RPA: Expanding New Horizons And Increasing Productivity

Petra Mainer

In this post, I will discuss robotic process automation (RPA), exploring its latest technical tools and analyzing its benefits and weaknesses compared to a human workforce.

The 21st century has rightly been called the time of technological boom. The number of new inventions and progressive ideas has grown exponentially, all aimed to simplify work, optimize processing timeframes, and increase business productivity. Robotic process automation is one of the latest smart services to take on human functions. But could it actually replace people? Let’s examine this idea in more detail.

What is RPA?

Robotic process automation is a technological application that configures computer software to automate repeated, rules-based, non-subjective process. It changes the approach to completing tasks that involve manual input and data processing. The main principle of RPA is grounded on playing between two technological applications through an existing user interface rather than an application programming interface (API).

Technical advantages of RPA

RPA does not alter the existing IT architecture. It comprises the core to enable quick, efficient implementation without additional expenditures. Monitoring and regulatory procedures and reporting also remain unchanged.

For many companies using outdated legacy systems, RPA can offer an efficient solution that addresses most common challenges.

RPA can also be used incrementally and can be installed on individual users’ computers, enabling specific employees to run processes to increase efficiency. In such instances, RPA can easily be disabled if necessary.

Benefits of RPA

  1. Increased productivity reduces expenditures. RPA can be implemented for relatively little investment, especially if it is open source, which requires no license costs.
  1. Robots do not make mistakes. Set up correctly, RPA eliminates errors and inaccuracies.
  1. RPA works 24-7. It can triple the productivity of a human worker.
  1. RPA reduces staff training investment and saves time. When the process should be modified, the company needs only change the initially preset rules (modify the script) not to train the employee.
  1. RPA registers 100% of processes. Regulatory compliance is especially important in today’s fast-paced business environment.
  1. RPA does not require a workplace and social guarantees. According some studies, one robot is the equivalent of three to eight human workers in terms of productivity. Experience shows that RPA can reduce business process costs up to 80%.

Does RPA undermine the value of human resources?

Regardless of all the advantages of RPA listed above, the answer is unambiguous: No.

Robotic systems are best suited to work with repeated, rules-based, and non-subjective processes. They cannot be applied effectively in areas where decisions are based on fuzzy criteria such as moral principles. They are also ineffective in conditions that involve rapid changes in processing rules, or for one-time tasks.

In conclusion, while RPA has unprecedented potential, it is still far from multifunctional and cannot compete with human intellect. It can and often should be used to free people from performing mundane, repetitive functions so they can focus on more complex business challenges.

How is robotics technology being used in finance? Read Accounting Robots: How Digitization Drives Automation In Finance.

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About Petra Mainer

Petra Mainer, the novice solopreneur (founder of GradeScout), studies Information Management and now she is eager to start writing about everything worth reading in the digital sphere. You can follow her on Twitter.

What Will The Customer Experience Look Like In 2020?

Mark de Bruijn

We are living in an age in which retail chains are going bust by the score, which means the need for an optimal customer experience is clearer than ever. Developing at breakneck speed, modern technology offers many opportunities to further enrich the experience. But what will the customer experience look like, say, four years from now?

Digital assistants everywhere, but the human touch retains value

Siri and Cortana are examples of the digital assistants we know now: nice little smartphone tools that amuse us with funny conversations and the occasional surprising answer. In short: an entertaining waste of time. However, digital assistants will evolve considerably in the near future. They will offer truly useful conversations and help us make informed choices. Some web shops are already experimenting with chatbots and wizards that attempt to offer helpful advice. By 2020, these systems will have matured and offer real and useful advice based on targeted open and closed questioning.

They will also become a staple in brick-and-mortar shops. One good example of where this technology is heading is Robot Pepper. This physical robot is able to start a conversation and—assuming it is programmed to do so—help you find and purchase the products that best meet your needs. By 2020, such robots will be taking part in advisory roles previously performed by humans.

I am convinced humans will always be needed, but their role will change. Robots will take care of basic customer care, while humans will remain indispensable for more advanced tasks that require a human touch. (Consider the many examples of organizations that have subjected too many of their customer care services to automation.)

