues and share their happiness with them. These social events can improve employee engagement and retention by diffusing the tension between different teams of employees and keeping them happy.

Appraisals and Insights

Employees’ performance should be analyzed regularly and critically evaluated to provide valuable insights on how they can improve their skills and performance. These appraisals can help track the growth and progress of the employees from their time of onboarding to their current position in the company. Proper guidance and insights regarding their performance and how they can improve their skills help channel their growth efficiently to boost their results. 

Conclusion

A good cordial relationship between the employees and management is essential to develop a congenial atmosphere for the growth of the company. We need to adopt new ideologies and trends to the work process to improve the efficiency of employees and keep up with the growing competition. Employee suggestions are often overlooked by seniors and management teams resulting in unhappy relationships that strain the office dynamic. Follow these simple steps to improve your workplace and create a highly productive environment for your teams to work peacefully in.

10 Lesser-Known Factors That Could Affect Your Career Growth

We live in a goal-oriented world with every one of us running behind our ambitions frantically deeming our merit by whether or not we achieve the goals we set for ourselves. Advancements in the fields of science and technology have opened up various uncharted career paths for young aspirants to explore and grow. The Internet has brought the world to our palms, with unlimited access to information from all places across the world. Vast exposure to different fields and sectors allows us to make informed decisions regarding our career choices and how we can choose the optimal route to success and growth. Here are a few factors that could affect your career growth and help you grow immensely if you manage them right.

  • Reach Out to Opportunities – Digitization of companies and enterprises across the different fields and sectors has greatly improved the range of opportunities available for people interested in various job positions. The conventional approach of waiting for new opportunities to come up without looking out for them has been phased out with the onset of online platforms that help people reach out to companies from countries across the globe. 

  • Online Job Platforms – Look out for new job opportunities in your fields in popular online platforms to gain insights on the skill and expertise requirements required for these jobs. There are various communities and online platforms like Glassdoor which provide useful information regarding the work environment and nature of work in different companies as reported by employees who are working there or who have had an experience working there. They help make critical decisions that will significantly boost your career.


  • Develop Relevant Skills and Expertise – The world is a rapidly growing place and we need to constantly adapt to the new changes to keep in pace with it. New software and technological advancements revolutionize companies and enterprises in all fields and sectors and we must stay updated regarding these new advancements to stay ahead of our competition. Constant efforts to keep up with the latest advancements will reward you with wonderful opportunities to grow and expand.

  • Community Outreach – Valuable contacts and experience are two factors that set apart seasoned professionals from amateurs just entering the fields. While experience is an accumulating asset, we must develop good stable relationships with our colleagues and eminent personalities in our fields. It puts us in a position of great advantage as the web of contacts acquired by developing such relationships provides us great leverage when looking out for new opportunities by offering vital information and suggestions propelling our growth.

  • Interpersonal Skills – Creativity and good communication are important factors that can improve your career growth. Do not restrict your thought processes to conventional norms and protocols. Venture out to new horizons and communicate well with people of all age groups to gain a diverse perspective that is not constrained by preset norms and thought processes that are too stubborn to adapt to change. Good relationships built with all sectors of your companies and enterprises will provide you with a holistic knowledge of the work processes helping you make innovative changes that can boost the productivity and efficiency of your team, helping you grow your career.

  • Work-Life Balance – A peaceful and supporting home environment can help you progress your career greatly. Chaotic personal relationships and instability in lives negatively impacts your performance and efficiency and undermines your general quality of life. It is essential to find the ideal balance between the time spent on bolstering your career and nurturing personal relationships as they go hand in hand in improving your lives and getting you successful. 


  • Financial Security – Financial security plays a critical role in most of the decision-making processes involving the growth of your career. Shifting to new jobs or setting up new startups requires confidence and financial security as they are huge risks that could set you back in your career. Adequate financial support helps you mitigate the risks significantly allowing you to explore and implement your ideas freely without being restrained by caution and worries.

  • Mental and Physical Health – This is the most important factor that most of us tend to ignore and take for granted, with a great impact on our growth and development. Adequate time must be devoted to physical exercise to stay healthy and sharp, helping you stay focused on the task at hand. Mental acuity and the ability to adapt to changing surroundings is vital to stay relevant in different fields and sectors and good mental health help us achieve the same. 

 

Conclusion

We live in a highly competitive world with new companies and enterprises getting set up in different fields and sectors fighting for dominance and profits. Adaptability and the continuous desire to learn new things to stay ahead of the curve is what will help you progress and achieve your desired goals and desires. You must handle these factors suitably to allow the smooth and constant progression of your career.

The Future with Futran: Guaranteed Growth and Excellence

As a one-stop shop for key business services and strategies, Futran Solutions knows exactly how to navigate challenges in countless domains to best serve our clients and their goals. Regardless of how fast technology changes or what software trends lie around the corner, we consistently aim for and achieve excellence through our artificial intelligence, software development, and data analytics savvy.

Our team focuses on maximizing business growth, cost effectiveness, and operational efficiency for all of our customers, and as the next step in our own development, we are excited to announce recognition of Futran Solutions by Clutch, The Manifest, and Visual Objects.

