AI, Robotics, and the Future of Jobs

This report is the latest in a sustained effort throughout 2014 by the Pew Research Center's Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-Lee (The Web at 25).The report covers experts' views about advances in artificial intelligence (AI) and robotics, and their impact on jobs and employment

Identifying Characteristics for Success of Robotic Process Automations

In the pursuit of digital transformation, the Air Force creates digital airmen. Digital airmen are robotic process automations designed to eliminate the repetitive high-volume low-cognitive tasks that absorb so much of our Airmen\u27s time. The automation product results in more time to focus on tasks that machines cannot sufficiently perform data analytics and improving the Air Force\u27s informed decision-making. This research investigates the assessment of potential automation cases to ensure that we choose viable tasks for automation and applies multivariate analysis to determine which factors indicate successful projects. The data is insufficient to provide significant insights

IT-palvelun tietotöiden automatisointi: Koneoppimismalli ohjelmistorobotille

Aging population, legacy systems and pressure on cost savings are all growing problems for modern-day companies. One relief for these problems is to automate business processes and IT-service desk tasks with software robots. The aim of the automation is to reduce employees' growing workload so that they have time to work with the more valuable tasks. One of the most limitating factors of using software robots are that the automated processes must be strictly rule-based and the input data must be highly structured. In business there has been a lot of talk about using machine learning and other AI-techniques for achieving more generic solutions, but the actual results have not yet been publicly recognised. In this thesis it is intended to examine the suitability of machine learning for software robotics by automating company's internal IT-services. The goal is to build a working solution and find a machine learning platform for the company that provides software robotic solutions as a service. The end result is a viable automation solution which uses machine learning model for probability-based decision making. Based on this research it is possible say that there exist synergy benefits between the two technologies, as long as there is a suitable application for them.Ikääntyvä väestö, vanhat järjestelmät ja paine kustannussäästöille ovat nykypäivän yritysten kasvavia ongelmia. Yksi helpotus näihin ongelmiin on liiketoimintaprosessien ja IT-palveluiden automatisointi ohjelmistorobottien avulla. Automatisoinnin pyrkimyksenä on vähentää työntekijöiden kasvavaa työtaakkaa, jotta heillä jää enemmän aikaa liiketoiminnalle tärkeämpien tehtävien hoitoon. Yksi suurimmista ohjelmistorobotiikan käyttöä rajoittavista tekijöistä on se, että automatisoitavien prosessien tulee olla tarkasti sääntöihin perustuvia, sekä hyödynnettävän tiedon tarkkaan jäsenneltyä. Alalla on ollut paljon puhetta koneoppimisen ja tekoälyn hyödyntämisestä geneerisempien ratkaisujen saavuttamiseen, mutta tuloksia näiden projektien onnistumisesta ei ole kantautunut suuren yleisön tietoisuuteen. Tässä työssä on tarkoitus tutkia koneoppimisen soveltuvuutta ohjelmistorobotiikkaan automatisoimalla yrityksen sisäisiä IT-palveluja. Päämääränä on rakentaa toimiva prototyyppi ja löytää koneoppimismalleja hyödyntävä alusta, jota prosessien automatisointia tarjoava yritys voisi alkaa käyttämään osana ratkaisukokonaisuuttaan. Lopputuloksena saatiin toimiva ratkaisu, joka höydyntää koneoppimista todennäköisyyksiin perustuvassa päätöksenteossa. Tutkimuksen avulla voidaan sanoa, että kahden tekniikan välillä on synergiahyötyjä, kunhan niille löydetään sopiva käyttökohde

Remanufacturing a Robotic Arm

The PUMA 260 is a 1980s robot manufactured by RP Automation. The purpose of the project is to assess the feasibility of redesigning the manufacturing process and making recommendations to the company based on our findings. The goal is twofold: to apply new and different technology to the existing system in order to lower the cost for the end user to enter a new market for inexpensive robotics and provide recommendations to RP Automation. In our assessment of the product, we provided future students with a body of knowledge that will help them understand how decisions were made throughout the process. We have also built the foundation for a future project for prototyping the new robot

