Public perception of artificial intelligence technology, seems to lie somewhere at the intersection of existential fear and cautious optimism. Yet there’s a growing movement of people who believe AI is crucial to the evolution of our species. These people aren’t outsiders or outliers — they’re actually directing research on the cutting edge at companies like Google.
Ray Kurzweil, Google’s guru of AI and futurism, spoke last week at the Council for Foreign Relations, in an intimate Q&A session. His views on the future of humanity might seem radical to a public that’s been cutting its teeth on doomsayer headlines featuring Elon Musk and Stephen Hawking warning about World War III.
He’s quick to point out that today, right now, is the best our species has ever had it. According to him, most people don’t know that the world we live in currently has less hunger and poverty than ever before. “Three billion people have smartphones, I thought it was two but I just found out it was three. In a few years that’ll be six billion.” he says.
The deadliest war in recorded human history, World War II, ended just 72 years ago. In the time since, humanity has engaged in what feels like countless skirmishes, police actions, and outright wars. And while the US remains engaged in the longest war in its history – with no end in sight – the human species is currently enjoying the most peaceful period in the history of our civilization.
The existential fear is that AI will somehow compromise this progress and send us careening into the next extinction-level event. If technology like the atom bomb made World War II so much worse than everything before it, doesn’t it follow that WWIII will be even more devastating?
It’s more complex than that, according to Kurzweil. He believes part of the reason we’re able to coexist so wonderfully (in the grand historical scheme) for so long is because democracy has begun to take hold globally. He also believes the rise of democracy is the direct result of advances made in communication technology. According to him:
You can count the number of democracies a century ago on the fingers of one hand, you can count the number of democracies two centuries ago on one finger. The world has become more peaceful. That doesn’t appear to be the case, because our information about what’s wrong with the world is getting exponentially better.
So what’s next? He believes we’ll all be less biological, because humans are always evolving, and the next step of our evolution will be the internal implementation of technology. The human-robot hybrid won’t be a monstrosity of metal. It’ll just be a chip in your brain instead of an iPhone in your hand.
In the future it’ll be no more shocking to think about the weather in Hong Kong and get an answer than it is to say “Hey Google, what’s the weather in China?” and receive accurate information from a glowing rectangle with a speaker inside of it.
Kursweil believes “medical robots will go inside our brain and connect our neo-cortex to the smart cloud” by the year 2029.
That’s a jaw-dropper, even for a technology journalist who writes about AI regularly. It’s pretty hard to imagine people walking around with their brains connected to the cloud before Justin Bieber turns 35.
But dismiss Ray Kurzweil’s predictions at your own peril: he’s seldom wrong. When it comes to technology he’s gone on the record with hundreds of predictions, which is what futurists do, and he’s correct over 90 percent of the time.
According to Kurzweil the future is incredible, but it’s also worth mentioning that his view of the present is pretty fantastic as well. He reminds us that “just a few years ago we had these devices that looked like smartphones but they didn’t work very well,” and he’s right.
Today’s smartphones know how to respond to complex voice commands like “find all the pictures from my trip to San Francisco” and “play Star Trek The Next Generation season three, episode 16.” Today’s phones can recognize who is talking, pick out your voice even when music is playing, and execute the command without a hitch.
But just a few years back, most of us quickly gave up on using voice control regularly, because we were sick of repeating ourselves. We figured we’d wait until the technology got better. Tada! It’s better now.
The truth about AI, according to experts such as Ray Kurzweil, is that there’s no part of our lives that won’t be directly affected by it. As individuals we probably won’t notice the changes in real-time, but our dependence on machine learning will increase at exponential rates.
The law of accelerating returns is behind the artificial intelligence revolution — and Ray Kurzweil’s predictions. The very limits of what is “possible” concerning machine learning are going to require reevaluation on a daily basis going forward.
Chatbots, computer programs that typically use text-based live chat as an interface to carry out tasks for customers on behalf of the business, are emerging as an inexpensive way to introduce artificial intelligence (AI) in banking.
New digitally savvy companies have found success attracting consumers with user-friendly offerings, while legacy banks are finding it difficult to invest in and adopt innovative products. To remain competitive, these large banks will have to adapt their traditional services by incorporating more robotics in banking that will attract more tech-savvy customers.
Chatbots in Banking
Chatbots in banking are a digital solution that is relatively inexpensive to develop and maintain. For starters, chatbots require less coding than standalone banking apps. And the current growth in popularity of messaging platforms saves banks the cost of developing their own channels, as well as saving on data storage thanks to chatbots’ cloud-based systems.
Companies such as Cleo, Stripe, and Wealthfront are giving traditional banks a run for their money. However, for these players it is more difficult to meet the demand of key bank products (such as loans) due to less restricted regulations that force their customers to spend heavily on compliance and maintain large capital cushions.
DBS uses Kasisto’s Kai, the underlying technology of MyKai, to allow customers to conduct transactions such as transfers and bill paying. Furthermore, they can ask about their personal finances using messaging applications such as Facebook Messenger and eventually WhatsApp and WeChat, all of which are the top messaging applications used across the world.