Clutch, a B2B research and reviews website that analyzes companies based on their market presence, strength of portfolio, and client feedback, recently named Futran Solutions among other leading data analytics companies in the industry.

Dev Gupta, a client of ours and editor of CS:GO related website, particularly praised our skills and support in a review on our profile. “Futran Solutions performed exceptionally in regards to project management,” Gupta touted.

Futran Solutions and their team are skilled in a plethora of services. Instead of having to go to a different agency for various parts of the project, they had everything in one place – from initial requirements, deployment, release, and digital marketing,” he said. “With Futran Solutions, we received an end-to-end solution. We made a great decision choosing Futran Solutions.”

Sister companies to Clutch, The Manifest and Visual Objects also highlighted our range of services.

The Manifest, a news and how-to website that shares business knowledge and rankings, has ranked us among other top AI companies, attesting to our quality service. Likewise, the portfolio-focused Visual Objects website now showcases examples of our work, supporting Futran Solutions in backing us and other quality creative design and development agencies on the platform.

We very much appreciate these features of our services, strategies, and skills, and our team at Futran welcomes you to contact us with any questions or if you are interested in exploring future partnerships with us. We look forward to hearing from you soon!

How to Balance RPA Risk and Opportunity in Seven Easy Steps

How to Balance RPA Risk and Opportunity in Seven Easy Steps

RPA is helping speed up processes in organizations of all shapes and sizes. It reduces costs and liberates human workers off grunt work to focus on research and innovation. The revolution is rampant in every industry from manufacturing to banking and customer services to research and development.

Today, RPA is one of the core technologies driving turnkey changes in products and services in every industry. Along with other ground-breaking technologies like AI, Big Data, and the Internet of Things, RPA promises to revolutionize things as they are.

Process automation is particularly impactful in banking. Simplified transaction processing means banks can cut costs while they can speed up customer services. In the finance industry, RPA helps streamline NAV calculations and trade reconciliations. In industry 4.0, which is the present industrial revolution we are living through, manufacturers depend on RPA for several applications including inventory rotation and process monitoring.

RPA Risk Dilemma

Multiple companies are skeptical of the impact of RPA on their organizations. Most of them have the perfectly legitimate apprehension of risking the productivity of a stable process by adding new technology. To help overcome the grand RPA dilemma, here are seven easy steps that will help you implement RPA solutions in your organization without having to deal with unnecessary risks.

1. Be Patient

The excitement of process automation can be overwhelming at times. Some companies can barge in head first, trying to automate as many processes as fast as possible. This brings us to the first major RPA risk. Frantically introducing new technology can throw the flow of data and products into disarray, causing disturbances in the workforce.

It’s way better to take out time to analyze what part of organizational workflow will benefit the most from automation. Then, you must plan to ensure that the team understands the role of RPA as well as RPA risks if any in automating the process.

2. Chase Small and Early Successes

In the larger industry equation, RPA is meant to tackle work-heavy and error-prone processes. And it can be tempting for organizations to do just that. However, the best approach is to start small and find a bunch of easy successes.

Starting with a limited scale execution of RPA lets the team understand the technology and reach a certain degree of comfort with it. Plus, it also avoids the risk of needless shipments and customer service. As the team handles more and more automated processes, they gain the experience to handle trickier processes.

3. Simpler the Better

Among the very first steps to executing RPA is to chart the existing process so you can better understand the steps and flow of information. It is easy for teams to become overzealous and try to implement every possible exception in the process. This could be to prove that human intervention is required or that a robot simply cannot handle some core process. Regardless of the nature of the motivation, it is important not to give in.

The wise way to avoid RPA risk here is to start with a process and let the automation handle the typical flow. Follow a case-by-case approach to eliminate the possibility of human intervention for exceptions. Complex rules can always be built at a later stage. In the beginning, follow a simple approach to RPA implementation.

4. Focus on Training

The infrastructure and business process of an organization can be heavily impacted with RPA. Train people rigorously on how the coming process will impact their workflows and methods. The IT team needs to learn the technology – that one’s a given. But the managers and workers cannot afford to miss out on key RPA sessions either.

Ample time and monetary budget must be spent on training the staff of the organization. Treat RPA less like another new technology and more like a digital transformation course. Only then can you overcome the temptation to rush or avoid this part.

5. Keep Communication Open

There aren’t many employees that will say ‘no’ to training. But most employees will also want to know what the training means to them. It is important to communicate to the employees what every training session means to them as well as what their future roles might look like.

Many employees have registered positive responses on automation handling routine tasks in their existing workloads. Some others have experienced the need for upgrading their own skill set to accommodate an automation-enriched work life. Being respectful to the team means that you help them understand the role they have to play in the changing work culture.

6. Assess the Criticality of RPA in Business Success

As new technologies are incorporated into business processes, the flow of energy is immense. RPA can only become a contributing factor in business success when you take time to plan for it. Jump into RPA only after taking clearly defining your objectives. Every potential milestone must be carefully deliberated upon before becoming an official entry into the list of objectives. Lastly, document everything right from the drawing board observations to the achieved milestones.

