Accelirate Inc. has created a guide for getting started with artificial intelligence. From enterprise use cases, to the technology involved, and even how to build a world class RPA and AI team, everyone can benefit from this all-inclusive guide to AI.
A Guide to Robotic Process Automation & Cognitive TechnologiesAccelirate Inc.
Accelirate Inc. is the largest Pure Play RPA and AI Services Company in the United States. We have written a Guide to RPA and Cognitive Technologies for both newcomers to the industry and those interested in learning more about RPA and learning about some of their business use cases.
Capgemini Robotic Process Automation special edition summer 2017UiPath
The rise of automation is bringing a plethora of opportunities to both organizations and individuals. Capgemini is at the forefront of this revolution – our Automation Drive is a unified, open and dynamic suite of automation tools and services that help our clients embark on a new journey of reimagining the way they do business. A number of experts from Capgemini's Business Services have shared their insights on various aspects of automation, and we hope that this collection of articles will help you navigate your business through the uncharted waters of this new age towards a productive automation environment.
Robotic Process Automation for Financial ServicesAppian
Robotic Process Automation (RPA) is emerging as a cost-effective technique to get work done in Financial Services Institutions (FSIs).
With the advent of RPA, executives should reconsider how they manage organizational business processes and support information technology.
Learn more about robotic process automation and the transformation continuum in this executive perspective: http://ap.pn/2jYWrMG
Digital Transformation And The Role of Robotic Process Automation (RPA)ARJUN S MEDA
Integrating digital technology within all the business areas to transform the operating model and deliver better value to customer is the essence of Digital Transformation. Empowering technology at the core to increase operational efficiency, improve employee performance and customer experience are a few key areas addressed by Digital Transformation.
It requires a cultural shift in the way the organizations operate and a strong leadership to identify the areas to be transformed and to explore new opportunities.
Understanding how RPA can enable digital transformation and upgrade the workplaces of the future is a key skill for C-suite executives.
Robotic process automation (RPA) is the use of software bots that can automate repetitive rule based tasks and reduce the manual intervention, thereby increasing the operational efficiency and reduce errors. It helps employees to focus on areas in which they can add value rather than spending efforts on repetitive and mundane tasks.
Key points of discussion:
What is Digital Transformation and why is it required
Framework for Digital Transformation
Elements of Digital Transformation
What is RPA
Role of RPA in Digital Transformation
Benefits of RPA
Design Centric RPA Approach: The Jidoka Examplevmic
Authored by "RPA Evangelist" Deepak Sharma, this paper is focused on taking a deep dive into exploring the Design Centric Approach to Robotic Process Automation, and bringing out insights why it is important for complex strategic RPA implementations, what are the key aspects of this approach, and how to implement this approach by taking the example of Jidoka RPA product.
Robotic Process Automation End-to-End Implementation RoadmapChazey Partners
RPA is a software solution, commonly referred to as “bots”, which mimic or automate tasks normally performed by humans interacting with data between systems. In common definitions RPA essentially comes down to removing human beings from operations that are repetitive evaluations requiring rules based decision criteria. Whereas automation addresses high volume repetitive tasks, RPA is the next step to include decision making under a controlled set of parameters. Like with other transformations, business leaders should adopt a structured framework with clear, tangible benefits and correctly defined expectations before embarking on an RPA journey. This Robotic Process Automation handbook offers you a clear, structured framework with which to launch and implement your RPA program. It ensures the technology is integrated well within existing systems and business operations and empowers your business to be future ready with dynamic adjustment of processes along your automation journey.
A Guide to Robotic Process Automation & Cognitive TechnologiesAccelirate Inc.
Accelirate Inc. is the largest Pure Play RPA and AI Services Company in the United States. We have written a Guide to RPA and Cognitive Technologies for both newcomers to the industry and those interested in learning more about RPA and learning about some of their business use cases.
Capgemini Robotic Process Automation special edition summer 2017UiPath
The rise of automation is bringing a plethora of opportunities to both organizations and individuals. Capgemini is at the forefront of this revolution – our Automation Drive is a unified, open and dynamic suite of automation tools and services that help our clients embark on a new journey of reimagining the way they do business. A number of experts from Capgemini's Business Services have shared their insights on various aspects of automation, and we hope that this collection of articles will help you navigate your business through the uncharted waters of this new age towards a productive automation environment.
Robotic Process Automation for Financial ServicesAppian
Robotic Process Automation (RPA) is emerging as a cost-effective technique to get work done in Financial Services Institutions (FSIs).
With the advent of RPA, executives should reconsider how they manage organizational business processes and support information technology.
Learn more about robotic process automation and the transformation continuum in this executive perspective: http://ap.pn/2jYWrMG
Digital Transformation And The Role of Robotic Process Automation (RPA)ARJUN S MEDA
Integrating digital technology within all the business areas to transform the operating model and deliver better value to customer is the essence of Digital Transformation. Empowering technology at the core to increase operational efficiency, improve employee performance and customer experience are a few key areas addressed by Digital Transformation.
It requires a cultural shift in the way the organizations operate and a strong leadership to identify the areas to be transformed and to explore new opportunities.