In 2016, Swedbank launched on its website and mobile application Nuance’s NINA, who helps answer customer inquiries more quickly by sourcing information relevant to their query using intuitive analysis.
Chatbots in Finance
The finance industry is built on processing information, which makes it an ideal industry for automation and reduction of salary expenditure, according to a new report from PwC. However, two-thirds of US financial services respondents said that they’re limited by operations, regulations, budgets, or resources to make the investment in such innovative development.
Fintech companies such as Plum, Digit, and Cleo use chatbots that drive microsaving by putting small amounts into savings each day for their users. These companies’ chatbot is their core product, unlike legacy banks that use it to supplement a core product.
These companies are improving various financial services that provide their customers more than just automated savings. Chatbots can provide wealth management for the masses, underwrite loans and insurance, provide data analyses and advanced analytics, and detect and notify of fraudulent behavior, all through an automated virtual assistant.
Bank of America uses ERICA to give customers key and real-time updates on their finances using a channel of their preference. Her predictive analytics and cognitive messaging helps customers make payments, pay down debts, and check their balances.
Chatbots Set to Grow
Although chatbots have been around for a long time, recently the underlying AI technology has made waves in the market.
BI Intelligence, Business Insider’s premium research service, has found that the technological advancements in AI has made leaps and bounds in recent years in financial services.
The growing popularity of messaging apps have made them reliable hosts for chatbots, and the increasing public acceptance of chatbots have created more trustworthy relationships with users, particularly for millennials, whom banks are trying to target.
“Cobots”, or collaborative robots, are making inroads into work previously considered too difficult to automate. But as cobots get better at performing tasks such as material handling or packaging, their designers are having to consider the effects on their colleagues of the machines’ improved ability to interact with humans.
In its early stages, this new technology has been safe if underwhelming, says David Mindell, a professor at Massachusetts Institute of Technology. Of the cobots, he says: “They don’t do much collaboration, but at least they won’t cut your head off.”
Small, light and slow moving, cobots are generally harmless — the sensors and machine-learning software that enable them to “understand” their environment have a simple override: if a human gets too close, they are programmed to shut down.
The first job has been to design the software models to allow robots to operate in the human world, says Manuela Veloso, head of machine learning at Carnegie Mellon’s School of Computer Science. “It’s very important to be able to envision a mobile creature moving around in our space,” she says. For instance, getting machines to work alongside people will require an understanding of “safety zones” of the body: “We’re trying to model a person. You don’t want to hit an eye — an elbow is less important.”
As the software becomes more sophisticated, it promises more flexible machines that can be released from their cages. “We’ve got people doing jobs today because the regular robots can’t do it,” says Jim Lawton, head of product and marketing at Rethink Robotics, a Boston-based maker of cobots. These often involve repetitive actions that strain human limbs, are mind-numbingly dull and consign workers to jobs with no chance of career advancement, he says.
Mindell, author of Our Robots, Ourselves, a 2015 book about human-robot interaction, agrees there is much to be gained in the way of worker wellbeing: “If your work is truly about to be augmented, or made less dangerous or less straining, these are good things.” But he says that limits in both the technology and imagination on how to apply it have made this more promise than reality.
Designing complex interactions between robots and people will take a change in mindset, he says, adding that the history of automation has largely been about treating humans like robots, to fit into automated processes. “The computer science world still has a long way to go before it has a clue about how to deal with people,” he says.
At a simple level, makers of cobots are working to reduce the sense of weirdness for people working alongside machines whose level of intelligence they find hard to judge. Rethink, for instance, experimented with putting smiling mouths on its robots to make them seem more “human”. The result was the opposite, says Lawton: people thought the machines were smirking at them, and found them “arrogant and condescending”. Moving into the “uncanny valley” where robots start to copy humans too closely “spooked people”, he says.
Veloso says there are hurdles that will have to be overcome to improve the human experience of working with the machines. One is that the machines have to be more understandable. “The more humans infer what a robot will do next, the safer it will be,” she says.
Rethink’s answer has been to give its robots “eyes” (an image on a tablet computer) that indicate the direction the machine is about to move in — a simple way to prepare people around them that they are about to do something, says Lawton.
The computer science world still has a long way to go on how to deal with people
Another key is to design a form of robot-human symbiosis in which each helps the other achieve its goal, says Veloso. That will mean teaching people to respond to requests from the robots, or to anticipate their needs, as much as the other way around. As interactions like this become more subtle and machines take over more work alongside people, the long-term impact on the wellbeing of human workers is hard to predict. Against the obvious benefits of taking dangerous or tedious work away from people, there may be unexpected side-effects. “When people invented keyboards, they weren’t imagining carpal tunnel syndrome,” Veloso points out.
As more automation creeps in, there may be subtle but far-reaching effects on the way work is designed. There is a fear that the iterative process improvements that are a product of lean manufacturing — constantly learning and implementing better ways of working — may be threatened, says Lawton. If existing work processes are automated, the result could be an ossific9ation that prevents this steady improvement.
