Tag Archives: automation


What is Robotic Process Automation (RPA)?

Robotic process automation (or RPA) is a form of business process automation technology based on metaphorical software robots (bots) or on artificial intelligence (AI)/digital workers. It is sometimes referred to as software robotics (not to be confused with robot software).

In traditional workflow automation tools, a software developer produces a list of actions to automate a task and interface to the back-end system using internal application programming interfaces (APIs) or dedicated scripting language. In contrast, RPA systems develop the action list by watching the user perform that task in the application’s graphical user interface (GUI), and then perform the automation by repeating those tasks directly in the GUI. This can lower the barrier to use of automation in products that might not otherwise feature APIs for this purpose.

RPA tools have strong technical similarities to graphical user interface testing tools. These tools also automate interactions with the GUI, and often do so by repeating a set of demonstration actions performed by a user. RPA tools differ from such systems that allow data to be handled in and between multiple applications, for instance, receiving email containing an invoice, extracting the data, and then typing that into a bookkeeping system.


The hosting of RPA services also aligns with the metaphor of a software robot, with each robotic instance having its own virtual workstation, much like a human worker. The robot uses keyboard and mouse controls to take actions and execute automations. Normally all of these actions take place in a virtual environment and not on screen; the robot does not need a physical screen to operate, rather it interprets the screen display electronically. The scalability of modern solutions based on architectures such as these owes much to the advent of virtualization technology, without which the scalability of large deployments would be limited by available capacity to manage physical hardware and by the associated costs. The implementation of RPA in business enterprises has shown dramatic cost savings when compared to traditional non-RPA solutions.

There are however several risks with RPA. Criticism include risks of stifling innovation and creating a more complex maintenance environment of existing software that now needs to consider the use of graphical user interfaces in a way they weren’t intended to be used.

Benefits of RPA

RPA provides organizations with the ability to reduce staffing costs and human error. David Schatsky, a managing director at Deloitte LP, points to a bank’s experience with implementing RPA, in which the bank redesigned its claims process by deploying 85 bots to run 13 processes, handling 1.5 million requests per year. The bank added capacity equivalent to more than 200 full-time employees at approximately 30 percent of the cost of recruiting more staff, Schatsky says.

Bots are typically low-cost and easy to implement, requiring no custom software or deep systems integration. Schatsky says such characteristics are crucial as organizations pursue growth without adding significant expenditures or friction among workers. “Companies are trying to get some breathing room so they can serve their business better by automating the low-value tasks,” Schatsky says.

Enterprises can also supercharge their automation efforts by injecting RPA with cognitive technologies such as ML, speech recognition, and natural language processing, automating higher-order tasks that in the past required the perceptual and judgment capabilities of humans.

Such RPA implementations, in which upwards of 15 to 20 steps may be automated, are part of a value chain known as intelligent automation (IA), Viadro says. “If we were to segment all of the major enterprises and ask them what’s on their agenda for 2018, close to 100 percent would say intelligent automation,” Viadro says.

By 2020, automation and artificial intelligence will reduce employee requirements in business shared-service centers by 65 percent, according to Gartner, which says the RPA market will top $1 billion by 2020. By that time, 40 percent of large enterprises will have adopted an RPA software tool, up from less than 10 percent today.

Tips for effective RPA

1. Set and manage expectations: Quick wins are possible with RPA, but propelling RPA to run at scale is a different animal. Dave Kuder, a principal with Deloitte Consulting LLP, says that many RPA hiccups stem from poor expectations management. Bold claims about RPA from vendors and implementation consultants haven’t helped. That’s why it’s crucial for CIOs to go in with a cautiously optimistic mindset. “If you go in with open eyes you’ll be a lot happier with the result,” Kuder says.

2. Consider business impact: RPA is often propped up as a mechanism to bolster return on investment or reduce costs. But Kris Fitzgerald, CTO of NTT Data Services, says more CIOs should use it to improve customer experience. For example, enterprises such as airlines employ thousands of customer service agents, yet customers are still waiting in the queue to have their call fielded. A chatbot, could help alleviate some of that wait. “You put that virtual agent in there and there is no downtime, no out sick and no bad attitude,” Fitzgerald says. “The client experience is the flag to hit.”

