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Futran Solutions appoints Anil Vazirani as its CEO

Futran Solutions, Inc. announced today that Anil Vazirani has been appointed the new CEO of the company. An experienced industry leader with a solid sales track record of 30 years, Anil brings to the fore new relationships and business partnerships.

Anil has IT industry experience across the Americas, Europe, and India. Prior to joining Futran Solutions, Anil had a 23-year long career at LTI where he was the Chief Business Officer for the Insurance and Healthcare verticals.

“In this post-pandemic situation, we at Futran believe that Mr. Vazirani has the right arsenal to take Futran to the next cycle of growth and add in new capabilities while bolstering the existing strengths of the team,” Krishna Vemuri, Chairman of Futran group of companies said.

“Apart from Futran Solutions core IT Services business, Mr. Vazirani will also take up the responsibilities of being the CEO of Futran-funded tech start-up, EZJobs®, Inc, a digital marketplace for blue and grey collar job seekers,” Krishna added.

Anil Vazirani said: “I am thrilled to join Futran Solutions and to be a part of such a talented and dedicated team. I look forward to working closely with Krishna, senior leadership, and the hugely talented employees of Futran to achieve multifold growth.”

Anil Vazirani has a B.Tech from the University of Bombay besides a management degree from Narsee Monjee Institute of Management Studies, Mumbai.

Artificial Intelligence In Telecom: 5 Groundbreaking Findings Shaping The Future

It doesn’t take much research to know that telecom is one of the most consumer-hated industries. Out of the blue, this tells us two things about telecom in general. One, people need telecom. And two, telecom sucks at expectation management. In comes artificial intelligence. Like with every other industry that it’s touched, AI has come to telecom riding atop a pedestal of promises. However, Artificial Intelligence in telecom has been playing a vital role in the IT industry.

As it turns out, AI is delivering the goods in the telecom sector. Most importantly, every telecommunication company worth its salt is turning to AI to augment user experience and improve network reliability. Then, there is a whole new avenue of predictive maintenance. AI is kicking in big time to predict (and correct) faults and failures before they surface. Most experts believe that’s just the surface of what AI can do in telecommunications. Combined with machine learning, AI can help revolutionise the telecom sector for the better.

Here are five big ways artificial intelligence and machine learning are transforming the telecom sector right before our eyes:

5G Made Accessibility Easy

The US, the UK, and Korea have already rolled out their 5G accessibility in 2019. 5G guarantees greater bandwidths, higher speeds, and greater privacy. However, these additional features were not possible in previous generations. 

Meanwhile, artificial intelligence in the 5th generation has improved accuracy. It has enabled new connectivity and also enhanced layers of security at a fundamental level. Likewise, Artificial intelligence and 5G working together play a very powerful role in the new generation.

The Process of Systems Working Together 

MWC19 was an event organized in Los Angeles. It stands for Mobile World Congress. It was held from 25 Feb 2019 to 28 Feb 2019. That is to say, because the main theme of this event was mobile interoperability. This comes under system convergence.

IT Systems works on different standardized languages. It is impossible for two systems to communicate without using different dialects. To figure out what comes next, we need to step in and simplify it for humans to understand.

Data Organization

After the interoperability of the systems, AI plays a vital role in organizing the data.  Most importantly, standardized data is anyway more impactful. On the other hand, Artificial Intelligence reviews large batches of data and organize it in a way that is understood easily. Similarly, this helps in making better-informed business decisions.

Integrated chatbots and voice recognition are some examples of AI used in data organization. Similarly, it has become popular because of more time allotted to human interaction and discussion than standardization. 

Personalizing the experience

Meanwhile, the data is organized, it should be efficient enough for the business world. How does it help the telecommunication industry? Moreover, AI proves to be instrumental here as well. 

Personalization of services based on user preferences are on the cusp of breaking into the telecommunications scene. Identifying the user’s behavior and preferences and enabling personalized services is the forte of AI.

Customer satisfaction

Firstly, the usage of AI has cut expenses to a great level. Chatbots and virtual assistants automate to these support requests. They can handle a bulk of such requests without human involvement.

These virtual assistants automate the requests, analyze, and escalate customer queries if necessary. AI helps in identifying sales. Similarly, it also alerts the customer about other products and services. Keeping a track of all of this, AI improves customer satisfaction.

DevOps, CIOs & Learning: The Handbook to Organizational Rollout of DevOps

CIOs and DevOps have worked together successfully for a while now. DevOps lets organizations deliver business transformation with the best customer experience. From the smallest organizations to large giants like Capital One, Walmart, Netflix and Target, the industry as a whole has eaten from the DevOps pie. However, the full potential of DevOps is still yet to be unlocked. This is where CIOs need to understand and lead the call for a change in the corporate culture.

