7 Ways in which Artificial Intelligence is Redefining Customer Experience in Contact Centers

A lot has already been said about Artificial Intelligence (AI). Some love it while others hate it. But today’s truth is that you can’t ignore AI. It has arrived and how. The advent of Big Data has further given a boost to AI. It helped companies to exploit the power of AI to provide the best customer experience.

According to a research by Gartner, by 2020, AI will be a top five investment priority for more than 30 percent of CIOs. But how exactly will it redefine or revolutionize the customer experience? Let us look at some ways through which artificial intelligence will improve the customer experience in contact centers:

1. All-time Customer Service
Providing convenient customer service is the need of the hour. When a customer has an issue, they want it resolved immediately. They don’t care what time of the day it is. Therefore, companies aim to provide round the clock customer support. Chatbots make this an efficient and seamless process. These are AI enabled devices which can be operational 24X7, unlike their human counterparts.

2. Omnichannel Integration
Customers today reach out to the companies through various channels – social media, phones, mobile apps, emails, etc. Therefore, it becomes important to integrate data from all these channels to provide a wholesome customer experience. For instance, a customer first reaches out through call and then also drops an email. The agent should get data from all the previous touch points in an integrated manner. AI helps in providing that omnichannel support to the agents.

3. Reduction in Waiting Time
AI streamlines the calling process. It helps in prioritizing the customers and thereby routing them to the best-suited agent in case of specific issues. Moreover, in case of general queries, the bot can route it to any available agent. This way, the customer doesn’t have to wait long and leads to satisfaction.

4. Repurposing Historical Data
By now we are accustomed to collecting Big Data. Companies try to gather data for all the possible domains and aspects – customer journey, operations, marketing, customer behavior and much more. Earlier, a lot of that data used to be dumped. But with AI, we can use this data to get a 360-degree view of the customer which helps us to improve CX.

5. Personalised Customer Interactions
Stemming from the previous point, the Chatbots being the virtual agents use the historical data to provide real-time information to the human agents. This information empowers the agent to be spontaneous and provide a customized experience to the customer. In addition to that, the customer will also be happy that the company is sensitive to her/his issues which might actually convert into brand loyalty.

6. Building Customer Relationships
Building strong customer relationships is the first step to brand loyalty. Unfortunately, humans have their limitations. AI can be effective in this situation. Bots can send an email to get timely feedback or an SMS on special occasions to make them feel valued.

7. Providing Future Opportunities
All the data crunching yields long-term results. Companies can analyze the historical trends to predict future trends. Based on sentiment analysis, machine learning and natural language processing data, companies can improve their products and target the right buyer persona.

Conclusion
Artificial Intelligence has proved its mettle, but there is still a long way to go before it gains acceptance from the majority. There always are people who are skeptical of new technology. AI has also not been left untouched by that. Some customers still feel more comfortable in interacting with live agents than virtual ones. Having said all that, AI is here to stay. We can expect an increase in its buy-in amongst companies in the near future.

Source: CustomerThink-7 Ways in which Artificial Intelligence is Redefining Customer Experience in Contact Centers

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What the heck is Robotic Process Automation?

Robotic Process Automation (RPA) is on the rise. This 7-step introduction to RPA is designed to dispel some urban myths. It’s the start of a journey in which I will share some of the challenges and benefits of RPA, best practices and success stories.

1. There are no physical robots involved!

RPA is an entirely software based technology, so don’t expect to see tiny robots coming to your office anytime soon! The robots are installed on normal computers such as your laptop or workstation. At a larger scale, they can also be deployed in server environments and/or virtual machines (including in the Cloud) – making flexibility one of RPA’s greatest strengths.

2. RPA emulates human execution

The core thing a robot does is to emulate human execution. That is, it performs tasks on a computer like a human does: launching applications, surfing the web, copy/pasting information, filling in forms, you name it. In other terms, imagine one of those self-playing pianos, but for your computer.

As such, you can configure a robot to execute a typical business process, or parts of it. Of course, there are some limitations to what a robot can do, both technically and cognitively, which means that not everything can be automated. At Accenture, we have common suitability and eligibility criteria to tackle these questions.

3. Robots use regular user interfaces

The way a robot manages to emulate human execution is by using the regular user interfaces on your computer (note to non-techies: the “regular user interface” is the normal application windows you have on your PC, including the buttons, fields, etc. you use to interact with them). This is incredibly powerful because it means that you do not require privileged or backdoor access to an application to be able to interact with it. The robot can interact with almost any type of application such as web apps, Java, win32 or even mainframes (and there are far more lying around than you would expect).

The downside of this is that the execution speed of the robot is limited by the application because it is subject to the same loading times and latencies as regular human users. Nevertheless, a robot is still generally much faster than its human counterpart.

4. RPA is a non-invasive technology

You should think of RPA as an extra layer on top of your existing technology stack. A robot is like any other worker in your organization, carrying out tasks by using the same applications as you do. There is no need to rewrite legacy software or integrate RPA with business-critical systems, as they are most likely to be supported out of the box. As such, it is a non-invasive technology.

“A start to end delivery of RPA from analysis to deployment can be as short as two weeks”

5. There is no need for complex coding

When meeting with clients, I like to use the analogy of a student worker to describe how to configure a robot: assume he knows nothing about your job and that he has never heard of the software you use. These are the two main things you need to build a robot: (1) what are the logic and flow of the task, and (2) what are the applications needed to carry out the said task. Coding is object oriented, which means that you can define the use of a certain application once and reuse that object with other robots as many times as you want, significantly reducing complexity.