Like much technology whose benefits are clear in the short term, even if their long-term effects on human wellbeing are hard to judge, the advance of the cobots is unlikely to be slowed. People are likely to take to their new robot colleagues as enthusiastically as they took to their smartphones, says Mindell. “People have their fears — in some ways, they are legitimate fears,” he says. “At the same time, they are addicted to their technology.”
‘Algorithms took our jobs’
Tom Gordon was 45 when his lucrative career as an oil trader suddenly faced a new threat. Electronic trading, which originally had been introduced to expand trading capacity overnight, was now operating head-to-head with Gordon and his colleagues on the floor of the exchange during the day.
Gordon says he used to handle between 500 and 750 trades a day. In his nearly 25 years as a trader he recalls recording only two months of losses. But even the high volumes that a successful trader like Gordon could handle were quickly overshadowed by the volumes electronic systems were capable of processing.
For Gordon, working alongside the electronic market was like being hit by a truck. “I saw the transition was coming and knew [traders] were going to get run over,” he says. He eventually left and retrained as a social worker.
He was wise to do so, because a few years later, in 2016, CME Group, which owns the New York Mercantile Exchange (Nymex), closed the last of its remaining commodity-trading pits.
Gordon says some of his former colleagues have struggled to cope in their new lives. “Some have done quite well, but for many of the people it really broke their lives and their spirit.”
Losing a job to a machine or algorithm carries a unique psychological burden, says Marty Nemko, a psychologist and career counsellor.
No training exists that can help a human match the speed and efficiency of artificial intelligence. “There is an inevitability of [one’s] inferior ability that accrues,” Nemko says.
Tim Leberecht, a consultant on business leadership, agrees: “If we lose our jobs due to automation and can’t get back into the workforce, then there is this huge void of purpose and meaning.”
“The big issue with this fourth industrial revolution is that we don’t have the social institutions that are facilitating and enabling the transition,” says Ravin Jesuthasan, managing director at Willis Towers Watson, and leader of the consulting group’s research area, “Future of Work”.
Research on the threat of automation paints a complicated picture. A 2016 OECD report found an average of 9 per cent of all jobs across the 21 countries the research covered could be automated, given current technology. A report by consultants McKinsey puts the global figure at less than 5 per cent.
Many researchers suggest the more nuanced effect of this transition will be on the handful of tasks across all sectors that are routine and repetitive.
According to another McKinsey report, more than 70 per cent of tasks performed by workers in the food service and hospitality sector could be carried out by machines. In manufacturing, nearly 60 per cent of tasks in jobs such as welding and maintaining equipment are at risk.
Higher-paying jobs are not immune from the disruption. McKinsey found that up to 50 per cent of tasks in the financial services industry could be automated, as could about a third of jobs in healthcare.
Jesuthasan says this refocusing of tasks can give people the space to do more meaningful work. “Leaving behind all of those routine things [creates] a huge emphasis on creativity and empathy and care,” he says.
After witnessing his original job as a trader vanish, it is perhaps no surprise that Gordon has found himself engrossed in work requiring these human characteristics. “I want to do my part,” he says. “Will I make a difference? I don’t know, but I’m going to give it a shot.”
Cheaper, more capable, and more ﬂexible technologies are accelerating the growth of fully automated production facilities. The key challenge for companies will be deciding how best to harness their power.
At one Fanuc plant in Oshino, Japan, industrial robots produce industrial robots, supervised by a staff of only four workers per shift. In a Philips plant producing electric razors in the Netherlands, robots outnumber the nine production workers by more than 14 to 1. Camera maker Canon began phasing out human labor at several of its factories in 2013.
This “lights out” production concept—where manufacturing activities and material flows are handled entirely automatically—is becoming an increasingly common attribute of modern manufacturing. In part, the new wave of automation will be driven by the same things that first brought robotics and automation into the workplace: to free human workers from dirty, dull, or dangerous jobs; to improve quality by eliminating errors and reducing variability; and to cut manufacturing costs by replacing increasingly expensive people with ever-cheaper machines. Today’s most advanced automation systems have additional capabilities, however, enabling their use in environments that have not been suitable for automation up to now and allowing the capture of entirely new sources of value in manufacturing.
Falling robot prices
As robot production has increased, costs have gone down. Over the past 30 years, the average robot price has fallen by half in real terms, and even further relative to labor costs (Exhibit 1). As demand from emerging economies encourages the production of robots to shift to lower-cost regions, they are likely to become cheaper still.
People with the skills required to design, install, operate, and maintain robotic production systems are becoming more widely available, too. Robotics engineers were once rare and expensive specialists. Today, these subjects are widely taught in schools and colleges around the world, either in dedicated courses or as part of more general education on manufacturing technologies or engineering design for manufacture. The availability of software, such as simulation packages and offline programming systems that can test robotic applications, has reduced engineering time and risk. It’s also made the task of programming robots easier and cheaper.
