The optimist’s guide to the robot apocalypse

Machines, you may have heard, are coming for all the jobs.

Robots flip burgers and work warehouses. Artificial intelligence handles insurance claims and basic bookkeeping, manages investment portfolios, does legal research, and performs basic HR tasks. Human labor doesn’t stand a chance against them—after the “automation apocalypse,” only those with spectacular abilities and the owners of the robots will thrive.

Or at least, that’s one plausible and completely valid theory. But before you start campaigning for a universal basic income and set up a bunker, you might want to also familiarize yourself with the competing theory: In the long run, we’re going to be just fine.

We’ve been here before

Our modern fear that robots will steal all the jobs fits a classic script. Nearly 500 years ago, Queen Elizabeth I cited the same fear when she denied an English inventor named William Lee a patent for an automated knitting contraption. “I have too much regard for the poor women and unprotected young maidens who obtain their daily bread by knitting to forward an invention which, by depriving them of employment, would reduce them to starvation,” she told Lee, according to one account of the incident. The lack of patent didn’t ultimately stop factories from adopting the machine.

Two hundred years later, Lee’s invention, still being vilified as a jobs killer, was among the machines destroyed by protestors during the Luddite movement in Britain. More than 100 hundred years after that, though computers had replaced knitting machines as the latest threat to jobs, the fear of technology’s impact on employment was the same. A group of high-profile economists warned President Lyndon Johnson of a “cybernation revolution” that would result in massive unemployment. Johnson’s labor secretary had recently commented that new machines had “skills equivalent to a high school diploma” (though then, and now, machines have trouble doing simple things like recognizing objects in photos or packing a box), and the economists were worried that machines would soon take over service industry jobs. Their recommendation: a universal basic income, in which the government pays everyone a low salary to put a floor on poverty.

Today’s version of this scenario isn’t much different. This time, we’re warned of the “Rise of Robots” and the “End of Work.” Thought leaders such as Elon Musk have once again turned to a universal basic income as a possible response.

But widespread unemployment due to technology has never materialized before. Why, argue the optimists, should this time be any different?

Automating a job can result in more of those jobs

Though Queen Elizabeth I had feared for jobs when she denied Lee’s patent, weaving technology ended up creating more jobs for weavers. By the end of the 19th century, there were four times as many factory weavers as there had been in 1830, according James Bessen, the author of Learning by Doing: The Real Connection between Innovation, Wages, and Wealth.

Each human could make more than 20 times the amount of cloth that she could have 100 years earlier. So how could more textile workers be needed?

According to the optimist’s viewpoint, a factory that saves money on labor through automation will either:

  1. Lower prices, which makes its products more appealing and creates an increased demand that may lead to the need for more workers.
  2. Generate more profit or pay higher wages. That may lead to increased investment or increased consumption, which can also lead to more production, and thus, more employment.

Amazon offers a more modern example of this phenomena. The company has over the last three years increased the number of robots working in its warehouses from 1,400 to 45,000. Over the same period, the rate at which it hires workers hasn’t changed.

The optimist’s take on this trend is that robots help Amazon keep prices low, which means people buy more stuff, which means the company needs more people to man its warehouses even though it needs fewer human hours of labor per package. Bruce Welty, the founder of a fulfillment company that ships more than $1 billion of ecommerce orders each year and another company called Locus Robotics that sells warehouse robots, says he thinks the threat to jobs from the latter is overblown—especially as the rise of ecommerce creates more demand for warehouse workers. His fulfillment company has 200 job openings at its warehouse.

A handful of modern studies have noted that there’s often a positive relationship between new technology and increasing employment—in manufacturing firms, across all sectors, and specifically in firms that adopted computers.

How automation impacts wages is a separate question. Warehouse jobs, for instance, have a reputation as grueling and low-paying. Will automation make them better or worse? In the case of the loom workers, wages went up when parts of their jobs became automated. According to Bessen, by the end of the 19th century, weavers at the famous Lowell factory earned more than twice what they earned per hour in 1830. That’s because a labor market had built up around the new skill (working the machines) and employers competed for skilled labor.

That, of course, is not the only option, but it is an outcome embraced by the optimist crowd. Similarly positive results of automation: If companies can make more money with the same number of workers, they can theoretically pay those workers better. If the price of goods drops, those workers can buy more without a raise.

As automation kills some jobs, it creates others

As the Industrial Revolution ended, about half of American workers were still employed in agriculture jobs, and almost all of those jobs were about to be lost to machines.

If nothing else had changed, the decrease in agriculture jobs could have led to a largely unemployed society. But that’s not what happened. Instead, as agricultural employment dwindled to less than 2% of American workers, jobs in other sectors grew during the same period. They involved working in factories, yes, but also working with computers, flying airplanes, and driving cargo across the country—occupations that weren’t feasible in 1900.

Today’s optimists believe that the latest automation technologies will create new jobs as well.

What kind of jobs, they really can’t say (this is where the optimism comes in handy). About a third of new jobs created in the United States over the past 25 years didn’t exist (or just barely existed) at the beginning of that period, and predicting what jobs might be created in the next 25 years is just guessing. In a report on artificial intelligence and the economy, the Obama White House suggested that automation might create jobs in supervising AI, repairing and maintaining new systems, and in reshaping infrastructure for developments like self-driving cars. But, the report’s authors note, “Predicting future job growth is extremely difficult, as it depends on technologies that do not exist today.”

