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Calculate the ROI on Infrastructure Automation

Programmable infrastructure and a world where you can take your data with you wherever is the future.

A new era has arrived, one in which software development practices are being applied to physical objects such as roads or bridges for greater efficiency; this idea of transparent skies could very well become a reality soon!

You can’t put a price on efficiency. The return on investment (ROI) of infrastructure automation is essential to consider before you start implementing changes that could be expensive and time-consuming.

How is Automation Valued?

The process of automating a long, manual task can be very beneficial. If you run it frequently enough and with the right system, your savings will grow exponentially as time goes on!

Regarding infrastructure automation, one of the first questions businesses ask is, “What’s the ROI?” In other words, what are the tangible benefits of automating tasks such as provisioning, configuration management, and deployments? And more importantly, how can we quantify those benefits?

In this blog post, we’ll walk you through a simple process for calculating the ROI of infrastructure automation. By the end, you’ll have a clear understanding of the financial benefits of automation and be able to make a strong case for why your business should invest in it.

The Advantages of Infrastructure Automation

Infrastructure automation is the process of automating IT infrastructure configuration, provisioning, and management. It can help organisations to manage their infrastructure more efficiently, improve service quality, and reduce operational costs. In this blog post, we will explore some of the main advantages of infrastructure automation. 

  • Improved Efficiency and Productivity

Infrastructure automation can improve efficiency and productivity. By automating configuration, provisioning, and management tasks, organisations can free up time for other activities, such as developing new features or products and providing customer support. These tasks can reduce errors and improve accuracy.

  • Improved Service Quality

Another advantage of infrastructure automation is that it can improve service quality. By automating tasks such as monitoring and maintenance, organisations can ensure their infrastructure is always running smoothly and efficiently. Additionally, automating these tasks can help identify problems early before they cause significant disruptions. Organisations can provide better service to customers.

  • Reduced Operational Costs

Finally, another advantage of infrastructure automation is that it can help to reduce operational costs. This is because automating tasks such as provisioning and management can help to reduce the need for manual intervention. Additionally, automating these tasks can help improve efficiency and productivity, which can lead to reduced labour costs. In addition, automating these tasks can also lead to reduced energy consumption and waste generation. As a result, organisations can save money on their operating costs. 

There are many advantages of infrastructure automation. Automating tasks such as configuration, provisioning, and management can help to improve efficiency and productivity, improve service quality, and reduce operational costs. If you are considering implementing infrastructure automation in your organisation, carefully weigh all of these factors to make the best decision for your business.

Calculating the ROI of Infrastructure Automation

Now that we’ve looked at some of the benefits of infrastructure automation let’s talk about how you can calculate its ROI. To do this, we’ll use a simple formula:

 (Total savings from automation – Cost of automation) / Cost of automation = ROI%

For example, you spend $5,000 per month on labour to manually provision and manage your servers. You estimate that by investing in an automated system, you could reduce that cost by 50%. The cost of the computerised system itself is $10,000 upfront plus $500 per month in maintenance costs. Using our formula, we get: 

 ($5,000 * 0.5 – $10,000 – $500) / ($10,000 + $500) = -9%

This means that over two years—the amount of time it would take to fully recoup your upfront investment—you would see a negative return on your investment (ROI). In other words, investing in automation wouldn’t make financial sense for your business now.

Conclusion:

As you can see from our example above, calculating the ROI of infrastructure automation is relatively simple. However, it’s important to note that other factors besides financial ones should be considered when deciding whether or not to automate your infrastructure. These include the size and complexity of your infrastructure, your company’s culture, and your willingness to embrace change. That said, we hope this blog post has given you a better understanding of how to calculate the ROI of infrastructure automation and why it’s such an important consideration for businesses today. Thanks for reading!

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Redefining Business Process Outsourcing through Business Process Automation

The business process outsourcing (BPO) industry is worth an estimated $190 billion. But what is BPO, and how has it evolved? In this blog post, we’ll explore the history of BPO and how business process automation (BPA) is redefining the industry.

Business Process Outsourcing: An Introduction

Business process outsourcing (BPO) has been a popular cost-cutting measure for businesses for many years. The concept is simple enough: rather than having an in-house team handle a specific process or task, you outsource it to a third-party provider. This often results in significant cost savings, as BPO providers can leverage economies of scale to deliver services at a lower cost than most businesses could achieve on their own.

However, new development is beginning to change the BPO landscape: business process automation (BPA). BPA involves using technology to automate tasks previously performed by human workers. This includes data entry, customer service, and even complex financial processes.