Most leaders now understand that institutionalized behavior in consumers cannot be changed. That is perhaps why most CIOs are generally averse to adopting new technologies. But still, CIOs must make the important realization of understanding when technology becomes imperative for survival.

DevOps Works Best in a Learning Culture

A culture conducive to learning is required for DevOps to work the way it should. Then, those who have the right skills should lead to the holistic automation of the entire business process. Most people that meet this challenge will have a definitive edge over their competition.

While it is easy to talk about holistic development through DevOps, a lot has to fall into place for it to happen successfully. At the organizational level, it must start with an assessment. There has to be an audit of the present infrastructure as a well-structured development pipeline. The DevOps team must also recognize the best set of tools for initiating business transformation.

Agile Takes Over Traditional, Linear Thinking

DevOps plays the most significant role in product/solutions based teams. Effective DevOps is about the fusion of development and operations in these teams. Product life cycles totally feed on product teams. Such reorganization takes away the issue of automation being reserved to just product-based silos.

It is imperative that automation will take over a lot of manual computing. Intelligent automation and its adoption across product lifecycles will eradicate human errors, reduce risks and drive enhanced service quality.

Less of Blame, More of Learning

Let’s admit it: whenever something goes wrong at the organizational level, one process is inadvertently made to be the scapegoat. Of course, other measures are also taken to prevent the next failure. In time, the processes pile up, adding tremendous delays as well as layer upon layer of defunct bureaucracy. Resultantly, there’s rarely (if ever) any future value stored in these processes. What follows is less confidence in future DevOps initiatives within the organization.

There has to be a lot of trusts for DevOps to work. The process has to be executed by teams that place trust in one another. Moreover, tasks must be executed together and failure must be used as an opportunity to learn more and drive greater value. In such collaborative environments, production failures will lead to a change in codes and automated testing schedule.

The whole point of placing such safeguards is ensuring that mistakes are not repeated. Plus, production changes are delivered well in time so that there is rapid rollback or resolution in the event of a failure. This process helps build a foundation for learning from past mistakes. Moreover, it also promotes an atmosphere conducive to learning through the whole organization.

Automation Silo Replacement

There is no running away from automation in Industry 4.0. And it has to happen at every stage of the life cycle of business transformation. At the moment, automation only exists within processes. It has only lately begun to work across different processes. The lack of automation affects knowledge management as well as the automation of handoffs.

Once human intervention and manual data processing are brought down significantly, process speeds will increase. Let’s say the operations team gets more performance management data and yet finds it difficult to share it with the development team. An automated feedback loop shall ensure that development teams receive the required information quickly. This way, any unforeseen event will affect the minimum number of customers.

Continuous Integration & Continuous Deployment

Continuous Integration and Continuous Deployment is the new benchmark through different stages of app development. The major stages that constitute CI/CD framework involve continuous integration, continuous delivery and continuous deployment.

With the CI/CD model, organizations can derive maximum output from DevOps integration. The CI/CD pipeline does away with a substantial amount of collaborative woes of development and operations teams.

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!

Six Top Data Management Practices Every Organization Must Follow

Six Top Data Management Practices Every Organization Must Follow

Storage silos in most traditional organizations are bursting open from the rapid evolution in big data. Most of these organizations are now concerned about data management practices in their organizations.

In the last decade or so, every industry from manufacturing to advertising has migrated to multichannel sourcing of data. This means each individual set of data now competes with every other set for analytical significance. Businesses can easily stretch out of their means of trying to fuel this process. Resultantly, very few companies can claim that they are making the best use of their data.

By all means, the answer lies in implementing a data management solution that is practical. Plus, it should improve the quality of the collected data. Moreover, it can also be a vital step toward solving productivity issues.

The focus is steadily shifting toward the production of well-analyzed, relevant and timely data. Such data allows businesses to make improved decisions and usher in substantial growth. Fitting in data management solutions in business could be challenging. And if you have not started yet, you might totally miss out on what’s actually covered in data management.

With a data management plan that is centered on specific business needs, every new asset in data will undergo extensive monitoring processes to make sure there are no security threats and data is kept safe. Here are some top data management principles and practices that will help your organization make the most of the available data assets.

Understand your business goals before data objectives

Over the next decade or so, the volume of data will snowball into a living data giant. In parts, this development will be propelled by the new digital devices that are constantly being added to systems and networks. The uninterrupted flow slows down data collected previously further down the silos as newer sets of data assume more importance.