Automation doesn’t necessarily make humans obsolete

In 2013, researchers at Oxford sparked fear of the robot revolution when they estimated that almost half of US occupations were likely to be automated. But three years later, McKinsey arrived at a very different number. After analyzing 830 occupations, it concluded that just 5% of them could be completely automated.

The two studies obviously counted differently. The Oxford researchers assessed the probability that occupations would be fully automated within a decade or two. But automation is more likely to replace part of a job than an entire job. When Amazon installs warehouse robots, they currently don’t replace full workers, but rather, the part of the job that involves fetching products from different shelves. Similarly, when my colleague used artificial intelligence to transcribe an interview, we didn’t fire him; he just worked on the other parts of his job. McKinsey’s researchers’ model didn’t attempt to sort jobs into “replaceable” and “not replaceable,” but rather to place them on a spectrum of automation potential.

Almost every occupation that McKinsey looked at had some aspect that could be automated. Even 25% of tasks inside of a CEO job, the analysis found, could be automated. But very few jobs could be entirely automated.

McKinsey’s conclusion was not that machines will take all of these jobs, but rather, “more occupations will change than will be automated away.” Our CEO, for example, won’t spend time analyzing reports if artificial intelligence can draw conclusions more efficiently, so he can spend more time coaching his team.

This part of the optimist’s theory argues that if humans aren’t bogged down by routine tasks, they will find something better to do. The weavers will learn the new job of operating the machines. My coworker will write more articles because he’s not transcribing interviews. The warehouse workers will each pack more boxes because they’re not running between shelves collecting each item to be packed.

“Any time in history we’ve seen automation occur, people don’t all of the sudden stop being creative and wanting to do interesting new things,” says Aaron Levie, the CEO of enterprise software company Box and an automation optimist. “We just don’t do a lot of the redundant, obsolete work.” He points to potential examples like automatically scheduled calendar appointments or automated research services. “Why won’t we make up that time with doing the next set of activities that we would have been doing?” he says. “What I think it does is make the world move faster.”

What might that look like? Sodexo’s CEO of corporate services, Sylvia Metayer, offers one example. She says the outsourcing company’s building maintenance crew has started using drones to survey roofs for maintenance needs in three locations. Before the drones arrived, a human climbed onto the roof to check things out. Now, that human stays on the ground, which is safer. “The service hasn’t changed, the clients still need someone to help maintain the roof,” she says. “If we do it with drones, the people who would have been going up on the roof have more value, talking with clients about what needs to be done.”

Examples also exist in back office automation. “From what we’ve actually seen on the ground, in real business operations, we’ve seen almost zero job loss,” says Alastair Bathgate, CEO of Blue Prism, a software company that helps automate tasks within customer service, accounting, and other jobs. One of his clients, a bank, trained the automation software to react when a customer overdrew an account by checking to see if there were a balance in another account that could be transferred to cover it. This was a process that had never been done by humans, because it would be too tedious and expensive. Another bank used the software to allow customer service representatives to direct customers who had a credit card stolen to an automated system that would input their information and close the account. What do they do now? “It allows them to take another call,” Bathgate says. On-hold time, not head count, went down.

We may need automation

As the birthrate in many countries declines, the share of the working age population will shrink. To maintain today’s GDP, those workers will each need to be more productive than workers today, and they’ll need to improve at a faster rate than they have in the past. Even if productivity continued to improve at the same rate that it has throughout the last 50 years—within which the computer and the internet both became mainstream tools—it wouldn’t be enough of an improvement to sustain GDP. Automation technology could be the answer. According to a McKinsey analysis, it could raise global productivity by as much as 0.8% to 1.4% annually—but only if humans keep working, as well.

Being an automation optimist doesn’t mean ignoring jobs lost to automation

The Industrial Revolution eventually led to an unprecedented high standard of living for ordinary workers.

But this prosperity didn’t immediately materialize. There was a period in which life inside of factories was miserable for the laboring class. It included paltry wages, terrible working conditions, and child labor.

Today, during what the World Economic Forum has dubbed the “fourth industrial revolution,” even optimists expect short-term labor displacement, wage depression, and, for some workers, pain. To take just one sector, the Obama White House estimated that nearly 3.1 million people could lose their job to the autonomous car. New jobs in other sectors could be created as these jobs disappear, but the people who are losing driving jobs won’t necessarily have the skills to fill the new ones. This is a big deal.

What separates the optimists from the pessimists is that they tend to believe that the economy as a whole will recover from this short-term adjustment period.

Pessimists argue that not everyone will benefit from this industrial revolution in the same way that the standard of living for ordinary workers rose after the last industrial revolution. Over the last two decades, most gains in productivity have gone to the owners of businesses rather than people who work for them. Global inequality has for the last several decades soared.

But there’s a lot of stuff going on outside of technological developments, argue the automation optimists, like the decline of unions, weakening of labor laws, tax laws that benefit rich people, and education policies that haven’t adapted to a changing world—these are policy problems, and we should fix them rather than blaming technology.

There is, however, one point that cannot be easily brushed aside. Pessimists point to the pace of innovation as a reason that, this time, advances in technology will impact jobs more brutally than they have in the past. “In the past, when you had disruption, the economy adjusted and jobs were created elsewhere,” says Ethan Pollack, an economist at the Aspen Institute’s Future of Work Initiative who says he wavers between optimism and pessimism on automation. “What happens if [in the near future], each period of disruption comes so quickly, that it never recovers?”

So who is right?

“There will be fewer and fewer jobs that a robot cannot do better,” Tesla and SpaceX CEO Elon Musk recently mused at the World Government Summit in Dubai, before suggesting that a universal basic income would be necessary. But even as he talked of the threat to jobs, he also spoke of positive impacts of automation technology. “With automation, there will come abundance,” he said. “Almost everything will get very cheap.”