Also, well-functioning self-service facilities will be indispensable by 2020. This includes self-service portals or active user communities where customers can ask questions.

Interactive shops make shopping districts more attractive

In 2020, brick-and-mortar stores will need to think more strategically about their relevance. The good news is that technology offers countless opportunities to boost the shopping experience. In 2020, the physical shopping experience will be transformed thanks to sensors, touch screens, and beacons—the only hurdle is creativity. That is a good thing, because shopping should be sexy.

Let’s consider, for example, a sneaker store. The moment a customer passes the store, he will get a personalized deal offer on his smartphone: The newest addition to his favorite line of sneakers is available with a 20 percent discount, and it is in stock at this store. Once inside, he explains to the digital assistant that he is looking for a sneaker suitable for a novice runner, and immediately, some of the shelves light up.

When he picks up one of the sneakers, a video on a large screen explains the shoe’s unique features. When fitting the sneaker, beamers project walking exercises on the shop floor to test some of its features. Finally, the mirror uses augmented reality to show the customer how different colors and other varieties would look on him.

Hyper-individualization brings product and shopping experience closer to the consumer

Stores and brands will collect increasing amounts of customer data in 2020, enabling them to generate detailed profiles based on a customer’s past purchases and the data they submit. Moreover, the Internet of Things will reveal important lessons about consumer behavior. Products will be equipped with sensors, generating information on which features are—and are not—successful. Brands can also advise individual customers about timely replacements, for example.

How will this play out in practice? In the interest of simplicity, let’s stick to the sneaker store. Customers who opt to have their walking patterns, soles, and weight measured can get advice on the footwear that best suits their needs, both in the physical store and online. Because the account of the sneaker store is linked to a fitness platform, the customer’s fitness level becomes a personal attribute.

The customer still controls the details they want to share. Privacy will remain an important topic in 2020, and the most successful companies will respect it.

Smartphone: a powerful “second screen”

Smartphones have become indispensable for most of us. We wake up with them and go to sleep with them. Mobile applications must work smoothly and be optimized for small screens, and should never feel like an impediment. An unpleasant mobile experience is unacceptable to consumers, and for good reason.

In 2020 the smartphone will be a very useful tool for retailers to further enrich customer experience in brick-and-mortar stores. Not only can customers get more product information by simply scanning its label, they can also find product reviews, information on related products, personalized advice, and perhaps even pricing history, using links with independent online comparison tools.

Supermarkets can also profit from smartphone apps. Not only can customers quickly create shopping lists at home, but in 2020 apps will also show the right picking order based on the layout of the store. It will also give personalized advice for additional products and availability information on alternative products in case one is unavailable.

Virtual reality offers a new home shopping experience

Virtual reality headsets will have a definitive presence in the living room in 2020. Consumers will use them for gaming as well as home shopping sessions, walking through virtual stores from the comfort of their couch. Retailers will enjoy the traditional advantages of brick-and-mortar stores in an online setting: the element of surprise, the ability to steer to impulse buying, and strategic product placement.

A digital setting also offers a unique feature that is not available for brick-and-mortar stores: The store’s layout and even the inventory can be fully customized to the preferences and habits of individual customers.

A new level of delivery

People who like to shop from the comfort of home still want to get their products as soon as possible. Today, web shops with even the tightest logistical operations need at least two days to close the delivery. In 2020, two days will be nothing special—by then we will have many more delivery options, and returning packages will be much easier than it is today.

A number of experiments with drones show potential, and perhaps drones will be able to pick up returns as well. This would require changes in law and regulations, however. Another exciting development: parcel pick-up at railway stations. By 2020, these parcel pickup stations will be much more widely available.

The customer at the center of the product lifecycle

Currently, the customer usually exists at the end of a product’s life cycle. However, by 2020, “crowdsourced design” will be an increasingly common approach to product life cycles. In this approach, the customer is actively involved in product design, variations, and choice. This enables producers to capitalize on customer creativity and wisdom, which results in products that meet specific target audience requirements. One example is Tesco, which actively involved  customers in the development of a new wine.