Understanding how RPA can enable digital transformation and upgrade the workplaces of the future is a key skill for C-suite executives.
Robotic process automation (RPA) is the use of software bots that can automate repetitive rule based tasks and reduce the manual intervention, thereby increasing the operational efficiency and reduce errors. It helps employees to focus on areas in which they can add value rather than spending efforts on repetitive and mundane tasks.
Key points of discussion:
What is Digital Transformation and why is it required
Framework for Digital Transformation
Elements of Digital Transformation
What is RPA
Role of RPA in Digital Transformation
Benefits of RPA
Design Centric RPA Approach: The Jidoka Examplevmic
Authored by "RPA Evangelist" Deepak Sharma, this paper is focused on taking a deep dive into exploring the Design Centric Approach to Robotic Process Automation, and bringing out insights why it is important for complex strategic RPA implementations, what are the key aspects of this approach, and how to implement this approach by taking the example of Jidoka RPA product.
Robotic Process Automation End-to-End Implementation RoadmapChazey Partners
RPA is a software solution, commonly referred to as “bots”, which mimic or automate tasks normally performed by humans interacting with data between systems. In common definitions RPA essentially comes down to removing human beings from operations that are repetitive evaluations requiring rules based decision criteria. Whereas automation addresses high volume repetitive tasks, RPA is the next step to include decision making under a controlled set of parameters. Like with other transformations, business leaders should adopt a structured framework with clear, tangible benefits and correctly defined expectations before embarking on an RPA journey. This Robotic Process Automation handbook offers you a clear, structured framework with which to launch and implement your RPA program. It ensures the technology is integrated well within existing systems and business operations and empowers your business to be future ready with dynamic adjustment of processes along your automation journey.
Make the office productivity step change with encanvas rpaNewton Day Uploads
In this presentation Mason Alexander summarises the things every business executive needs to know about the impact and opportunity of Robotic Process Automation (RPA). It's a fabuluous start-point if you know nothing of the topic.
This is an article published by Mason Alexander of NDMC Consulting that provides a simple explanation of what Robotic Proces Automation (RPA) is and why it's expected to be a game changer in knowledge worker productivity.
Automatonophobia is the fear of anything that falsely
represents a sentient being. And when it comes to process
automation, many organizations have this fear about getting
started.
Indeed, robots are fast advancing, enabling you to improve
the accuracy, consistency, speed, and delivery cost of any
activity that requires human labor. They do not need sleep,
overtime salary, or breaks, and they can do everything
from opening an Internet browser and executing a program
to validating data, answering questions, and supporting
decisions. But in a dynamic and somewhat ambiguous
technical landscape, with a lack of established marketplace
examples, some companies have spent the past 12 months
just talking about robotic automation, while early movers are
already saving millions of dollars.
In the webinar, Eric Liebross, Auxis Senior VP of Back Office Optimization, provides insights into some of the truths and myths of Robotic Process Automation (RPA), some of the significant benefits it can provide, demonstrated a real-world live demo, and more.
We hope you find the highlighted information in this presentation useful for your RPA initiatives.
View the live demo here: https://www.auxis.com/rpa-demo
http://aitomation.com The Complete Robotic Process Automation guide. It explains what it is. What are the advantages of using it. Why it is important to use in business nowadays and all other valuable resources.
Using agile automation methodology to automate business processesUiPath
Daniel Dines, CEO of UiPath, talks to Innovation Nation
about how UiPath’s Robotic Process Automation (RPA)
technology has become an integral part of Capgemini’s
Business Services solutions, as well as dispelling some
myths about the hype around robotics and automation.
Robotics Process Automation (RPA) - Hands on knowledgeJulen Mohanty
Presented this on in various forums due to agreements with organizers and sponsors, i had to block few slides. In case you absolutely need those, pls contact me,.
Chazey europe ritz hotel london-shared services roundtable-rpaDaniel Lawrence
Beginners guide to Intelligent Process Automation and RPA, with hints, tips, best practices and case studies to help navigate RPA and start your automation roadmap and journey on the right footing
Intelligent Automation: Exploring Enterprise Opportunities for Systems that D...Cognizant
To compete in an era of globalization and fast-moving business change, organizations need to apply smart technologies, which can reduce costs, increase scalability, improve accuracy, boost speed and make better use of human efforts.
How to get started and scale in Robotic Process AutomationConduent
New approaches to automation are offering vast opportunities to organizations to work better and smarter, freeing up employees for more value-added tasks. Learn how in this business process services slideshare from Conduent.
Take this opportunity to learn more about how Robotic Process Automation (RPA) play a role with your ERP. Learn more about the use of RPA for ERP-driven processes and how they can help organizations like yours make routine, time-consuming tasks less expensive and less labor-intensive.
Presented by Lewis Hopkins, Senior Technology Specialist, Smart ERP Solutions, discusses current automation trends and challenges along with providing insight on how the automation technology can have a significant impact on your organization. This webinar will included a demonstration of how bots can assist organizations with better workflows and enhanced customer service.
Secure and Scalable PeopleSoft--Sensitive and High Volume Deployment Case Stu...Smart ERP Solutions, Inc.
