What is Robotic Process Automation, and what benefits could it bring to your enterprise?

What is the key to making Robotic Process Automation a success? HCL Technologies’ Kalyan Kumar looks at how business can benefit from leveraging the power of AI and automation.

It’s an age-old problem for the C-suite to solve: how do you do better and do more with less? Having invested in technological advances such as cloud and digitalisation over the last few years, many businesses are now at the part of the roadmap where they have budgeted for spend to level-out, and the forecasted benefits to roll in. However, investment is still needed in most cases, so how can enterprises continue to build when the resources available to them have ceased to grow? The answer is getting technology to lend even more of a helping hand than it is currently doing, in the form of analytics and Artificial Intelligence (AI) integrated Robotic Process Automation.

What form will Robotic Process Automation with AI take in the business arena?

On a practical level, Gartner says AI will manifest itself in the continued rise of the ‘smart machine,’ something it predicts to be one of the biggest technology trends over the coming decade. It says enterprises will increasingly draw on growing computing power and ever-increasing sources of data to adapt to new situations, solve problems and ultimately get ahead of the competition. One of the key ways that they can do this is by automating routine processes and using AI, so that the efforts of skilled employees can be redirected to areas that will be more beneficial to the business than ever before.

This is where Robotic Process Automation (RPA) with AI comes in, enabling business and IT teams in an enterprise to automate processes using a virtual software robot. This robot interprets activities and stimuli within the business and then responds with an appropriate action, based on the parameters defined by the business. In effect, RPA with AI emulates a human operator, or acts as a tool to carry out repeatable processes or tasks. For example, it could be used by a bank to auto-complete registration forms when processing a higher than expected number of applications for a recently-launched type of account. Firms in a range of other sectors could also enjoy the benefits of being able to handle a sudden peak in inbound calls using of a virtual service desk employee to route inquiries more efficiently.

Reaping the benefits

Businesses will benefit greatly from having functions and processes automated at scale, and with repeatability. In addition to bringing potential cost reductions, RPA can also streamline processes and enhance the overall end-user experience. There are four big benefits enterprises should be able to draw from this:

• A more consistent experience than ever before. With robots following specific formulae and layouts, and performing at a uniform speed, a standard level of output should be more achievable than ever before.
• Deeper insights into business / IT performance and customer experience.
• A reduction in the level of human error. We all become fatigued and make mistakes on occasion; this potential for error is limited by the use of RPA.
• More speedy execution than ever before, with some areas of the business able to run 24/7. This means enterprises will be in a much better position to keep things moving even when it’s the end of the working day for its human employees.

Is it for everyone?

As with the adoption of any new kind of technology, enterprises must build a realistic business case for Robotic Process Automation before taking the plunge, or it could just be a wasted effort. This means taking the time to map out costs and expected benefits before budgets can be assigned and work can get underway. The key to making this work is thinking about RPA with analytics and AI from a wider strategic perspective: it’s no good just making vague statements about the potential benefits it can bring. Be clear on exactly what the end goal is, and how it will bring improvements to different existing processes within the business.

Once funds have been secured, businesses must develop a clear idea of the internal processes that are already running: what duty does everything perform, and what does it connect with? As the wires can become increasingly tangled here, it goes without saying that processes that have not previously been integrated and automated in the past will be much easier to improve through the use of RPA. Businesses will also need to consider how processes that are supported by their legacy IT systems will be impacted, as it will be much more difficult to integrate Robotic Process Automation with older technologies. The good news is that it is relatively simple to integrate RPA with existing automation and orchestration platforms, so those that have already invested in machine learning technologies may have much of the groundwork already in place.

Success as part of the wider picture

It’s also important to realise that the benefits won’t be so great if you’re automating a
single or standalone process. To be truly effective, RPA must be integrated with a complete service delivery chain to streamline the entire process, rather than just one small part of it. Furthermore, when Robotic Process Automation is integrated with Cognitive/Machine Learning capabilities, it will be able to learn to complete new processes and functions by itself, which is where it will really start to have a positive impact for the workforce.

The key to making RPA a success is taking the time to ensure it is embedded deep within existing systems and business operations. By skipping that stage, there is the risk that RPA will be tacked on simply for its own sake and is unlikely to deliver the benefits it can provide. If they get it right however, enterprises will be perfectly placed to leverage the power of AI and automation to accelerate the adoption and dynamic adjustment of process change in the digital world, proving a real springboard to success for the 21^st Century enterprise.

Source: cbronline.com -What is Robotic Process Automation, and what benefits could it bring to your enterprise?

Automation + Jobs: Not a Zero-Sum Equation

There’s been no shortage of hand wringing over the job costs of automation—and it’s not without cause. Automated devices are increasingly replacing people in all sorts of occupations. Just as a long list of other technologies have done before. Because dramatic workplace transformations do not occur often, it’s easy to see such shifts as zero-sum occurrences and overlook the new options presented as the familiar ones fade away.