Using data to understand and realize business goals is quite common as a practice. But a data would scientist would recommend that organizations keep referring to the business goals throughout the process of data planning. This helps companies identify the most important data sets and understand whether or not those need to be placed in a silo.

As an organization, you also need to consider how every dataset can impact the KPI that you would want to improve. Based on the goal you set, you will have to make a decision on what data you want to store. At the moment, most organizations do shoddy data management.  They store a lot of data without a well-defined purpose or store mechanism.

The best way to work around this is to know and decide how much data and associated technologies you will need to crack the goal.

Club AI and machine learning in data management

The more datasets an organization accrues, the more time it takes to conduct analysis and reporting on every one of them. With new techniques like artificial intelligence, the extraction levels on the collected datasets are all set to go deeper with machines getting contributing a bigger chunk in the analysis. Data companies are already championing inter-technology collaboration to better facilitate GDPR guidelines.

The other big factor in data management is big data. Given how big data has become in the past few years, artificial intelligence will be an even bigger factor in the months and years to follow. AI can deliver fast, economical and high-quality intelligence from ginormous sets of data. It is beyond impossible for humans to derive actionable insights from such data volumes.

With the onset of GDPR, almost any organization that dealing in significantly large volumes of data will need artificial intelligence. The major ways in which AI will help companies in better data keeping include:

  • The ability for consumers to check in and out of official communications
  • Supply consumers with reports on what data the company collects from them
  • Give consumers easy ways to delete all data the company has about them

Without artificial intelligence supplying the necessary technology, these processes will become heavily time-consuming for businesses.

Ensure the right people manage data

A good data strategy for a business starts with placing the best practices and principles in place. However, what you want to know is that success is a result of the right people managing data for your organization.

Start with planned data governance. Deriving maximum value from data is critical to any data strategy of a business. Perhaps, the first of many steps in data strategy is to include data governance as a principle. For one, this will make sure that the data being used in the business continues to stay of the highest quality throughout its lifecycle.

Data governance is a process in the evolution of new businesses. Since it’s based on integrity, usability, and availability, it allows for the whole industry to make use of the data. With big data and analytics, companies can improve security, reduce costs, ensure compliance, improve data quality and derive meaningful insight.

Implementing an enterprise-wide governance framework to reduce the cost of operation and risk associated in the subsequent projects.

Make data accessible

Data security is as important for an SME as it is for a Fortune 500 company. But in a mad bid to secure data, companies cannot afford to forget data, which might, in the long run, make it defunct altogether. Data needs to be stored securely, but without compromising on the accessibility for those who need the data. Imperatively, the same data should not be available to those who do not have the proper clearance.

Staying on the top of data access protocols is key to cope up with the rapid leaps into the digital age. Organizations must make sure that data is stored at places where relevant groups can have easy access to them. The age that is coming is more data-driven than we would think. At that, it is relevant that organizations are adequately prepared to extract data from dashboards. The message here is simple – silo data is not of any particular use to a company.

Defend cybersecurity threats

Most companies have an Incident Response Plan by now. But the common mistake that most companies end up making is to deviate from that plan. So first of all, there has to be a clear plan accentuated with decision points in times of crisis. That will let companies know if there is a legal requirement, good faith or regulation to find a breach which is either potential or realized.

To start with, an Incident Response Plan should be established before the occurrence of any major incident. The plan should include all the points that will help in recovery, eradication, containment and also supply with expert testimony.

Democratize data management 

Data management principles and practices must be kept up collectively by a business. Using a holistic method to work lets every member in the company to gain access to data infrastructure and create a way for better data management processes. Along with solid governance, this method can introduce successful master management of data as well. But for company-wide success, the integration must first happen within the company.

Data management practice aids in the study of data in the correct perspective to arrive at conclusions that align with business objectives. Now that organizations are hoarding lots and lots of data, the key is in classifying the data well and making a senior official in the company accountable for it.

Data democratization is desirable. There’s no question about that. However, it has surpassed desirability. With GDPR rolling into action faster than most would have imagined, someone within an organization has to take responsibility for the data of their users. Moreover, implementing stricter data guidelines will also ensure that companies are aware of the kind of data that flows through their organization.

If you follow these recommended data practices, you will be that much closer to making holistic use of data.

Futran Solutions specializes in delivering composite data management and analytics for small and medium enterprises. As applications of data management in business keep evolving, so do the resources that shoulder these needs within an organization. Speak to a Futran Data Analytics specialist today. Find out how we help you achieve your business and marketing objectives.

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.

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.