The optimism camp tends to have similarly mixed feelings about automation’s impact. “AI can seem dystopian,” tweeted Box CEO Levie, “because it’s easier to describe existing jobs disappearing than to imagine industries that never existed appearing.” He doesn’t deny that automated technology will make some labor obsolete—he just focuses on the long-term, big-picture opportunity for potential benefits.

Both sides generally agree that there should be measures in place to reduce the impact of labor displacement from automation, like education programs for re-skilling workers who will lose their jobs. One side just tends to have a more darker view of what happens after that.

So which side is right? If history is any guide, both.

In the 1930s, economist John Maynard Keynes famously coined the term “technological unemployment.” Less famous is the argument he was making at the time. His case wasn’t that impending technology doomed society to prolonged massive unemployment, but rather that a reaction to new technology should neither assume the end of the world or refuse to recognize that world had changed. From his essay, Economic Possibilities For Our Grandchildren:

The prevailing world depression, the enormous anomaly of unemployment in a world full of wants, the disastrous mistakes we have made, blind us to what is going on under the surface to the true interpretation, of the trend of things. For I predict that both of the two opposed errors of pessimism which now make so much noise in the world will be proved wrong in our own time-the pessimism of the revolutionaries who think that things are so bad that nothing can save us but violent change, and the pessimism of the reactionaries who consider the balance of our economic and social life so precarious that we must risk no experiments.

The Obama White House, in a report about how automation may impact jobs, recommended responding to automation by investing in education; creating training programs for workers, like drivers, who will be displaced by automation technology; and strengthening the social safety net. Bill Gates has suggested that we tax robots’ productivity similar to how we tax humans’ income in order to finance retraining programs and jobs for which humans are well-suited, like care-taking. Others have suggested wage subsidies and direct government employment programs. These proposed solutions are not so dissimilar to those provided to President Johnson in 1964, which included “a massive program to build up our educational system” and “a major revision of our tax structure.”

Even so, little progress has been made since then in making the US more resilient to job displacement caused by automation. The cost of college education has never been higher. As a society, the US has not shown a commitment in building effective, equal-opportunity re-skilling programs. Inequality continues to increase. And the Trump Administration has so far focused on preventing companies from hiring people into manufacturing jobs overseas rather than preparing the economy for the impact of automation. This is an insufficient approach.

As MIT’s Erik Brynjolfsson and Andrew McAfee put it more recently than Keynes in their 2014 book about automation’s economic impact, The Second Machine Age: “Our generation has inherited more opportunities to transform the world than any other. That’s a cause for optimism, but only if we’re mindful of our choices.”

Source: Quartz-The optimist’s guide to the robot apocalypse

Five questions to ask before considering robotic process automation

Speed. Efficiency. Higher quality. Lower costs. These are the key benchmarks by which enterprise IT has been measured since the earliest days of its existence. These outcomes have always been achieved by some combination of man and machine, but with advances in cloud and digital technology, the balance is shifting away from human labor and moving toward technology-driven solutions.

Nowhere is this more evident than in the rise of Robotic Process Automation (RPA). Software robots are increasingly performing important and onerous tasks that have traditionally been done by humans: taking over work processes, manipulating data, communicating between systems, and processing and recording transactions.

Not surprisingly, RPA can do these tasks at a fraction of the cost of human equivalents, with dramatic improvement in quality and reliability. In fact, in the healthcare industry, one RPA bot – which can be procured at a cost of $10,000 -$15,000 – can process claims at the rate of 5-10 humans working full-time. Not to mention RPA allows businesses to dramatically reengineer and improve key processes.

Even the jobs question is not as sticky as one might expect. It’s true that full-time equivalent savings are essential to the business case for investing in RPA, and this often sets the stage for conflict between those advocating for RPA and those whose positions will be eliminated. However, there is a silver lining found in two essential elements of all RPA implementations:

1. The ability to move humans from routine, rote jobs into higher-value roles with work that is far more engaging increases employee satisfaction and often fills open positions with staff that have “tribal knowledge” that would take new employees years to build

2. Organizations seem to gain a greater appreciation for the importance of organizational change management – the communications, expectation management and planning – that lays the groundwork for successfully transferring work from humans to robots.

So, with relatively little investment and simple implementation, RPA may seem like an opportunity no-brainer – but realizing the real payoff of automation requires some careful deliberation. Here are five questions to ask when considering RPA for the first time:

1. Where are your pain points?

Where in your organization are humans spinning their wheels with repetitive and mundane tasks that take them away from more important, higher value work? Where are kinks hindering the workflow? Where are employees transferring data from one application to another or keying in large volumes of information by hand. Where are errors most likely to occur? Where is your staff not able to meet your SLA’s? Pinpointing these scenarios in your organization will help you create an “automate-ability map,” a picture of the processes that may be easily “automatable” with RPA tools. Start with a small number of these use cases so you can talk about the application of RPA in concrete terms and deliver quick results.

2. What are your priorities for an investment in automation?

After mapping out which processes in your environment are automatable, develop and evaluate the business case for doing so. Estimating the investment and the potential advantages for a particular use case is a smart way to initiate the RPA conversation with leadership. Enterprises across industries – including insurance, banking, telecommunications and manufacturing – are creating compelling business cases for deploying RPA to process invoices for payment or generate weekly reports that once required people to extract data from one application into a spreadsheet and manually save it into another. Estimated time and cost savings, and accurate estimates of what it will take to configure and deploy the automation, should be an important part of the decision matrix.