The History of Business Process Outsourcing

BPO had its roots in the late 1800s when American companies began outsourcing manual labour to countries with lower wages, such as China and India. This practice continued into the 20th century with the rise of telephone operators and data entry clerks. However, it wasn’t until the 1990s that BPO began to take off. 

This was primarily due to technological advances that allowed communication and collaboration across vast distances. Suddenly, businesses could outsource not just manual labour but also knowledge work to countries with lower living costs. This led to the rise of call centres and other forms of customer service outsourcing.

In recent years, there has been a shift away from traditional BPO models. This is due to several factors, including the increasing cost of labour in countries like China and India, as well as the advent of new technologies that make it possible to automate many business processes.

Benefits of BPA 

There are several benefits that businesses can reap from implementing BPA: 

  • Cost savings: One of the primary benefits of BPA is that it can help businesses save money. Companies can reduce their labour costs by automating tasks that human workers previously performed. In some cases, BPA can also help enterprises improve their efficiency and reduce other costs, such as errors and rework.
  • Improved quality: Another benefit of BPA is that it can help improve the quality of work. BPA is designed to follow pre-determined rules and procedures. This contrasts with human workers, who may make mistakes or take shortcuts that result in lower-quality work.
  • Increased capacity: A final benefit of BPA is that it can help businesses increase their capacity without incurring additional costs. This is because BPAs can work faster and for longer hours than human workers. In some cases, this increased capacity can help businesses meet spikes in demand or complete time-sensitive tasks more quickly. 

Implementing BPA

If you’re interested in implementing BPA within your business, there are a few things you’ll need to do:

  1. Determine which processes you want to automate: The first step is to evaluate your business processes and determine which ones would be candidates for automation. To do this, you’ll need to consider factors such as the complexity of the process, the frequency with which it needs to be performed, and the availability of data and applications required to support it.
  2. Identify the right tools: Once you’ve identified which processes you want to automate, you’ll need to select the right tools. There are many different types of BPAs available on the market today, so you’ll need to evaluate your needs and choose the tool best suited for your specific requirements.
  3. Define success criteria: Before beginning any automation project, it’s essential to define what success looks like. This will help you select an appropriate tool and set realistic expectations for the project’s outcomes.
  4. Implement and test: Once you’ve chosen an agency and defined success criteria, you’re ready to implement your BPA solution. Be sure to test it thoroughly before rolling it out into production to address any potential issues before they cause problems for your business operations.

Business Process Automation: The Future of BPO?

Business process automation (BPA) is the use of technology to automate repetitive, low-value tasks typically performed by human workers. BPA can be used to automate a wide variety of business processes, including data entry, invoice processing, and lead generation.

One of the critical benefits of BPA is that it can help businesses reduce their dependence on human labour. This is especially important in today’s economy, where many companies struggle to find enough qualified workers to fill open positions. By automating low-value tasks, companies can free their employees to focus on more strategic initiatives.

BPA is also more efficient and accurate than human workers, and machines can work 24 hours a day, 365 days a year, without getting tired or making mistakes. This increased efficiency can help businesses save money and increase profits.

Conclusion:

The business process outsourcing industry is evolving thanks to advances in technology rapidly. Business process automation is redefining what is possible in terms of outsourcing and helping businesses save money and increase efficiency in the process. In the future, we can only expect BPA to become more prevalent as companies continue to search for ways to cut costs and improve performance.

How to Embed Tableau or Power BI Dashboards into Web Pages without Using an Iframe

Iframe: An Introduction

Iframes are an HTML element that allows you to embed one HTML document inside another. While they are commonly used to embed videos or maps on websites, they can also be used to embed dashboards created in Tableau or Power BI. However, iframes can cause problems with security and website loading times, which is why some developers prefer to avoid using them. So, how can you embed a Tableau or Power BI dashboard on a webpage without using an iframe? Keep reading to find out.

Ways to Embed iFrame to your Web Pages

Many web developers shy away from using iframes because they can be difficult to work with. However, iframes are often the only way to embed Tableau or Power BI dashboards into web pages. If you’re looking for a way to embed your dashboards without using an iframe, read on!

Let us walk you through 3 methods for embedding Tableau or Power BI dashboards into web pages. These methods are:

1. Use Tableau’s or Power BI’s JavaScript API

2. Use a third-party service like Publitas

3. Use an open-source solution like Koalas

We’ll also provide a brief overview of each method so that you can decide which one is right for you. 