3. Involve IT early and often: COOs initially bought RPA and hit a wall during implementation, prompting them to ask IT’s help (and forgiveness), Viadro says. Now “citizen developers” without technical expertise are using cloud software to implement RPA right in their business units, Kuder says. Often, the CIO tends to step in and block them. Kuder and Viadro say that business heads must involve IT from the outset to ensure they get the resources they require.

4. Poor design, change management can wreak havoc: Many implementations fail because design and change are poorly managed, says Sanjay Srivastava, chief digital officer of Genpact. In the rush to get something deployed, some companies overlook communication exchanges, between the various bots, which can break a business process. “Before you implement, you must think about the operating model design,” Srivastava says. “You need to map out how you expect the various bots to work together.” Alternatively, some CIOs will neglect to negotiate the changes new operations will have on an organization’s business processes. CIOs must plan for this well in advance to avoid business disruption.

5. Don’t fall down the data rabbit hole: A bank deploying thousands of bots to automate manual data entry or to monitor software operations generates a ton of data. This can lure CIOs and their business peers into an unfortunate scenario where they are looking to leverage the data. Srivastava says it’s not uncommon for companies to run ML on the data their bots generate, and then throw a chatbot on the front to enable users to more easily query the data. Suddenly, the RPA project has become an ML project that hasn’t been properly scoped as an ML project. “The puck keeps moving,” and CIOs struggle to catch up to it, Srivastava says. He recommends CIOs consider RPA as a long-term arc, rather than as piecemeal projects that evolve into something unwieldy.

Impact on employment

According to Harvard Business Review, most operations groups adopting RPA have promised their employees that automation would not result in layoffs. Instead, workers have been redeployed to do more interesting work. One academic study highlighted that knowledge workers did not feel threatened by automation: they embraced it and viewed the robots as team-mates. The same study highlighted that, rather than resulting in a lower “headcount”, the technology was deployed in such a way as to achieve more work and greater productivity with the same number of people.

Conversely, however, some analysts proffer that RPA represents a threat to the business process outsourcing (BPO) industry. The thesis behind this notion is that RPA will enable enterprises to “repatriate” processes from offshore locations into local data centers, with the benefit of this new technology. The effect, if true, will be to create high-value jobs for skilled process designers in onshore locations (and within the associated supply chain of IT hardware, data center management, etc.) but to decrease the available opportunity to low skilled workers offshore. On the other hand, this discussion appears to be healthy ground for debate as another academic study was at pains to counter the so-called “myth” that RPA will bring back many jobs from offshore.

Impact on society

Academic studies project that RPA, among other technological trends, is expected to drive a new wave of productivity and efficiency gains in the global labour market. Although not directly attributable to RPA alone, Oxford University conjectures that up to 35% of all jobs may have been automated by 2035.

In a TEDx talk hosted by University College London (UCL), entrepreneur David Moss explains that digital labour in the form of RPA is not only likely to revolutionize the cost model of the services industry by driving the price of products and services down, but that it is likely to drive up service levels, quality of outcomes and create increased opportunity for the personalization of services.

In a separate TEDx in 2019 talks, Japanese business executive, and former CIO of Barclays bank, Koichi Hasegawa noted that digital robots can be a positive effect on society if we start using a robot with empathy to help every person. He provides a case study of the Japanese insurance companies – Sompo Japan and Aioi – both of whom deployed bots to speed up the process of insurance pay-outs in past massive disaster incidents.

Meanwhile, Professor Willcocks, author of the LSE paper cited above, speaks of increased job satisfaction and intellectual stimulation, characterising the technology as having the ability to “take the robot out of the human”, a reference to the notion that robots will take over the mundane and repetitive portions of people’s daily workload, leaving them to be redeployed into more interpersonal roles or to concentrate on the remaining, more meaningful, portions of their day.


What is Artificial Intelligence (AI)?

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal.