The last time such a major shift took place was during the transition from an agricultural-based economy to an industrial-based economy. And though the tractor and other automated farm equipment did put many farmers and other agricultural workers out of work, people did successfully make the shift. This example has been used so frequently and is so far removed from our current reality that many dismiss this comparison and say that the disruption being brought by automation today is different from the change brought by automation to farming a century ago.

David Autor, an economist who assesses the labor market consequences of technological change and globalization, disagrees. In a recent TedTalk to explain why automation does not just eliminate jobs, Autor highlighted an interesting employment development that has taken place in the banking industry since the advent of the ATM. In his presentation, Autor said: “In the 45 years since the introduction of the automated teller machine…the number of human bank tellers employed in the United States has roughly doubled, from about a quarter of a million to a half a million. A quarter of a million in 1970 to about a half a million today, with 100,000 added since the year 2000.”

Though the number of tellers per bank dropped by roughly a third due to the ATM, banks also discovered that, as a result of the ATM, it was less costly to open new branches, said Autor. He added that the number of bank branches has increased about 40 percent since the appearance of the ATM. That’s why there are more tellers now than there were before ATMs replaced so many of them.

Of course, tellers today do different work than they did historically. “As their routine, cash-handling tasks receded, they became less like checkout clerks and more like salespeople, forging relationships with customers, solving problems and introducing them to new products like credit cards, loans and investments,” said Autor. The tellers are now performing “a more cognitively demanding job.”

The same thing has been happening in the manufacturing and processing industries for decades now. While automation has eliminated many industrial jobs, it is also largely responsible for the plethora of industrial jobs that have been coming back to the U.S. It’s also the reason so many new manufacturing jobs are starting here rather than elsewhere. But today’s manufacturing jobs—just like today’s bank teller jobs—are clearly different from what they used to be. Autor pointed out, “As our tools improve, technology magnifies our leverage and increases the importance of our expertise and our judgment and our creativity.”

In the interim phase we find ourselves in—between manufacturing’s past and its future—we face a challenge. A challenge we have faced before. The last time was a century ago, during the transition from the agricultural economy to an industrial one. As automation was eliminating agricultural jobs, the farm states “took the radical step of requiring that their entire youth population remain in school and continue their education to the ripe old age of 16,” said Autor. “This was called the high school movement, and it was a radically expensive thing to do. It also turned out to be one of the best investments the U.S. made in the 20th century. It gave us the most skilled, the most flexible and the most productive workforce in the world.”

Autor pointed out that, when surveying our current industrial employment situation with an eye toward correcting its current course, “It’s foolish to say there’s nothing to worry about. Clearly we can get this wrong. If the U.S. had not invested in its schools and in its skills a century ago with the high school movement, we would be a less prosperous, a less mobile and probably a lot less happy society. But it’s equally foolish to say that our fates are sealed. That’s not decided by the machines. It’s not even decided by the market. It’s decided by us and by our institutions.”

Given the current state of political matters in the U.S., it’s clear than any large scale, government-driven support for an educational solution—like the one Autor referenced—is highly unlikely. However, action is being taken on a smaller scale in localized areas, such as in Ohio and Indiana, and even in the manufacturing corridor in upstate South Carolina. Despite such positive actions by many of our institutions, the scale of the problem will require additional help from the automation industry and its manufacturing customers.

An example of how automation suppliers are taking action can be seen in Festo’s efforts to bring the German apprenticeship model to the U.S. Yaskawa is another automation technology supplier stepping up to the plate to address the automation-versus-jobs issue through its robotic training programs.

Having seen his former aerospace manufacturing employer go through massive layoffs in the mid-1990s—reducing its workforce by more than 60 percent in three years—Buddy Smith, who is now manager of the Yaskawa Academy for Yaskawa America, knows firsthand how technology suppliers and their manufacturing customers can play a more active role in improving today’s manufacturing workforce situation.

“If a manufacturer of any size decides to add robots to its workforce,” said Smith, “the company’s owners and executives can and should prepare the workforce in advance. There are a lot of communication opportunities to start a dialogue with workers. For example, explain why robotics will help workers, boost production and increase profit sharing. When a company capitalizes on these opportunities, it creates excitement by outlining how robotics can open doors to new training, provide a chance to tackle new responsibilities and reduce injuries.”

To help manufacturing customers who have purchased a robot communicate more effectively with their workers, Smith explained Yaskawa Motoman offers customers a range of robotics classes covering everything from programming to maintenance. “For our maintenance training, a student can spend one week with us and learn about 85 percent of what they’ll face on the job. That’s a terrific example of how retraining an already-employed worker can give them new skills and reap rewards for his or her employers,” he said.

Smith noted that different companies will, of course, have different workforce needs. For example, a large manufacturer may already have some technically inclined workers on staff who “could transition easily into robotics training,” he said. A small job shop, however, may need to look outside its walls to hire a robotics-qualified employee.

“Each company has to think about how to get a return on its robotics investment,” Smith said. “Improving production is not just about purchasing a robot. It’s also about weighing the decision to bring in someone skilled at programming a robot or retraining an employee to program and work with a newly purchased robot. Ultimately, though, the answer is not really a choice between retaining, retraining or recruiting. The solution is a mix of these things depending on the company’s position.”