Particularly for HCM deployments, many PeopleSoft customers require a very secure and/or highly scalable architecture due to the nature of their business. This session provides a number of case studies of successful production deployments involving alternate architectures involving multiple database instances to address extreme security requirements and high volumes of transactions.
Make the office productivity step change with encanvas rpaNewton Day Uploads
In this presentation Mason Alexander summarises the things every business executive needs to know about the impact and opportunity of Robotic Process Automation (RPA). It's a fabuluous start-point if you know nothing of the topic.
This is an article published by Mason Alexander of NDMC Consulting that provides a simple explanation of what Robotic Proces Automation (RPA) is and why it's expected to be a game changer in knowledge worker productivity.
Automatonophobia is the fear of anything that falsely
represents a sentient being. And when it comes to process
automation, many organizations have this fear about getting
started.
Indeed, robots are fast advancing, enabling you to improve
the accuracy, consistency, speed, and delivery cost of any
activity that requires human labor. They do not need sleep,
overtime salary, or breaks, and they can do everything
from opening an Internet browser and executing a program
to validating data, answering questions, and supporting
decisions. But in a dynamic and somewhat ambiguous
technical landscape, with a lack of established marketplace
examples, some companies have spent the past 12 months
just talking about robotic automation, while early movers are
already saving millions of dollars.
In the webinar, Eric Liebross, Auxis Senior VP of Back Office Optimization, provides insights into some of the truths and myths of Robotic Process Automation (RPA), some of the significant benefits it can provide, demonstrated a real-world live demo, and more.
We hope you find the highlighted information in this presentation useful for your RPA initiatives.
View the live demo here: https://www.auxis.com/rpa-demo
http://aitomation.com The Complete Robotic Process Automation guide. It explains what it is. What are the advantages of using it. Why it is important to use in business nowadays and all other valuable resources.
Using agile automation methodology to automate business processesUiPath
Daniel Dines, CEO of UiPath, talks to Innovation Nation
about how UiPath’s Robotic Process Automation (RPA)
technology has become an integral part of Capgemini’s
Business Services solutions, as well as dispelling some
myths about the hype around robotics and automation.
Robotics Process Automation (RPA) - Hands on knowledgeJulen Mohanty
Presented this on in various forums due to agreements with organizers and sponsors, i had to block few slides. In case you absolutely need those, pls contact me,.
Chazey europe ritz hotel london-shared services roundtable-rpaDaniel Lawrence
Beginners guide to Intelligent Process Automation and RPA, with hints, tips, best practices and case studies to help navigate RPA and start your automation roadmap and journey on the right footing
Intelligent Automation: Exploring Enterprise Opportunities for Systems that D...Cognizant
To compete in an era of globalization and fast-moving business change, organizations need to apply smart technologies, which can reduce costs, increase scalability, improve accuracy, boost speed and make better use of human efforts.
How to get started and scale in Robotic Process AutomationConduent
New approaches to automation are offering vast opportunities to organizations to work better and smarter, freeing up employees for more value-added tasks. Learn how in this business process services slideshare from Conduent.
Take this opportunity to learn more about how Robotic Process Automation (RPA) play a role with your ERP. Learn more about the use of RPA for ERP-driven processes and how they can help organizations like yours make routine, time-consuming tasks less expensive and less labor-intensive.
Presented by Lewis Hopkins, Senior Technology Specialist, Smart ERP Solutions, discusses current automation trends and challenges along with providing insight on how the automation technology can have a significant impact on your organization. This webinar will included a demonstration of how bots can assist organizations with better workflows and enhanced customer service.
Secure and Scalable PeopleSoft--Sensitive and High Volume Deployment Case Stu...Smart ERP Solutions, Inc.
Particularly for HCM deployments, many PeopleSoft customers require a very secure and/or highly scalable architecture due to the nature of their business. This session provides a number of case studies of successful production deployments involving alternate architectures involving multiple database instances to address extreme security requirements and high volumes of transactions.
A Guide on How AI Contributes to Businesses in Today’s Era to Watch in 2023.Techugo
Artificial Intelligence and Machine Learning have become the main focus of the scene. Artificial intelligence can be used for a wide variety of uses in business, including streamlining processes and aggregating the performance of companies. Researchers are still determining what AI will mean for businesses shortly. AI is predicted to shift technological advancement away from the traditional two-dimensional screen and towards the three-dimensional physical space surrounding the person.
Although the acceptance by society in general for AI does not mean anything new. The idea itself isn’t. Artificial intelligence is a broad field of business application. Indeed, most of us interact with AI in some way or another. Artificial Intelligence is changing all aspects of business across every industry. To know more, visit the post.
In today's tech-driven world, the integration of artificial intelligence (AI) into applications has become increasingly prevalent. From personalized recommendations to intelligent chatbots, AI enhances user experiences and optimizes processes. However, building an AI app can seem daunting to those unfamiliar with the process. Fear not! This guide aims to demystify the journey, offering step-by-step insights into how to build an AI app from scratch.