Organizational adoption of Robotic Process Automation: managing the performativity of hype

Robotic process automation (RPA) has recently been subject to colossal hype. Although hype and expectations around technological innovation have been researched at length, there is limited research into the impact of hype at a firm level from an adopter’s perspective. Through an inductive multi-case study of five organizations from the banking, financial services and insurance (BFSI) sector that have adopted RPA over the past five years, we attempted to answer the question: how does RPA technology hype reach the shores of organizations and what adoption behaviour and decision making does it drive? Findings point to the critical role of senior management as instigators of adoption and legitimation, which goes beyond the sponsorship role identified in extant theory. Results also demonstrate that RPA adoption is driven by a ‘hunt’ for use cases by interdisciplinary teams, which exposes long-standing operational problems while at the same time offers opportunities for organizational learning. We contribute to a theoretical understanding of the organizational performativity of hype and draw lessons for industry practitioners considering RPA and other hyped technologies for organizational adoption

Automated testing in robotic process automation projects

Robotic process automation (RPA) has received increasing attention in recent years. It enables task automation by software components, which interact with user interfaces in a similar way to that of humans. An RPA project life cycle is closely resembling a software project one. However, in certain contexts (e.g., business process outsourcing), a testing environment is not always available. Thus, deploying the robots in the production environment entails high risk. To mitigate it, an innovative approach to automatically generate a testing environment and a test suite for an RPA project is presented. The activities of the humans whose processes are to be robotized are monitored and a UI log is confirmed. On one side, the test environment is generated as a fake application, which mimics the real environment by leveraging the UI log information. The control flow of the application is governed by an invisible control layer that decides which image to show depending on the interface actions that it receives. On the other side, the test case checks whether the robot can reproduce the behaviour of the UI log. Promising results were obtained and a number of limitations were identified such that it may be applied in more realistic domains.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-R (POLOLAS)Servinform, S.A. P114-16/E0

Robotic process automation : impact and best practices in portuguese banks

Robotic Process automation is the technology that allows companies to configure software to emulate the interaction of an employee with digital systems. This software is also known as “robots” and they have the innate ability to perform tasks faster, more accurately and to work 24 hours a day. In banks, most support functions are located in back-offices and the tasks performed there are mainly repetitive tasks that follow a set of rules. These types of processes are a good candidate for RPA automation. The Portuguese banking sector is still in an early stage of digitalization. In general, banks are innovating in their front-offices, however, apart from a few players in the industry, their back-offices still heavily rely on paper and currently have a low level of automation. By conducting interviews with topical experts that have been present in RPA implementations in banks, this study determined the impacts of the technology in Portuguese bank’s back-offices and the best practices that should be followed in order for implementations to be successful in future endeavors. The results show that RPA can have a significant impact in the industry, namely a reduction in operational costs. Furthermore, always considering scalability for future RPA projects and providing functional and technical documentation are among the resulting best practices.Automação Robótica de Processos é a tecnologia que permite às empresas configurar software com o objetivo de reproduzir a interação de um funcionário com sistemas digitais. Este tipo de software é tambem conhecido por “robots”. Estes têm a particular capacidade de executar tarefas em menos tempo, com maior precisão e de trabalhar 24 horas por dia. Nos bancos, a maioria das funções de suporte encontram-se no back-office. As tarefas relacionadas com estas funções são muitas vezes de natureza repetitiva e seguem regras estabelecidas. Este tipo de processos são bons candidatos para automação através de RPA. O sector bancário em Portugal ainda se encontra numa fase inicial da sua digitalização. De uma forma geral, os bancos estão a inovar serviços no seu front-office, mas, excluindo alguns bancos na indústria, os back-offices ainda funcionam muito à base de papel e têm um nível de automação baixo. Através de entrevistas com especialistas em RPA que já integraram equipas em implementações da tecnologia em bancos, este estudo determinou os impactos da tecnologia nos back-offices dos bancos em Portugal e as melhores práticas a seguir para garantir o sucesso de futuras implementações. Os resultados mostram que RPA pode ter um impacto significativo na indústria, nomeadamente a nível da redução de custos operacionais. Adicionalmente, ter sempre em consideração a hipótese de escalabilidade e entregar documentação técnica e funcional foram algumas das práticas reveladas no estudo