By 2020, customer and brand will be more closely aligned than ever before. Thanks to modern technology, customers will be able to give instant feedback, be involved in design, and choose different product variants, while at the same time the Internet of Things provides insight in actual use so companies can improve products more quickly and efficiently.

Of course, predicting the future is never a sure thing. But one thing is absolutely clear: Enterprises that know their customers best—and that map the customer adventure most effectively—will prevail.

Assess your current strength in key phases of the customer journey with a free Customer Experience Assessment. 

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Am I Alone In Thinking AI-Run Governments Could Be A Good Thing?

Glenn Sawyer

Put Skynet from the Terminator movies in the back of your mind for a minute, and stay with me on this one.

Certain political leaders remind us of their fragile humanity with increasing frequency these days. Prone to wild acts of emotion, and unable to resist the urge to push their personal agenda at the expense of the greater good, these leaders’ behavior is enough to make the concept of an artificial intelligence (AI)-controlled government sound utopian by comparison.

I’m not quite naïve enough to think we’re at the point where our human leaders could be replaced by an all-seeing, all-knowing, all-doing machine, but AI and machine learning are becoming ever more tantalizing in their potential to simplify, accelerate, and improve many aspects of society and our lives.

Governments are beginning to realize this. We’re already seeing small crumbs of evidence that they understand how AI can make public services more efficient and citizen-friendly. But these are very early days in discussing and figuring out how such technology could help us enforce laws, organize labor and welfare, etc., in ways most people would be comfortable with.

And if the Facebook AI story is anything to go by, we’re still pretty spooked by the idea of an intelligence that can “think,” communicate, and potentially make decisions using methods we might not always understand, so a future in which we’re willingly ruled by a digital overlord remains very distant.

What’s more likely – dare I say, inevitable – is that governments will find ways to take advantage of AI in smaller increments, and this will eventually compound to form a political system in which machines are doing most of the “thinking” work.

Unless you believe the singularity is possible, AI’s “thinking” will remain under the control of a far more streamlined government made up of regular, everyday humans. Our greatest hope is that the AI-run aspects of governance are powerful and transparent enough that those humans can’t get away with the deceit, selfishness, and emotion-based political decisions that plague us today.

That said, it would likely be a very different group of people, compared to today, running an AI government. If governments do come to rely heavily on technology, it could be a few technologists at the top of the tree – the ones who understand how it all works – who find themselves wielding immense power. With the likes of Mark Zuckerberg already accruing vast political influence, to use as they please, that’s a worrying prospect.

Thankfully, it won’t happen in the way some are fearing it might. Government decision making is so complex, with so many interlinked aspects, that no one person or small group of technological minds could comprehend and control it entirely. I also don’t believe people generally hold the Silicon Valley view that technology alone can solve everything. What I’m saying is, let’s embrace AI, safe in the knowledge that collectively we’ll be able to keep it and its programmers in check.

If we do, we’re opening a whole new world of possibilities in efficient, logical, and honest governance. It’s essential we don’t let the same thing happen with a tech-run government that we’re letting happen with the Internet – where power is consolidating into too few hands. That will take a combination of remembering the democratic principles that got us to this point and educating enough people to understand the technology overseeing us. I, for one, am optimistic we can get there. Please tell me I’m not alone.

I’m delivering the keynote at Our Digital Future Summit on September 14, which you can livestream. The summit is part of Our Digital Future film series presented by SAP at Toronto International Film Festival 2017. For more, watch a thought-provoking discussion about how technology is already transforming governments, work and the economy.

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Glenn Sawyer

About Glenn Sawyer

As an industry expert and Internet of Things thought leader, Glenn Sawyer is the National Director of IoT Digital Transformation for SAP Canada.

Diving Deep Into Digital Experiences

Kai Goerlich

 

Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.


What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”


Download the executive brief Diving Deep Into Digital Experiences.


Read the full article Swimming in the Immersive Digital Experience.