20 Useful Applications of AI Machine Learning in Your Business ProcessesKashish Trivedi
A 2017 study from Pew Research found that more than 70% of the U.S. is scared that robots are going to take over our lives. And, while we can’t perfectly predict the emergence of a Skynet singularity, we can say with some certainty that technology is set to take over the repetitive, dehumanizing elements of our jobs instead of putting us out of work. Artificial intelligence (AI) is a strategic priority for 84% of businesses, and in some cases has been used to improve sales team efficiency by over 50%. Even I’ve used AI in the past to generate hundreds of relevant hashtags for social media posts at the click of a button. It was once the stuff of utopian science fiction and huge enterprises, but now practically anyone can take advantage. For this post, we will dive into 20 different applications of AI in the real world.
Even though RPA is only now starting to mature, adding Artificial Intelligence to it will allow the creation of a true digital workforce that will automate activities typically characteristic to knowledge workers. An application such as Intelligent Process Automationconverges AI, RPA and other cognitive tools to enhance productivity. The quality of work will provide service continuity and availability and will reduce costs. AI will be responsible for the “thinking” component of the digital workforce. It will create the new rules for machine learning and will perform supervised learning, reinforcement learning, and unsupervised learning. AI will trigger RPA, which will be handle workflows and APIs, computer command scripts and mechanical robotics.
20 Useful Applications of AI Machine Learning in Your Business ProcessesKashish Trivedi
The fear of robots taking over our lives has been a prevalent concern, with over 70% of the U.S. population expressing apprehension, as highlighted by a 2017 Pew Research study. However, while the emergence of a Skynet-like scenario remains uncertain, it's evident that technology, particularly artificial intelligence (AI), is poised to revolutionize various aspects of our daily tasks, freeing us from repetitive and dehumanizing job elements rather than rendering us obsolete. With AI being a strategic priority for 84% of businesses, its implementation has shown remarkable efficiency enhancements, such as boosting sales team productivity by over 50%. The accessibility of AI tools has expanded significantly, enabling practically anyone to leverage its benefits. In this discourse, we'll explore 20 diverse real-world applications of AI, ranging from healthcare and finance to entertainment and government, illustrating its pervasive impact on modern society.
Machine Learning Assignment: How JD utilizes Artificial Intelligence?Total Assignment Help
In this Machine Learning Assignment, a detailed analysis is being provided about the latest Machine Learning that is being used by JD, an online retail company.
Building an AI App: A Comprehensive Guide for BeginnersChristopherTHyatt
"Discover the steps to create your own AI app: Choose a framework, define your app's purpose, collect and prepare data, train the model, integrate a user-friendly interface, and deploy successfully."
From Alexa and Siri to factory robots and financial chatbots, intelligent systems are reshaping industries. But the biggest changes are still to come, giving companies time to create winning AI strategies
The power and potential of artificial intelligence cannot be overstated. It has transformed how we interact with technology, from introducing us to robots that can perform tasks with precision to bringing us to the brink of an era of self-driving vehicles and rockets. And this is just the beginning. With a staggering 270% growth in business adoption in the past four years, it has been clear that AI is not just a tool for solving mathematical problems but a transformative force that will shape the future of our society and economy.
Artificial Intelligence (AI) has become an increasingly common presence in our lives, from robots that can perform tasks with precision to autonomous cars that are changing how we travel. It has become an essential part of everything, from large-scale manufacturing units to the small screens of our smartwatches. Today, companies of all sizes and industries are turning to AI to improve customer satisfaction and boost sales. AI is the next big thing, making its way into the inner workings of Fortune 500 companies to help them automate their business processes. Investing in AI can be beneficial for businesses looking to stay competitive in a fast-paced business world.
Seven things CIOs and software buyers should know about artificial intelligenceAndy Mura
Here are seven things I think everybody should know before making any decisions regarding AI applications of any kind.
by Andy Mura
ExB Group
More about AI and Intelligent Document Processing - https://exb.de/intelligent-document-processing/
RPA and AI impact on Banking - 4th Annual Back Office Operations Forum, ViennaUiPath
There is no doubt the universe of artificial intelligence extends far beyond the world of robotic process automation. AI is actually an umbrella covering a broad set of methods, algorithms and technologies that make software ‘smart’. Machine learning, computer vision, natural language processing, robotics and related topics are all part of AI. They collectively form a subset of AI broadly termed ‘cognitive technologies’.
Machine learning is a term thrown around in technology circles with an ever-increasing intensity. Major
technology companies have attached themselves to
this buzzword to receive capital investments, and every
major technology company is pushing its even shinier
parentartificial intelligence (AI).
How Can Businesses Adopt AI Technology to Achieve Their GoalsKavika Roy
https://www.datatobiz.com/blog/businesses-adopt-ai-technology/
Artificial intelligence is a dynamic force that keeps the industry moving forward to conquer more technologies. From manufacturing to hospitality to retail and aerospace, AI is being adopted by several organizations across all industries. The global AI market is worth $327.5 billion in 2021.