Ease of integration
Advances in computing power, software-development techniques, and networking technologies have made assembling, installing, and maintaining robots faster and less costly than before. For example, while sensors and actuators once had to be individually connected to robot controllers with dedicated wiring through terminal racks, connectors, and junction boxes, they now use plug-and-play technologies in which components can be connected using simpler network wiring. The components will identify themselves automatically to the control system, greatly reducing setup time. These sensors and actuators can also monitor themselves and report their status to the control system, to aid process control and collect data for maintenance, and for continuous improvement and troubleshooting purposes. Other standards and network technologies make it similarly straightforward to link robots to wider production systems.
Robots are getting smarter, too. Where early robots blindly followed the same path, and later iterations used lasers or vision systems to detect the orientation of parts and materials, the latest generations of robots can integrate information from multiple sensors and adapt their movements in real time. This allows them, for example, to use force feedback to mimic the skill of a craftsman in grinding, deburring, or polishing applications. They can also make use of more powerful computer technology and big data–style analysis. For instance, they can use spectral analysis to check the quality of a weld as it is being made, dramatically reducing the amount of postmanufacture inspection required.
Robots take on new roles
Today, these factors are helping to boost robot adoption in the kinds of application they already excel at today: repetitive, high-volume production activities. As the cost and complexity of automating tasks with robots goes down, it is likely that the kinds of companies already using robots will use even more of them. In the next five to ten years, however, we expect a more fundamental change in the kinds of tasks for which robots become both technically and economically viable (Exhibit 2). Here are some examples.
The inherent flexibility of a device that can be programmed quickly and easily will greatly reduce the number of times a robot needs to repeat a given task to justify the cost of buying and commissioning it. This will lower the threshold of volume and make robots an economical choice for niche tasks, where annual volumes are measured in the tens or hundreds rather than in the thousands or hundreds of thousands. It will also make them viable for companies working with small batch sizes and significant product variety. For example, flex track products now used in aerospace can “crawl” on a fuselage using vision to direct their work. The cost savings offered by this kind of low-volume automation will benefit many different kinds of organizations: small companies will be able to access robot technology for the first time, and larger ones could increase the variety of their product offerings.
Emerging technologies are likely to simplify robot programming even further. While it is already common to teach robots by leading them through a series of movements, for example, rapidly improving voice-recognition technology means it may soon be possible to give them verbal instructions, too.
Highly variable tasks
Advances in artificial intelligence and sensor technologies will allow robots to cope with a far greater degree of task-to-task variability. The ability to adapt their actions in response to changes in their environment will create opportunities for automation in areas such as the processing of agricultural products, where there is significant part-to-part variability. In Japan, trials have already demonstrated that robots can cut the time required to harvest strawberries by up to 40 percent, using a stereoscopic imaging system to identify the location of fruit and evaluate its ripeness.
These same capabilities will also drive quality improvements in all sectors. Robots will be able to compensate for potential quality issues during manufacturing. Examples here include altering the force used to assemble two parts based on the dimensional differences between them, or selecting and combining different sized components to achieve the right final dimensions.
Robot-generated data, and the advanced analysis techniques to make better use of them, will also be useful in understanding the underlying drivers of quality. If higher-than-normal torque requirements during assembly turn out to be associated with premature product failures in the field, for example, manufacturing processes can be adapted to detect and fix such issues during production.
While today’s general-purpose robots can control their movement to within 0.10 millimeters, some current configurations of robots have repeatable accuracy of 0.02 millimeters. Future generations are likely to offer even higher levels of precision. Such capabilities will allow them to participate in increasingly delicate tasks, such as threading needles or assembling highly sophisticated electronic devices. Robots are also becoming better coordinated, with the availability of controllers that can simultaneously drive dozens of axes, allowing multiple robots to work together on the same task.
Finally, advanced sensor technologies, and the computer power needed to analyze the data from those sensors, will allow robots to take on tasks like cutting gemstones that previously required highly skilled craftspeople. The same technologies may even permit activities that cannot be done at all today: for example, adjusting the thickness or composition of coatings in real time as they are applied to compensate for deviations in the underlying material, or “painting” electronic circuits on the surface of structures.
Working alongside people
Companies will also have far more freedom to decide which tasks to automate with robots and which to conduct manually. Advanced safety systems mean robots can take up new positions next to their human colleagues. If sensors indicate the risk of a collision with an operator, the robot will automatically slow down or alter its path to avoid it. This technology permits the use of robots for individual tasks on otherwise manual assembly lines. And the removal of safety fences and interlocks mean lower costs—a boon for smaller companies. The ability to put robots and people side by side and to reallocate tasks between them also helps productivity, since it allows companies to rebalance production lines as demand fluctuates.
Robots that can operate safely in proximity to people will also pave the way for applications away from the tightly controlled environment of the factory floor. Internet retailers and logistics companies are already adopting forms of robotic automation in their warehouses. Imagine the productivity benefits available to a parcel courier, though, if an onboard robot could presort packages in the delivery vehicle between drops.