Source:  automationworld-Automation + Jobs: Not a Zero-Sum Equation

Bring on the Bots

Artificial intelligence is moving from science fiction to practical reality fast.

AI — technology that teaches machines to learn so they can perform cognitive tasks and interact with people — is suddenly accessible to many companies. Costs associated with the advanced computing and data-storage hardware behind AI are plummeting. A growing number of vendors also offer AI tools such as robotic processing automation that can be configured without the help of a rocket scientist.

So this is clearly an area more banks will need to pay attention to going forward.

Already some AI pioneers have emerged in the financial industry just over the past year: Bank of New York Mellon‘s use of robotic process automation in trade settlement and other back-office operations; Nasdaq‘s search for signs of market tampering with an assist from AI; UBS’ initiative to answer basic customer-service questions through Amazon’s virtual assistant, Alexa; and USAA‘s development of its own virtual assistant.

Most large banks are considering using AI wherever mundane or repetitive tasks could be offloaded to a computer fairly easily.

What It Can Do

Here are some examples of where AI could make the biggest difference.

• Customer conversations. Chatbots, natural language processing and speech processing could all be used to improve social interactions. In addition to USAA, Bank of America, Capital One Financial, Barclays and BBVA are experimenting with AI-powered virtual assistants.

“The vision that excites me is the one where we have seamless interactions, where I’m interacting with people, with the bank, with systems in the bank, and at the end of the day what the bank is giving me is exactly what I want,” said Marco Bressan, chief data scientist at BBVA. “We shouldn’t have a fixed idea of what the customer wants. There are some customers that the less they see their banks the better, as long as their money is well taken care of. Other customers want to see their bank every day. We have to serve both. And communicating with each of those from an AI perspective is very different. One has to do with full automation, and the other has to do with a smart interface.”

• Automated investment advice. AI is used to help investment advisers and robo-advisers make better recommendations to customers. Australia’s ANZ Group has been using IBM’s Watson in its wealth management division for three years. Watson can read and understand unstructured data found in contracts and other documents, comb through millions of data points in seconds, and learn how to draw conclusions from the data. It can assess a new customer’s financial situation more quickly and comprehensively than a human being, and it never forgets anything.

BlackRock uses AI to improve investment decision-making. The startup Kensho combines big data and machine-learning techniques to analyze how real-world events affect markets.

• Faster, better underwriting. BBVA uses artificial intelligence to improve its risk scoring of small and midsize businesses. “We realized we could update data in real time and integrate it with what the risk analysts were doing to have a much deeper understanding of their own portfolio,” Bressan said.

Some online lenders use AI to speed up their process. The software can look at hundreds or thousands of attributes, such as personal financial data and transaction data, to determine creditworthiness in a split second. The system learns as it goes — when a lender gets payment information on loans, that information gets fed back into the system, so its knowledge evolves.

However, some people question whether AI programs can be trusted to make sound, unbiased lending decisions.

• Streamlined operations. BNY Mellon, Deutsche Bank and others are using bots in their back offices to automate repetitive tasks like data lookups.

• Assisted account opening. Account origination can be a slow, cumbersome process. Some banks are experimenting with robotically automating some elements, such as data verifications.

• Fraud detection. Card issuers and payment processors like PayPal use AI to compare current card transactions to the user’s past behavior as well as to general profiles of fraud behavior. Human analysts teach the model to discern the difference between legal and fraudulent transactions.

• General efficiency. “The financial industry is an enormous percentage of the GDP,” said Robin Hanson, an associate professor at George Mason University. “A lot of it is due to various regulations and rules about who has to do what and how. It’s entrenched in regulatory practices, and it’s really hard to innovate in finance because you run into some of these obstacles.”

For example, Hanson wanted to sell some books at a convention. To do so, he had to apply for a tax ID, pay a fee and cover $25 in sales taxes. That required him to go to his bank to get a cashier’s check, for which he had to pay a $5 transaction fee and postage. “That’s an enormously expensive, awkward process,” he said. “If we had an efficient financial system, that would cost pennies.”

Unintended Consequences

As AI is used to improve the speed and efficiency of tasks now performed by humans, there are potential unintended consequences. For one, people in lower-paying jobs in operations, branches, compliance and customer service are likely to lose those jobs.

“Bank executives say they’re going to take those people and put them into high-tech, high-pay jobs to help us code, help us do this, help us do that. It’s just not going to happen,” said Christine Duhaime, a lawyer in Canada with a practice in anti-money-laundering, counterterrorist financing and foreign asset recovery and the founder of the Digital Finance Institute. However, “the bank may end up with the same number of employees,” as it sheds customer-facing jobs and hires trained software developers to code.

There are also privacy concerns around the use of AI in financial services. “From a consumer protection point of view, there are concerns people need to take into account when it comes to AI, machine learning and algorithmic decision-making,” said Steve Ehrlich, an associate at Spitzberg Partners, a boutique corporate advisory and investment firm in New York. “Say a company wants to look at your social media or your search engine history to determine your creditworthiness. They go into Facebook and find a picture of you that you didn’t upload. It’s a picture of you at a bachelor party or gambling at a casino. That data gets fed into the algorithm. For one, they should tell you they were going to be taking that information.”