6. RPA can be rapidly deployed

Depending on the complexity of the process (number of steps, decisions and application screens) and organization maturity (existence of RPA objects library, strong Center of Excellence/governance model), a start to end delivery of RPA from analysis to deployment can be as short as two weeks. These short deployment times mean that Agile is particularly well suited to this technology.

7. Robots are managed by business users

Once approval has been reached to put a robot into production, it is transferred from the delivery team to the execution team. Typically, the latter is composed of business users who have a good understanding of what the robot does. Their job is mainly to manage the robot, make sure everything is running smoothly and take care of exceptions and incidents.

Source: Accenture-What the heck is Robotic Process Automation?

Here’s How Your Company Needs To Prepare For AI

In the late 1960s and early 70s, the first computer-aided design (CAD) software packages began to appear. Initially, they were mostly used for high-end engineering tasks, but as they got cheaper and simpler to use, they became a basic tool to automate the work of engineers and architects.

According to a certain logic, with so much of the heavy work being shifted to machines, a lot of engineers and architects must have been put out of work, but in fact just the opposite happened. There are far more of them today than 20 years ago and employment in the sector is supposed to grow another 7% by 2024.

Still, while the dystopian visions of robots taking our jobs are almost certainly overblown, Josh Sutton, Global Head, Data & Artificial Intelligence at Publicis.Sapient, sees no small amount of disruption ahead. Unlike the fairly narrow effect of CAD software, AI will transform every industry and not every organization will be able to make the shift. The time to prepare is now.

Shifting Value To Different Tasks

One of the most important distinctions Sutton makes is between jobs and tasks. Just as CAD software replaced the drudgery of drafting, which allowed architects to spend more time with clients and coming up with creative solutions to their needs, automation from AI is shifting work to more of what humans excel at.

For example, in the financial industry, many of what were once considered core functions, such as trading, portfolio allocation and research, have been automated to a large extent. These were once considered high-level tasks that paid well, but computers do them much better and more cheaply.

However, the resources that are saved by automating those tasks are being shifted to ones that humans excel at, like long-term forecasting. “”Humans are much better at that sort of thing,” Sutton says. He also points out that the time and effort being saved with basic functions frees up a lot of time to focus on customers and has opened up a new market in “mass affluent” wealth management.

Finally, humans need to keep an eye on the machines, which for all of their massive computational prowess, still lack basic common sense. Earlier this year, when Dow Jones erroneously reported that Google was buying Apple for $9 billion — a report no thinking person would take seriously — the algorithms bought it and moved markets until humans stepped in.

Human-Machine Collaboration

Another aspect of the AI-driven world that’s emerging is the opportunity for machine learning to extend the capabilities of humans. For example, when a freestyle chess tournament that included both humans and machines was organized, the winner was not a chess master nor a supercomputer, but two amateurs running three simple programs in parallel.

In a similar way, Google Health, IBM’s Watson division and many others as well are using machine learning to partner with humans to achieve results that neither could achieve alone. One study cited by a White House report during the Obama Administration found that while machines had a 7.5 percent error rate in reading radiology images and humans had a 3.5% error rate, when humans combined their work with machines the error rate dropped to 0.5%.

There is also evidence that machine learning can vastly improve research. Back in 2005, when The Cancer Genome Atlas first began sequencing thousands of tumors, no one knew what to expect. But using artificial intelligence researchers have been able to identify specific patterns in that huge mountain of data that humans would have never been able to identify alone.

Sutton points out that we will never run out of problems to solve, especially when it comes to health, so increasing efficiency does not reduce the work for humans as much as it increases their potential to make a positive impact.

Making New Jobs Possible

A third aspect of the AI-driven world is that it is making it possible to do work that people couldn’t do without help from machines. Much like earlier machines extended our physical capabilities and allowed us to tunnel through mountains and build enormous skyscrapers, today’s cognitive systems are enabling us to extend our minds.

Sutton points to the work of his own agency as an example. In a campaign for Dove covering sport events, algorithms scoured thousands of articles and highlighted coverage that focused on the appearance of female athletes rather than their performance. It sent a powerful message about the double standard that women are subjected to.

Sutton estimates that it would have taken a staff of hundreds of people reading articles every day to manage the campaign in real time, which wouldn’t have been feasible. However, with the help of sophisticated algorithms his firm designed, the same work was able to be done with just a few staffers.

Increasing efficiency through automation doesn’t necessarily mean jobs disappear. In fact, over the past eight years, as automation has increased, unemployment in the US has fallen from 10% to 4.2%, a rate associated with full employment. In manufacturing, where you would expect machines to replace humans at the fastest rate, there is actually a significant labor shortage.

The Lump Of Labor Fallacy

The fear that robots will take our jobs is rooted in what economists call the lump of labor fallacy, the false notion that there is a fixed amount of work to do in an economy. Value rarely, if ever, disappears, it just moves to a new place. Automation, by shifting jobs, increases our effectiveness and creates the capacity to do new work, which increases our capacity for prosperity.

However, while machines will not replace humans, it’s become fairly clear that it can disrupt businesses. For example, one thing we are seeing is a shift from cognitive skills to social skills, in which machines take over rote tasks and value shifts to human centered activity. So it is imperative that every enterprise adapt to a new mode of value creation.

“The first step is understanding how leveraging cognitive capabilities will create changes in your industry,” Sutton says, “and that will help you understand the data and technologies you need to move forward. Then you have to look at how that can not only improve present operations, but open up new opportunities that will become feasible in an AI driven world.”