Agile production systems
Automation systems are becoming increasingly flexible and intelligent, adapting their behavior automatically to maximize output or minimize cost per unit. Expert systems used in beverage filling and packing lines can automatically adjust the speed of the whole production line to suit whichever activity is the critical constraint for a given batch. In automotive production, expert systems can automatically make tiny adjustments in line speed to improve the overall balance of individual lines and maximize the effectiveness of the whole manufacturing system.
While the vast majority of robots in use today still operate in high-speed, high-volume production applications, the most advanced systems can make adjustments on the fly, switching seamlessly between product types without the need to stop the line to change programs or reconfigure tooling. Many current and emerging production technologies, from computerized-numerical-control (CNC) cutting to 3-D printing, allow component geometry to be adjusted without any need for tool changes, making it possible to produce in batch sizes of one. One manufacturer of industrial components, for example, uses real-time communication from radio-frequency identification (RFID) tags to adjust components’ shapes to suit the requirements of different models.
The replacement of fixed conveyor systems with automated guided vehicles (AGVs) even lets plants reconfigure the flow of products and components seamlessly between different workstations, allowing manufacturing sequences with entirely different process steps to be completed in a fully automated fashion. This kind of flexibility delivers a host of benefits: facilitating shorter lead times and a tighter link between supply and demand, accelerating new product introduction, and simplifying the manufacture of highly customized products.
Making the right automation decisions
With so much technological potential at their fingertips, how do companies decide on the best automation strategy? It can be all too easy to get carried away with automation for its own sake, but the result of this approach is almost always projects that cost too much, take too long to implement, and fail to deliver against their business objectives.
A successful automation strategy requires good decisions on multiple levels. Companies must choose which activities to automate, what level of automation to use (from simple programmable-logic controllers to highly sophisticated robots guided by sensors and smart adaptive algorithms), and which technologies to adopt. At each of these levels, companies should ensure that their plans meet the following criteria.
Automation strategy must align with business and operations strategy. As we have noted above, automation can achieve four key objectives: improving worker safety, reducing costs, improving quality, and increasing flexibility. Done well, automation may deliver improvements in all these areas, but the balance of benefits may vary with different technologies and approaches. The right balance for any organization will depend on its overall operations strategy and its business goals.
Automation programs must start with a clear articulation of the problem. It’s also important that this includes the reasons automation is the right solution. Every project should be able to identify where and how automation can offer improvements and show how these improvements link to the company’s overall strategy.
Automation must show a clear return on investment. Companies, especially large ones, should take care not to overspecify, overcomplicate, or overspend on their automation investments. Choosing the right level of complexity to meet current and foreseeable future needs requires a deep understanding of the organization’s processes and manufacturing systems.
Platforming and integration
Companies face increasing pressure to maximize the return on their capital investments and to reduce the time required to take new products from design to full-scale production. Building automation systems that are suitable only for a single line of products runs counter to both those aims, requiring repeated, lengthy, and expensive cycles of equipment design, procurement, and commissioning. A better approach is the use of production systems, cells, lines, and factories that can be easily modified and adapted.
Just as platforming and modularization strategies have simplified and reduced the cost of managing complex product portfolios, so a platform approach will become increasingly important for manufacturers seeking to maximize flexibility and economies of scale in their automation strategies.
Process platforms, such as a robot arm equipped with a weld gun, power supply, and control electronics, can be standardized, applied, and reused in multiple applications, simplifying programming, maintenance, and product support.
Automation systems will also need to be highly integrated into the organization’s other systems. That integration starts with communication between machines on the factory floor, something that is made more straightforward by modern industrial-networking technologies. But it should also extend into the wider organization. Direct integration with computer-aided design, computer-integrated engineering, and enterprise-resource-planning systems will accelerate the design and deployment of new manufacturing configurations and allow flexible systems to respond in near real time to changes in demand or material availability. Data on process variables and manufacturing performance flowing the other way will be recorded for quality-assurance purposes and used to inform design improvements and future product generations.
Integration will also extend beyond the walls of the plant. Companies won’t just require close collaboration and seamless exchange of information with customers and suppliers; they will also need to build such relationships with the manufacturers of processing equipment, who will increasingly hold much of the know-how and intellectual property required to make automation systems perform optimally. The technology required to permit this integration is becoming increasingly accessible, thanks to the availability of open architectures and networking protocols, but changes in culture, management processes, and mind-sets will be needed in order to balance the costs, benefits, and risks.
Cheaper, smarter, and more adaptable automation systems are already transforming manufacturing in a host of different ways. While the technology will become more straightforward to implement, the business decisions will not. To capture the full value of the opportunities presented by these new systems, companies will need to take a holistic and systematic approach, aligning their automation strategy closely with the current and future needs of the business.