Method 1: Use Tableau’s or Power BI’s JavaScript API

  • If you’re aTableau or Power BI user, then you’re in luck! Both platforms offer a JavaScript API that allows you to embed your dashboards into web pages without using an iframe. 

The biggest advantage of using the JavaScript API is that it gives you full control over how your dashboard is rendered on the page. For example, you can choose to display the dashboard as a lightbox pop-up or inline within the page. You can also specify the size and position of the dashboard on the page. 

Another advantage of using the JavaScript API is that it’s relatively simple to set up and use. However, one downside is that it requires some knowledge of HTML and CSS in order to properly configure it. 

Method 2: Use a Third-Party Service like Publitas 

  • If you’re not a web developer and don’t have any knowledge of HTML or CSS, then using a third-party service like Publitas is probably your best bet. Publitas offers an easy-to-use platform that allows you to embed your Tableau or Power BI dashboards into web pages with just a few clicks. 

The biggest advantage of using Publitas is that it’s very user-friendly and doesn’t require any knowledge of HTML or CSS. Another advantage is that Publitas offers a wide range of customization options so that you can control how your dashboard looks on the page. 

However, there are some downsides to using Publitas. First off, it’s a paid service, so you’ll need to sign up for one of their subscription plans in order to use it. Additionally, because Publitas is a third-party service, there’s always the potential for compatibility issues between their platform and your dashboard software (e.g., Tableau or Power BI). 

Method 3: Use an Open-Source Solution like Koalas

  • Koalas is an open-source solution that allows you to embed Tableau or Power BI dashboards into web pages without using an iframe. The advantage of using Koalas is that it’s free to use and doesn’t require any knowledge of HTML or CSS. Additionally, Koalas offers a wide range of customization options so that you can control how your dashboard looks on the page. 

There are some downsides to using Koalas, however. First off, because it’s an open-source solution, there’s always the potential for compatibility issues between Koalas and your dashboard software (e.g., Tableau or Power BI). Additionally, Koalas doesn’t offer as many features as Publitas (e .g . , lightbox pop – ups), so keep that in mind when deciding which solution is right for you. 

Choosing the right method for embedding your Tableau or Power BI dashboard into a web page depends on several factors, including your level of technical expertise, budget, and desired features. We hope this blog post has helped you better understand your options so that you can make an informed decision about which method is right for you.

Conclusion: 

Iframes are commonly used to embed dashboards created in Tableau or Power BI onto websites. However, they can cause problems with security and website loading times. As such, some developers prefer to avoid using them altogether. Luckily, there are two methods that you can use to embed a Tableau or Power BI dashboard on a webpage without using an iframe. So, whether you’re a developer who wants more control over the code or someone who just wants an easy solution, there’s a method here for you.

5 Impactful Ways IPA is Disrupting the Insurance Industry  

The use of Intelligent Automation (IPA) in insurance is making big waves in a once archaic industry. By operating in a highly regulated environment with demanding clients, insurers advocate the introduction of artificial intelligence into their daily processes. 

To gain a competitive advantage, several insurers have already deployed an automation strategy in areas like new Business Processing, Claims Processing, and Finance. The insurance industry is currently intensively evaluating use cases for intelligent automation in order to increase the efficiency of current processes and thus reduce operating costs. 

According to McKinsey: “IPA can cut the cost of insurance claims processing by up to 30% and a large insurer could more than double profits over 5 years by digitizing existing business.” On that note, let’s quickly understand how IPA is disrupting the insurance industry.  

1. Digitally powering a changing insurance landscape  

The insurance market has become competitive and more robust in recent years with the arrival of online P2P insurers, technology operators, and insurance players. While digitization in the insurance sector is bound to create opportunities, it also presents new challenges for traditional players. 

With low-interest rate scenarios, the income streams of old insurance companies quickly dry up, as premiums from clients investing in different financial institutions do not pay the same returns as in recent years. In the future, these challenges may increase, due to the rapid pace of digitization and changing customer preferences. 

However, insurers who have taken the pulse of the digital revolution and innovated their products and value chain have grown. 

2. Analyzing operations and optimizing business processes 

The insurance industry is taking an approach where it holistically implements intelligent process automation more effectively to improve its performance.  

The whole approach is to analyze end-to-end operations and find new and efficient ways to use automation technologies to optimize business processes. The technology not only liberates the employees from the hassle of doing repetitive, paper-based tasks but focuses more on people-oriented tasks and helps in delivering personalized, top-quality customer service backed by complicated and real-time data profiles. 