There is also the chance that bots and AI engines could run amok and make poor lending decisions, or commit an operations error that a human with common sense could have averted.

What Banks Can Do

These caveats aside, banks’ wisest course is to prepare to be part of the revolution.

One thing they can do is create an internal center of excellence where a group of people become experts and help bring AI to other parts of the company. They could test technology and use cases and guide the business units in their adoption of bots and AI. Citigroup and BBVA are among the banks doing this. BNY Mellon has a robotics process automation team that partners with businesses and has come up with eight pilots, including settlement and data reconciliation.

Banks also can try to encourage people to embrace AI — even if their jobs are at risk. It helps to communicate that there could be some benefit to them. “People in operations and data analysts don’t want to be doing this work anyway — swivel-chair work, mindless copying and pasting and keying in data,” said Adam Devine, head of marketing at WorkFusion, a robotics process automation software provider that competes with Blue Prism and Automation Anywhere.

David Weiss, senior analyst at Aite Group, also sees the trend as an eventual positive for employees. “I personally argue for human augmentation — go after the peak human problems first,” he said. “There, you’re not going to cut jobs, you’re just going to make people more functional, and leverage their inorganic intelligence more.”

But there’s no question the workplace will change and people will have to adapt.

Source: AmericanBanker.com-Bring on the Bots

Building a business case for offshore robotic process automation

For years, business case for the offshore captive IT center model — whereby companies set up their own wholly owned IT service centers abroad — has centered on the benefits of labor arbitrage to generate cost savings. However, as the return on salary differentials has dwindled and the pressure on captive centers to create additional value, companies are looking to other sources of lower costs and increased efficiencies.

The current rise of robotic process automation (RPA) presents an opportunity for IT organizations to wring more benefits from their offshore delivery centers. The rapidly advancing technology that is used to automate rules-based and repetitive tasks with limited or no human involvement is growing in popularity among the captive center set, says Sarah Burnett, vice president of research with outsourcing research firm and consultancy Everest Group. RPA offers a number of benefits: incremental cost savings over traditional offshore delivery; improved service delivery in the form of process quality, speed, governance, security and continuity; relatively shorter investment recovery periods; and a general ease of implementation.

CIO.com asked Burnett about the increased adoption of RPA and offshore captive centers, the hard benefits of implementation, and the best way to build a business case for automation in offshore IT delivery centers.

CIO.com: Why are functions that are already offshored ripe for the application of RPA? Are onshore IT and business operations also candidates?

Sarah Burnett: RPA is a no brainer for most transactional services irrespective of whether they are offshored or not. RPA can help lower costs while increasing the efficiency of operations. This can help global In-house centers or shared service centers achieve their year-on-year efficiency targets.

[Our recent research] shows that costs of operations in offshore global in-house centers can be lowered by 20 to 25 percent. The savings would be even higher for onshore centers. RPA can also address specific issues such as shortage of resources and skills and where there is a high rate of staff attrition due to the repetitive and boring nature of transactional work.

CIO.com: You’ve noted that RPA has the potential to reduce headcount by 25 to 45 percent resulting in significant cost savings. Does the business case for RPA need to address more than headcount reductions?

Burnett: Headcount reduction is enough of a factor for some enterprises, but not all automate with that as a top priority. Some want to keep the staff and create capacity for other more complex work or address issues such as an influx of new work. It is also important to note that automation is not just about headcount reduction, but also increased quality and standardization of work.

CIO.com: What are the biggest factors that would impact the business case of RPA in an offshore location?

Burnett: I think increasing salaries and shortage of skills could drive demand for automation. Clients of offshore centers are also driving automation for increased efficiency and throughput. This is part of their continuous and year-on-year improvement. One factor that could adversely affect automation in offshore centers is lack of skills for deployment.

CIO.com: What should organizations consider in order to build a realistic business case for RPA?

Burnett: The existing and potential costs and benefits of all of these [issues] should be factored into the business case. There are costs that are easy to measure, e.g., cost of RPA software licenses. [But] there are also qualitative values, such as reduced error rates, that are difficult to measure but these must be factored in for a comprehensive business case.

CIO.com: What other advice would you offer IT organizations considering implementing RPA in captive centers?

Burnett: It is important to benchmark existing operations to work out the benefits of automation and build a business case for deployment and scaling up.

Source: CIO.com.au-Building a business case for offshore robotic process automation

Amplifying human cognition with cognitive computing

Throughout history, humankind has created technologies that amplified our strengths. As an extension of the strength of our arms, we created the hammer; as an extension of the strength of our backs, the steam engine was born; and as an extension of our intelligence and skills, we created cognitive computing, a form of artificial intelligence (AI).