In 1900, 30 million people in the United States were farmers. By 1990 that number had fallen to under 3 million even as the population more than tripled. So, in a matter of speaking, 90% of American agriculture workers lost their jobs, mostly due to automation. Yet somehow, the 20th century was still seen as an era of unprecedented prosperity.
In the decades to come, we are likely to see similar shifts. Today, just like then, many people’s jobs will be taken over by machines and many of the jobs of the future haven’t been invented yet. That inspires fear in some, excitement in others, but everybody will need to plan for a future that we can barely comprehend today.
This creates a dilemma for leaders. Clearly, any enterprise that doesn’t embrace automation won’t be able to survive any better than a farmer with a horse-drawn plow. At the same time, managers need to continue to motivate employees who fear their jobs being replaced by robots. In this new era of automation, leaders will need to identify new sources of value creation.
Identify Value At A Higher Level
It’s fun to make lists of things we thought machines could never do. It was said that that only humans could recognize faces, play chess, drive a car, and do many other things that are automated today. Yet while machines have taken over tasks, they haven’t actually replaced humans. Although the workforce has doubled since 1970, unemployment remains fairly low, especially among those that have more than a high school level of education. In fact, overall labor force participation for working age adults has risen from around 70% in 1970 to over 80% today.
Once a task becomes automated, it also becomes largely commoditized. Value is then created on a higher level than when people were busy doing more basic things. The value of bank branches, for example, is no longer to manually process deposits, but to solve more complex customer problems like providing mortgages. In much the same way, nobody calls a travel agency to book a simple flight anymore. They expect something more, like designing a dream vacation. Administrative assistants aren’t valuable because they take dictation and type it up on a typewriter, but because they serve as gatekeepers who prioritize tasks in an era of information overload.
So the first challenge for business leaders facing a new age of automation is not try to simply to cut costs, but to identify the next big area of value creation. How can we use technology to extend the skills of humans in ways that aren’t immediately clear, but will seem obvious a decade from now? Whoever identifies those areas of value first will have a leg up on the competition.
Innovate Business Models
Amazon may be the most successfully automated company in the world. Everything from its supply chain to its customer relationship management are optimized through its use of big data and artificial intelligence. Its dominance online has become so complete that during the most recent Christmas season it achieved a whopping 36.9% market share in online sales.
So a lot of people were surprised when it launched a brick and mortar book store, but as Apple has shown with its highly successful retail operation, there’s a big advantage to having stores staffed with well trained people. They can answer questions, give advice, and interact with customers in ways that a machine never could.
Notice as well that the Apple and Amazon stores are not your typical mom-and-pop shops, but are largely automated themselves, with industrial age conventions like cash registers and shopping aisles disappearing altogether. That allows the sales associates to focus on serving customers rather than wasting time and energy managing transactions.
When Xerox executives first got a glimpse of the Alto, the early personal computer that inspired Steve Jobs to create the Macintosh, they weren’t impressed. To them, it looked more like a machine that automated secretarial work than something that would be valuable to executives. Today, of course, few professionals could function without word processing or spreadsheets.
We’re already seeing a similar process of redesign with artificially intelligent technologies. Scott Eckert, CEO of Rethink Robotics, which makes the popular Baxter and Sawyer robots told me, “We have seen in many cases that not only does throughput improve significantly, but jobs are redesigned in a way that makes them more interesting and rewarding for the employee.” Factory jobs are shifting from manual tasks to designing the work of robots.
Lynda Chin, who co-developed the Oncology Expert Advisor at MD Andersonpowered by IBM’s Watson, believes that automating cognitive tasks in medicine can help physicians focus more on patients. “Instead of spending 12 minutes searching for information and three with the patient, imagine the doctor getting prepared in three minutes and spending 12 with the patient,” she says.
“This will change how doctors will interact with patients.” she continues. “When doctors have the world’s medical knowledge at their fingertips, they can devote more of their mental energy to understanding the patient as a person, not just a medical diagnosis. This will help them take lifestyle, family situation and other factors into account when prescribing care.”
Humanity Is Becoming The Scarce Resource
Before the industrial revolution, most people earned their living through physical labor. Much like today, many tradesman saw mechanization as a threat — and indeed it was. There’s not much work for blacksmiths or loom weavers these days. What wasn’t clear at the time was that industrialization would create a knowledge economy and demand for higher paid cognitive work.
Today we’re seeing a similar shift from cognitive skills to social skills. When we all carry supercomputers in our pocket that can access the collective knowledge of the world in an instant, skills like being able to retain information or manipulate numbers are in less demand, while the ability to collaborate, with humans and machines, are rising to the fore.
There are, quite clearly, some things machines will never do. They will never strike out in Little League, get their heart broken, or worry about how their kids are doing in school. These limitations mean that they will never be able to share human experiences or show genuine empathy. We will always need humans to collaborate with other humans.
As the futurist Dr. James Canton put it to me, “It is largely a matter of coevolution. With automation driving down value in some activities and increasing the value of others, we redesign our work processes so that people are focused on the areas where they can deliver the most value by partnering with machines to become more productive.”