3. How ready is your organization for change?

Automating business processes with RPA requires more than simply giving people a head’s up that change is coming. The scope, nature and complexity of the processes being automated will determine the requirements for managing organizational change, which impacts the broader culture and operating characteristics of the organization. Managing major change is part art and part science. Before you dive into the RPA pool, determine your organization’s aptitude for change, organizational redesign and transition planning. Also take the time to determine the complexity of the automation scripts you are considering implementing so you know what change management practices will be needed, in which functional areas and across which parts of the business.

4. What is IT’s role?

Even though RPA tools can be implemented by business users and, once up and running, require relatively few IT resources, it’s important to involve IT early on – even as early as the consideration phase. RPA is a corporate asset; if it is going to deliver its full potential in terms of process automation and data integration, it needs to be configured correctly on a secure server in the IT environment and included in IT’s critical processes that support security management and back up/contingency planning. RPA also can be considered a boon to IT, since many times it can be leveraged by business users to automate requests that are otherwise pending with IT, thus reducing the backlog list of modifications and enhancements IT needs to deliver.
5. What are the security requirements for this particular initiative?

IT will need to know how your RPA tools are going to interact with IT infrastructure and applications. Credentials will be required to enable your bots to access network resources and interface with applications and file structures. Remember that bots often have an advantage here – while human workers may browse the Internet, shop and share information across contacts, RPA tools will execute only the scripts or commands they are programmed to execute, making them more secure than many human users. Still, be prepared to have the conversation with IT about how to credential RPA tools and how to govern their access to the network.

Thinking through these considerations upfront can make both the business and the CIO winners. By freeing workers from dull and monotonous back-office processes – and by offsetting the IT work required to support countless applications throughout the enterprise – RPA can set up the business to make higher-order, more creative work happen, all while driving savings to bottom line.

Source: questions to ask before considering robotic process automation

Will Automation will take away all out jobs?

TEDx Cambridge Sept 2016 by David Autor

Here’s a paradox you don’t hear much about: despite a century of creating machines to do our  work for us, the proportion of adults in the US with a job has consistently gone up for the past 125 years. Why hasn’t human labor become redundant and our skills obsolete? In this talk about the future of work, economist David Autor addresses the question of why there are still so many jobs and comes up with a surprising, hopeful answer.

Source: TEDx Cambridge- Will Automation will take away all out jobs?

Robotics Process Automation Deja Vu

Those of us who have been around the Robotic Process Automation (RPA) world are starting to get a sense of déjà vu. More and more companies are expecting non-technology resources within the organization to know how to use RPA. Although RPA technologies were originally intended for business resources to more easily develop and manage automation, it really wasn’t realistic as software providers promised: “If you can use Excel Macros, you can use X RPA solution.”

So what should be the make-up of a successful RPA implementation team?

Reality bites

When the rubber meets the road at the outset of an implementation, it becomes very clear that “macro monkeyswere building robots that had one of two problems:

  1. They broke when they got to real-world situations.
  2. They were not maintainable in the long run.

In fact, not only was it not possible for business resources to create reliable, real-world automation, but even hard-core technical resources needed a certain aptitude and specialized training. To develop good automation, specific engineering discipline is required, and most developers need training to cultivate it.

The rise of the machines

As RPA has risen in popularity over the last year or two, we have seen a resurgence in expectations that business resources will be able to develop automation. Is there hope that we will be successful this time around? Spoiler alert: It is possible, but definitely not easy.

At Cognizant, we call our vision “Robot Utopia” (more on that in a future post). Robot Utopia makes heavy use of the ability of business resources to identify, build, execute, monitor, and maintain automation.

Key automation ingredients

Our research on this is not yet complete, but there are a couple of principles I’d like to share with you.

The first is that the business resources cannot be trusted to engineer a good solution. But when properly trained, they can identify opportunities that are the foundation for creating reliable components for automation. It is very important to have professional automation engineers involved to provide reliable reusable components (think Lego blocks).

The second major lesson relates to engagement. The business resources have a real job to do and they will only invest in automation to the extent that automation will make their job easier. Convincing them to spend their time learning relevant automation techniques, and then building, operating, and maintaining automation is a critical component.

The edge of tomorrow

If business resources are going to be part of your RPA development strategy, take special care not to repeat the mistakes of the past. The best way that I can recommend to do that is to work closely with a partner who has experience in exactly these matters. Ask the tough questions. Get comfortable with the plan and make sure that your partner has attended this rodeo before.

Source: Process Automation Deja Vu

RPA Technical Insights, Part 21: Transformation Begins With Education, A Guide to RPA Training Documentation – Symphony

This is part 21 of a 22 part blog series by the leading experts at Symphony Ventures. It addresses how to choose the right RPA tools for your business needs. Drawing from our global team’s extensive knowledge in automation consulting, implementation, and managed services across a range of diverse industries, we’ve drilled into the technical criteria to consider when selecting which RPA software best enables your company’s digital operation strategy.

Read part 20, Why You Shouldn’t Blindly Pile Work Onto Your Automation.

When choosing which RPA tool to employ, the maturity of the vendor’s support and training services should contribute to your decision-making process. Vendors strive to make their products powerful yet accessible, often supplementing training material and additional assistance to ease the transition to RPA. If you are new to this technology, this material can prove to be invaluable in getting you started.