Organizational adoption of Robotic Process Automation: managing the performativity of hype

Robotic process automation (RPA) has recently been subject to colossal hype. Although hype and expectations around technological innovation have been researched at length, there is limited research into the impact of hype at a firm level from an adopter’s perspective. Through an inductive multi-case study of five organizations from the banking, financial services and insurance (BFSI) sector that have adopted RPA over the past five years, we attempted to answer the question: how does RPA technology hype reach the shores of organizations and what adoption behaviour and decision making does it drive? Findings point to the critical role of senior management as instigators of adoption and legitimation, which goes beyond the sponsorship role identified in extant theory. Results also demonstrate that RPA adoption is driven by a ‘hunt’ for use cases by interdisciplinary teams, which exposes long-standing operational problems while at the same time offers opportunities for organizational learning. We contribute to a theoretical understanding of the organizational performativity of hype and draw lessons for industry practitioners considering RPA and other hyped technologies for organizational adoption

The Intersection of Robotic Process Automation and Lean Six Sigma Applied to Unstructured Data

While new Artificial Intelligence (AI) technologies gain traction in the workplace, there seems to be more buzz around these newer advances, including Robotic Process Automation (RPA), than more established process improvement techniques such as Lean Six Sigma. This praxis research uses Lean Six Sigma as a framework for effectively deploying these emerging technologies, a challenge for 86% of companies (Ernst & Young, 2021). This research is applied to one of the legal industry’s most resource intensive processes – eDiscovery in the environment of a Big 4 accounting firm that provides services to corporations and legal professionals alike. Electronic discovery (also known as e-discovery, ediscovery, eDiscovery, or e-Discovery) is the process of identifying, collecting, producing, and presenting electronically stored information (ESI) in response to a request for production in a law suit or investigation. ESI can include any type of electronically stored file and commonly includes emails, documents, databases, media files, social media, and web sites. The lifecycle of eDiscovery has been defined by the Electronic Discovery Reference Model (EDRM) as having the following phases: Information Governance, Identification, Preservation. Collection, Processing, Review, Analysis, Production, and Presentation. To move through the phases of the EDRM historically requires a significant investment in time, technology, and human resources. This project had its origins as an automation effort driven by the technical advances in RPA solutions. However, RPA became a tool for to enable the program – not the solution itself. The DMAIC framework (Define, Measure, Analyze, Improve, Control) of Lean Six Sigma laid the foundation for a more wholistic analysis of the EDRM including the identification of processes that required revision prior to their automation. The Define phase identified the resource intensive strain moving through the EDRM causes corporations, vendors, and litigators. Through the measure phase, an opportunity to provide better results faster, and therefore cheaper was quickly identified. Through the analysis, several unnecessary handoffs, extraneous processes, and general bottlenecks in the process were refined. Through the Improve phase, automation played a significant part in realizing the efficiencies identified in the analyze phase. Finally, the controls phase not only put these improved processes into place but also quantified the value of ensuring these procedures were thoroughly deployed. This research is organized using the DMAIC framework to articulate the process for completing the research, the gains and efficiencies made throughout the analysis, and to measure the impact and success of the overall program enhancements. The impact of this project is measurable not only in the reduction of defects as defined by Lean Six Sigma, but also a significant improvement in time required to complete these processes. Even more satisfying, these efficiencies have a measurable, financial impact that has currently been realized north of $5 million USD in one year alone. This impact led to the solution becoming a finalist for an industry award where it was presented to over 3,000 industry professionals. Furthermore, the reduction and automation of manual, tedious tasks have also led to more enriching work for resources