So the key to winning in the era of automation, where robots do jobs formerly performed by humans, is not simply more efficiency, but to explore and identify how greater efficiency creates demand for new jobs to be done.
Marna Ricker has her own personal robot.
While it doesn’t shoot lasers or clean her Minneapolis office Roomba-style, her “bot” can do some of her digital data dirty work so she doesn’t have to.
“I don’t want to sit at my computer and do process type of work,” said Ricker, the central region tax managing partner at Ernst & Young.
Accounting firms locally and nationally have recently employed the virtual bots in their own offices, as well as advised clients to use them as a faster, cheaper and often more accurate option to complete repetitive tasks.
Robotic process automation (RPA) is the use of a software robot or “bot” that replicates the actions of a human to execute tasks across multiple computer systems. According to professional services organization Deloitte, a minute of work for a robot is equal to about 15 minutes of work for a human.
For example, a bot could scan an invoice in a PDF document attached to an e-mail, save the data into an Excel spreadsheet, log into a web system and enter the data to generate a report, all before e-mailing an employee to say the work is done.
Robotics is predicted to automate or eliminate up to 40 percent of transactional accounting work by 2020, a 2015 Accenture report found.
Bill Cline, the national advisory leader for digital labor at global audit, tax and advisory firm KPMG LLP, said robotics is the biggest inflection point of the industry since global sourcing.
“I think a lot of people know that physical robots are being used in factories, or even that AI [artificial intelligence] is being used in medical diagnoses, but I don’t think many people understand how extensively software bots are being used to automate previously manual business processes and functions,” he said.
Ernst & Young has built in the last 18 months an army of about 200 bots in the firm’s tax practice operations that has resulted in saving several hundred thousand hours of process time annually. The firm, which also offers assurance, transaction and advisory services, uses bots for its own core business functions, including finance and performance management.
The bots can have accuracy rates as high as 99 percent and can reduce operating costs by 25 to 40 percent or more, Ricker said.
“They work 24/7. They are happy. They don’t take vacation,” Ricker said.
Bots can allow humans to focus on higher level tasks, she said. Ernst & Young is in its second full year of training staff on RPA. Over the last year, the firm also has started to help their clients use RPA in areas such as finance, procurement and human resources.
Eventually robotic software will be as freely used in accounting as Excel, Ricker said.
Besides time and cost savings, RPA and other types of automation could have several other benefits.
Intelligent automation can provide greater accuracy, accountability and defensibility by logging every process step executed and data source used, Cline said. Furthermore, automation allows for larger amounts of information to be analyzed for audits, risk analysis, and predictive analytics instead of depending on a smaller sample size that has been the norm when done manually, he said.
The use of RPA and other intelligent automation is also leading to a decline in offshore outsourcing for the array of tasks that can be replaced by digital workers, Cline said. In turn, that would also save companies money and give them more control.
KPMG has used various degrees of intelligent automation for more than three years. In the future, Cline predicts use of automation will become more sophisticated with advancements in natural language processing and artificial intelligence.
“Bots are getting smarter,” Cline said. “The lines are blurring between those classes of automation. Now some of the newer bots can actually watch a human worker do work and learn through observation.”
With all the talk of advancement, there has been speculation on whether digital robots could start to completely take the place of their human counterparts and pose a threat to the everyday accountant.
Cline said he acknowledged that the end result could be that it will take fewer people to do a task with the help of automation. But he said automation can also help open the door for companies to expand their services. In general, the clients that KPMG is helping with automation are trying to keep costs under control as they expand their capabilities, he said.
In some ways, robotics will create more jobs because it requires tech-savvy workers, he said. More firms will need staffers who understand how automation works.
“There are not enough people in the market right now that know tax and technology,” Ricker said.
John A. Knutson and Co., a smaller accounting firm in Falcon Heights, doesn’t have the automated tools that the Big Four do, but Kyla Hansen, a director, said they will be useful.
“Right now, accountants coming out of college are high in demand,” she said. “So it doesn’t scare me at all to automate whatever we can to make best use of the workforce that we have because there is a shortage right now.”
Source: startribune.com-Robotic software sweeping large accounting firms and clients
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.
Many people believe robots are a distant reality, however, the reality is that technological advancements in robotics is currently underway and are beginning to enter the workforce.
In partnership with Kofax from Lexmark, OmniChannel Media hosted a roundtable discussion in Brisbane regarding the phenomena that is Robotics Process Automation (RPA).
Moderated by Daniel Fowell, attendees included some of the industry’s leading experts and featured a keynote presentation from Tim Sheedy, Principal Analyst at Forrester.
“RPA is a very simple piece of technology capability that is taking the world by storm at the moment,” Sheedy said.
RPA is the application of technology to change normal day-to-day processes that are easily repeated, and shifting them to automated procedures which can increase the efficiency and cut unnecessary costs.
“RPA is a simple solution to save a lot of money and time, and cut costs out of the business to take humans out of the process.”
With robotics integrating itself into every industry, there are many examples of RPA which casual customers don’t notice but take for granted.