The Importance of Basic Training Material

The focus of this blog is training documentation. Offerings like structured training materials are extremely useful for getting developers and users off the ground in terms of understanding how to use the software. These often come in the form of user manuals, with pages of structured content that walk a user through a development path. Ideally, they can be downloaded and viewed offline, allowing for easy access and readability. Having a user manual or training guide present and accessible means that users will have a baseline understanding of the tools abilities, which are invaluable when it comes to design and deployment.

The Growing Popularity of Video Tutorials

Aside from training manuals, other forms of visually informative content like video tutorials are being produced by a few top RPA vendors. A training video, for example, can provide a demonstration of the step-by-step process of designing a practical workflow or how to use one of the tools abilities. This sort of content is best presented in a format that viewers can follow along to. In general, you will find that visual training materials are especially helpful for RPA, since most RPA software utilize a visual interface.

Other Training Outlets

As a seeker of RPA information, you might have noticed that vendor training content is relatively scarce. Fortunately, this may not be true for long. The RPA world is currently undergoing a massive expansion of training materials and opportunities to improve accessibility and meet growing demand. Some of the top vendors are currently developing online training courses, to provide comprehensive e-learning environments. E-learning is beneficial because it offers the whole suite of training, practicing, and testing.

There are other outlets to receive training outside of vendor supported material. For instance, Symphony host RPA and AI training workshops in collaboration with The Global Sourcing Association (GSA UK). These are not designed as development courses, but rather focus on technical and business subjects, aiming to teach participants how to strategize their business around RPA. Outside of these, Symphony offers specialized training for clients as one of our main services. So, be on the lookout for these if you want to gain valuable knowledge from the experts and grow your internal capabilities.


In today’s fast-paced, digital environment, it is critical to avoid being slowed down by a lack of proper training. It is prudent to expect your RPA vendor or service provider to give the necessary tools to help your team succeed. Whether it is a structured manual, training guide or a series of video tutorials, make sure that quality training material is available. Most of all, don’t hesitate to reach out to the experts, like Symphony, for advice. We try our best to provide the proper tools and education to help your business succeed with RPA.

Be sure not to miss the last part of our RPA Technical Insights, where investigate the benefits of comprehensive implementation support from RPA vendors.

Source: RPA Technical Insights, Part 21: Transformation Begins With Education, A Guide to RPA Training Documentation – Symphony

Why branch bankers shouldn’t fear bots

Many bank employees likely fear robots will replace them, but to U.S. Bank’s Dominic Venturo, some bots are just there to be their wingman.

Much of the talk about artificial intelligence in banking has been about how technology can replace some functions currently performed by humans. But AI could help human bankers do their jobs more effectively by giving them quicker access to relevant information than ever before.

“AI won’t totally replace, but rather augment, a human’s ability to do their job in a lot of ways,” said Venturo, the Minneapolis-based bank’s chief innovation officer.

U.S. Bank is one of a number of financial institutions experimenting with how AI and machine learning can transform the financial industry. This year the bank announced the formation of an artificial intelligence enterprise solutions unit, which sits inside its payments, virtual solutions and innovation group.

The bank has begun experimenting with how artificial intelligence can help bankers serve customers that have a question on a product or service that is “infrequently asked about,” Venturo said. If a customer walks into a branch to ask a banker about such a product, the banker may not immediately know the answer. The banker may need to call a different expert in another location or spend time looking up additional information. That makes for a not-so-great experience for the customer. But if AI can contain an encyclopedic knowledge of U.S. Bank’s entire suite of products and services, the banker can quickly pull up information in seconds, he said.

“Let’s say there’s potentially hundreds of products a banker can get asked about; AI can give them the ability to get at the right answer for any of them in a rapid way,” Venturo added. “It enables your banker to become almost superhumanly smart about every product or service you might offer.”

This is important, he said, because although artificial intelligence can automate many repetitive tasks, and customers are more comfortable using digital financial services, they still want to talk to a human for many financial needs, Venturo said.

“The reality is there are many individuals who still prefer to talk to humans, and AI can augment that experience,” Venturo said.

Further, if the branch-based banker now doesn’t have to call a centralized contact center expert for information, that expert has more time to work on something more complicated.

“Now those folks can spend their time on other issues that require a higher level of service,” Venturo said.

AI can help improve not only in-person interactions between banks and customers, but human-initiated digital experiences, said Mitch Siegel, head of strategy for the financial services practice at KPMG. A wealth manager could use artificial intelligence to analyze customer’s financial data and send a personalized, automated report daily or weekly, for instance. If that client then had a further question or wanted to talk about something more complicated, such as changing investment strategies, they could then pick up the phone or talk to the wealth manager in person, Siegel said.

“There’s an opportunity for applying AI to better service customers, and give them a more personalized experience without getting rid of the human touch,” he said.

AI can also make suggestions to help banks build loyalty with customers by suggesting products based on their behavior. AI might be able to detect retail customers who typically have money left in their checking account when their next paycheck arrives. In that case, the bot can notify the bank, which can then push a digital message to the customers asking if they would like the bank to automatically sweep the remaining money into a savings account when the next check arrives.

“That’s something that could really deepen a customer relationship and a suggestion the customer might really appreciate,” Siegel said. “Insights into the ways you spend money, how you pay bills or savings habits are usually appreciated. Lower-value needs like these, customers are generally comfortable doing digitally, and when the customer has a financial need they want to speak to a human, with, like, a mortgage,” they’re then more likely to come to that bank first, he said.

AI can also free up bank employees to act in a more consultative, advisory role, said Adam Devine, head of marketing for WorkFusion, which provides robotic process automation and artificial intelligence solutions to several industries, including banks.

One example is contact centers where about a quarter of the calls are about the same topics — basic questions about transactions.