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Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

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Why Artificial Intelligence Is Not Really Artificial – It Is Very Tangible

Sven Denecken

The topic of artificial intelligence (AI) is buzzing through academic conferences, dominating business strategy sessions, and making waves in the public discussion. Every presentation I see includes it, even if it’s only used as a buzzword – its frequency is rivaling the use of “Uber for X” that’s been so popular in recent years.

While AI is a trending topic, it’s not mere buzz. It is already deeply ingrained into the strategy and design of our products – well beyond a mere shout-out in presentations. As we strive to optimize our products to better serve our customers and partners, it is worth taking AI seriously because of its unique role in product innovation.

AI will be inherently disruptive. Now that it has left the realm of academic projects and theoretical discussion – now that it is directly driving speed and hyper-automation in the business world – it is important to start with a review that de-mystifies the serious decisions facing business leaders and clarifies the value for users, customers, and partners. I’ll also share some experiences on how AI is contributing to solutions that run business today.

Let’s first start with the basics: the difference between AI, machine learning, and deep learning.

  • Artificial intelligence (AI) is broadly defined to include any simulation of human intelligence exhibited by machines. This is a growth area that is branching into multiple areas of research, development, and investment. Examples of AI include autonomous robotics, rule-based reasoning, natural language processing (NLP), knowledge representation techniques (knowledge graphs), and more.
  • Machine learning (ML) is a subfield of AI that aims to teach computers how to accomplish tasks using data inputs, but without explicit rule-based programming. In enterprise software, ML is currently the best method to approach the goals of AI.
  • Deep learning (DL) is a subfield of ML describing the application of (typically multilayer) artificial neural networks. Neural networks take inspiration from the human brain, with processors consisting of small neuron-like computing units connected in ways that resemble biological structures. These networks can learn complex, non-linear problems from input data. The layering of the networks allows cascaded learning and abstraction levels. This can accomplish tasks like: starting with line recognition, progressing to identifications of shapes, then objects, then full scene. In recent years, DL has led to breakthroughs in a series of AI tasks including speech, vision, and language processing.

AI applications for cloud ERP solutions

Industry 4.0 describes the trend of automation and data exchange in manufacturing. This comprises cyber-physical systems, the Internet of Things (IoT), cloud computing, and cognitive computing – everything that adds up to create a “smart factory.” There is a parallel in the world beyond manufacturing, where data- and service-based sectors need to capture and analyze more data quickly and act on that information for competitive advantage.

By serving as the digital core of the organization, enterprise resource planning (ERP) solutions play a key role in business transformation for companies adapting to the emerging reality of Industry 4.0. AI solutions powered by ML will be a broad, high-impact class of technologies that serve as a key pillar of more responsive business capabilities – both in manufacturing and all the sectors beyond. As such, ERP must embrace AI to deliver the vision for the future: smarter, more efficient, more flexible, more automated operations.

Enterprise applications powered by AI and ML will drive massive productivity gains via automation. This is not automation in the sense of repetitive, preprogrammed processes, but rather capabilities for software to handle administrative tasks and learn from user behavior to anticipate what every individual in the company might need next.

Cloud-based ERP is ideal for companies looking to accelerate transformation with AI and ML because it delivers innovation faster and more reliably than any onsite deployment. Users can take advantage of rapid iterations and optimize their processes around outcomes rather than upkeep.

Case in point: intelligent ERP applications need to include a digital assistant. This should be context-aware, designed to make business processes more efficient and automated. By providing information or suggestions based on the business context of the user and the situation, the digital assistant will allow every user to spend more time to concentrate on higher-value thinking instead of on repetitive tasks. Combined with built-in collaboration tools, this upgrade will speed reaction to changing conditions and create more time for innovation.

Imagine a system that, like a highly capable assistant, can greet you in the morning with a helpful insight: “Hello Sven, I have assessed your situation and the most recent data – here are the areas you should focus on first.” This approach to contextualized analysis of real-time data is far more effective than a hard-programmed workflow or dump of information that leaves you to sort through outdated information.