However, businesses are still in varying stages of adopting AI in their enterprises. While the top companies have added AI technology as an integral part of their systems, SMEs still use AI to develop pilot projects for certain departments like sales, marketing, etc.
leewayhertz.com-How to build an AI app.pdfrobertsamuel23
The power and potential of artificial intelligence cannot be overstated. It has transformed
how we interact with technology, from introducing us to robots that can perform tasks
with precision to bringing us to the brink of an era of self-driving vehicles and rockets
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
2. Table of Contents
Introduction to Artificial Intelligence……………………………………………………3
Machine Learning Use Cases……………………………………………………………….8
Where and How do I use AI?...................................................................10
AI Training (Packaged Machine Learning)………………………….13
AI Implementation……………………………………………………………………………..15
Enterprise Use Cases for AI………………………………………………………………..17
Chat Bots………………………………………………………………………….19
Natural Language Processing……………………………………………………………..20
Analytics & Prediction………………………………………………………………………..22
Robotic Process Automation………………………………………………………………24
AI Consumption Model and AI Toolsets………………………………………........26
Building World Class AI and RPA Teams………………………………………………29
Data Scientists………………………………………………………………....31
Machine Learning Engineers…………………………………………….33
RPA Architects and Engineers………………………………………….……...34
Developers……………………………………………………………………….35
Business Analysts & Business Intelligence Developers……..36
Managers, Project Managers, and Executives…………………..37
Conclusion…………………………………………………………………………………………38 2
4. Introduction to Artificial Intelligence
4
Not a day goes by that you don’t hear of the dire predictions about Artificial Intelligence taking most of the human jobs
away. When you look around and hear about self-driving cars, Alexa, Siri, etc., it feels like humans may not have much to do
in a few years. The reality however is not that bleak…
AI can be taught to drive cars, but the same AI can’t be used to clean the tables. AI can beat humans on the “Go” board
game (AlphaGo) but the same AI program does not know how to play chess. So, today’s AI and Machine Learning can
perform incredibly well at tasks that we train the computers on, but without the proper “labeling” or training of the
algorithms, it’s still a garbage in and garbage out scenario. Today’s AI technology is fundamentally great at processing huge
amounts of data and can use supervised, semi-supervised, and unsupervised AI techniques to solve a narrow set of
problems that its trained on.
The term “Narrow AI” describes the state of AI Technology today as compared to human-like “True AI” which still may be a
few decades away.
So why use AI?
5. Introduction to Artificial Intelligence
5
Businesses can use AI to solve a lot of prediction problems using their own internal data, as well as, combine their data
with publicly available external datasets. For example, if a financial institution is trying to predict sales for the next few
quarters, the accuracy of the prediction may be much better if they use not only their own existing datasets but also utilize
macro-economic data, such as interest rates, to better align their sales forecast with broader market factors. So, Business
Intelligence (BI) is probably one of the first places where AI and Machine Learning technologies can have a huge impact as
it allows BI groups to go beyond their traditional retrospective and predictive analytics and add a prescriptive element to
their analytics.
Business Process Automation is another area where there are tremendous applications for AI. For example, many
businesses have large document management and OCR system deployments but they still have a lot of manual Business
Processes around such implementations. AI and ML can potentially lead businesses towards autonomous execution of such
processes using AI technologies like Natural Language Processing, Robotic Process Automation, etc.
Some organizations have large deployments around Business Process Management (BPM) systems which use Workflow and
rules-based software to “assemble” business applications. AI and RPA technologies can complement such systems by
enabling significant additional end point automations by utilizing UI based surface automation (RPA) as well as AI
algorithms for decision making beyond the scope of rules engines.
6. Introduction to Artificial Intelligence
6
Marketing can use AI to create better customer engagement through the company’s existing customer facing channels such
as their Websites, Mobile Apps, and so on. For example, AI technologies such as sentiment analysis can help in real time
social brand management. Chatbots and Customer Service Rep Assistive Bots can help to create better overall customer
engagement for customers. The list of business use cases goes on and on…
In summary, AI is NOT magic (at least for now). But the ability of machines to learn and program themselves, even within
the bound of “Narrow AI”, is an incredibly powerful evolution of computing which has been termed as the 4th Industrial
Revolution and rightfully so. In this white paper, we will go into a bit more detail on the who, what, when, where, and why
questions around Artificial Intelligence. The technical definition of AI can be found all over the Internet; it is essentially
defined as a technology that is designed for computers to mimic intelligent human behavior. Computers were originally
designed to solve data processing problems where humans were just not as fast or efficient and could not handle large
amounts of data. Traditional computing has come a long way since the early days but still primarily solves the data
processing, workflow, and rules problems.
7. Introduction to Artificial Intelligence
7
On the other hand, AI takes datasets as the input, processes the data using Machine Learning Algorithms and Models, then
outputs patterns found in the input datasets. The process can run cyclically as the AI models are fed more data they
become better at detecting patterns. Hence the AI systems are fundamentally designed to learn through feedback loops
and additional datasets. The algorithms can be supervised, unsupervised and semi-supervised. Supervised algorithms use
both sets of input and output data to predict an outcome (for e.g. estimating the value of a house by using past price
history of similar homes). Unsupervised algorithms, on the other hand, use only input data to detect patterns in the data
(for e.g. if a classroom has male and female students and the only data provided is the weight and or physical attributes of
students, the algorithm can classify the gender of the student).
There are other types of Algorithms such as reinforcement learning and you can find more information out there on the
internet but our point here is to differentiate between AI and traditional computing. The image below outlines the types of
problems that can be solved by these algorithms. This of course by no means is reflective of all AI algorithms as the field of
AI is very wide.