7. Brave the Bot Change

Automation does not necessarily amount to the cancelation of risk. Organizations can aim to maintain an optimal processes-to-bots ratio. But there is always the risk to threaten the status quo itself with drastic systemic change.

The human teams running the business must strive hike up their knowledge on par with their bots. Find a way to engage and store the automation processes so they can be easily updated and accessed by teams at any place and time. That way, the process can recover in minimum time should automation fall off the grid for any reason. Most importantly, it is the people who must create the best processes for bots. Only this can instill a sense of genuine responsibility among teams.

The above seven ways coupled with a more organic approach can sure achievement of RPA objectives. Plus, you can dive a long way away from any potential risk that the new technology might carry along with it.

Futran Solutions specializes in delivering composite RPA solutions and resources. As Blockchain technology is evolving, so are the resources that shoulder the needs of the industries. Speak to a Futran blockchain RPA specialist today to find out how we help you achieve your business and marketing objectives.

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.
Five Applications of Machine Learning in the Pharma Industry

Five Applications of Machine Learning in the Pharma Industry

Machine learning seamlessly is integrating with other industries right before our eyes. Like with so many so many other industries, more data means greater effectiveness in the pharma industry. A McKinsey report estimates that machine learning and big data could generate a combined business value of $100B annually. The value optimization involves optimal innovation, better decision making, greater efficiency for clinical/research trials, and additional tool creation for medical professionals.

What is the source of so much data? Regular streams like research and development, clinics, physicians, patients, and caregivers do their bit. The disparate origin points of data sets form a large part of the problem when we talk of synchronizing all the data and improving the healthcare industry as a whole. The core of the problem is to find ways to effectively collect different types of data sets for better treatment, analysis, and ultimately treatment.  

Today, the applications of Machine Learning are sprouting in manifold ways. All these applications give us a glimpse of a future where the analysis and synchronization of data is already a reality. Here’s a collection of some of the most important applications of machine learning in the pharma industry.

1. Behavioral Modification

With machine learning, personalized medication will soon be a reality. Such treatment is based on individual health data along with a decent dose of predictive analysis. In fact, this is one of the most hotly worked on topics on machine learning and behavioral modification.

At the moment, this area is dominated largely by a combination of genetic information and supervised learning. This basically allows physicians to choose from a set of diagnoses. The next decade is crucial for health optimization with the help of machine learning. Micr biosensors will see a further rise in the application and there will be a similar rise in use remote monitoring capabilities.

A gamut of startups is emerging in fields like cancer identification and treatment. While the success of these innovations is still far from desirable, there is a good chance of a significant breakthrough in the coming decade.

2. Research and Clinical Trial

Machine learning has already tasted decent success in shaping direct research and clinical trial. The application of predictive analytics in identifying candidates for clinical trials could see a lot of additional data pouring in compared to the volume of data we see today. Clubbed with genetic information, this will help in quicker and more cost-effective trials in the coming days.

The application of Machine Learning also spans over access to real-time data for heightened safety. One critical area is the monitoring of biological signals for any visible sign of harm or fatality. McKinsey suggests that there is a whole lot of other applications that help in augmenting the efficiency of a clinical trial. This includes the discovery of the best sample sizes for greater efficiency.   

3. Drug Manufacturing

ML has tremendous scope in the early stages of drug discovery. The application starts at the initial screening of compounds and moves over to the predicted success rate based on a number of biological factors. We are also looking at discovery technologies in R&D like next-generation sequencing.

Precision medicine, a genre that involves mechanism identification for multifactorial diseases, is the frontrunner in this race. Since a lot of this research is based on unsupervised learning, the game revolves around identifying data patterns without the use of prediction of any kind.  

4. Identification and Diagnosis of Disease

Most of the present efforts in Machine Learning research for the pharma industry is geared toward disease identification. In a 2015 report released by the PRMA, the number of cancer medicine and vaccines in trial went over 800. The larger challenge, however, is to make justified use of all the data that comes out as a result of these studies.

It is here that the need for biologists working with information scientists and machine learning experts will become extremely vital. It doesn’t come as a surprise that the bigger players were the first to jump on the bandwagon. IBM Watson Genomics came into existence in 2016. It partnered with Quest Diagnostics to take rapid strides in precision medicine.   

5. Epidemic Control

At present, AI and ML technologies are also being used to monitor and foretell epidemics around the world. The predictions are based on satellite data, real-time updates on the social media as well as other sources. Scientists have already made use of artificial neural networks and support vector machines to predict outbreaks of malaria. The analysis took into account factors like average monthly rainfall, temperature, data points, and the number of positive cases.

The prediction of outbreaks (and their severity) becomes even more important in third-world countries where epidemics claim lives in the hundreds. This also alerts governments to implement prevention protocols and if needed quick treatment measures, too.

Futran Solutions works with a talented pool of resources that are ML experts in the pharma industry. Call us today to know how we can help you with project implementation and digital consulting in machine learning.

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