Today, an architect needs to be far more than a draftsman, a waiter needs to do more than place orders and a travel agent needs to do more than book flights. Automation has commoditized those tasks, but opened up possibilities to do far more. We need to focus less on where value is shifting from and more on where value is shifting to.

Source: Inc.-Here’s How Your Company Needs To Prepare For AI

Using Robotic Process Automation To Prepare For GDPR Compliance

Creating ‘forget’ robots may help your business avoid fines

Many businesses are scrambling now, to be prepared for the impending changes in May 2018, to the General Data Protection Regulations (GDPR). The EU is going to the next level in its attempts to protect consumers from a data privacy (DP) perspective. One area that has a lot of companies very anxious is the right to be forgotten.

As of May 2018, any consumer can request to be forgotten. The request must be complied with to avoid significant fines. Each business will need a documented process of how they will scrub or remove the personally identifiable information (PII) connected to that consumer, in all their systems if there is no legal right or obligation to retain it. This can be a daunting task, depending on how many systems and cross system shares that may be in place.

This an area where Robotic Process Automation (RPA) may be the best answer. The first step in designing a “Forget Robot” is to document the details of all the places where data is stored (RPA 101 – requirements and process documentation). If this documentation doesn’t already exist, the RPA team needs to start compiling it now to be ready for May 2018! Once you identify all the places holding personally identifiable information, you will need to work with your data protection lead and your business stakeholders to decide if specific field data can be deleted or replaced, or if you need to delete the entire record. Some companies may wish to keep a record of a sale made to a male/female, in a specific age bracket, within a specific city for example, but would not be allowed to retain the PII connected to the transaction. A robot might just replace the PII fields with “*******”. System constraints may come in to play here also, with respect to how you may or may not be able to manipulate this data. In some cases you may have no choice but to delete the record. Clearly at this stage, you are designing the robot steps.

I have learned that PII fields sometimes come down to context. What other information is connected to a specific piece of data? If it is possible to derive a person’s identity through connected data, you will need to scrub the field in some manner. Your DP lead will be advising you to err on the side of caution as the fines can be significant.

The next challenge you will need to review with your DP Lead is what kind of detail that can be stored in the RPA logs relative to the task the “Forget Robots” carry out. The logs cannot contain any PPI information about the data that was just manipulated. At this stage you have moved from designing the Robot steps into the process, reporting and audit log documentation.

In some companies, there may not be resources available to carry out the right to be forgotten tasks. Based on the nature of the task, it is primed for RPA which adds a further degree of risk mitigation for your company as the robot will never miss a step or make a mistake. Your data privacy team likely has budget already, as most companies are anticipating new processes and controls will be required. This is your chance to show initiative, risk mitigation and save on costs by promoting “Forget Robots” to your organisation.

 

Source: Disruption Hub-Using Robotic Process Automation To Prepare For GDPR Compliance

6 Hot AI Automation Technologies Destroying And Creating Jobs

Forrester

Physical and software robots rise

Nothing gets the Silicon Valley-obsessed media more excited than watching the online mud-wrestling of two tech titans, especially when the fight is over the hottest topic of the day: Will AI destroy our jobs or will it be a force for good?

It all started with Elon Musk declaring that “robots will be able to do everything better than us,” creating the “biggest risk that we face as a civilization.” To which Mark Zuckerberg responded that the “naysayers” drumming up “doomsday scenarios” are “pretty irresponsible.” Musk retorted on Twitter (where else?) “I’ve talked to Mark about this. His understanding of the subject is limited,” and Zuckerberg blogged on Facebook (where else?) that he is “excited about all the progress [in AI] and it’s [sic] potential to make the world better.”

And so it goes. I don’t agree with the notion that only people who are actually doing AI can comment on AI and I’m sure both Musk’s and Zuckerberg’s understanding of AI is not limited. Like the rest of us, however, they inject into the debate their own biases, perspectives, and ambitions. It may help anyone interested in the question of what AI will do or not do to our jobs and civilization to study its history (you may want to start here), to look for evidence refuting what we believe in, and to assessments of the current and future impact of AI technologies that are based on relevant data analyzed with minimal assumptions.

Surveys, interviews and conversations with the people that actually make decisions about creating or eliminating jobs are an example of the latter category and they often serve as the basis for market landscape descriptions and better-informed speculations from industry analysts. A recent case in point—and recommended reading—is “Automation technologies, Robotics, and AI in the Workplace, Q2 2017” from Forrester’s J.P. Gownder (his blog post on the report is here).

Gownder and his Forrester colleagues discuss in detail (33 dense pages instead of 140 characters) a dozen “automation technologies”—all based on what we now generally refer to as “artificial intelligence”—that were selected because they play a role in either eliminating or augmenting jobs, require long-term planning for maximum impact, and (most importantly, in my opinion), generate questions from Forrester’s clients. In addition to assessing the developmental stage and long-term impact on jobs and businesses, Forrester provides definitions of the AI technologies/categories they discuss, valuable simply because definitions are often sorely missing from discussions of “artificial intelligence.”