“So much of it is rules-based and repetitive,” Devine said.

If chatbots then could be trained to automatically handle these common calls, that could free up contact center employees to focus on providing more valuable services, Devine said.

“It allows the customer service agents to provide more of an advisory and recommendation service based on nuance and conversation,” he added. “It becomes less about transactions and provides more revenue opportunity for banks.”

In general, Devine said bankers he has met with as clients or potential clients have pointed to AI projects as top priorities.

“Two to three years ago we’d walk in [to a bank] and talk about machine learning and AI and they’d look at us like we had five heads,” he explained. “Now they’re showing us PowerPoint presentations about how it’s part of their core strategy.” For many banks, “if it’s not on their 2017 road map, it’s on their 2018 road map,” he added.

Source: branch bankers shouldn’t fear bots

For the Next Election, Don’t Recount the Vote. Encrypt It

Let’s be honest: The 2016 election wasn’t a sterling display of American democracy. Its problems extended beyond Russian hackers and trolls trying to thumb the scale, and the winner’s baseless, ongoing claims of voter fraud. For computer scientist Ben Adida, the most troubling part came afterward, when voting security experts and Green Party candidate Jill Stein called for a recount of the vote in three thin-margin swing states, raised millions of dollars to do it—and still mostly failed.

While Stein successfully triggered a Wisconsin recount, federal judges in Pennsylvania and Michigan put an early stop to her efforts. In the latter case, a judge ruled that Stein had “not presented evidence of tampering or mistake” in the electronic voting machines. It was a vexing catch-22, says Adida, an engineer and applied cryptographer at the education startup Clever. If the Michigan vote was tainted, the paper backup ballots Stein wanted to recount were the evidence that could prove it. But Stein didn’t have any evidence to justify looking at the evidence.

“Recounts don’t actually happen, because if you can’t bring a shred of evidence to the table that something went wrong, you sound like a lunatic,” Adida says. “That’s what 2016 proves. We need to build a voting system that inherently provides that evidence in case something goes wrong.”

Encrypt the Vote

At the Enigma security conference next week in Oakland, Adida will make the case for a decade-old voting system that provides that inherent evidence, what Adida and other voting security experts call “end-to-end verification.” Since 2007, thousands of people, including organizations like the Association of Computing Machinery and Greenpeace, have used Adida’s election software, called Helios to solve that core problem. Helios encrypts every vote, and then publishes an online list of encrypted results by voter in a form that allows anyone from an election-monitoring organization to individual voters themselves to check the results.

“The whole idea that paper ballots are going to save us is well-intentioned but flawed,” says Adida. “I think we can do better. We can provide true end-to-end proof that an election works.”

Now that same system will be put into practice for the first time in actual government: A voting scheme, known as STAR-Vote—for Secure, Transparent, Auditable, and Reliable—uses a similar cryptographic system to Helios, but with real, physical voting machines and ballots. One Texas county is even set to implement it before the 2020 presidential election.

“STAR-Vote allows the general public to verify the vote themselves,” says Dana DeBeauvoir, the county clerk of Travis County, Texas, which includes the city of Austin. “We’re trying to build a better mousetrap and share it with everyone else.”

How It Works

Here’s the clever—and somewhat convoluted—way that end-to-end verified voting system works: Registered voters input their vote on a touchscreen machine. When they’re done, the machine prints their ballot with their choices, along with a “receipt” at the bottom that they can take home. That input machine also encrypts the results, shares the encrypted vote data with all the other voting machines at the polling place, and also enters it into a database of all the encrypted votes that will be published online at the end of the election day. Then voters feed their printed ballot into a ballot box with a scanner that reads a barcode on the ballot and confirms to the network that the vote has been cast.

After the votes are published, anyone can use a tracking number on their receipt to look up their vote online and confirm that it was registered. But crucially, no one can see who voted for whom. Not even the voter can decrypt their own vote; if they could prove who they voted for, they might be coerced or paid to vote for a certain candidate.

In fact, thanks to some mathematical sleight-of-hand known as “homomorphic encryption,” not even the election officials counting up the results can decrypt any individual votes. Homomorphic encryption allows simple arithmetic to be performed on encrypted data without decrypting it. So the encrypted votes can be added up and published online to produce an encrypted, public total tally that remains accurate without ever exposing anyone’s vote. Election officials decrypt only that final result, and even they can only do so when a certain number of overseers combine their secret passwords. After the results are decrypted and declared, anyone can re-encrypt them to check that they match the online encrypted tally, to prevent the officials from colluding to falsify the count.

That somewhat mind-bending process still leaves another question: How can voters check that the STAR-Vote machine not only registered their encrypted vote, but registered the correct vote rather than slyly switching it? To solve that problem, the system offers voters one more feature it calls a “challenge.” When the vote is encrypted and declared to the other voting machines—but before the voter scans it and puts it in the ballot box—the voter can choose to challenge it instead of confirming it, essentially declaring the ballot to have been a test of the system. If a ballot is challenged, it’s not counted, and the machine where the voter input their choice uses a special key that only it possesses to decrypt the encrypted vote it just declared to reveal who that challenged vote was for; it’s then shared with the local network and the public database. (The voter, meanwhile, starts over and votes again.)