11. Where and How do I use AI?
11
AI, at a high level, has 3 main branches that are relevant for business (there may be others)
1. Machine Learning
2. Natural Language Processing
3. Robotics
These are very wide topics and they are relevant to business; but where do we apply them? Finance, HR, Operations, IT, the
Executive Branch? The answer is simple; all of the above. Next question, how do we apply them?
Well, most businesses fundamentally have processes, Manufacturing Processes and/or Business Processes, and they also
have Managers and Executives who make the management decisions. More and more Executives are making these
decisions based on data that is generated by the business and the operations. So, at a high level, AI and its branches can be
used for the following:
1. Prediction & Forecasting Problems
2. Process Automation Problems
12. Where and How do I use AI?
12
Prediction problems are usually addressed by advanced business intelligence and analytics groups within enterprises. Machine
Learning and Data Science expands the horizons of current BI systems by bringing in the ability to analyze vast amounts of external
and internal structured and unstructured data and by applying the “learning” concept that is inherent to Machine Learning. In a
nut shell, by applying Machine Learning and Data Science you can probably increase the accuracy of the prediction and forecasts
that you are able achieve today using existing BI technologies. This sounds simple but this is a huge competitive advantage to
many businesses. For example, loss prediction and mitigation in Insurance and fraud prediction and prevention in Banking. As you
can imagine, this list goes on and on, however, the skill sets required to solve such prediction problems is highly technical.
Process Automation problems are usually addressed by all sorts of enterprise software. ERP, CRM, BPM, etc. Enterprise Software
today is pretty good at Workflow and Rules based process problems but is still reliant on humans to make inputs into the systems
and interpret the output of the systems. This is where manual Business Processes start to pile up. The field of Robotics (Robotic
Process Automation) is one such solution to these problems, but RPA inherently can only “automate” tasks in between various
systems without any “judgement”. In other words, RPA, to a certain extent, is “dumb” automation.
This is where the evolution of “smart” process automation comes in, using technologies such as NLP Data Extraction, Text
Analytics, Sentiment Analysis, etc. If you are already evaluating such technologies, it’s important that you separate out the actual
AI implementation problems from AI training problems because the skill sets required for these problems are different. AI Training
requires a much lower level of skills as compared to AI implementation. AI Training is essentially the “labeling” of data to describe
the data.
Let’s clarify the AI implementation and training problems further.
13. AITraining (Packaged Machine Learning)
13
Many AI enabled software platforms already solve some of the common business problems, however, almost all of them
require training for specific business use cases. These use cases require subject matter expert Business and Technical
Analysts to train these systems, however, they do not necessarily require Data Scientists or Machine Learning Engineers.
Consider a few examples below:
OCR is one such area where there are plenty of existing OCR platforms that focus on solving data extraction problems using
Computer Vision and Machine Learning. Most of this software allows the users to “teach” their document structures to the
system. So, this job does not require highly technical skill sets. However, if the objective of the process automation is not
just to extract name/value data but to also contextually classify blob data (legal contracts, etc.) then you either need
additional packaged text analytics software or Machine Learning engineers who are experts in text analytics. The skill sets
are dramatically different for data extraction vs. text extraction and analytics.
14. AITraining (Packaged Machine Learning)
14
There is plenty of Chatbot software out there that uses some sort of an AI Engine from either IBM, Microsoft, Google or
Amazon to figure out user intent from a chat question and then match that to an answer. Once again, in most cases this is a
training problem where the Chatbot needs to be “trained” on a set of questions and configured as per the business’s
requirements. There are plenty of Chatbot software and platforms (ChatFuel, Microsoft Bot Framework, etc.) which
essentially “package” the AI required to develop the Chatbots. Therefore, the training of Chatbots does not require
technical skills but if you are embedding Chatbots within your applications you need Developers who are familiar with the
APIs of these Chatbot platforms and these developers do not necessarily have to be Machine Learning Experts.
Splunk (www.splunk.com) is used widely in the Enterprise to analyze infrastructure and software log data which is used for
various purposes including investigating infrastructure issues, etc. Splunk uses Data Science and Machine Learning
techniques to analyze the information but the Technical and Business Analysts do not need to know the underlying details
of algorithms utilized. This is also referred to as “packaged machine learning”.
16. AI Implementation
This is where you are trying to solve a problem which is not
already solved by an existing off the shelf software. For
example, you want to create a system which automatically
analyzes the chat or blog content on your website. This
problem requires an in-depth problem definition, analyzing
existing datasets, algorithm selection, and implementation.
Such problems require your teams to have Data Science
and Machine Learning skills which are much higher-level
skill sets than those of folks who are just training the AI
systems.
In summation, it’s important to differentiate between the
Operator, Modeler, Consumer, and Developer roles that
exist within the AI and Machine Learning world.
16
18. Enterprise Use Cases for AI
18
Let’s take some high-level examples of AI Technology Use Cases within Customer Engagement, or Customer Service and
Customer Acquisition:
AI Technologies can also be used across Enterprise Operations. A few examples are provided below:
AI is not just limited to the Technology Applications above; the list of AI Applications is endless. Let’s go into a bit more
detail about the AI Applications identified above.