Here is my summary of the 6 AI technologies that will have the most impact on jobs—positive and negative—in the near future:

  1. Customer Self-Service: Customer-facing physical solutions such as kiosks, interactive digital signage, and self-checkout. Improved by recent innovations (better touchscreens, faster processors, improved connectivity and sensors), it is also entering new markets and applications—a prime example being the experimental Amazon Go convenience store. Example vendors: ECRS, Four Winds Interactive, Fujitsu, Kiosks Information Systems, NCR, Olea Kiosks, Panasonic, Protouch Manufacturing, Samsung, and Stratacache.
  2. AI-Assisted Robotic Process Automation: Automating organizational workflows and processes using software bots. Analyzing 160 AI-related Deloitte consulting projects, Tom Davenport found it to be one of the fastest growing AI applications, an observation confirmed by Forrester. Example vendors: Automation Anywhere, Blue Prism, Contextor, EdgeVerve Systems, Kofax, Kryon Systems, NICE, Pegasystems, Redwood Software, Softomotive, Symphony Ventures, UiPath, and WorkFusion.
  3. Industrial Robots: Physical robots that execute tasks in manufacturing, agriculture, construction, and similar verticals with heavy, industrial-scale workloads. The Internet of Things, improved software and algorithms, data analytics, and advanced electronics have contributed to a wider array of form factors, ability to perform in semi- and unstructured environments, and the “intelligence” to learn and operate autonomously. A rising sub-category is collaborative robots (cobots), working safely alongside humans. Example vendors: ABB, Aethon, Blue River Technology (agriculture), Clearpath Robotics (autonomous, multiterrain), Denso, FANUC (traditional robots and cobots), Kawasaki, Kuka, Mitsubishi, Nachi Robotics, OptoFidelity, RB3D (cobots), Rethink Robotics (cobots), and Yaskawa.
  4. Retail and Warehouse Robots: Physical robots with autonomous movement capabilities used in retailing and/or warehousing. Picking up objects is still the biggest challenge, but retailers such as Hudson’s Bay and JD.com, and of course Amazon, are investing in potential solutions. Example vendors: Amazon Kiva Systems (structured environments), Fetch Robotics (unstructured), Locus Robotics (unstructured), and Simbe Robotics (retail scanning robots for product restocking).
  5. Virtual Assistants: Personal digital concierges that know users and their data and are discerning enough to interpret their needs and make decisions on their behalf. Developed for the consumer market just a few years ago, these assistants can be used by companies in a business-to-consumer setting (e.g., answer questions at home or augment the work of call center employees) or inside the business organization (e.g., serve as subject matter experts or support business processes). Example vendors: Amazon Alexa, Apple Siri, Dynatrace for ITSM, Google Now and Google Assistant, IBM Watson conversational interface, IBM Watson Virtual Agent, IPsoft Amelia, Microsoft Cortana, Nuance Communications Nina, and Samsung Bixby.
  6. Sensory AI: Improving computers ability to identify, “understand,” and even express human sensory faculties and emotions via image and video analysis, facial recognition, speech analytics, and/or text analytics. Example vendors: Affectiva, Amazon Lex, Amazon Rekognition, Aurora Computer Services, Caffe, Clarifai, Deepomatic, Ditto, Equals 3 Lucy, FaceFirst, Google Cloud Platform APIs, HyperVerge, IBM Watson Developer Cloud, KeyLemon, Linkface, Microsoft Cognitive Services, Microsoft Cortana Intelligence Suite, ModiFace, Nuance Communications, OpenText, Revuze, Talkwalker, and Verint Systems.

The first 4 categories have been around for a while (Forrester calls them “mature”) but have recently become energized by hardware and software innovations. It is interesting to note that the key reason for the recent excitement about and fear of AI—the rapid advancement in a number of narrow AI tasks (e.g. object identification) due to improvements in deep learning techniques—has not contributed greatly to the newly-found sexiness of these 4 categories. But deep learning has been a key contributor to the nascent success of the other 2 hot categories—virtual assistants and sensory AI. My general conclusion from these observations is that the excitement (and fear) generated by specific “triumphs” of AI technologies can obscure for us a very fundamental fact of technology adoption throughout history, including recent history—it takes a very long time. This has important implications for our assumptions and projections regarding the question when will AI eliminate (lots of) jobs.

It’s tough to make predictions about the timeframe and magnitude of job elimination, especially when we consider the future of employment (to paraphrase a very wise man). But the difficulties inherent in saying anything about the future, especially the future of jobs in a dynamic, constantly evolving, and multi-faceted economy (e.g., persistent low wages may postpone the adoption of robots), have never stood in the way of people writing and/or analyzing and/or speaking for fame and fortune (or more simply, for continuous employment).

The current cycle of here-are-authoritative-numbers-on-how-many-jobs-will-be-eliminated-by-AI started 4 years ago by two Oxford academics (47% percent of jobs in the US are at risk of being automated in the next 20 years). Forrester’s analysts could not resist the much in-demand forecasting exercise and, in what became “one of the five best-read among all reports at Forrester,” estimated that automation will destroy 17% of US jobs by 2027. But, unlike many other commentators on the subject, they also looked at the glass-half-full and estimated that automation will add 10% of new jobs to the US economy by 2027, for a net loss of 7%.

Whether it will be 7% or 47% or any other quantitative or qualitative speculation about the future impact of AI on employment, the debate over when and how muchdoes not even take into consideration the question of if. Will robots really “be able to do everything better than us,” as Musk believes, and not just in 20 or 100 years, but anytime in the future? I know, it’s tough to make predictions, especially about the future of technology. What is certain is that inquiry minds steeped in the scientific ethos, such as Musk’s, should consider all possibilities and avoid making dogmatic statements, either of the AI-will-destroy-civilization type or AI-will-cure-all-diseases kind. Why not consider the possibility that intelligent machines will not take over because they will never be human and that the futile quest for “human-level intelligence” has actually slowed down progress in AI research?