Key industries that illustrate this change are the food and transport industries. The most prominent case is seen in grocery stores where self-serve checkouts have replaced many of the aisle checkouts, and therefore nullified the purpose of human assistance.
Many organisations are increasingly realising that technology can be more dependable than humans, with Sheedy providing GE as an example of an organisation that is shifting towards RPA in order to avoid human error.
“GE, one of the world’s largest organisations, are talking about closing all of their books at the end of every month, quarter or year and using…RPA capability to do that. Their justification is that humans make 1-2 mistakes each and there’re 200-300 people involved in closing the books globally, and so that’s 600 mistakes that flow into their accounts.”
In the transport industry, the arrival of self-driving cars is becoming a further example of how RPA is expanding rapidly across multiple industries.
Although the emergence of RPA has caused many citizens to begin fearing for the safety of their jobs, this will inevitably encourage humans to up-skill and specialise in order to harness the evolving digital world.
US bank Citi recently suggested that its retail banking workforce could be reduced by 30% over the forthcoming years on the account of RPA. Although we are yet to see the full force of RPA transpire, it has the potential to disrupt the fabric of organisations from the bottom up.
The use of computers to automate human tasks and simulate the human thought process—the field now widely called cognitive computing—is attracting the attention and investment of some of the most well-known names in technology. More and more often, we are seeing automation and cognitive computing solutions that use statistical modelling, machine learning, natural language processing and other sophisticated capabilities to accomplish complex and learned operations. Google’s RankBrain artificial intelligence system processes search data to produce increasingly relevant search results based on what it has “learned” from past searches. Twitter has recently announced the acquisition of Magic Pony Technology that uses neural networks and machine learning for visual processing. Jack Dorsey, Twitter CEO and co-founder said, “Machine learning is increasingly at the core of everything we build at Twitter.”
For the past few years, advances in automation and cognitive computing also have been making their mark on the IT and business process services world, making certain repetitive, rules-based and mundane tasks faster and less labor-intensive. Because they can make such a dramatic impact in specific domains, these technologies are changing the way companies operate. Expectations for these solutions are high—and potential returns are great. For many enterprises, robotic automation has delivered 20-50 percent direct savings.
Though it’s easy to get caught up in imagining the potential of these solutions, their efficacy in the context of IT services can be validated by just a couple of simple proof points:
Do they achieve higher efficiency? How much faster, cheaper and more consistently than humans do software bots complete repetitive tasks?
Do they improve solutions for complex problems? How well can cognitive technologies solve (or assist a human in solving) higher-order problems that yield better results than is possible using human judgement and data?
While automation solutions can help achieve the first of these, cognitive solutions, powered by artificial intelligence, can help achieve the second. But various automation and cognitive solutions are at differing levels of maturity, making it difficult to know which will work best in any given environment. Enterprises need to be able to reap the efficiency gains in the short-term, while they experiment and solve higher-order problems in the longer term.
In today’s market, we are seeing four basic models for implementing automation and cognitive computing solutions.
In this operating model, the outsourcing service provider drives the automation initiative to achieve higher efficiencies for specific tasks in a traditional outsourced delivery model. The investments in automation and the benefits (typically cost savings) are shared with the client, depending on the nature of the commercial model. Since the service provider drives the automation, the client has less control over the nature and degree of automation. This model is ideally suited for less complex use cases.
Automation as a Service
Here the enterprise client identifies an objective for implementing automation and engages an automation-savvy provider to automate these specific process areas. To support the initiative, some enterprises create an internal automation center of excellence that engages with the ecosystem and internal business units to evangelize and manage change. Because the enterprise drives the initiative, it realizes the benefits (and savings) and compensates the automation provider for the services. This model is best for clients that prefer more control over the automation initiative and for use cases that are low-to-medium complexity. Automation as a Service is the most mature operating model in today’s market.
Provider-owned point solutions
Certain service providers have invested in developing best-in-class point solutions for specific domains. Most of these solutions contain elements of cognitive technology, such as social listening for marketing, image matching and natural language processing. This model is ideal for cases in which the business problem is complex and there are specialized solutions available in the market. In such cases, enterprises find it more beneficial to plug in such point solutions than trying to build their own, though of course this means the intellectual property is the provider’s or software vendor’s.
Client-incubated innovation labs
This is ideally suited to address complex industry-specific problems for which solution components (but not whole solutions) exist. Given the iterative nature of such pursuits, this model works when a buy-side enterprise partners with a capable provider to set up a joint innovation lab to develop a custom solution in an agile approach. The ownership of the intellectual property and commercial model varies depending on the capabilities and investment of the two parties. Though the client-incubated innovation lab model is the least mature in the market today, it shows the greatest promise for taking advantage of cognitive software solutions that provide competitive advantages.
Automation, driven by A.I., and cognitive computing offer exciting opportunities to help organizations drive business results. Understanding which model is the best fit is the first step to enabling successful automation and cognitive implementation.