19. Chat Bots
19
Customer Engagement includes Customer acquisition, Customer service, and other front office related functions. Many
firms have self-service apps including: web, web mobile apps, and IVR systems to allow customers to manage their
accounts. However, customers rely on call center agents if the apps are not able to service their requests. Live chat has also
been around a while, allowing Customer Service Reps to potentially handle multiple customers at a time without getting on
the phone (convenient for the customer).
AI enabled Chatbots are an emerging customer engagement channel where chat responses can be handled by “Bot” agents
which are capable of parsing customer questions and customer intent, providing them with an appropriate response. These
Bots have to be trained on variations of incoming questions and just like other Machine Learning applications, they get
better over a period of time as they are trained. We also see many product vendors focusing on Chatbot niche areas where
they train their Bots for a specific industry or sub-industry (e.g. https://kasisto.com/ bots are purely focused on retail
banking solutions)
However, there are risks associated with deploying Chatbots too quickly and without the proper training. In addition, the
Chatbot workflows must be designed in such a way that a human CSR can take over if a Chatbot is leading the customer to
confusion. AI is not designed to be an alternative to humans at this stage but is more of an assistive technology.
21. Natural Language Processing (NLP)
21
Businesses of all size deal with text. In most cases, text has to be extracted, interpreted, and utilized for business processes
and workflows. For example, after collecting loan documents, the Loan Processors have to extract the key information from
those documents, interpret it, and process it based on the appropriate workflows and rules. NLP is a broad term used to
define the AI driven technology that is used to process different types of text.
For example, NLP can be used to extract relevant information from Legal Contracts (the text within contracts has to be
“labeled” first over a period of time to train the AI algorithms). NLP can also be used to determine the sentiment polarity of
text. For example, analyzing the sentiment of tweets and social posts which is an essential part of “dynamic brand
management”. NLP can also be used to summarize text as well; it can even be used to “generate” text from data. There are
many websites that use NLG (Natural Language Generation) to generate content by feeding data into NLP templates. Like
sports sites such as ESPN using NLG to generate the summary of games and other sporting events.
23. Analytics & Prediction
Many firms utilize advanced business intelligence and data
visualization platforms to analyze the vast amount of
information they collect so they can understand business
trends and forecast their sales, understand their risk
profiles, and so on. Machine Learning takes these
capabilities a step further, it makes analytic models more
accurate by training the models with past and present
internal data as well as external datasets.
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25. Robotic Process Automation
(RPA)
RPA can be used to automate business processes that are
repetitive in nature and, as mentioned earlier, can also
evolve by having RPA Bots use Machine Learning
algorithms to take those automations further.
Let’s look at an example:
Automatic Reconciliation has been a goal for Corporate
Accounting for a long time. Today’s G/L systems are quite
powerful in matching and reconciling transactions based on
primary and secondary keys and as well as a set of rules.
However, they still leave many exceptions which have to be
sorted out by humans. Using RPA and Machine Learning,
one can train these G/L systems to handle these exceptions
and over time these systems can achieve up to 99%
accuracy. See the SAP example here.
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27. AI Consumption Model
AI Tools are evolving fast but you have several options to evaluate from.
You can use a combination of the options as well if you want to build AI
capabilities within your Enterprise Applications; some of the options you
have include:
• OpenSource tools such as Appache PredictionIO
• Custom code with Python and readily available algorithm libraries
with public/ private datasets
• AI Platforms such as Azure Machine Learning, IBM Watson, and
Google TensorFlow
• Get algorithms and code from sites like Algorithmia or put up a Data
Science challenge on Kaggle
• Depending on what problem you are trying to solve, you can create
your own internal datasets or procure publicly available data sets
We discussed earlier the about concept of “packaged machine
learning”. Many Enterprise Software Vendors are adding Machine
Learning capabilities to their software where they already have
implemented a lot of algorithms within their products and part of that
implementation is to train those algorithms. 27
AI Consumption Model
28. AITool Sets
28
Earlier we discussed the concept of “packaged machine learning”. Many Enterprise Software Vendors are adding
Machine Learning capabilities to their software where they already have implemented a lot of algorithms into
their products; part of that implementation is to train those algorithms.
30. BuildingWorld Class AI and RPATeams
30
We have talked about different types of AI Technologies and how/where they can be used in the enterprise. It is also
important to understand the different types of skill sets and organizational structures that can support businesses with
these types of initiatives.
A typical lean organizational structure we see emerging is one in which independent Enterprise Technology Optimization
groups are established and aligned with business but are governed by IT. We also see some organizations aligning AI and
RPA initiatives within their broader shared service organizations. Although both models have their pros and cons, the
choice depends on the organization and its culture. Larger organizations may even have multiple such groups in different
silos.
We will now go into a bit more detail about the different types of roles that exist within AI.
31. Data Scientists
31
There are lots of non-qualified “data scientist” profiles on job websites. Just because someone has been writing SQL queries
and working on BI platforms does not necessarily make them a Data Scientist. Even folks with experience in Data Warehousing
Technologies or Big Data platforms such as Hadoop still does not necessarily make them a Data Scientist. It is important to
understand the distinction between a Database Developer and/or Data Engineer vs. a Data Scientist.