There is no question that we will continue to see in the future the same disruption in the job market that we have witnessed in the last sixty-plus years of computer technology creating and destroying jobs (like other technologies that preceded it). The type of disruption that has created Facebook and Tesla. Facebook had a handful of employees in 2004 and today employs 20,000. Tesla was founded in 2003 and today has 33,000 employees. Whether AI technologies progress fast or slow and whether AI will continue to excel only at narrow tasks or succeed in performing multi-dimensional activities, entrepreneurs like Zuckerberg and Musk (and Jack Ma and Vijay Shekhar Singh Sharma and Masayoshi Son) will seize new business opportunities to both destroy and create jobs. Humans, unlike bots and robots (now and possibly forever), adapt to changing circumstances.

Source: Forbes-6 Hot AI Automation Technologies Destroying And Creating Jobs

Automation Is About More Than Saving Money

With the working population shrinking as the population ages, there will be fewer people doing more work. And that means automating more tasks will become the norm. I spoke with Blue Prism’s Alistair Bathgate about this as we looked into the world of Robotic Process Automation.

Bathgate is the CEO of Blue Prism. The company has created a software class dubbed Robotic Process Automation (RPA). He likens RPA to an electronic piano that is programmed to move the keys and play music – it’s about using software robots.

“It logs on, and orchestrates and interacts with the systems autonomously in exactly the same ways a user would,” he explained. “It’s more like training robots than writing code”.

This differs from traditional automation and orchestration because it is business-led he said. Business people train the robots rather than relying on technical resources from the IT team to write code to program the robots.

According to Bathgate creating the robots was a challenge but the hardest part was making the system accessible and friendly for business users to operate.

However, in order to ensure there isn’t an explosion of automation that creates issues within systems, Bathgate said there are controls in place so the business can work with the IT team as well as robotics deployment and governance model that supports the use of RPA in a efficacious way.

Blue Prism’s RPA software is used in banks, healthcare providers and insurance companies across the world.

One of the benefits of this approach, said Bathgate, is that the systems can be scaled up easily. So, as workloads increase the ability for the user-created robots to absorb increased workloads is limited only by compute capacity – something that can easily scaled to accomodate the shifting workloads.

The benefits of RPA, said Bathgate, go beyond just reducing the number of people needed to complete the work that is required. For example, European mobile network operator Telefonica has employed Blue Prism’s solution to massively improve customer service.

In the UK, Telefonica operates the Orange mobile phone network. In the past, when a customer requested a SIM card swap, such as when they upgraded their phone, the process to activate the card could take up to 24 hours. This was because the backoffice processes required significant input from people.

However, once the business-led RPA was applied, customers could leave the store with a new, working SIM rather than the promise that the SIM would be active within 24 hours.

With regulatory obligations and compliance with rules becoming increasingly complex and prevalent, RPA also delivers a consistent process and outcome that can be audited and amended as rules change.

Another application of RPA came from a major bank. Bathgate said that the time between when a credit card was reported lost or stolen in a bank until it was deactivated was around 25 minutes as there were a number of systems and authorities that needed to be notified. That time that could be used by a thief to steal funds.

“That 25 minute process was delegated to a virtual backoffice or digital employee that has freed the agent to be six times more productive”.

A bank in New Zealand was able to take an existing six-day process for rejected transactions and shortened it to 11 minutes, added Bathgate.

A recent survey undertaken by Infosys found 40% of 16-to 25-year-olds think their current jobs will be replaced by some form of automation within the next decade. So, are jobs under threat?

Bathgate said first world economies are suffering from shrinking work forces as populations age. As a result, productivity is, potentially, under threat. He says there are two main ways this can be overcome; automation and immigration.

But with immigration becoming increasingly unpopular politically – he noted this was particularly so in “Donald Trump’s America and Teresa May’s UK” and the world’s third largest economy, Japan, not having high levels of immigration that automation will be part of the answer.

The said there is a “global, macro-economic argument for automation”.

In Bathgate’s dealings with clients, he has not seen jobs lost when RPA or other automation technologies are introduced. Rather, people are redeployed to other tasks. In particular, as regulatory and compliance obligation become more onerous, he has seen many people move into those roles.

One of the most often used reasons for using automation technologies such as RPA is cost reduction. But Bathgate says this is not the ideal lens with which to make decisions about auto=tomation. One of the biggest benefits, he said, comes from reduced error rates.

One Saudi bank where Bathgate deployed the Blue Prism solution saw a 70% reduction in flaws across 1.5 million transactions.

“Robots are faster, more accurate, more compliant,” he said.

Source: lifehacker.com.au-Automation Is About More Than Saving Money

The Raw Truth About RPA

The market for robotic process automation (RPA) and Intelligent Automation continues to be obfuscated by smoke and mirrors. If you listen to our friends at Gartner, satisfaction levels are allegedly at an unprecedented 96% while our own data rather suggest that roughly only half of the deployments led to satisfactory levels. So where is the market really at and what needs to be done to accelerate the journey? But more importantly what can be learned from the early deployments? Thus, our recent Summit in Chicago was a welcome opportunity to check what really is on buyers’ minds.

Buyers struggle to scale RPA projects

When we asked buyers in Chicago, how satisfied they are with their RPA projects, the results that can be seen in Exhibit 1 were astounding. The surprise was less around the low scores at the suggestion that their expectations were fully achieved, but more that many are struggling to scale projects and that they didn’t anticipate the impact on adjacent workflows and processes. Those suggestions are food for thought and are building on HfS’ much more detailed research on RPA satisfaction levels.

Exhibit 1: Polling question from HfS Summit in Chicago “Buyers, how satisfied are you with your RPA projects?”