When we think about AI, it’s often about technology like natural language processing, smart homes and cars and virtual personal assistants. These solutions make people’s lives easier, and some may think they replace the need for human intervention altogether. But rather than replacing human minds, the purpose of cognitive computing is to make human cognition even stronger, even better. Cognitive computing enables people to see a perspective they wouldn’t have seen on their own; to recognize something they otherwise would have missed; to help them build an idea; to strengthen their creative processes.

Learn about the IBM Conference at Mobile World Congress

Using cognitive computing to help save lives

IBM Watson for Oncology is able to assist oncologists when making decisions on how to treat their patients. Doctors do not have to rely solely on reading medical journals or finding treatments. By using cognitive computing, doctors can start with an understanding of the patient by extracting information from medical records. IBM Watson for Oncology is able to linguistically analyze clinical literature to recognize the intended meaning in the literature and whether it is relevant to the patient’s case, rather than processing a straight translation like a simple keyword search.

By performing micro-segmentation for population similarities and combining that with an analysis of the patients’ current disease states, possible treatments and regimes, and by monitoring progress, this cognitive system allows oncologists to predict and better prepare for treating side effects. The system is also able to analyze all clinical trials a patient may be eligible for to quickly get patients placed in clinical trials that best fit them. With less time analyzing reports on their own, oncologists are able to spend more time with their patients and making decisions, knowing they have all the crucial information they need.

Looking toward a future with cognitive computing

AI is used to inspire and assist creative processes. It doesn’t just perform individual tasks or answer single questions, it shapes conversations with people that help to build out ideas. People work collaboratively to come up with and build on ideas in the presence of a cognitive system. Rather than thinking about AI like natural language processing — as a simple back-and-forth conversation — we look at it as a conversation between human and machine. The outcome of this dialogue is an amplification of human intelligence.

In our session at Mobile World Congress 2017, we will discuss how cognitive computing is evolving to further amplify human cognition. We will describe how, with devices that people carry or locate in the world, cognitive systems will create a presence with people, whose presence can be useful in activating and accelerating human creativity.

Cognitive computing is set to revolutionize how we interact with our world (in fact, it’s already started). Join me at Mobile World Congress 2017 at the session, “Artificial Intelligence: Chatbots and Virtual Assistants” on 27 February to discover more.
Source: mobilebusinessinsights.com-Amplifying human cognition with cognitive computing

How to do automation right

Automating manual or inefficient processes is the bread and butter of any technology organization. Here are nine considerations for approaching process automation tasks the right way, and ultimately delivering successfully.

1. Avoid ‘automated crap’

A colleague of mine once quipped that automating a ‘crap’ process just results in ‘automated crap,’ and while the language might be a bit uncouth, the sentiment is absolutely correct. It can be tempting when tasked with automating a process to immediately start considering the systems and software to deploy; however, it’s worth determining whether the process is currently valid, effective, and necessary before diving into automating it. Furthermore, some processes simply should not be automated.

2. Check your options

Sometimes pure technology is not the right answer for process automation, as the whole offshore process outsourcing business can attest to. While there are myriad risks and considerations to process outsourcing, considering non-technology options for process ‘automation’ should be part of your evaluation and due diligence process.

3. Look for cheap fixes

Similar to avoiding ‘automated crap,’ before digging into the technical aspects of automation, consider whether there are low- or zero-cost changes that can make the process more efficient. In particular, look for areas where data are painstakingly gathered but no one knows why they’re needed, or complex workflows ‘ping-pong’ a process between multiple operators when one person could perform several steps. Automation should be icing on the cake of a well-designed process; if you don’t have the cake, all the icing in the world won’t prepare you for the birthday party.

4. Size the solution to the problem

I was once tasked with building a complex automated order management process for ‘selling’ scrap paper that had dozens of unique tracking and invoicing requirements. As we contemplated solutions, I asked how much revenue was generated by this process, and was solemnly told it was “as much as $500 annually.” While the goal of integrating all sales processes in one system was laudable, a $50,000 solution to a $500 problem was laughable. Simply disposing of the scrap paper was a far more cost-effective solution than expensive automation.

5. Consider the KPIs

When we automate, we usually consider increased speed or reduced cost as the ultimate successful outcome of the automation effort. However, these may not be the right KPIs in some cases. Shortening the time to handle a customer sales call might increase the number of calls you can process, but will reduce sales revenue since reps no longer have time to cross sell, just as a confusing automated system can actually increase customer service calls, producing the exact opposite of the intended outcome. Before reflexively considering cost and speed as your key KPIs, think a level deeper and add KPIs that will mitigate unintended outcomes.

6. Consider the user experience (UX)

Along with well-defined KPIs, User Experience (UX) should also factor into your automation concerns, both from the perspective of the process operator(s) as well as the consumer of the process. Automation that’s targeted toward an employee with minimal training will require more robust error handling than one that’s in the hands of a skilled operator. Similarly, extra time spent ensuring the process is clearly articulated and displayed toward the operator and end customer will ultimately make the process more efficient by reducing errors and confusion. For example, self-checkout systems at the grocery store were supposed to be a superior option to human-assisted checkout. However, users quickly learned that manually finding and keying codes for produce and bagging their own groceries was inferior and slower than checkouts staffed by humans, resulting in low adoption rates of self-checkout. Obviously, these systems save the store staffing costs, but ultimately they increase customer frustration.