IPA can be effectively utilized by insurance companies to scan photographs and analyze and clear damage or settlements in a more effective and quick manner. Nowadays, the use of high-end voice technologies can help bring down the insurance settlement error rates to about 6 percent (which equals that of an average human). The combination of voice analysis and image recognition technology can help an insurer reduce the challenges or complexities associated with adjusting and assessing claims. 

3. Addressing challenges faced by the insurance industry  

Insurance companies face multiple challenges in this increasingly digitized world. Here are some of the main hurdles insurers face: 

Manual Data-Entry  

Insurers suffer from tedious, prolonged, and tiresome activities like collecting and entering data which is done by humans. These processes are expensive, prone to inaccuracies due to human error, and result in slow response times.  

Accommodates Strict Regulatory Standards  

Operating in multiple regulatory jurisdictions, insurers must adhere to evolving rules regarding capital regulation, transparency, reporting, and interactions with clients. Insurance companies can face heavy fines and damage their reputation if they fail to comply.  

Legacy Applications and Miscellaneous Systems  

Insurers’ banking processes are heavily dependent on legacy systems and software. These programs can no longer meet the needs of modern customers and processes. Companies must invest a large part of their budget and resources to implement new technologies capable of meeting these requirements 

Scalability 

Insurers are subject to seasonal peaks which only become more difficult in the event of a large-scale disaster. Such spikes require the complaint process to be efficient and accurate when dealing with a large amount of incoming data.  

Increasing Demand for Better Client Service 

Today’s insurance customers are tech-savvy and have little patience for bad customer service. They expect transparency, customizable options, and fast response times and are ready to go around a large number of competitors in the market if their needs are not met. 

4. Deploying bots for rudimentary decisions 

IPA is already changing the way insurance companies operate. The bot’s hardworking workforce not only emulates repetitive, rule-based transactional and administrative tasks but also handles some basic decision-making levels. Experts in the human domain are therefore free to focus on more strategic tasks involving reasoning, judgment, and emotional intelligence.  

This digital workforce of bots is getting smarter with Intelligent Automation enabled by transformational technologies like Machine Learning (ML), Computer Vision, Artificial Intelligence (AI), and Natural Language Processing (NLP). Typical areas where intelligent automation makes a huge difference are: 

  • Policy Administration and Servicing  
  • Renewal Processing 
  • Claims Processing  
  • Underwriting 
  • Regulatory Compliance  
  • Process and Business Analytics 
  • Sales and Distribution  
  • Finance and Accounts 

5. Future-proofing the insurance industry  

With IPA in the increasingly sophisticated insurance industry, it will provide a cost-effective way to meet rapidly changing regulatory requirements and help businesses focus on long-term strategic issues. AI-based support can reduce documentation times by up to 80%, making insurance companies more profitable.  

Today’s businesses need to rethink how they want to use human potential and how much they can trust a computer to run their operations. In this way, one thing becomes clear: human contact will become paramount in the future, especially when robots will perform most of the repetitive and mundane tasks.  

Although high-frequency products are digitized and automated to a greater extent, it can be difficult to convert existing policies in this new field, it is not only a technological challenge, but the product must also evolve to meet the needs of current market conditions and regulatory landscape. Insurers will need to consider digitization from a broader perspective that includes product innovation, services, and business models. 

Conclusion 

Insurers can profit vastly from Intelligent Automation, and those who have already turned to automated solutions are already seeing the impact. By effectively planning and implementing AI solutions, businesses can transform their operations, reduce costs, increase employee engagement, and improve the customer experience. 

Futran Solutions has partnered with leaders in the industry (e.g. Automation Anywhere) to provide a solution that is rooted in domain experience, built on a solid foundation, and can be scaled incrementally. We specialize in AI and RPA technologies, however, we understand that the critical success factor for automation programs is the domain knowledge about the processes, and not only the technology. 

Futran Solutions has worked with insurtech start-ups as well as global insurance leaders to develop Intelligent Automation solutions for improving business processes. Our team has experience in creating custom automation applications that use visual recognition, machine learning, and other AI tools that simplify the management of routine tasks, whether it’s underwriting, claims processing, or customer experience. 

Futran Solutions is a Digital Technology organization focused on Data Analytics, Cloud, Automation, and New Age App Development. 

Automation in Recruitment Sector

Automation in HR/Recruitment Sector

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

Introduction 

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

 

Applications of Automation in Recruitment

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

 

  • Sourcing of Candidates

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

 

  • Communication

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

 

  • Enhanced Marketing Strategy

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

 

  • Interview Scheduling

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

 

How to Efficiently Tap in Automation Resources

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

 

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

 

Conclusion

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

 

The 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!

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.