Before the voter has even left the voting place you have all the information you need to catch the machine cheating. Dan Wallach, STAR-Vote Inventor
Thanks to some proven cryptographic math, there’s no way for the computer to believably decrypt the ballot without revealing which candidate it was about to register a vote for. So if the machine’s answer in the public database doesn’t match the voter’s choices, the voter can look up the challenged vote, spot the mismatch and report the machine’s fraudulent behavior. That makes any attempt at tampering with voting machines highly risky. “Before the voter has even left the voting place you have all the information you need to catch the machine cheating in its electronic representation of your ballot,” says Dan Wallach, a cryptographer at Rice University and one of STAR-Vote’s inventors.

Baking in the Evidence

All of those cryptographic checks aren’t meant to replace paper ballot backups, Wallach and Adida say, which would still serve as the ultimate record in any recount. But with STAR-Vote, the hints of tampering that trigger that recount would be far easier to spot. And just as importantly, says Travis County Clerk Dana Debeauvoir, all that cryptographic complexity remains hidden from any voter that doesn’t want to deal with it. “It has to be something that mom and pop can operate,” she says.

Next month, Travis County, which has about 720,000 registered voters, will reveal the results of a request-for-proposal it issued last year for tech firms to code and build its STAR-Vote machines. Debeauvoir hopes to put the system to use for the first time in local elections in 2019, so that any bugs will be worked out before the 2020 presidential election. She says she expects the system to cost between $8 and $12 million to develop, but argues that’s still less in the long run than licensing the currently available, less-verifiable systems.

Supporters hope that if it catches on, STAR-Vote could serve as a key reassurance for the American electoral system, and save millions of dollars spent on wasted paper recounts. Rather than lawsuits, sore loser accusations, and expensive audits, the audit would be baked into the system, says Adida. “Instead of having to seek the evidence, the system would provide evidence of correct operation by virtue of the process of voting itself,” says Adida. Imagined voter fraud and Russian trolls aside, that might actually be a system all Americans can trust.

Source: Wired-For the Next Election, Don’t Recount the Vote. Encrypt It

Artificial intelligence will increase productivity

Research shows that software robots will soon automate 80% of repetitive tasks currently being done by people and increase productivity by freeing up humans to use their brains.

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Businesses will need to develop a balance of artificial and human intelligence as different roles require a mix of the two, found the academic study by Goldsmiths, University of London and artificial intelligence (AI) supplier IPsoft,

It said by automating and redeploying humans away from repetitive jobs to tasks that require creativity and innovation, organisations can increase productivity three times over.

The FuturaCorp: Artificial Intelligence & the Freedom to be Human report outlines the future workplace where humans and machines together increase output.

The report described three tasks requiring a different mix of human and artificial intelligence.

It said deterministic tasks are repetitive and process-oriented, while probabilistic tasks require a human in concert with machines. Then there are cross-functional reasoning jobs that rely on connections that can only be made by the human brain.

The report said that 80% of deterministic tasks will be done by machines in the not-too-distant future, probabilistic jobs will be shared 50:50, while humans will do 80% of cross-functional reasoning tasks.

Read more about artificial intelligence in banking

  • SEB bank is currently integrating AI into its customer services channels, following an internal trial of the technology.
  • Ahead of its annual meeting in Davos, the World Economic Forum warns that AI needs strong governance.
  • Read why enterprises need to worry just as much – if not more – about the business implications, rather than the technical challenges when implementing cognitive software.
  • Middle East banking group Emirates NBD is piloting an intelligent virtual assistant with selected customers and plans to launch it soon.

“The real productivity benefits of AI will not be simply a factor of automating existing processes. The arrival of AI will engender entirely new, unknown possibilities for humans and what they can achieve,” said Chris Brauer, senior lecturer at Goldsmiths, University of London.

“It is this new configuration of humans working alongside intelligent machines that will be the source of sustained competitive advantage. The result will be FuturaCorp – a Fortune 500 with the innovative flexibility of a Silicon Valley startup, or a startup with the IT power of a Fortune 500.”

Chetan Dube, CEO at IPsoft, said CEOs must be prepared to redefine their business in order to capitalise on the productivity potential of AI. “That journey begins with fundamental change to organisation structure, who they hire for which roles, and how they use the new relationship between humans and machines to maximise efficiency and innovation.”

“AI engenders emergent individual qualities, which push us to access the more complex parts of our minds. When routine work is automated, we will be able – and required – to flex our most human of skills. The future of society relies on individuals accessing higher reasoning, critical thinking and complex problem-solving skills,” said Dube.

Amelia’s reading power

IPSofts AI platform, known as Amelia, was launched in 2014. It has an understanding of the semantics of language and can learn to solve business process queries like a human. It can read 300 pages in 30 seconds and learn through experience by observing the interactions between human agents and customers.

If Amelia can’t answer a question, it passes the query on to a human, but remains in the conversation to learn how to solve similar issues in future. It understands 20 languages, as well as context, can apply logic and infer implications.

The software is used for services such as technology helpdesks, contact centres, procurement processing and to advise field engineers, among other business processes.

AI’s benefit to productivity is now being predicted. According to recent research by Vanson Bourne for Infosys, businesses that have adopted AI technologies expect their revenues to increase by 39% and costs to drop by 37% by 2020. Some 64% say their future growth depends on large-scale AI adoption.

But there are hurdles to overcome. The World Economic Forum’s Global Risk Report 2017 has highlighted risks associated with AI. Based on a survey of 750 experts, the report warned that AI, biotech and robotics have among the highest benefits to society, but they also require the most legislation.

The World Economic Forum warned that governance of emerging technologies is patchy. Some are regulated heavily, and others hardly at all because they do not fit under the remit of any existing regulatory body.