7. Test the edge cases

A key failure point for many automated systems is poor testing. The automation is great ‘most of the time’ but completely breaks down during an edge case or error condition. While there’s a risk of ‘over-testing’ and becoming obsessively focused on convoluted and unlikely use cases, it’s imperative that the automation recover gracefully from predictable errors. What happens if unexpected data are entered? How does the automation respond to a system that’s down? How does the automation recover and guide a user when failures occur?

8. Avoid ‘orphaned’ automation

Like any IT project, once automation is successfully deployed it’s tempting to cross the item off the organizational to-do list, and not revisit the automation until it’s obsolete or fails. As part of your regular maintenance activities, check the performance of key automated processes. Perhaps there’s a new tool or technique that could quickly be adopted for significant benefit, or a minor technology upgrade could provide dramatic returns. Applying continuous improvement practices to your process automation efforts can reap additional benefits from your automation at lower costs than new efforts.

9. Innovate

It can be easy to apply the same solutions and techniques to your ongoing automation efforts, but take a moment to consider if there are new technologies or services that might be more effective at accomplishing your automation goals. Emerging technologies like Robotic Process Automation (RPA) or Artificial Intelligence (AI) technologies could be relevant to the automation problem at hand, or perhaps you’re tasked with automating a low-risk process that could serve as a test case for advanced tools and techniques.

While process automation is old hat to many IT organizations, to the point that it’s seen as a low-level activity that can be given limited attention, there are very real considerations, concerns, and benefits to effective process automation. Executing a difficult technical task often has less visible impact, and associated financial benefit, to shaving some time or improving the quality of a process that’s performed thousands of times each day.

 

Source: How to do automation right

Image Credit: iStock

Why bots are poised to disrupt the enterprise

The proliferation of robots completing manual tasks traditionally done by humans suggests we have entered the machine automation age. And while nothing captures the imagination like self-directing machines shuttling merchandise around warehouses, most automation today comes courtesy of software bots that perform clerical tasks such as data entry.

Here’s the good news: Far from a frontal assault on cubicle inhabitants, these software agents 7may eventually net more jobs than they consume, as they pave the way for companies to create new knowledge domain and customer-facing positons for employees, analysts say.

The approach, known as robotic process automation (RPA), automates tasks that office workers would normally conduct with the assistance of a computer, says Deloitte LLP Managing Director David Schatsky, who recently published research on the topic. RPA’s potential will grow as it is combined with cognitive technologies to make bots more intelligent, ideally increasing their value to businesses. Globally, the RPA market will grow to $5 billion by 2020 from just $183 million in 2013, predicts Transparency Market Research.

Bots mimic activities a human would perform, including anything from populating electronic forms to changing data in a customer account. Some bots log into an application, extract information from a web page, modify it and enter it into another application. At AT&T, bots pull sales leads from multiple systems, enabling staff to spend more time with customers.

Rise of the machines yield greater productivity

Bot benefits include the capability to cut staffing costs, reduce error rates associated with humans and improve customer engagement. For example, Schatsky says a bank redesigned its claims process and deployed 85 or bots running 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.

Deloitte analysis

Man vs. Bot (Click for larger image.)

A major appeal of bots is that they 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.

There’s little question that many workers will lose their jobs as companies automate more business processes. Forrester Research last November estimated that RPA software will threaten the livelihood of 230 million or more knowledge workers, or approximately 9 percent of the global workforce. However, RPA will create new jobs as workers train up and pivot to new roles within their companies.

RPA, along with physical, intelligent machines and other automation capabilities, will replace 16 percent of U.S. jobs but create the equivalent of 9 percent, yielding a net loss of 7 percent of jobs by 2025, says Craig Le Clair, a Forrester analyst who tracks the impact of automation technologies on the corporate sector. For instance, Wolters Kluwer reallocated money saved using RPA to close its books to hire a financial analysts to analyze profits, revenue, planning and forecasting.

Le Clair also says increased RPA will give rise to “cognitive sommeliers,” or staff who understand domains and curate knowledge bases for an application area. Moreover, as the glut of information increases, particularly in financial services, customers will need more human advice than ever before, Le Clair says.

Most bots stick strictly to their business logic rules but that is changing. If the machines can become smarter, the popular thinking goes, businesses will be able to use them in more complex operations. Paired with chatbots, natural language processing, machine learning and other tools, RPA can extract and structure information from audio, text, or images, as well as identify patterns and pass that information to the next step of the process.

Bots are already making a difference in how businesess interact with customer. At Vanguard Group, sophisticated algorithms called “roboadvisors” pair with humans to offer clients tailored investment advice.Virgin Trains has deployed cognitive RPA to automatically refund customers for late running trains. As customer emails arrive, a natural language processing tool gauges meaning and sentiment and then recognizes key information in the text to service the customer, reducing daily processing time and manual labor involved in dealing with customer emails by 85 percent. “Cognitive RPA has the potential to go beyond basic automation to deliver business outcomes such as greater customer satisfaction, lower churn, and increased revenues,” Schatsky says.