Source: HfS Research 2017, n=36

One buyer succinctly articulated the implications of those concerns: “You don’t buy RPA, AI or Blockchain, you buy an outcome, yet providers’ organizational issues are pulling us back to technology.” It is here where the overselling of the supply side cuts in. Until compensation schemes and organizational issues change, we have to cope with an enormous amount of smoke and mirrors. Another buyer built on these issues in progressing on the automation journey, “AIis nothing you take off the shelf, it is a disparate set of capabilities, it is about orchestration, ecosystem, data.” We had heard similar concerns at our last Summit in New York: “We need to move beyond technology by being specific, in particular, specific about the use cases. And we have to move from bots to data.” This raises a plethora of questions from compliance to governance.

So, what holds the future for RPA? When we asked the audience in Chicago where they see RPA in 12 months’ time, we got clear answers. As exhibit 2 highlights, 34% reinforced the message that RPA will be all about transformation and not products. Slightly surprising 20% suggests that either Google, Microsoft, or AWS will enter and disrupt the market. While we have argued that around AI will see a shift toward mega ISVs, a direct involvement in RPA would certainly come as a surprise to us.

Exhibit 2: Polling question from HfS Summit in Chicago “Where do you see RPA in 12 months’ time?”

Source: HfS Research 2017, n=59

To get a more nuanced feedback on the issues surrounding RPA deployments, HfS did run two breakouts titled “The raw truth about RPA”. In those sessions we leverage a simplified Design Thinking method that facilitates constructive feedback on any given topic, using simple statements that convey feelings – I LikeI Wish, and What If? In both sessions, we saw a surprising convergence of thoughts, experiences and ideas by a wide range of RPA stakeholders – services buyers (both new to RPA and experienced practitioners), RPA vendors, and sourcing and automation advisors. We present the synthesized RPA experiences in the same design format below.

Constructive feedback on RPA to the services industry

I LIKE:

  • That RPA works! It brings us efficiency and quality, increased throughput, and helps us in managing volatility.
  • That RPA has a low entry barrier. It has a relatively low entry cost, allows us to test quickly and fail fast, is relative ease of use, and has a good time to value.
  • That RPA solves stubborn business problems. Things we couldn’t address or bring up with IT before can now be dealt with by ourselves.
  • That RPA brings operations and IT together. Without change management, projects are likely to fail.
  • That RPA creates a new source of value to clients. It brings us (service providers) closer to clients. The branding alone is valuable – robotics sells.
  • That RPA documents undocumented processes! We can derive intelligence from automation, standardization, thus creating new levels of transparency.

I WISH:

  • We could stop calling RPA new. The basic concepts have been around for a decade.
  • We had more realistic expectations for all stakeholders – and definitions, offer insights as to what is reality and what is hype.
  • We had more RPA maturity overall. Maturity around change management and more education on the realities, use cases, examples of failures and successes.
  • We wouldn’t see Machine Learning and RPA as silver bullets or a cure. We need broader education around this. Machine Learning is not about having the best algorithm, it’s about the best integration into the fabric of the process.
  • We could get agiler. That is experiment faster, leverage bot libraries; furthermore, that we had reusable business knowledge, central business rules engines
  • We could rethink our RPA business case. Take a more strategic view, longer-term – albeit with softer criteria. We would move beyond narrow notions of cost. Fundamentally, it is not about FTE reduction, we need more clarity of the underlying costs including attrition
  • We would look at the investments as a strategic opportunity, in particular, that at least half of the cost savings would be used for transformational projects.

WHAT IF:

  • RPA was free? It is already commoditizing; RPA costs get reduced year-on-year.
  • We had common RPA standards? Furthermore, a knowledge hub for adjacent knowledge where FTEs get freed up.
  • There were people in the industry with functional expertise who were also experts in RPA? The talent that understands the impact of RPA on process chains and workflows is scarce.
  • We could share data across industries? The same applies for benchmarks and metrics.
  • We could measure less tangible benefits! Thus, the business case and communication internally would become easier.
  • Risk education was more advanced? We could overcome data breaches, security.
  • We don’t need bots in the future? Because of the speed of digital transformation, we could leapfrog legacy and have native automation.
  • We could look at this as a continuum? RPA/RDA could be part of something bigger (reengineering), thus RPA is a wake-up call!
  • We truly transformed to a customer-centric platform that is companies with legacy processes. OneOffice in all but name!

Bottom-line: RPA needs to support outcomes through orchestration of disparate sets of technology and data

The voices of the RPA community in Chicago were loud and clear: On a basic level RPA works and yields results. However, buyers are struggling to scale projects and often lack an understanding how they can advance to more data-centric models. On this journey, standards that can help with the communication, and case studies that convey the lessons learned would go a long way. While they acknowledge that RPA could evolve into a crucial lever for progressing toward the OneOffice, the buyers criticize that cost savings are not being reinvested for transformational projects. They are in agreement that in order to support outcomes, RPA needs to be integrated with other disparate sets of technologies as well as data. To succeed with those projects, the industry urgently needs a new breed of talent that blends functional experience with practical understanding of those innovative technologies.

Source: HFS-The Raw Truth About RPA

RPA and the role of the CIO

Robotic Process Automation is the process of using certain RPA tools to automate manual processes.

These manual processes are typically those that are built up around existing computer systems or those systems that are currently not automated.

For example, preparing data for entry into an automated system, or taking data manually from system and keying it into another system.

Implementation of RPA can be hugely beneficial.