Machine Learning – the core of AI

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

But, using the classic algorithms of machine learning, text is considered as a sequence of keywords; instead, an approach based on semantic analysis mimics the human ability to understand the meaning of a text.

Uses of AI:

  • Chatbots: Chatbots are artificial intelligence software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites and mobile apps or through the telephone. A chatbot is often described as one of the most advanced and promising expressions of interaction between humans and machines. However, from a technological point of view, a chatbot only represents the natural evolution of a Question Answering system leveraging Natural Language Processing (NLP). Formulating responses to questions in natural language is one of the most typical Examples of Natural Language Processing applied in various enterprises’ end-use applications. Together, chatbots and AI can create a very powerful experience. Artificial Intelligence serves as the learning mechanism for many chatbots. Chatbot AI teaches the bots how to respond to your inquiries and helps the bot learn about your personal preferences. AI bots are responsible for engaging in meaningful conversation with an end-user using chatbot AI as a source of intellect.
  • Healthcare: Artificial intelligence in healthcare is the use of complex algorithms and software in another words artificial intelligence (AI) to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of computer algorithms to approximate conclusions without direct human input. What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user. AI does this through machine learning algorithms and deep learning. The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. Large technology companies such as IBM and Google have also developed AI algorithms for healthcare. Additionally, hospitals are looking to AI software to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs. Companies are developing predictive analytics solutions that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.
  • Cyber Security: Cybercriminals will steal an estimated 33 billion records in 2023 according to the projections mentioned in the 2018 study from Juniper Research. That’s something very grave to watch for, and this sheds light on the pressing need to fortify cybersecurity across organizations. To tackle cybersecurity proactively and accurately, infusing intelligence across the entire security ecosystem of an organization is the need of the hour”. If the recent history is any indication, the targeted cyber-attacks that happened recently and even the past attacks suggest that no one is immune to these threats unless companies are fully prepared to respond. This calls for the implementation of fortified cyber security measures. Just the stand-alone cyber security solutions would not suffice. Artificial Intelligence has a huge potential to boost the cyber security profile of a company. AI could work in harmony with the cyber security implementations, to accelerate risk reduction exponentially. Many of the Cyber Security Solutions in the market uses AI models.  For example, LogRhythm uses machine learning to profile and detect threats, compromised accounts, privilege abuse and other anomalies. The Versive Security Engine (VSE) uses artificial intelligence to separate critical risks from routine network activity.  Cybereason’s AI-powered hunting technology determines whether an organization is under attack.

Latest Developments in AI

  •  Robotics is a prime area of development for the AI community so it’s no surprise that there are plenty of start-ups conducting research with the intention of taking the field further. Seattle company Olis Robotics caught the attention of GeekWire earlier this year with a solution designed to take robotics not just to the next level, but somewhere else entirely. According to CEO Don Pickering, “Olis Robotics’ innovation currently manifests in a plug-and-play controller loaded with our AI-driven software platform. The controller and our proprietary software can operate tethered robots on the ocean floor, satellite servicing robots using high-latency satellite links in space, or industrial robots cleaning up a dangerous chemical spill on land using 4G/5G networks. Our innovation will exponentially expand the role of robots to make an impact on human advancement and exploration.”
  • New AI software developed by researchers at the University of Oxford can recognize and track the faces of individual chimpanzees in their natural habitats. The software will allow researchers and wildlife conservationists to significantly cut back on time and resources spent analyzing video footage, according to a new paper. In Science Daily, Dan Schofield, researcher and DPhil student at Oxford University’s Primate Models Lab, School of Anthropology explained, “For species like chimpanzees, which have complex social lives and live for many years, getting snapshots of their behavior from short-term field research can only tell us so much. By harnessing the power of machine learning to unlock large video archives, it makes it feasible to measure behavior over the long term, for example observing how the social interactions of a group change over several generations.’ The computer vision model was trained using over 10 million images from Kyoto University’s Primate Research Institute (PRI) video archive of wild chimpanzees in Guinea, West Africa. The team at Oxford hopes the new software will help improve conservation efforts in areas where chimpanzees are endangered.