Personal assistants have been around in the consumer space for some time now, but it takes an ML-based approach to bring that experience, and all its benefits, to the enterprise. Based on the pace of change in ML, a cloud-based ERP can best deliver the latest innovations to users in a form that has immediate business applications.

An early application of ML in the enterprise will be intelligence derived from past patterns. The system will capture much richer detail of customer- and use-case-specific behavior, without the costs of manually defining hard rule sets. ML can apply predictive detection methods, which are trained to support specific business use cases. And unlike pre-programmed rules, ML updates regularly as strategies – not monthly or weekly – but by the day, hour, and minute.

How ML and AI are making cloud ERP increasingly more intelligent

Digital has disrupted the world and changed the way businesses operate, creating a new level of complexity and speed. To stay competitive, businesses must transform to achieve a new level of agility. At the same time, advances in consumer technology (Siri, Alexa, and Google Now in the personal assistant space, and countless mobile apps beyond that) have created a desire and need for intuitive user interfaces that anticipate the user’s needs. Building powerful tools that are easy to interact with will rely on ML and predictive analytics solutions – all of which are uniquely suited to cloud deployment.

The next wave of innovation in enterprise solutions will integrate IoT, ML, and AI into daily operations. The tools will operate on every type of device and will apply native-device capabilities, especially around natural language processing and natural language interfaces. Augment this interface with machine learning, and you’ll see a system that deeply understands users and supports them with incredible speed.

What are some use cases for this intelligent ERP?

Digital assistants already help users keep better notes and take intelligent screenshots. They also link notes to the apps users were working on when they were created. Intelligent screenshots allow users to navigate to the app where the screenshot was taken and apply the same filter parameters. They recognize business objects within the application context and allow you to add them to your collection of notes and screenshots. Users can chat right from the business application without entering a separate collaboration room. Because the digital assistants are powered by ML, they help you move faster the more you use them.

In the future, intelligent cloud ERP with ML will deliver value in many ways. To name just a few examples (just scratching the surface):

  1. Finance accruals. Finance teams use a highly manual and speculative process to determine bonus accruals. Applying ML to these calculations could instead generate a set of unbiased accrual figures, so finance teams have more time during closing periods for activities that require review and judgment.
  1. Project bidding. Companies rely heavily on personal experience when deciding to bid for commercial projects. ML would give sales and project teams access to decades-worth of projects from around the world at the touch of a button. This capability would help firms decide whether to bid, how much to bid, and how to plan projects for greatest profitability.
  1. Procurement negotiation. Procurement involves a wide range of information and continuous supplier communication. Because costs go directly to the bottom line, anything that improves efficiencies and reduces inventory will make a real difference. ML can mine historical data to predict contract lifecycles and forecast when a purchasing contract is expected so that you can renegotiate to suit actual needs, rather than basing decisions on a hunch.

What does the near future hold?

An intelligent ERP puts the customer at the center of the solution. It delivers flexible automation using AI, ML, IoT, and predictive analytics to drive digital transformation of the business. It delivers a better experience for end users by providing live information in context and learning what the user needs in every scenario. It eliminates decisions made on incomplete or outdated reports.

Digitization continues to disrupt the world and change the way businesses operate, creating a new level of complexity and speed that companies must navigate to stay competitive. Powering business innovation in the digital age will be possible by building and deploying the latest in AI-powered capabilities. We intend to stay deeply engaged with our most innovative partners, our trusted customers, and end users to achieve the promises of the digital age – and we will judge our success by the extent to which everyone who uses our system can drive innovation.

Learn how SAP is helping customers deploy new capabilities based on AI, ML, and IoT to deliver the latest technology seamlessly within their systems

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Sven Denecken

About Sven Denecken

Sven Denecken is Senior Vice President, Product Management and Co-Innovation of SAP S/4HANA, at SAP. His experience working with customers and partners for decades and networking with the SAP field organization and industry analysts allows him to bring client issues and challenges directly into the solution development process, ensuring that next-generation software solutions address customer requirements to focus on business outcome and help customers gain competitive advantage. Connect with Sven on Twitter @SDenecken or e-mail at sven.denecken@sap.com.