Generally the technology can be cost saving, improve quality, reduce error in relaying and retrieving data, aid productivity and output, and improve the performance of the business.

RPA, however, is not being utilised to its full potential.

Business leaders often bypass IT when implementing RPA initiatives, because RPA systems don’t require extensive IT support, and IT is often seen as a potential roadblock to operational improvement initiatives.

However, RPA systems do require a level of IT support and involvement to ensure performance, and lack of CIO involvement can lead to risks associated with any technology projects, such as disconnected technology, performance issues, security lapses and decreased value delivery.

Why is IT so important in implementing RPA initiatives?

It’s essential for IT to be engaged and preferably supportive of RPA.

When an organisation selects an RPA initiative or tool, and it makes the commitment to apply resources, it becomes a technology in that organisation.

So, the CIO’s view is inclusive in the strategy of implementing RPA technology. There is a portion of this that needs his or her’s help.

These technologies are optimally installed on a server-type device in the organisation, and that’s usually managed by IT.

So, in order to get RPA installed in an optimum way, we need IT’s help.

The CIO controls those resources, and so they have to be deployed to assist the users; get the installation correct, make sure it’s installed properly within the infrastructure, and oversee the right back up.

IT’s involvement is essential, so that the RPA technology can operate just as efficiently as any other major technology in the business; continuously and reliably.

What is the impact of integrating RPA on IT systems?

Generally speaking the impact on IT is minimal. RPA technologies don’t require complex interfaces at the data level.

The robot mimics what the user does, so it typically does not store data, and does not create a growing database of its own.

As a result there’s a minimum impact on infrastructure, on the server, on memory on bandwidth, on storage and so forth.

It doesn’t require any new operating system, it’s almost agnostic

How can RPA and IT work together?

IT can leverage this tool in two different ways specifically.

One is, IT also has manual things that it does. When IT deploys systems they have to test modifications and enhancements.

These RPA tools can be used for functional and regression testing [type of software testing that verifies that software previously developed and tested still performs correctly after it was changed or interfaced with other software].

Secondly, and from my standpoint most significantly for the CIO, it presents an opportunity to see where these RPA tools can be used in place of modification and enhancement requests that the users might have.

For example, most IT departments maintain some sort of backlog list of requests that are used to modify or enhance existing applications. It would be possible to look at these requests and find ones that could be partially, or entirely solved by using an RPA tool.

How has the role of the CIO changed with regard to RPA initiatives?

In one respect I’m not sure if it’s changed very much, and in another respect it has changed.

For years businesses, or business users have been implementing their own tools in their organisations; there’s reporting tools, there’s office tools.

So the CIO, for years, has had to be aware of and had to address user departments that want their own technologies, all to help the business advance and to meet their own goals.

From that standpoint the CIO’s role is very similar.

The way it’s different is that most organisations do not have RPA tools in place.

There is a learning exercise, an education process, in place.

This is a new technology aiding the implementation of manual tasks.

The CIO has a role to support the infrastructure, provide the application facilities, figure out how to optimise it throughout the organisation, and to drive the benefits into the business.

What are the advantages of using RPA?

In many organisations there is a ROI (return on investment) implementing robotics.

Robots can do more work than one full time employee, which significantly reduces cost.

There are financial advantages in implementing a robot that can work after hours and on the weekend, 24 hours a day.

Developing these robots is also efficient, and it can take weeks or months, rather than years.

We often see companies that have very high ROI percentages, because these robots can be deployed quickly, they can do the work of more than one full time equivalent (FTE / employee), and the cost is less than an FTE.

Another advantage is that when a process changes, human training can be a huge undertaking, it has to be well planned and well timed. It becomes a huge initiative.

With a robot it’s very simple to modify the systems, test, and re-deploy the robot. The whole process becomes much simpler.

Accuracy is the final factor. When the robot is working its processes it should work at 100%, and so the errors go away.

Are there security risks?

The robots are only going to do what they’re configured to do.

Unlike the user, the robot doesn’t need internet access, so the robot will only do what it is told to do.

In some cases, the robot is even more secure than a human on the network.

The robot will conform to the existing security policies that are in place at the organisation.

Ultimately, RPA tools appear to be an emerging technology that increases productivity and saves money, while not upsetting the established IT order.

Source: information-age.com-RPA and the role of the CIO

5 Steps to More Effective Change Management When Implementing Automation Solutions

Change management is often the most difficult part of an IT project, regardless of the technology that’s being implemented. Many companies struggle with getting buy in from key stakeholders and encouraging adoption when end users have been doing things a particular way for years and are resistant to change.

This is especially true when implementing solutions that are as transformative as automated Enterprise Content Management (ECM) and Enterprise Information Management(EIM). New technologies have the power to dramatically improve productivity, profitability and innovation. They also can cause significant changes to staffing needs, workflow and day to day operations. This often makes it challenging to ensure that the change goes smoothly and that the company exploits the full benefits of automation.

More effective change management

In order to make sure new automation solutions have the high-level backing to be implemented successfully and that employees actually use them, companies should follow change management best practices and tailor their approach to the unique challenges presented by automation. This means working closely with key people at all levels of the company, addressing major challenges and focusing on the benefits that the new solutions will bring.

1. Address challenges head on – Automation is a disruptive technology that will have a major impact on job outlook and the way people work. Companies shouldn’t avoid these issues but instead embrace them and focus on positives. Although automation tools will change the way work is being done, it also frees workers time to focus on more creative and innovative areas. New tools also allow workers to perform their tasks faster and with fewer headaches, reducing stress and making work more enjoyable.