Source: intelligence will increase productivity

How accelerating automation is positively disrupting industries

If CIOs are not already looking at RPA as a digital enablement to their digital strategy, they are already behind the eight ball a little bit

A bank slashed the time it would approve an application for credit from 21 days to 11 minutes, by deploying robotic process automation (RPA).

With RPA, the bank was able to process all paperwork and data for credit, criminal and employment checks, to determine whether the bank should be lending more money to a customer.

For David Snell, an independent sourcing advisor, this case study is just one of the ways RPA brings “positive disruption” to industries.

“Its potential to positively disrupt the workplace is incredible, while its potential to disrupt the outsourcing market is far reaching,” says Snell, who was New Zealand country manager for Alsbridge (now part of Information Services Group).

He argues RPA and related automation technologies should already be on the agenda for CIOs.

“If CIOs are not already looking at RPA as a digital enablement to their digital strategy, they are already behind the eight ball a little bit,” Snell tells CIO New Zealand.

“Early adopters are already starting to see significant benefits and not only in their own operations,” he states.

He predicts there will be a rise in the uptake of RPA, because of the net benefits of potential productivity, cost savings and better CX (customer experience).

Apart from banking, RPA has practical applications in insurance, healthcare and manufacturing according to a white paper written by Craig Nelson, Alsbridge managing director and now partner at Information Services Group.

“RPA is a disruptive and game-changing technology that presents an opportunity to adapt, change and achieve significant results,” writes Nelson.

Though before embarking implementing RPA, ICT managers need to assess the interests of key stakeholders, their current and desired states of operation, their existing talent pool and senior executive willingness to sponsor the integration of human and virtual labour, according to the paper.

RPA sponsors, Nelson believes, must be prepared to address three questions:

  • How will work processes change?
  • How will skills requirements change?
  • How will the organisation change?

Big data challenge

Snell, on the other hand, links the take-up of RPA to the challenges of data management.

“Getting data into the right place in a usable form is massive and it continues to be a big challenge for a lot of the organisations,” he states.

“Unless you are able to feed that data in those powerful algorithms, you really are not going to get the results that will give you some predictive capabilities.”

For Snell, the most important part of an RPA deployment is change management.

“The technology is not difficult,” he states, but there will always be “people, politics and an appetite for change” that will be involved in the deployment.

Source: accelerating automation is positively disrupting industries

How bots will shape the future of work

Picture this. You are in an elevator and your watch starts vibrating and one swipe tells you that it’s an important alert from your bank. You are facing some issues with your banking transaction and you are connected to a chatbot (an intelligent robot at the bank’s contact centre) and over a series of messages, the bot meticulously addresses your problem. This indicates the onset of a digital era which is set to transform the way people work and interact in daily business activities.

Intelligent automation, software robotics, cognitive computing, augmented reality and machine learning have the potential to increase productivity, improve efficiency, and reduce time-to-market and reshape the manner in which people and enterprises function.

Many industry experts believe that the use of artificial intelligence (AI) will become pervasive across industries in the next decade. Machines with natural language processing skills will help to automate routine and repetitive tasks.

One of the world’s oldest and largest news gathering organizations is using AI to develop more than 4,000 corporate earnings stories.

Leading IT services companies have developed AI platforms to streamline different aspects of their project delivery. For instance, a tier-1 Indian IT services player plans to use its AI platform to automate application maintenance projects. This will help the company save up to $50 million and free around 3,000 engineers from routine software maintenance activities. A leading utility company is using AI in the area of financial reporting and accounting statement preparation.

The future of the workforce is poised to change dramatically. In the new wave of automation led by software robots, AI is also expected to redefine jobs which were previously considered immune to technological displacement such as white-collar work. For instance, algorithms are bringing deeper insights in the financial services space (high-frequency trading) and automation is making its presence felt in the healthcare segment by altering the manner in which healthcare services are being delivered (mobile health apps, robotic surgery and diagnosis by algorithms).

The technological advances will redefine the notion of what a “job” is and change the “eight-hours-a-day-work” paradigm. The workplace will become a hyper-connected network of people and machines and no longer be confined to traditional working hours or places.

However, the proverbial glass is half full. Previous experience has shown that automation has redefined what were then considered as traditional jobs and created new jobs in the manufacturing ecosystem.

Rather, the digital disruption brought about by automation is likely to create new companies and opportunities in hitherto unheard of economic activities. However, it is observed that a majority of the workforce is not adequately prepared to deal with these technology changes.

Both employees and enterprises will have to adapt, reinvent and upskill themselves to stay relevant.

As technology drives and reshapes business needs, enterprises will be required to revamp their learning and development programmes to re-skill their employees and enable them to take on tasks beyond the purview of bots and algorithms. Technical training needs to be oriented towards meeting the dynamic needs of the next-generation workforce. Furthermore, the next-generation workforce will have to take ownership to build their own ‘brand’ to take on jobs which match their new skill-sets.

At the same time, to attract the new-age digitally-connected workforce, companies will need to provide an entrepreneurial environment where employees can experiment innovative ideas. The changing nature of work will free up individuals to pursue other interests and has the potential to spur a new wave of entrepreneurship.

An important stakeholder in the journey to the next-generation workplace is the government, which can act both as a monitor and an enabler. Governments around the world will have an important role to play in the new paradigm as the employer-employee relationship evolves drastically.

Next-generation enterprises, whose businesses are based mainly on AI/automation, argue that existing regulations were designed for another era and are not in line with the demands of the industry. There is a need to re-examine existing regulations and develop new regulatory regimes which are designed for the future—nimble, real time and responsive to the needs of the work force.

Source: Livemint-How bots will shape the future of work