Change management and integration challenges loom

However, Schatsky cautions, cognitive RPA has been slow-going due to the complex nature of blending the technologies with existing systems, as well as the lack of required skills to implement them. To bridge that talent gap, IBM and Blue Prism have inked a joint agreement to work together on cognitive RPA.

Organizations piloting the most basic bots must clear change management, governance and security hurdles, says Forrester’s Le Clair. Implementing too many interdependent bots can wreak havoc on existing systems. Bots can also create turmoil when paired with workers who must learn to work with their new virtual colleagues.

Moreover, because most RPA tools reside on desktops, implementations in environments that are highly virtualized — where information from thousands of PCs resides on centralized servers — can be clumsy. “They have integration problems with more sophisticated VDI [virtual desktop infrastructure] implementations,” Le Clair says.

Schatsky says that CIOs should introduce RPA quickly in increments but scale it up slowly. “Start small and start fast,” Schatsky says. “If you see early success, you need to take a step back and start thinking more strategically about how you scale up in terms of governance and your staffing model.”

 

Source: CIO.com-Why bots are poised to disrupt the enterprise

Image Credit: Thinkstock

Looking to Improve Back-Office Efficiency? Maybe It’s Time to Send in the Robots

M&As are on the rise and retailers from grocery chains to high-end fashion outlets face pressure to maintain margins amidst a constantly fluctuating and highly uncertain marketplace. In this environment, the automation of critical back-office processes to increase efficiency and reduce costs has become a top priority of CIO, CFO and CEO alike.

To address this objective, retailers are taking a long hard look at Robotic Process Automation (RPA) — software tools that use rules-based logic to execute repetitive, manual tasks traditionally performed by humans. In retail, RPA solutions are being applied to automate typical back-office functions such as store auditing, month-end close, accounts receivable (AR) and other financial reporting activities. By linking multiple standalone systems without requiring duplicate data input or analysis, RPA also improves merchandising and in-store planning, order fulfillment to stores and supply chain efficiency.

In retail, where multiple partners deliver products to multiple store locations, the ability to match up and reconcile information from these various partners (CPG manufacturers and distributors) is imperative. This loading, reconciling and researching activity is typically a very manual, repetitive and time-consuming task, one that involves many data files with thousands of products in each. In some cases, loading these files can take hours. Software robots can handle these manual, repetitive tasks so people don’t have to, accomplishing many hours of work in a matter of minutes. Additionally, by freeing human data specialists from these tedious, rote activities, RPA enables humans to focus on managing exceptions and adding value.

RPA can also significantly improve IT-related processes. Many large retailers that still use Microsoft Excel spreadsheets to track IT assets can take advantage of RPA to easily extract data from any store or DB2 system, put it into Excel, and evaluate, scrub and feed it back into the system – often error-free and with minimal human intervention.

Given that retail by definition represents an ecosystem of partners, and those partners share a ton of data each week with retailers, processing that information quickly for month-end reporting is paramount. As a result, financial and operational departments within retail face constant pressure to conduct month-end reporting faster. In most cases, the cost of replacing antiquated home-grown legacy systems is prohibitive; here, RPA offers a cost-effective alternative to automating finance and auditing functions.

Outlined below are some considerations for retailers seeking to leverage RPA capabilities.

Ease of implementation and integration with IT. A key advantage of RPA technology is speed and ease of integration. In contrast to traditional IT automation systems that can take 6 to 12 months to implement, RPA solutions can often be deployed in 3 to 4 months. Moreover, since they reside on the application layer of IT systems, RPA tools – while requiring some level of IT support and integration – involve minimal disruption to IT infrastructure and have a negligible impact on IT resources. As such, CIOs would be well-advised to embrace RPA and partner with business advocates early and often in an automation initiative. This can benefit the business by easing the integration process. IT, meanwhile, can apply RPA tools to optimize its internal resources, and ensure that business units don’t circumvent IT when deploying RPA solutions.

A job changer for retail data processors. In addition to reducing the head count of human data entry specialists or processors, RPA dramatically changes the job description for the staffers who remain. Human processors can spend their time more strategically, addressing unusual cases that don’t follow prescribed rules – such as, for example, a distributor’s shipment that substantially exceeds historical volumes. This situation would require industry experience and knowledge and a review of the retailer’s policies and guidelines against the distributor’s recent gross shipment reports to determine if an error occurred.

An alternative to offshoring. Many retailers have turned to offshoring to reduce costs and improve the efficiency of back-office processes. By undermining the fundamental competitive differentiator of labor arbitrage, RPA makes geographic location and low labor costs less relevant as sourcing strategy criteria, and expands the range of options available to retailers.
Since today’s retailers are constantly challenged by changing customer preferences, encroaching competition and empowered consumers who want greater user experiences in-store and online, the last thing they want to worry about is integrating and optimizing back-office processes. By automating time-consuming repetitive processes and providing more accurate and faster financial reporting and improved analytics, RPA offers a strategically “disruptive” technology solution that requires a minimal level of operational disruption.