In our opinion, to qualify as a Data Scientist, one needs to have the necessary academic or proven background in advanced
statistics, applied mathematics, as well as, computer science. We will spare you from the technical Wikipedia definitions of
Data Scientists, however, it’s important for you to understand the types of things a Data Scientist is expected to do:
• Create and execute strategies for analyzing and extracting insights from large structured and unstructured data. The skills
required to do so are the ability to query using traditional SQL as well as query big data sources such as Hadoop, etc. using
Appache Hive, Stinger, etc.
• Create and execute strategies around the ETL transformation of traditional, as well as, big data. Familiarity with ETL
techniques and tools for data migration, cleansing, and transformation is a must
• Create and execute strategies around statistical data modeling and machine learning. Expert knowledge in breadth of
machine learning algorithms and the ability to find the best approach to a specific problem. Familiarity with several
supervised and unsupervised learning algorithms such as Ensemble Methods (Random forests), Logistic Regression,
Regularized Linear Regression, SVMs, Deep Neural Networks, Extreme Gradient Boosting, Decision Trees, KMeans, Gaussian
Mixture Models, Hierarchical models, and time series models (ARIMA, GARCH, VARCH, etc.)
32. Data Scientists
As you can see, the talent pool with such skill sets is a very
limited one and reskilling an existing Data Engineer into a
Data Scientist is not an easy, if not impossible, task.
Most of the talented Data Scientists usually find jobs in
large companies such as Google, Facebook, Amazon, etc.
So, one strategy, in addition to looking for lateral hires, is to
look for Masters or PHD graduates from Universities that
have very strong programs around Data Science. As an
evolving, but very important discipline, we feel that
investing in the right early stage talent can pay big
dividends over a period of time.
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33. Machine Learning Engineers
33
A Machine Learning Engineer shares some skills as a Data Scientist but, nevertheless, they have an important skill set which
makes them not as readily available in the market. A Machine Learning Engineer needs to have the following skill sets:
• Familiarity with the traditional Software Development Lifecycle (SDLC) and Programing using ML friendly languages like
Python, R, etc.
• Familiarity with Probability and Statistics and an understanding of some of the Algorithms. (These folks may be tasked to
select appropriate algorithms for specific problems but may not need to understand the inner workings of the
algorithms in depth)
We believe that while some of the traditional Developers can reskill themselves to become Machine Learning Engineers,
but not all of them are capable of doing so. To be a Machine Learning Engineer, the skills require the relevant academic
background, aptitude, and talent.
Unfortunately, technology evolves quite fast and the skills of yesterday, although helpful, may not necessarily translate into
the experience required to move into these newly evolving engineering disciplines.
34. RPA Architects and Engineers
RPA Architects and Engineers usually come from either a
QA automation background or a traditional development
background. RPA is a fairly advanced area with plenty of
packaged enterprise software offerings like UIPath,
BluePrism, AutomationAnywhere, and so on. RPA Engineers
need to be familiar with not only RPA software but they
must also be well versed in automation strategies and the
infrastructure related issues that come up with any RPA
program designed to scale. The role of RPA Architect is an
advanced role for someone who has an extensive technical
architecture background and has a thorough understanding
of how to set up Centers of Excellence for RPA Programs.
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35. Developers
35
A lot of traditional Developers have updated their LinkedIn profile to say Machine Learning Developer. Although it’s
certainly admirable that folks are upskilling themselves, we advise that if you are looking to hire Machine Learning
Engineers, you go through a thorough vetting process to qualify them.
If history is any sort of guide, some COBOL developers did not make a successful transition to GUI based application
development and many GUI based Application Developers could not make a successful transition to mobile and social
application development, not because they were not smart, but because they were stuck in maintaining the legacy
codebases.
So, just because a Developer can train and consume a Chatbot in their application with a simple API, it does not mean that
they are a Machine Learning Engineer.
36. Business Analysts & Business Intelligence Developers
36
Business Analysts
Traditional Business Analysts have always been more successful when using their business subject matter expertise along
with their data analysis skills. We recommend that Business Analysts take basic trainings on AI and Machine Learning
technologies to discover what they can do for business. This will allow them to adapt quickly to the endless applications of
AI and ML technologies.
Business Intelligence Developers
We see a huge opportunity for BI Developers who are able to upskill their strong data analysis skills using ML and AI. BI
Developers, Data Scientists, and Machine Learning Engineers are the ones at the core of being able to solve some of the
fundamental prediction and forecasting problems for businesses.
37. Managers, Project Managers, and Executives
We believe that Executive Project Managers, Program
Managers, and the Executive Suite folks should take
strategy courses in Artificial Intelligence, Machine Learning,
and Robotic Process Automation. Only by taking the time to
understand these technologies can they be ready for the
hyper completive business environment that is rapidly
evolving in front of us. There is a reason it is being called
the 4th Industrial Revolution.
37
38. Conclusion
AI implementations require a fundamentally different
mindset than that of traditional IT Operating models.
Therefore, it is critical that the Executive Management
takes the time to train themselves on how these
technologies work so that they can think through operating
and resource models that are optimal in taking advantage
of these technologies.
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