2. Emphasize integration – Many people postpone adoption because they are afraid their productivity will take a hit while learning new systems. Modern ECM solutions are designed to integrate well with existing systems, letting users access the content and information they need directly from the Enterprise Resource Planning (ERP) or Human Resource Information System (HRIS) screens they are familiar with. This means that users don’t have to learn an entirely new system and casual users can work within their Outlook applications without even opening the ECM application.

3. Leverage flexibility – By focusing on the flexibility of automated ECM solutions, companies can help their employees see the benefits of using them. They often offer multiple end user clients and ways to access content, from mobile apps and web browsers to Outlook. This allows users to access their data and work with the system however it suits them.

4. Make training a priority – When implementing any new solution, effective training is critical, helping to educate about benefits, ensure effective usage and encourage adoption. In-house teams and consultants from the solution provider can help with implementation and training, easing the burden on your own IT staff.

5. Monitor progress and make improvements – No solution implementation is perfect in its first weeks or months. The project team should keep working past rollout, monitoring adoption rates and problem areas while developing solutions to address any issues.

Companies that leverage the power of automated EIM and ECM solutions can benefit from major increases in productivity and lower labor costs, but their efforts will not be effective unless they implement effective change management programs. By taking steps to minimize negative impacts of the change and smooth the transition, your organization can increase adoption and ROI for the solutions.

Source: IRPAAI-5 Steps to More Effective Change Management When Implementing Automation Solutions

Robotics Process Automation For B2B FinTech

In corporate finance, automation changed the game. Accountants and other financial professionals once tasked with manual processes and number-crunching were freed up to focus on more strategic initiatives.

But there’s a new FinTech trend that wants to empower financial execs even more, with some players acknowledging that basic automation falls short.

Robotics process automation (RPA) is a recent favorite among some B2B FinTechs. Corporate accounting software company Gappify, for instance, announced only weeks ago that it is rolling out an RPA-fueled bot to automate many accounting processes — that is, to automate processes without requiring human intervention to initiate that automation. Around the same time as Gappify’s news, another corporate accounting software firm, FloQast, revealed the launch of Cloud Connect, an RPA-based solution to help companies more easily access data stored within on-premise ERP systems.

Investors are perking up to the sudden interest in robotics process automation.

Earlier this week, Kryon Systems announced a Series B fundraising round to the tune of $12 million, led by Aquiline Technology Growth and Vertex Ventures.

Speaking with PYMNTS, Kryon CEO Harel Tayeb explained why robotics process automation has such vast potential in B2B payments.

“RPA offers any business that relies on rule-based processes, whether that be financial or otherwise, the opportunity to boost profitability, increase operational efficiency and maintain a competitive advantage by automating much of the high-volume and repetitive processes,” Tayeb said.

This type of technology, he continued, means financial executives can “offload a huge swatch of operational tasks” — those processes that are tedious and time-consuming, but necessary, like invoice processing, expense report auditing and so on. RPA integrates what Harel Tayeb described as a “virtual workforce of software robots … [that] can perform these tasks more efficiently than human employees, thus making employees available for more creative assignments.”

While robotics process automation is a relatively young technology, the executive said it’s already proven itself as a tool that can “drastically improve efficiency and process execution for enterprises of all shapes and sizes.”

“RPA and other digital tools are quickly becoming indispensable to the corporate finance industry as the pressure for accounting and financial professionals to accomplish more with fewer resources and less time is constantly on the rise,” stated Tayeb.

Automation initiated this capability for many organizations. But, according to Tayeb, RPA goes further, offering a more flexible technology.

“Workplaces are dynamic,” he said, “in that they have a shifting set of needs and rely on employees to wear different hats when the need arises and be efficient in executing tasks to make the business successful.”

“Traditional automation services require enormous amounts of work for IT departments, while RPA was developed to be as simple as possible for enterprises to adopt, integrate and evolve as their internal needs change,” the CEO continued.

As the technology continues to evolve, and as more use cases are defined, the potential for RPA to disrupt not just corporate accounting but also the way in which businesses operate overall, is significant. But that doesn’t mean the tool will have an easy time stepping into that role. The initial emergence of automation led to widespread concern that accountants and other professionals would lose their jobs to robots; critics of RPA could have the same fears.

“We still need to educate the workforce that the myth that digital transformation will take away millions of jobs is just that: a myth,” Tayeb said, noting that strategic deployment of RPA solutions can result in a hybrid workforce of both bots and humans. “New technologies always lead to new jobs.”

Without doubt, companies will need a bit more convincing. Startups like Kryon and other B2B FinTechs won’t just be some of the first to put RPA to use in the enterprise, they’ll be on the front lines of pushing adoption of the solution. If Kryon’s latest funding is any sign, it looks like venture capitalists are ready to throw their support behind the effort too. The company said it will use the $12 million to focus on sales and marketing, as well as on engineering efforts to continue building out its solutions.

Its CEO said he is optimistic about RPA’s ability to break through enterprise doubts.

“Businesses … are eager to optimize their operations, which is why RPA as a disruptive technology has been, and will continue to be, well-received,” said Tayeb. “Innovation is critical to the viability and success of businesses, especially in competitive, fast-paced markets.

“RPA enables a substantial amount of work processes to be handed over to a virtual ‘bot’ workforce, but human employees are not about to disappear,” he emphasized. “Rather, RPA enables businesses to maximize the potential and productivity of employees by freeing up their time and focus on different tasks that require a more human touch.”

Source: pymnts.com-Robotics Process Automation For B2B FinTech