Source: risnews.com-Looking to Improve Back-Office Efficiency? Maybe It’s Time to Send in the Robots

What Is The Difference Between Deep Learning, Machine Learning and AI?

 

Over the past few years, the term “deep learning” has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries.

Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is. But what exactly is it? And is it just another fad being used to push “old fashioned” AI on us, under a sexy new label?

In my last article I wrote about the difference between AI and Machine Learning (ML). While ML is often described as a sub-discipline of AI, it’s better to think of it as the current state-of-the-art – it’s the field of AI which today is showing the most promise at providing tools that industry and society can use to drive change.

In turn, it’s probably most helpful to think of Deep Learning as the cutting-edge of the cutting-edge. ML takes some of the core ideas of AI and focuses them on solving real-world problems with neural networks designed to mimic our own decision-making. Deep Learning focuses even more narrowly on a subset of ML tools and techniques, and applies them to solving just about any problem which requires “thought” – human or artificial.

How does it work?

Essentially Deep Learning involves feeding a computer system a lot of data, which it can use to make decisions about other data. This data is fed through neural networks, as is the case in machine learning. These networks – logical constructions which ask a series of binary true/false questions, or extract a numerical value, of every bit of data which pass through them, and classify it according to the answers received.

Because Deep Learning work is focused on developing these networks, they become what are known as Deep Neural Networks – logic networks of the complexity needed to deal with classifying datasets as large as, say, Google’s image library, or Twitter’s firehose of tweets.

With datasets as comprehensive as these, and logical networks sophisticated enough to handle their classification, it becomes trivial for a computer to take an image and state with a high probability of accuracy what it represents to humans.

Pictures present a great example of how this works, because they contain a lot of different elements and it isn’t easy for us to grasp how a computer, with its one-track, calculation-focused mind, can learn to interpret them in the same way as us. But Deep Learning can be applied to any form of data – machine signals, audio, video, speech, written words – to produce conclusions that seem as if they have been arrived at by humans – very, very fast ones. Let’s look at a practical example.

Take a system designed to automatically record and report how many vehicles of a particular make and model passed along a public road. First, it would be given access to a huge database of car types, including their shape, size and even engine sound. This could be manually compiled or, in more advanced use cases, automatically gathered by the system if it is programmed to search the internet, and ingest the data it finds there.

Next it would take the data that needs to be processed – real-world data which contains the insights, in this case captured by roadside cameras and microphones. By comparing the data from its sensors with the data it has “learned”, it can classify, with a certain probability of accuracy, passing vehicles by their make and model.

So far this is all relatively straightforward. Where the “deep” part comes in, is that the system, as time goes on and it gains more experience, can increase its probability of a correct classification, by “training” itself on the new data it receives. In other words it can learn from its mistakes -just like us. For example it may incorrectly decide that a particular vehicle was a certain make and model, based on their similar size and engine noise, overlooking another differentiator which it determined had a low probability of being important to the decision. By learning that this differentiator is, in fact, vital to understanding the difference between two vehicles, it improves the probability of a correct outcome next time.

So what can Deep Learning do?

Probably the best way to finish this article and give some insight into why this is all so ground breaking is to give some more examples of how Deep Learning is being used today. Some impressive applications which are either deployed or being worked on right now include:

Navigation of self-driving cars – Using sensors and onboard analytics, cars are learning to recognize obstacles and react to them appropriately using Deep Learning.

Recoloring black and white images – by teaching computers to recognize objects and learn what they should look like to humans, color can be returned to black and white pictures and video.

Predicting the outcome of legal proceedings – A system developed a team of British and American researchers was recently shown to be able to correctly predict a court’s decision, when fed the basic facts of the case.

Precision medicine – Deep Learning techniques are being used to develop medicines genetically tailored to an individual’s genome.

Automated analysis and reporting – Systems can analyze data and report insights from it in natural sounding, human language, accompanied with infographics which we can easily digest.

Game playing – Deep Learning systems have been taught to play (and win) games such as the board game Go, and the Atari video game Breakout.

It is somewhat easy to get carried away with the hype and hyperbole which is often used when these cutting edge technologies are discussed (and particularly, sold). But in truth, it’s often deserved. It isn’t uncommon to hear data scientists say they have tools and technology available to them which they did not expect to see this soon – and much of it is thanks to the advances that Machine Learning and Deep Learning have made possible.

Bernard Marr is a best-selling author & keynote speaker on business, technology and big data. His new book is Data Strategy. To read his future posts simply join his network here.

Source: Forbes.com-What Is The Difference Between Deep Learning, Machine Learning and AI?

Why serverless computing is the next step in cloud evolution

Watch this video about erverless computing is the next step in cloud evolution

http://www.cio.com/video/71961/why-serverless-computing-is-the-next-step-in-cloud-evolution

Source: CIO.com-Why serverless computing is the next step in cloud evolution